This PDF file contains the front matter associated with SPIE Proceedings Volume 13394 including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Beidou dual-mode receiver has the functions of positioning, navigation, timing and message communication, and has been widely used in various industries. Based on the transition from the BDS-2 regional system to the BDS-3 global system, the impact of the upgrade of BDS on the service performance of the Beidou dual-mode receiver and the countermeasures are studied. Firstly, the differences between BDS-2 and BDS-3 in signal type, constellation scale and service performance are compared. Next the system composition and working principle of Beidou dual-mode receiver are briefly introduced. Then combined with the specific application scenario, the impact of the upgrade of BDS on the service performance of the Beidou dual-mode receiver are analyzed. Finally, the countermeasures of Beidou dual-mode receiver are discussed to reduce or eliminate the impact of BDS upgrade on the service performance of the receiver, and provide continuous and reliable services for users. The research results can provide reference for the design, development and application of Beidou dual-mode receivers.

In this paper, an inductive coupling feeding structure is proposed. Then a plasma antenna prototype is designed and measured based on this inductive coupling feeding structure. This antenna is composed of the fluorescent tubes filled with low pressure gas, an inductive coupling feeding structure, and a microwave feeding line. The inductive coupling feeding structure is a solenoid structure which is wrapped around the fluorescent tubes. The microwave signal is excited into the plasma through the inductive coupling feeding structure. The dependence between the microwave feeding structure and the plasma is significantly reduced due to the magnetic coupling. Then the designed antenna has excellent reconstruction characteristics by only changing the coupling inductance of the inductive coupling feeding structure. The effectiveness of the proposed antenna is verified by using the measured reflection coefficients and transmission coefficient before and after establishing the plasma states. Experimental results show that the reflection coefficient of the antenna is decreased from -2.07dB to -34.36dB at the frequency point of 382.4MHz. At the same time, the transmission link level is significantly improved after the plasma state is established.

On-board orbit determination and timing are the most convenient ways for low earth orbit (LEO) constellations to achieve spatiotemporal datum, but different calculation methods lead to different error coupling methods, and there is a lack of research on the impact of orbit determination methods on the characteristics of LEO clock bias. Therefore, this article is mainly based on the publicly measured data of 7 LEO satellites in orbit, including gravity recovery and climate experiment follow-on (GRACE-FO), sentinel-3, and swarm, combined with indicators such as accuracy, frequency stability and period term to comprehensively evaluate the impact of different orbit determination methods on the LEO clock bias characteristics. The results show that the clock bias calculated from reduced dynamic are smoother and less gross compared to kinematic, and the stability of clock bias obtained by reduced dynamic is higher based on better on-board clocks and observations. However, two orbit determination methods have no significant impact on the accuracy and periodic term of clock bias. Meanwhile, under the same orbit determination method, the GRACE-FO clock has the highest accuracy, better than 1ns, reach E-13 level for 100s and E-14 for 1000s stability, slightly better than BeiDou-2 rubidium clock, equivalent to Beidou-3 rubidium clock and inferior to BeiDou-3 Passive Hydrogen Maser clock. Next is sentinel-3, the worst is swarm, and the clock bias of sentinel-3 and swarm have obvious periodic characteristics.

In recent years, the Integrated Satellite Aerial Terrestrial (I-SAT) network has emerged as an innovative and comprehensive communication system and has gained wide attention. However, in the face of a complex external environment, it will still be affected by other factors. This paper considers the integration of Unmanned Aerial Vehicle (UAV) with Reconfigurable Intelligent Surfaces (RIS) and introduces a novel sub-connected active RIS architecture under the energy consumption constraints of the system. Firstly, a joint RIS phase shift, amplification, and UAV trajectory optimization algorithm is proposed to achieve a high achievable sum rate. Secondly, a Deep Deterministic Policy Gradient (DDPG) algorithm is utilized to solve the optimization problem and converge in a continuous action space. Finally, simulation results confirm that the proposed approach significantly enhances the system performance compared to the other schemes.

In order to meet the requirements of users for flexible use and secure communication, an integrated design method of satellite communication relay terminal station supporting multi-network, multi-mode and interconnection is proposed. The content mainly includes design analysis, system composition, function performance and design description. The terminal station has the characteristics of flexibility, simple operation and strong environmental adaptability, and can quickly deploy and establish satellite communication links to transmit voice, video, data and other transmission services.

This paper investigates the application of Multi-User Multiple Input Multiple Output (MU-MIMO) techniques in Power Line Communication (PLC) systems to enhance throughput and network efficiency. Focusing on the unique challenges posed by PLC environments, the study introduces the Null Space Based Multi-User Interference Reduction (NS-MUIR) algorithm, a novel precoding and detection methodology designed to mitigate Multi-User Interference (MUI). We present a detailed system model for both Single Transmitter (ST) and Multiple Transmitter (MT) MU-MIMO configurations, followed by an extensive performance evaluation through simulation. The performance evaluation compares the NS-MUIR algorithm against traditional methods such as Zero-Forcing (ZF), Minimum Mean Square Error (MMSE), and Maximum Likelihood Detection (MLD), demonstrating that NS-MUIR not only approaches the performance of MLD with significantly reduced computational complexity but also outperforms ZF and MMSE in various scenarios. The results confirm the efficacy of NS-MUIR in enhancing data transmission rates and system reliability in MU-MIMO PLC systems. This paper contributes to the advancement of PLC technology by providing a robust solution for deploying efficient and high-capacity MU-MIMO systems in challenging communication environments.

A highly efficient, low-cost, and practical reception method is proposed for the reception of reverse link signals in the L-band digital aviation communication system (L-DACS1). The signal reception, demodulation, and decoding are completed by combining electronic components and computer software tools. The effectiveness of this method is verified by the calculation results of the received signal superframe power spectrum, Multi-Frame constellation diagram and error rate.

In applications like the Metaverse, human brain information can be collected, identified, stored and transmitted with the help of hardware and software systems like brain-computer interface. Not only human personal privacy information can be leaked, but also human behavior and consciousness may be interfered and controlled, which will bring serious information security problems. Therefore, this paper explores the possibility and possible methods of human brain information encryption and protection. First, the definition of human brain information encryption is introduced. Second, possible methods of human brain information encryption and protection at the stages of collection, identification, storage and transmission are proposed. This paper may provide a reference for the future human brain information protection and utilization.

To recover pure speech from observed speech, this paper presents a time-domain multi-channel Wiener filter speech enhancement algorithm for the distributed speech model. For reducing the noise from noisy speech in time domain, this paper first gives the formula about the energy of noise reduction and speech distortion, then establishes the optimization problem on noise reduction and speech distortion, and finally solves the optimization problem to get the formula of the optimal linear filter. In addition, this paper uses an iterative method to estimate the source speech signal autocorrelation matrix, which improves the estimation accuracy. Simulation experiment results show the proposed algorithm can obtain the better performance than several classical multi-channel speech enhancement algorithms.

China's power grid is progressively advancing towards smart technology. With increasing substation voltage levels, more 5G base stations are being integrated into substations. The presence of external antennas poses challenges for electromagnetic protection in these 5G base stations. This study focuses on deploying 5G base stations within substations, selecting a specific substation for physical modeling. It simulates the complex power-frequency electromagnetic field environment within the station and compares these simulations with actual measurements to validate their accuracy. Ultimately, by comparing and analyzing the simulation results with relevant standards for substation electromagnetic environments, the study determines the optimal deployment scope for 5G communication equipment in substations. This research provides theoretical insights for designing effective 5G deployment strategies in substations.

