bluuebunny/crossref_metadata_2025 huggingface dataset:
Study and early warning of comprehensive distribution box state based on maximum likelihood parameter estimation of Weibull distribution
Authors: Ji Degui , Sun Bo , Chen Xiaolei , Zhang Yan
Published: December 2022

With the expansion of the power grid scale and the improvement of distribution box automation, the structure of the distribution network has become more complex, and the maintenance task is heavy. The traditional post-maintenance and regular maintenance modes could not satisfy the requirements of distribution network equipment operation reliability. Firstly, according to the maintenance mode, the failure rate curve is proposed based on Weibull distribution. The service age reduction factor and the current failure rate of the equipment are mapped in this paper to the failure rate curve based on Weibull distribution. Secondarily, they are used to calculate the actual service age of equipment. The annual failure rate of equipment is used to count and determine the failure rate increment factor. Thirdly, the ant colony algorithm is applied to Weibull distribution maximum likelihood parameter estimation. Finally, an example analysis verified that the parameter accuracy of ant colony algorithm estimation is higher. Thus, the correctness of the state-based equipment failure rate estimation method is verified, which lays a foundation for the formulation of a maintenance plan.
Design of A Grid-connected Control System for Distributed Photovoltaic Power Generation Based on PLC
Authors: Zhu Shaofei , Hu Chao
Published: December 2022

Distributed photovoltaics interfere with continuous power generation after grid connection. In the face of the failure of a single module, the current grid-connected control system needs to readjust system parameters to flexibly adapt to equipment operating conditions. Therefore, this paper is researching a photovoltaic power generation grid-connected control system based on PLC. In the hardware part, PLC is used to complete power generation control, monitoring MCU, data acquisition, control, and other modules. In the software part, the grid-connected state is optimized and controlled according to the distributed photovoltaic output power and the remaining energy storage capacity. The PLC is the execution center, which will control the on-off state of the equipment switch after the data operation. The experimental results show that the designed system can optimally control the power generation and energy storage units according to the power change, reduce the cost of electricity consumption through the adjustment of charge and discharge, reduce the IO usage rate, and improve the concurrent response ability of users. The design system not only guarantees the quality and efficiency of photovoltaic grid connection but also realizes rapid networking and independent tracking of power points to meet the requirements of flexible communication.
Construction of Marginal Effect Model for Reliability Improvement of Urban Distribution Network
Authors: Zhang Ying , Gong Ruibang , Gu Jiangbo , Wang Yuchen
Published: December 2022

With the development of the social economy in China, China’s power grid is facing severe challenges. In addition to effective supply, the more important thing is to provide the reliability of the power supply for the existing distribution network. The power supply reliability of the distribution network directly affects the safety and reliability of power users, and also relates to the development of power supply enterprises. To realize the safer and more stable operation and development of urban distribution networks, this paper researches the construction of a reliability enhancement marginal effect model of urban distribution networks. By determining the factors that affect the reliability of distribution network operation, the marginal benefit model is selected to construct the index. The specific value of the index and the boundary conditions of the model are set. The reliability improvement marginal benefit curve is analyzed in depth; and finally, the specific value of the reliability index marginal benefit is calculated. The new model is applied to the distribution network in the old urban area of a city, and the relationship between each index and reliability and marginal benefit is clarified. According to the analysis results, the future construction and development direction of the distribution network in the old urban area of the city is fully clarified, and the path scheme of reliability improvement is given according to the degree of urgency. Through the use of the marginal effect model, it can provide a more effective reference for the improvement of national distribution network reliability and the decision-making of distribution network construction planning projects.
Research on Urban High Voltage Power Network Harmonic Transfer and Emission Level
Authors: Ye Shangxing , Liu