Occasionally occurring events in the actual transmission process can lead to the deletion of signal-generated data, which in turn alters the signal's structure. The aim of this is to modify the structure of the missing signals and to reduce the volume of data. This paper introduces a method for diagnosing and integrating multi-dimensional features within a wide-tree model through migration. Initially, entities are extracted from various dimensions based on the sample structure, and the entity subspace for the target domain is established by eliminating redundant entities. Subsequently, within the Bagged-Tree ensemble classification model, the multi-dimensional feature space is crafted by leveraging the incremental dimensionality reduction from the sampling diagnosis of the sub-classifiers. Decisions at the sub-classification level are integrated using the ensemble classification layer, and ultimately, the target domain diagnosis is accomplished through the fine-tuning of the monitoring sample parameters.

When Kevin Ashton, direct of the Auto-ID Center at Massachusetts Institute of Technology, introduced the term “Internet of Things” (IoT) to the world in 1998, IoT has been developing rapidly and applied to more and more technology industries. The concept of IoT has had major changes in manufacturing production processes. By means of this, this article used a case study to understand the advantage of IoT and how it may benefit manufacturing processes.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), a satellite focused on changes in the heights of ice caps, clouds, and the land surface, provides valuable data resources for global Earth science research through its advanced laser altimetry technology. Since the ICESat-2 satellite transmits weak pulses that are susceptible to solar radiation and other factors, there is a large amount of noise in the data. In this paper, a new denoising model (ConvDS) is proposed. We use a combination of discrete convolution algorithms and cluster analysis algorithms to reduce the noise photon points under the ground to some extent. Comparing the ConvDS model with the other two methods, in steep terrain, the ConvDS model is 2.76% better than the improved DBSCAN algorithm and 1.48% better than the improved OPTICS algorithm. In hilly terrain, the denoising accuracy of the ConvDS model is 2.09% higher than the improved DBSCAN algorithm and 0.48% higher than the improved OPTICS algorithm. It can be concluded that the denoising effect of the ConvDS model is superior.

In order to adapt to the special requirements of different measurement systems for channel simulators, especially the differentiated needs of input and output signal power, this paper proposes an innovative design method for intelligent channel simulators based on adaptive link optimisation of signal power. The design adopts an all-link power adaptive link optimisation algorithm, which realises the precise control and intelligent adjustment of the signal power of each link inside the channel simulator. Compared with the traditional methods of input link power independent control and output link power independent control, this method maintains the best output signal quality through the adaptive detection and matching of RF input and output signal power, as well as the fast response power adjustment of key components such as the receiving channel, the sampling preprocessing module, and the signal recovery processing module, and so on. The test results show that the intelligent channel simulator can automatically identify and match the input and output signal powers of different systems under test, the output signal power accuracy converges rapidly, the signal quality is good in a large dynamic range of input and output power, and it meets the design requirements of channel simulators in the field of modern wireless communication in terms of intelligence and accuracy control.

Heavy hazy weather blurs airport or target images obtained through visible light photoelectricity sensors, and hazy images reduce the efficiency of target recognition. With the deployment of panoramic systems and takeoff and landing monitoring systems in the test field, the research on image dehazing algorithms has increasingly crucial theoretical significance and practical application value. In this paper, we propose an image dehazing method based on the Double Discriminator Generative Adversarial Network (DDGAN-DF), which generates multi-layer semantic awareness and produces highquality, reliable haze-free images.

With the tide of “software defined car”, this paper analyzes the importance of software from the dimension of vehicle architecture, and puts forward a new vehicle architecture such as “SDA six-tier architecture”. In order to ensure the efficiency and quality of vehicle software delivery, this paper proposes a development model based on agile iteration, from agile iterative development process, vehicle software integration, as well as integrated IT quality management tools and set process quality objectives. Through 7 iterations of pilot projects, on the basis of the brand new architecture platform and the brand new Software R&D mode, the quality state of the traditional project at the same time is achieved, and the individual quality index is better than that of the traditional project. This quality management approach has also changed from focusing on version of the test bug to software integration process bug quickly solving. Through the setting of Dx iterative process quality valve, with the help of IT tool chain, integration process problems can be quickly located and tracked, and the software defects can be moved forward and the release delivery risk can be greatly reduced. It provides a good reference for automotive software quality management and achieves the research goal.

Static Random-Access Memory (SRAM) type Field Programmable Gate Array(FPGA) has large-scale logic units, higher operating speed, and larger capacity, and are widely used in electronic systems to undertake increasingly complex control and information processing tasks, improving the flexibility and efficiency of system design. For the convenience of remote debugging and increasing efficiency, this article introduces the configuration method of SRAM-type FPGA, with a focus on the 32-bit SelectMAP port configuration. It also provides a parallel loading scheme using the SelectMAP interface from the SPI interface PROM through the ZYNQ series FPGA.

By applying the data life cycle theory, this study provides a systematic logical progression for the data security governance of the big data industry, linking the internal and external governance processes, dividing them into the four phases of data collection, data processing, data utilization and data maintenance, and carrying out risk analysis and deconstruction, and exploring their external characteristics and internal causes. Through the perspective of "system-technology-society" trust mechanism, a cyclic analysis framework combining internal and external aspects is constructed in the context of data security governance of the whole life cycle of the big data industry, and this cycle is effectively operated through the trust mechanism, which provides an effective mechanism and governance path for deconstructing the risk of data governance of the big data industry and realizing effective governance.

Unmanned aerial vehicles (UAVs) have been widely used in many scenes in our lives, such as photography, emergency rescue, express delivery, and so on. Because of its small size, moving mobility and low cost, the UAV has become a major threat to soldiers in modern warfare. Therefore, the detection and recognition of UAVs are important in modern radar technology. This paper establishes the mathematical models of radar echo signals for different aerial targets such as UAVs, airplanes, birds, etc. Then we utilize the short-time Fourier transform (STFT) to process echo signals, and we will obtain the micro-Doppler spectrums of targets. The spectrums reflect their unique micro-Doppler features caused by their physical structures and micro-motion characteristics, so we can use that to recognize different targets more efficiently. In this paper, a convolutional neural network based on micro-Doppler spectrums (DS-CNN) is trained to realize the recognition. The network mainly consists of 5 convolutional sequences and is aimed to classify 6 different micro-Doppler spectrums of targets. The simulation results show that the DS-CNN has a high recognition accuracy even in 10dB noise and an appropriate run time to recognize each spectrum.

This paper investigates gas turbine modeling techniques based on machine learning algorithms. Utilizing polynomial regression (PR), K-Nearest Neighbor (KNN), decision trees (DT), and multi-layer perceptrons (MLP), it constructs models from one year's operational data from a power plant's heavy-duty gas turbine. Predictions encompass key parameters such as compressor outlet temperature, compressor outlet pressure, power, and turbine exhaust temperature. Experimental results demonstrate that the MLP algorithm achieves high prediction accuracy for compressor outlet temperature (MAPE: 0.112%), compressor outlet pressure (0.201%), power (0.421%), and turbine outlet temperature (0.070%), highlighting its effectiveness in gas turbine modeling.

With the development of Low Earth Orbit (LEO) satellite internet, the number of satellites in LEO constellations has been increasing. To reduce the construction costs of LEO satellites, they are often equipped with secondary frequency standards (primarily crystal oscillators and rubidium atomic clocks) as their time-frequency sources. In order to enhance the performance of these time-frequency sources, many scholars have been investigating methods to optimize secondary frequency standards. Concerning clock control algorithms, classical methods such as PID and Ping-Pong (PP) suffer from slow tuning and uncertainty about optimization. This paper introduces a clock control algorithm based on the Linear Quadratic Regulator (LQR), which determines the optimal adjustment through a designed cost function. The advantages of this algorithm include a clear optimization strategy and ease of parameter tuning. The algorithm proposed in this paper achieves superior performance in terms of both time deviation standard deviation and frequency stability when compared to the PID and Ping-Pong algorithms. After control, for the PID, PP, and LQR algorithms, the standard deviations of the crystal oscillator are 4.2E-9, 2.7E-9 and 1.8E-9, and it’s frequency stability (10000s) has improved to 8.1E-14, 6.1E-14 and 2.8E-14, and the rubidium clock's standard deviations enhanced to 2.6E-9, 5.2E-10 and 3.4E-10, the rubidium clock's frequency stability (10000s) has improved to 6.8E-14, 2.1E-14 and 2.1E-15.

Several methods for constructing Hermitian self-orthogonal codes over the quaternary field F₄ are presented. For each n≥8 and n≠47,62, we can construct an [n,4]₄ optimal Hermitian self-orthogonal code.

The analysis and application of big data technology bring innovative methods for intelligent information processing, and give rise to a new governance pattern of technological ethics: build an intelligent information processing model of "evaluation-empowerment−effectiveness" by reconstructing governance concepts with data thinking, using data information as decision-making basis, restructuring practical practices with data technology, and achieve disruptive innovation in governance process methods, content structures and interactive mode of technology ethics governance. Ethical governance of big data technology based on intelligent information processing completes the dual task of "transforming data" and "dataization" through data assetization, data idealization and data productization, promotes governance elements evolution of exploring laws, forming plans, implementing shape and soft adjustments, reshapes the innovative ways, cognitive schemas, ethical formation and behavior development of technology actors, and finally realizes the intelligence, efficiency and precision of technology ethics governance.

The multi-sensor fusion SLAM localisation method has been widely used in robot navigation and other fields. For the problem of SLAM localization accuracy and robustness reduction caused by environment degradation in practical applications, this paper proposes a multi-sensor fusion SLAM localization method based on multi-metric evaluation of environment degradation. The visual environment degradation evaluation function is constructed by three evaluation indicators: the number of feature points, the feature point tracking success rate, and the feature point distribution uniformity. The LVI-SAM multi-sensor fusion SLAM localisation algorithm is improved by taking the environmental degradation evaluation result as the independent variable of the robust kernel function, and the output of the robust kernel function is used to adjust the weight of the visual odometry factor in the LVI-SAM back-end optimisation of the multi-sensor fusion SLAM localisation algorithm, so that the SLAM localisation algorithm can better adapt to the environmental changes. Experiments on public datasets show that the multi-sensor fusion SLAM localisation method proposed in this paper, based on multi-metric evaluation of visual environment degradation, reduces the absolute position error root mean square error (APE RMSE) and relative position error root mean square error (RPE RMSE) by at least 7.6% and 16.9%, respectively, compared to the LVI-SAM algorithm without visual environment degradation judgement and the LVI-SAM algorithm using the number of feature points to judge the environment degradation, and effectively improves the localisation accuracy and robustness.

Aiming at the limitations of traditional fire detection algorithms in terms of accuracy and real-time detection, a lightweight fire detection algorithm based on improved YOLOv8 is proposed. The Slim-neck is used to improve the Neck network, reduce the number of parameters and computation of the model, and improve the detection performance of the model. The C2f-Star module is designed, and the Star block is introduced to replace the bottleneck structure of the C2f module in the Backbone network, to better capture the information of the image, and further reduce the complexity of the model. The Focaler WIoU boundary loss function is used instead of the original loss function, which reduces the influence of low-quality samples and increases the regressivity of the network bounding box. The experimental results show that the number of parameters and the computational volume of the improved model are reduced by 14.0% and 16.0%, while the precision and the mean average precision are improved by 3.0% and 1.2%, compared with the original model, which can help real-time monitoring and early warning of fire.

In the face of the new demands of power services, the routing planning under the existing OTN technology has obvious deficiencies in meeting the different power service requirements. To improve the ability of the current routing planning scheme to handle small-granular bandwidth and reduce communication delay, this paper proposes a new routing planning algorithm based on the characteristics of OSU technology. First, the actual needs of different existing power communication services are explained, and a targeted analysis is conducted on the delay and the number of business bearers in combination with the characteristics of OSU technology. Finally, a routing planning strategy based on the needs of power services is proposed using the hierarchical analysis method. Experiments show that this routing planning algorithm optimization scheme is applicable and feasible, and can provide a new idea for future power communication networking related issues.

Improving the reliability of integrated navigation systems requires effective fault detection and isolation strategies. In response to false alarms in jump soft fault detection grounded on the orthogonality principle, this study proposes refinements to the approach for data selection within a sliding window. The refined method introduces an improved orthogonality fault detection algorithm, which enhances detection accuracy and reduces the likelihood of false alarms. By harnessing data within the sliding window optimally, the algorithm computes the orthogonal average as the test statistic without necessitating an increase in the sliding window length. Furthermore, by combining the traditional residual chi-square test and extrapolation chi-square test, more precise judgments can be made regarding fault type and occurrence time. Ultimately, through the integration of traditional residual chi-square and residual extrapolation chi-square detection, this methodology enhances fault classification capabilities, reduces false alarm rates, and enables the effective identification of pulse, abrupt, and gradual faults even in scenarios with limited fault signal strength.

This paper designs an efficient range query protocol for large-scale RFID systems that addresses the existence of unknown tags while simultaneously identifying missing tags. Range query aims to classify target tags according to the ranges specified by users. However, existing range query protocols do not consider the existence of missing tags, leading to incorrect classification results. To handle this issue, we propose a protocol called Missing Tag Identification and Range Query Scheme (MRQS), which selects singleton slots and 2-collision slots to receive range information and identify missing tags. Expected empty slots are used to filter out unknown tags. Compared with the related range query protocols, simulation results show that the proposed MRQS scheme yields accurate classification results for target tags in the presence of missing tags and unknown tags.

In response to the problems of high cost and low work efficiency faced in the trial cutting of non-circular gear (NCG) machining, an in-depth study of the working principle of CNC gear shaping machine based on meshing theory was conducted. A conjugate motion model was established that satisfies the non-sliding rolling motion of the tool pitch circle along the NCG pitch curve, and calculation formulas for workpiece angle, tool angle, and center distance were obtained. A computer simulation algorithm was proposed to simulate the CNC gear shaping process of NCGs; Secondly, a criterion model for preventing root cutting and top cutting (RCTC) of NCG was established, and a visual method for determining RCTC of NCG was proposed. A computer-aided design virtual simulation system for NCG CNC shaping program was designed to verify the rationality of NCG pitch curve parameters and shaping tool parameters. Finally, the correctness and effectiveness of the algorithm proposed in this paper were verified through computer virtual simulation.

Aiming at the problem of low detection accuracy and poor real-time performance in infrared ship target detection, a lightweight detection method based on improved YOLOX is proposed, which can effectively improve detection real-time performance while ensuring detection accuracy. The algorithm first adopts an anchor free box design, then improves the backbone and neck of the network, introduces task alignment learning methods, assigns labels, and finally strengthens the detection head in a targeted manner. Through experimental verification, the mAP of this method can reach 50.1%, and the FPS can reach 78.3%, with better accuracy and real-time performance than other similar algorithms.

Photovoltaic power generation is greatly affected by the weather, and with the growth of photovoltaic installed capacity, large-scale grid-connection poses challenges to the security and stability of the grid. In order to solve this problem, 10 regression models were trained by using 10-fold cross-validation combined with the operating parameters of photovoltaic panels and weather parameters. The optimal four models were selected and the random grid search method was used to adjust their hyperparameters, and then the feature importance of the four models was obtained respectively. Then, three optimal models that are adjusted with Stacking are integrated. Through the study of the importance of features, the influencing factors of photovoltaic power generation are studied. The results showed that the MAPE of the integrated model was 0.0243%. Considering that the integration model may ignore the dependence between factors, this study uses Bayesian networks to construct the influence path of photovoltaic power generation and form a visual network. In the model established above, the paper introduces the operating parameters of photovoltaic power generation panels and presents the important influencing factors and influencing paths of photovoltaic power generation through the model of Stacking and Bayesian network, which provides an effective reference for photovoltaic power generation prediction.

To investigate subjective comfort, we examine the comfort scores of seats toward obtaining a means of body-pressure detections based on a mechanical sensing device. With respect to the body pressures of a human-chair contact interface, the relationship between comfort and seat surface pressure is demonstrated. Primary and secondary school students are selected with statures in the range of 1.20–1.74m. Considering human-machine interaction, they are asked to be participants with “ordinary,” “comfortable,” and “very comfortable” using as survey criteria for pressure of <1.25Ncm^-2. Accordingly, “less comfortable” and “uncomfortable” were equal criteria for measuring pressure in the range of 1.36 to 1.41Ncm^-2. Under a static sitting posture, the pressure coefficient (D_c) of buttock support is calculated. Through on-site records of the configuration of ordinary school chairs, sitting postures have a significant effect on the comfort score, where the upright position exhibits the largest D_c. The average acceptable subjective well-being was supposed to have a score of ≥3 (ordinary), and we expect that chairs that are designed to satisfy students’ comfort requirements.

With the ongoing digital transformation of the traditional Chinese medicine (TCM) manufacturing industry, effectively processing and managing large-scale device operation data has become a significant challenge. The diversity and complexity inherent in TCM production, coupled with the vast amounts of data generated, necessitate innovative approaches to ensure robust data management. This paper proposes comprehensive strategies to address performance bottlenecks in data collection, storage, and real-time display. By optimizing JSON data format, designing efficient database structures, implementing read-write separation, and employing dynamic table partitioning, the efficiency of data processing and the capability for real-time display are significantly enhanced.

3D map creation based on machine vision in a weak texture environment is a difficult problem of robot SLAM. Therefore, this paper tries a new method of low-texture map creation based on 3D Gaussian Splatting (3DGS). Through the acquisition of RGB images of the weak texture environment (lawn) by an Unmanned Aerial Vehicle (UAV) equipped with a monocular camera, the improved Structure from Motion (SfM) and 3DGS processing is used to generate a high-fidelity 3D map of complex and weak texture terrain. Experiments show that the peak signal-to-noise ratio (PSNR) of the training set is more than 40dB. For the new view, the Structural Similarity (SSIM) index in the frequency domain is improved by more than 0.7 in the low and mid-frequency range. While limited in high-frequency detail reconstruction, the method excels in global consistency and medium-scale feature capture, with efficient reconstruction and real-time rendering capabilities. This approach sets the stage for more advanced semantic mapping, promising applications in desert landscapes, barn interiors, and wilderness first aid scenarios.

In this study, the complexity of an ICU environment in terms of both static and dynamic objects when building a 3D real-time map for mobile robot localization is solved. YOLOv5, probability dynamic feature points culling algorithms and ORB-SLAM3 are fused and dynamic objects are culled using a probability based culling rule which eliminates the dynamic objects and retains the static ones. The algorithm utilizes a point cloud construction thread to establish a dense point cloud map from the sparse point cloud results output through a point cloud stitching thread after the elimination of the dynamic feature points and this is converted and saved as octree map for memory efficiency. Evaluation of the accuracy of the proposed 3D real-time map building algorithm performed using the f3/walking_xyz dataset from the TUM RGB-D dataset sequence utilizes the relative pose error (RPE) and absolute trajectory error (ATE) as metrics for dynamic obstacle culling and mapping accuracy. For a dynamic feature point elimination experiment involving 8 keyframes. The study reveals that 28 dynamic feature points remain in 8 keyframes, with a success rate of 89.74% for dynamic feature point elimination. The remaining feature points are mainly in "chair" type objects. The algorithm, based on YOLOv5s-PROBABILITY CULLING-ORB-SLAM3 fusion effectively eliminates dynamic objects, with low false rejection rates, optimized feature point extraction, and significantly improves mapping effect and accuracy by 91.4% compared to ORB-SLAM3 implemented alone which yields 72% in environment like ICU, where frequent movement is prevalent, demonstrating its superior accuracy, effectiveness and precision.

Industrial images are often captured under full-time and full-weather conditions, leading to inevitable noise during the imaging process, which can impact subsequent detection algorithms. In recent years, image denoising with neural networks has been the rapid development. However, training such networks typically requires a large dataset, which is scarce in publicly available industrial image databases. In this paper, we propose a novel approach termed Zero-Shot Industrial Image Lightweight Denoising (ZSILD) network, which effectively denoises single noisy industrial image without the need for datasets. First, we sample the paired neighbour pixels of a random noisy industrial image, which are then utilized to train a lightweight denoising network. Second, we design a lightweight depthwise convolutions network based on bottleneck residual structure with shortcut connections. Finally, this network is trained on the sampled pairs using a novel loss function aimed at enhancing denoising performance. Our experiments conduct on real-world industrial ambient noise demonstrate that our ZSILD method outperforms existing denoising techniques, all while requiring comparatively minimal computational resources.

The tobacco moisture is one of the key indexes in the process of making cut tobacco, which directly affects the aroma and overall quality of tobacco, thereby deciding the quality of cigarette. This paper takes the tobacco moisture as subject investigated, the process parameters data of the influencing factors of tobacco moisture are collected, and the Variational Autoencoder is adopted to extract the key potential variables of these data. A moisture prediction method based on Particle Swarm Optimization Bidirectional Long Short-Term Memory network is proposed. The results of MAPE=0.02 and RMSE=0.03 showed that the method can achieve the accurate prediction of tobacco moisture.

Aiming at the infrared imaging deterioration of porcelain insulator non-contact detection, this research uses a regional network based on the proposed detection: the infrared image deterioration of porcelain insulator was achieved using a Faster R-CNN algorithm. Faster R-CNN has excellent generality and robustness, it can be easily applied to a variety of datasets, and the target categories can be easily adjusted to effectively improve the performance of the test model. To address the limitations of Faster R-CNN in multi-scale detection, this study introduces an enhancement via the Feature Pyramid Network (FPN), which augments the detection accuracy. The FPN enhances the Region Proposal Network (RPN) by integrating the high-resolution features from lower layers with the semantic richness of upper layers, thus bolstering the detection of targets across various scales. Furthermore, the research employs the ResNet101 residual network as a substitute for the VGG16 network, reducing the computational burden of convolutional operations and preserving more informative features of the insulators. This approach facilitates the extraction of detailed features from smaller targets, enhancing detection performance. Experimental comparisons demonstrate that the refined Faster R-CNN algorithm significantly improves the efficacy and precision in identifying insulator defects.

Once quality abnormalities such as tobacco mildew and excessive moisture occur in cigarette production, it often necessitates the lockdown or even scrapping of a significant portion of the inventory. This can result in widespread market complaints. Therefore, timely identification of moisture anomalies in cigarettes is of paramount importance to adjust relevant parameters or operational processes for subsequent batches promptly. This paper proposes a method for identifying cigarette moisture anomaly risks based on an improved NGBoost algorithm. This method focuses on the moisture content of finished products, involves cleansing time-series data of moisture chain-related influencing parameters, extracting feature parameters using SHAP Value, and ultimately establishing a moisture prediction model using NGBoost. Trend analysis is conducted on the residuals between predicted and actual values on a weekly basis. A change in trend in the residuals serves as a timely alert for moisture anomalies. The results indicate that in 2023, the model identified moisture anomaly risks a total of 18 times, with 14 confirmed as actual risky states. There were 6 instances of false positives. The identification accuracy reached 77.8%, effectively mitigating the quality risks associated with moisture anomalies.

Aiming at the problem of decreased detection accuracy and low reliability caused by the large variation of wind turbine blade defect scale, this paper proposes a wind turbine blade defect detection method based on improved YOLOv8. First, the CBAM attention mechanism is used to optimize the YOLOv8 network, and the C2f module is replaced by the C2f-CBAM module in the Neck part, which enhances the network's ability to extract and dynamically adjust the important features, so that it is able to detect the information of defects at different scales more effectively. Second, the GIoU loss function is used to replace the IoU loss function, which provides more stable and accurate gradient information and improves the positioning accuracy of the bounding box by calculating the generalized IoU of the predicted box and the real box. The experimental results show that for wind turbine blade defects at different scales, the CBAM attention mechanism has better applicability than the SE attention mechanism and the LSK attention mechanism, and the YOLOv8-CBAM-GIoU network proposed in this paper has significantly improved detection accuracy and robustness than the YOLOv8 network.

Aiming at the problems of poor robustness and low accuracy of 5G positioning caused by non-line-of-sight errors and environmental occlusion in indoor environments, this paper proposes a 5G indoor positioning method based on Chan-White Shark optimization algorithm. The method uses Chan algorithm and White Shark optimization algorithm for co-localization, adopts Chan algorithm as the first level of co-localization algorithm, and initially solves the rough position of the moving target, which is used as the basis for the initial population generation of the White Shark algorithm, and then searches for the optimal solution within the moving target's movement area using White Shark optimization algorithm to optimize the Chan algorithm solution results. The experimental results show that the White Shark algorithm is better than the particle swarm optimization algorithm and the sparrow search algorithm in the cooperative positioning algorithm, and the 5G positioning method based on the Chan-White Shark optimization algorithm has higher positioning accuracy and robustness than the 5G positioning method based on the single Chan algorithm or the White Shark optimization algorithm.

The pantograph-catenary system, as a vital link in the energy supply chain of electrified railways, has a direct impact on the overall reliability of the railway system. The occurrence of pantograph arc not only affects the quality of power collection by locomotives but also poses significant safety concerns. Therefore, the quest for accurate and efficient methods to identify pantograph arcs is of paramount practical importance. This paper proposes a pantograph arc recognition strategy based on Zebra Optimization Algorithm (ZOA) optimized Radial Basis Function Neural Network (RBFNN). Four groups of current collection experiments were conducted under different operating conditions using a self-developed Pantograph arc experimental simulator; And based on the consideration of zero-rest phenomenon during arc occurrence, the collected current data was selected from the data of the middle half cycle of the power frequency for feature calculation. Principal Component Analysis (PCA) was used to screen the features with higher contribution rate as the recognition basis; Furthermore, the ZOA was used to optimize the RBFNN for training and learning the features of the four groups of experimental, the average test level of the model is 98.5%, and the overall level is above 98%.The testing results of the model verified the generalization feasibility of this strategy; Finally, the superiority of this strategy was demonstrated through comparison with optimization and testing results of other types of algorithms.

Transmission tower is an important equipment for overhead transmission and distribution lines, and its structural strength directly affects the safety of transmission lines. ABAQUS software was used to establish the finite element model of 66kV drum-type tower, and the calculation methods of gravity load and wind load were presented. The stress distribution and displacement of 66kV drum-type tower under gravity load only and under the combined action of gravity load and wind load are obtained. The results show that the maximum stress of the drum-type tower under combined action of gravity load and wind load is much larger than that under gravity load only. The maximum stress points under combined action of gravity load and wind load are the windward surface at the connection between the cross-arm and the main material and the windward surface at the main material of the tower legs, whereas the maximum stress point under gravity load only is located in the vicinity of the cross-arm of the drum-type tower.

The hot spot temperature of an oil-immersed transformer is a critical indicator of its service life and operational performance. In this study, a detailed examination of a 66kV naturally cooled transformer is conducted by establishing a two-dimensional physical model and simulating the transient temperature field under natural oil circulation conditions using finite element software. This analysis primarily focuses on the temperature rise from the initial moment until thermal equilibrium is reached, providing insights into the thermal dynamics of the transformer. Additionally, the study investigates the behavior of the hot spot temperature under varying conditions, particularly assessing the influence of load rate and ambient temperature. The findings reveal that both factors significantly impact the hot spot temperature, thereby affecting the transformer's overall thermal performance. Furthermore, the research evaluates the effect of the oil baffle plate's position relative to the winding on heat dissipation efficiency. Results indicate that the strategic installation of an oil baffle plate can effectively reduce the hot spot temperature, with the optimal configuration being the placement of the oil baffle plate between the two windings to enhance heat dissipation performance. In conclusion, this study underscores the importance of precise thermal management in oil-immersed transformers. By comprehensively understanding the effects of load rate, ambient temperature, and the placement of oil baffle plates, it is possible to significantly improve the thermal performance and extend the service life of these transformers. This research contributes valuable knowledge to the field, offering practical guidelines for optimizing transformer design and operation.

Relying on the subgrade engineering of an expressway in Guangdong Province, according to the compiled test scheme, six FWD survey lines were laid in the test area according to the load levels of 3t, 4t and 5t, and the subgrade surface deflection test under different FWD loads was carried out. The average value of the deflection data and load data of the last 4 hammers after 5 blows is used as the final deflection data and load data of this testing point, and the unstable first hammer data is discarded. Then, based on the obtained single-point data, the elastic modulus value of soil foundation at this test point is obtained by inverse calculation through the displacement formula of elastic half-space under single circular load. The abnormal test data is removed by using the 3-fold standard deviation method, and the representative values of deflection and soil foundation modulus of each survey line are calculated by the unilateral confidence level of 95%. The results show that the surface deflection value of single-point subgrade and the elastic modulus of soil foundation are fluctuating, and they are not proportional to FWD load, but the representative values obtained according to the survey line are linear with the load.

In order to improve the load balancing quality and efficiency of the UAV (Unmanned Aerial Vehicle) network controlled by SDN (software-Defined Networking), 3MMO (three m migrated optimization) load balancing strategy is proposed, which considers the minimum migration rate, the minimum migration cost and the maximum migration efficiency. Through the comparison of the load balance of DALB (Dynamic and adaptive load Balancing), CAMD (controller adaption and migration decision), ISMDA (an improved switch migration decision algorithm) three algorithms simulation, the average packet loss rate is 0.5% lower than that of CAMD, the migration efficiency is 3 times that of CAMD, and the number of migration is 13.6% less than CAMD. The strategy of 3MMO not only solves the problem of unbalanced load, reduces additional overhead caused by frequent load migration, improves migration efficiency, and reduces the cost of migration. It has greatly improved the communication quality and performance of the UAV network.

Artificial intelligence (AI) technology is an important means to promote industrial development and technological innovation. Due to the lack of high-dimensional high-quality data, the risk of leakage of important production data, and the defects of deep neural network algorithms, the application of AI in smart power plants faces many challenges. This paper introduces the main technologies of AI and its application status in intelligent power stations. The basic technical architecture of AI application in intelligent power stations is analyzed, and the typical application scenarios of AI in intelligent power stations are proposed. Finally, the electric power AI technology is proposed, which combines other technologies such as the Internet of Things (IoT) and big data analysis. The deep integration and application in smart power stations will further enhance the level of power station intelligence.

This research focuses on wind speed under complex outdoor conditions, employing an 'Internet + UAV' based shortrange wind measurement system as the research method. It aims to address the critical issue of strong winds negatively impacting construction efficiency and safety factors during aerial engineering operations. Research indicates that the short-range wind measurement system composed of 'Internet + UAV' can effectively forecast strong winds, thereby reducing the occurrence rate of safety accidents in engineering construction and improving the effective working period for aerial operations. This research will provide technical and theoretical support for enhancing the quality and efficiency of engineering aerial operations under complex outdoor conditions.

The use of point clouds in computer graphics is increasing, providing a versatile geometric representation for various applications and serving as the main output of many 3D data acquisition tools. Despite the traditional use of hand-designed attributes on point clouds in graphics and vision fields, recent success of Convolutional Neural Networks (CNNs) in image analysis suggests that using CNN understandings to process point clouds holds significant promise. To address the lack of inherent topological information in point clouds, we enhanced the DGCNN network by introducing a lightweight attention module, named DGCNN-PA (Dynamic Graph CNN based on the Point Attention module). Additionally, creating extensive datasets is crucial to meet industry demand and support unsupervised macro-model training. We propose a methodology for generating three-dimensional point cloud using a two-dimensional segmentation model and relative depth estimation. Our model achieves an mIoU of 86.2 on the ShapeNetPart dataset and 64.2 on the hot-rolled steel Strip Surface Inspection dataset.

Carbon nanotube yarns have extremely high flexibility and tensile resistance, as well as excellent conductivity and thermal conductivity, making them widely used in fields such as textiles, electronic devices, medical devices, aerospace, and more. The electrical characteristics is important for the application of carbon nanotube yarns. Thus, the relationship between the current and voltage of carbon nanotube yarns is analyzed by experimental research in the paper. Based on the experimental results, a backlash model is used to describe this relationship, and the parameters of backlash model are identified using the batch least squares method. Finally, the experimental results show that the established model can well describe this nonlinear relationship of carbon nanotube yarns.

The stability of the moisture content at the front end of cut tobacco dryer (MC-FECD) reflects the ability to control the moisture content of tobacco leaves in the preprocessing section of the tobacco shreds production, and it affects the control effect of the moisture content of cut tobacco in the drying process. Therefore, this paper adopts the correlation analysis method to determine the factors that affect the MC-FECD, and proposes to establish a support vector machine learning model using the "Modeling method of moisture content difference". After research and production verification, it is found that the R² of the "Modeling method of moisture content difference" established in this paper is improved by 58.33% compared with the direct Modeling method. Compared with the expert experience model, the standard deviation of the MC-FECD between batches of the intelligent production control model established in this paper is reduced from 0.06% to 0.03%. The qualified rate increased from 20% to 50%. This study provides a research foundation and ideas for stable regulation of MC-FECD.

The processing of integral beam structures in quartz sensor devices is mostly done by wet etching, and the anisotropy of quartz etching makes the results of the etching process unpredictable, so it is necessary to predict the results of the process by simulation before the etching process. In this paper, a quartz beam structure wet etching simulation system is developed based on the hull method. The system can realise the simulation of beam structure of quartz crystal plate with different cutting type. By inputting the width of etching mask, offset distance and thickness of crystal plate, the etching samples of beams with different widths and thicknesses under different etching time are simulated, which provides guidance for the processing of quartz integral beam structure.

This study utilizes the Hubbard interpolation method to partition the etching directions of quartz crystal into 96 triangular regions based on 13 crystal planes. The etching rates in various directions on each crystal plane of quartz can be obtained through mask design and rate measurement experiment. By measuring the etching rates of multiple types of crystal-cut wafers and considering the symmetry of quartz, the etching rates in various directions on the 13 crystal planes can be determined. At the boundaries of the triangular regions, a series of sampling points are selected to further subdivide the triangular regions into smaller triangles. Utilizing the Hubbard interpolation method, the etching rates of crystal orientations within each small region can be computed. Finally, employing Hubbard interpolation on each triangular region yields the complete quartz crystal etching rate database covering all directions.

To fully utilize the big data accumulated by the lightning positioning system to provide lightning protection services, combined with the lightning fault characteristics and typical tower models of various voltage levels of transmission lines in Qinghai Power Grid, a simulation calculation study on typical towers of transmission lines above 110kV was carried out. ATP-EMTP was used to build a model to simulate and calculate the range of dangerous lightning current for counterattack, and the electrical geometric model method considering the strike distance coefficient and ground inclination angle was used to calculate the range of dangerous lightning current for bypass. At the same time, based on the dangerous lightning current range and average annual lightning situation in Qinghai region, the risk distribution map of lightning strikes in Qinghai region is drawn according to four voltage levels: I, II, III, and IV. This provides technical support for the design of lightning protection engineering in Qinghai power grid and the prevention and reduction of lightning disasters.

Significant Wave Height (SWH) is a critical meteorological parameter that significantly impacts oceanic navigation, coastal engineering, and disaster prediction. Accurate and timely prediction of SWH relies on precise and rapid retrieval techniques. The vast expanse of the ocean and long satellite revisit times have historically posed significant challenges for SWH observation. The advent of GNSS Reflectometry (GNSS-R) technology has facilitated the rapid acquisition of sea surface information by using reflected GNSS signals. This technology, combined with advanced deep learning methods, presents a promising approach for retrieving SWH. Currently, mainstream methodologies primarily utilize GNSS-R data from NASA's Cyclone Global Navigation Satellite System (CYGNSS) satellites. In contrast, GNSS-R data from China's Fengyun satellite series have been underutilized. In 2021, China launched the first Fengyun satellites capable of receiving and processing GNSS-R signals, with official GNSS-R data products released in 2022. Given the recent availability of this data, research leveraging these domestic datasets for SWH retrieval remains limited, and their potential has not been fully realized. This study addresses this gap by focusing on the retrieval of SWH using GNSS-R data from the Fengyun satellites, employing deep learning methodologies. We designed and evaluated multiple models tailored to different characteristics of GNSS-R data. WaveANN model retrieves SWH based on one-dimensional feature elements; WaveCNN model utilizes the Delay-Doppler Map (DDM) power delay spectrum for two-dimensional feature retrieval; and HybridWaveNet model is a hybrid model that integrates both one-dimensional and two-dimensional features for enhanced SWH retrieval. Among these, HybridWaveNet demonstrated superior performance, achieving a root mean square error (RMSE) of 0.7 meters, thereby meeting international standards for SWH retrieval accuracy.

In distribution network systems where the neutral point is grounded through arc suppression coils, the existing accuracy of the single-phase grounding fault line selection method requires enhancement due to the transient characteristics arising from arc suppression coil compensation and potential polarity errors in zero-sequence current transformers. The singlephase grounding fault line selection method based on the compensation characteristics of arc suppression coils holds the potential to theoretically improve the accuracy of selecting single-phase grounding fault lines. This paper delves into the practical application of this method within distribution network systems grounded through the neutral point via arc suppression coil compensation. An analysis is conducted on the characteristics of actual single-phase grounding fault lines under both overcompensation and under-compensation scenarios, as well as the characteristics of fault lines under the influence of polarity errors in zero-sequence current transformers at the line outlet. The results of the analysis demonstrate that the single-phase grounding fault line selection method, based on the compensation characteristics of arc suppression coils, can effectively overcome the impact of both the transient characteristics associated with arc suppression coil compensation and the errors in polarity of zero-sequence current transformers.

With the rapid development of artificial intelligence technology, the intelligent manufacturing and logistics transportation industries are undergoing profound changes. The rise of China's manufacturing industry has further intensified the growth in demand for the transportation of large and heavy objects. Traditional transportation methods such as cranes, manned freight, rail cars, etc. are no longer able to meet the growing demand and safety requirements. Therefore, heavy-duty Automated Guided Vehicle(AGV) emerged as the times require. Our company has successfully manufactured an Automated Guided Vehicle(AGV) with a load capacity of 360 tons and put it into actual use. In order to solve the problem of load stability of heavy-duty Automated Guided Vehicle(AGV) on complex roads, this paper designs an automatic leveling control system based on a multi-point hydraulic platform and constructs the transfer function of the system. Through simulation verification, the fusion of the highest-point leveling strategy and the fuzzy PID algorithm proposed in this article perform well in the leveling control of heavy-load Automated Guided Vehicle(AGV). Compared with the traditional algorithm, there is no overshoot phenomenon, high stability, and can be achieved within 2.5 seconds. Reach a steady state with smaller steady-state error.

Bio-robot is a kind of creature controlled by human beings by applying intervention signals through control technology to regulate biological behavior. The animal can be controlled artificially and become a kind of animal robot by stimulating the brain motor area of the animal. To realize the precise control of the carp robot, it is necessary to monitor its motion state and quantify its motion behavior. Therefore, the article proposes a method of applying computer vision technology to detect the control effect of the carp robot. In this study, the method of combining inter-frame difference with Gaussian mixture background modeling was used to extract the target, the mass cancroid tracking algorithm was used to track the target, and the parallax method was used to obtain the three-dimensional motion trajectory and various motion parameters. The carp robots were divided into the Experimental group and the Control group, with 30 fish in each group. In the Experimental group, the computer vision technology was used for real-time detection, and electrical stimulation parameters was adjusted according to the measured data to control the movement of the carp robot. In the Control group, the underwater control experiment was carried out without computer vision technology. The results showed that the success rate of control in the Experimental group was significantly higher than that of the Control group (P < 0.05). This study indicated that the computer vision detection method could effectively improve the control effect of the carp robot, and had effectiveness and practicability.

To achieve long-term stable operation of nuclear fusion under high-performance conditions, the full-superconducting tokamak experimental device (EAST) was established. Neutral Beam Injection (NBI), an efficient auxiliary heating method, is the foundation for EAST experiments to reach their upper limit. The NBI Control System (NBICS) centrally manages NBI experiment operations and achieves field control. With the increase in experimental achievements and the increasing goals of EAST experiments, more requirements are appearing on the neutral beam injection. To ensure the richness of future experimental operations, a multi-mode injection control system for neutral beams has been developed. The new system adds feedback control and follow-up control modes to the existing open-loop control. Compared with the past, feedback control perfectly solves the problem of beam current slowly decreasing under long pulse widths and follow-up control improves the richness of experiments. The multi-mode control system uses LabVIEW language, RT operating system, and FPGA hardware which achieve functions such as parameter configuration and PID adjustment. This paper introduces the design and development of the multi-mode power output control system for neutral beams.

Aiming at the problem that the early fault signal of wind turbine gearbox is weak and difficult to extract effective features, this paper proposes a fault diagnosis method based on convolutional neural network (CNN) and improved support vector machine (SVM). The S-transform is used to convert the one-dimensional gearbox vibration signal into a two-dimensional feature map containing time-frequency characteristics, and the CNN is constructed to extract features from the time-frequency map. The Latin hypercube and golden sine strategies are introduced to improve the Subtractive average Optimization Algorithm (SABO), and the improved SABO algorithm is used for parameter searching of the SVM, and an ISABO-SVM classifier is designed to classify the extracted fault features and derive the fault diagnosis results. The experimental results show that the CNN-ISABO-SVM wind turbine gearbox fault diagnosis model proposed in this paper has higher accuracy compared to the CNN-SVM and CNN-SABO-SVM fault diagnosis models.

An adaptive tracking controller for an n-link robot subject to model uncertainties and position constraints is addressed in this article. The robot's position is constrained in a time-invariant compact set by using the nominal-model-based control scheme, and the position is constrained in a time-varying compact set by utilizing the uncertain-model-based control strategy. It is widely known that certain systems and constant constraints are just special cases in practical engineering, therefore, the uncertain-model-based control method can be more versatile in addressing the practical problems of the robot with state constraints. To prevent constraints from being violated, the barrier Lyapunov function is employed, and the disturbance observer is utilized to estimate the model uncertainty. It can demonstrate the asymptotic stability of the closed-loop systems by utilizing Lyapunov analysis. The effectiveness of the proposed approach is verified by completing the simulation studies.

The inspection path requirements of a four-wheeled omnidirectional robot in substations are met using an initial A* algorithm-generated path and a trajectory optimized with the cubic non-uniform B-spline algorithm. A fuzzy PID controller is used to regulate the robot in real-time dynamically, ensuring the robot's four-wheel drive speed and rotation angle are properly regulated, reducing the robot's motion error. The motion control design proposed in this paper is verified using a joint simulation environment built using Coppeliasim and Matlab, which effectively avoids collisions between the robot and obstacles, optimizing the smoothness of the cornering path

As a high-efficiency DC-DC converter, the dual active bridge (DAB) converter can be connected in series on the input side to improve the withstand voltage capacity and in parallel on the output side to enhance the current processing ability. This configuration helps meet the energy storage system's demand for efficient energy conversion. Sharing input voltage and output current between modules is crucial to ensure the normal operation of the series input parallel output system. To overcome the limitations of the traditional inverse droop control strategy, which fails to achieve voltage and current sharing and zero steady-state error simultaneously, and to enhance the adaptability and stability of the system in response to input voltage and load changes, as well as improve the dynamic performance. The proposed strategy combines the inverse droop control strategy with correction and the inverse droop strategy of fixed voltage feed-forward. It integrates a PI correction module with real-time input voltage changes and conducts stability analysis. A simulation model of a series input parallel output system with two DAB modules is developed using MATLAB simulation software to verify the control strategy. The simulation results confirm the effectiveness of the proposed control strategy.

The operational status of power grid equipment directly affects the safety of the grid operation, making intelligent equipment health analysis methods particularly important. In order to address the shortcomings of low efficiency in manual reporting of status indicators, single feature quantities in status evaluation, and retrospective bias in health assessment encountered in power equipment health analysis, this paper proposes a method centered on equipment and components, guided by "equipment health codes," to study the establishment of a graph relationship between key equipment status characteristics and equipment failures. Through the combination of expert experience and rational intelligent agent technology, automated and intelligent health analysis is achieved, improving the analysis efficiency of operation and maintenance personnel and evaluators, and enhancing the level of equipment reliability.

Aiming at the problem that passive suspension cannot be adjusted independently to achieve good buffering effect on different road surfaces, a dynamic adaptive radial basis function (RBF) neural network PID controller is designed based on the two-degree-of-freedom hydro-pneumatic suspension model of 1/4 car body, which improves the traditional RBF neural network structure and relies on the self-learning behavior of neural network to dynamically adjust PID parameters, The simulation model was built in Matlab/Simulink, the simulation results showed that, under the condition of B-class and C-class uneven pavement input, compared with the multimode self-switching PID control, the root mean square error (RMSE) of tire dynamic load, body centroids acceleration and dynamic deflection of hydro-pneumatic suspension under the improved RBF-PID control are reduced by 42%, 64% and 3%, respectively, which verifies the superiority and effectiveness of the proposed control strategy.

This paper investigates the application of nonlinear forecasting models in Short-term Load Forecasting (STLF). Given the limitations of traditional linear models in dealing with complex load data, nonlinear forecasting models are invoked to enhance forecasting accuracy. Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM) are constructed and validated, and the initial use of ensemble learning idea to integrate single nonlinear models is proposed. The empirical analyses showed that the nonlinear models outperform the linear ones in terms of prediction accuracy and stability. The results demonstrated that MLP-SDLW models with 10 variables performed best with Mean Average Percentage Error (MAPE) 5.047%. Meanwhile the ensemble models reflected the advantage of higher accuracy in observation.

In this paper, an optimal control strategy with inverse model is designed for the stability analysis of Hammerstein systems with backlash. In this control strategy, the inverse model compensation mechanism is set up to offset the adverse influence of nonlinear nonsmooth plant on the control system. Subsequently, by minimizing the defined objective function integrating with stability analysis, the stabilized optimal control strategy is proposed. The derived control strategy and stability criterion are substantiated via the results of simulation based on the mechanical transmission system.

Due to the use of turn-off device IGBT in flexible HVDC transmission, the fault current is a short time change process. In the initial stage of flexible DC protection failure, if the converter valve cannot be quickly closed and the AC test switch cannot be turned off, it will bring great harm to the converter equipment. At present, the test of flexible DC control protection mainly depends on RTDS or PSCAD simulation test. This testing method needs to build a very complex test environment, and the workload of on-site configuration and debugging is very huge. Therefore, this paper establishes an integrated test system of flexible DC control and protection based on modelling decoupling to solve the above problems. The integrated test system realizes the field testing of flexible DC protection control systems with different configurations, which greatly reduces the workload of on-site debugging personnel and improves the safe and stable operation ability of the power grid.

Eddy current testing (ECT) is a method used to detect defects in conductive materials. The estimation of defect sizes from detected signals plays a crucial role in evaluating product quality and guiding production processes. However, the determination of defect sizes during testing is susceptible to variations in lift-off distances, electrical noise, and often relies on human expertise. This paper evaluates defect sizes using a convolutional neural network regression model based on the impedance signals of defects. First, a through-type eddy current testing system is constructed, resembling those used in industrial production, to obtain high-quality eddy current testing data. Secondly, data are partitioned to locate defect signals, which are then converted into time-frequency matrices using continuous wavelet transform. A pseudo-colour enhancement algorithm is utilized to transform matrices into RGB images and preserve discernibility of defect information. Finally, regression models are constructed to estimate defect sizes. Experimental results demonstrate that this method can accurately evaluate defect sizes with a resolution of 0.05mm, achieving an accuracy of 96.02% with an error margin of ±0.01mm. For defect sizes ranging from 0.3mm to 1.0mm, the mean absolute error in defect estimation is 3.0201×10^-3mm.

With the increasing attention to environmental electromagnetic radiation, the calibration of near-zone field strength measuring instruments, which are important measurement tools, has always been of great concern. This article proposes a calibration device for a near-zone field strength measuring instrument based on GTEM Cell and field strength probe. The calibration device utilizes a field strength probe to transmit the standard field strength, combined with a power meter to monitor the effective field strength value in the selected area, which is then set up for automatic detection by the test software. It realizes the automatic calibration of the near-zone field strength measuring instrument in GTEM Cell, which not only reduces the establishment cost but also improves work efficiency.

The thermal mass flowmeter has simple structure and wide measurement range. It can be divided into capillary thermal mass flowmeter and plug-in thermal mass flowmeter. In order to accurately measure the thermal gas mass flowmeter, the passive gas piston provers (standard device) was selected to measure it. and the measurement uncertainty of the standard device was analyzed. Three test points are selected to test at three pistons, and the repeatability of the measurement results and the expanded uncertainty of the measurement results are analyzed at the same time. The expanded uncertainty U_rel is 0.19% (k=2), which is proved that the calibration of the thermal gas mass flowmeter with passive gas piston provers (standard device) is effective. The uncertainty of the measurement results is small and the reliability is high.

For real-time analysis of pneumatic feeding on cut tobacco running status, and to realize fast fault warning, data was collected over nearly three years from a tobacco factory cigarette machine, focusing on 12 key parameters of various production running states including dust removal airflow, tobacco feed negative pressure, tobacco feed air speed, tobacco feed frequency, air leakage coefficient, dust removal power, and valve opening degree. Utilizing the random forest algorithm for parameter selection, a fault prediction model for the air supply cut tobacco system was established through the SVM algorithm. Experimental results show the three parameters, namely the dust removal airflow, tobacco feed negative pressure, and tobacco feed airflow are the most significant factors contributing to air supply cut tobacco abnormalities. The built prediction model exhibited an accuracy of 86.87%. Application of the model in the cigarette production line led to a 72% reduction in cut tobacco delivery failure rate and reduced problem analysis time from 20 minutes to 3 minutes. This intelligent discrimination method of air supply cut tobacco feeding state holds significant application value for enhancing the intelligent manufacturing level of enterprises [1].

The paper mainly adopts simulation analysis to consider the contact friction form and characteristics of the metal cushions from the perspective of the microstructure model. It models the metal wire contacts, and then deduces the microstructure to the macro model by analysing the internal structure of the metal cushions. The overall idea is mainly to abstract a three-dimensional model with regular internal structure arrangement that is easy to study based on the preparation process and microstructure composition of the metal cushions. Furtherly, contact simulation is carried out by using finite element method, and the mechanical characteristics of the metal cushion are obtained. The validation of the model for the description of the metal cushion internal structure is verified by combining solid contact theory. The results indicate that the special characteristics of metal cushion such as hysteretic behaviour, nonlinear stiffness, and dry friction damping property are predicted by the model, with the comparison between the theoretically calculated and simulated results yielding differences in numerical values lower than 3% under the assumptions considered in this paper.

Unmanned Aircraft System (UAS) are central to low-altitude flight activities, and their safety and reliability are crucial to the development of the entire low-altitude economy. This article provides a detailed analysis of low-altitude drone airworthiness certification policies, UA operational risk levels, and UAS safety objectives. It conducts an in-depth study of the operational risks and functional hazard assessments of UAS Command and Control links in logistics operation scenarios and innovatively proposes a safety analysis method and process suitable for civil UAS Command and Control links, which is highly practical and operable. The research findings are of significant importance for the airworthiness certification of drone Command and Control links, offering clear guidance and reference for the safety design and improvement of drone communication links, while also providing technological support for technological innovation and industrial upgrading in the low-altitude economy. More importantly, the processes and methods of this research have a wide range of applicability and reference value. They are not only applicable to logistics operation scenarios but can also be extended to other scenarios such as power line inspection, agricultural and forestry plant protection, freight express delivery, environmental monitoring, emergency rescue, forest fire prevention, artificial rainmaking, and carrying passengers and goods, or to the analysis of key factors in the airworthiness safety of other communication system links. They are of great practical significance and value in preventing and mitigating safety risks in drone operations, providing valuable experience and technical support for the safe operation and risk management of the UAS industry.

This article aims at the problem that the automated production line of standard packaged cigarettes cannot effectively produce special-shaped cigarettes. Through the analysis of the packaging method and external dimensions of packaged cigarettes, as well as the study of the transportation stability of packaged cigarettes, this paper proposes a common method for different and standard finished cigarettes. The optimal size parameters of the line conveying equipment were determined, and a buffer channel for co-line conveying of finished cigarettes was designed. After the research results of this article were put into operation in the packaging workshop of Hangzhou Cigarette Factory, the fully automatic production logistics of special-shaped cigarettes was realized, reducing the number of 6 full-time cigarette box palletizing outsourcing workers. While improving production efficiency and accuracy, it also reduced the cost Labor costs and human error. This research result effectively solves the problems of transportation, caching and separation of finished cigarettes in the co-line production of cigarettes, and provides important reference and practical experience for similar technical transformation projects in the industry.

In the modern society with highly developed rationalism, micro-landscape reproduces a highly integrated ecosystem of humanity and nature in the most condensed and lightweight form. Therefore, micro-landscape has become an effective way for breeders to improve their health and get close to nature. However, the change of environmental factors will restrict the aesthetic ingenuity of micro-landscape. Therefore, it is of great significance to study the application of temperature and humidity automatic regulation micro-landscape system based on STM32 micro-controller in environmental adaptive control. Based on the sensor technology and biological principles, this study aims to improve the survival rate and efficiency of the micro-ecosystem, and discusses the optimization of sensor technology in the ecosystem, in order to provide more scientific and effective conservation guidance for breeders and promote the development of the application of sensor technology.

Transmission towers are the most important power equipment in transmission lines, and monitoring the structural safety of transmission towers is of great significance. A three-dimensional finite element model of 66kV drum-type tower was established and the static strength of the drum-type tower under wind load was analyzed. It is indicated that the maximum stress of the drum-type tower tower under wind load is much greater than that under gravity load only. Based on the simulation results of the maximum stress location, the optimal layout method of stress sensors was obtained, and a stress monitoring system for transmission towers was designed.

In order to meet the oil trade measurement or enterprise internal work requirements, this paper designs a mobile detection device. The device adopts two mutually leaning half-column metal gauges as the main measuring instruments or standards, supplemented by level measurement, temperature measurement, leveling and fastening, shell and other designs, to achieve the dual-use function of oil quantity measurement and online calibration of oil flow meter. This paper describes in detail the composition of the device and the function of each component, after the field test verification, the test results to meet the customer's needs.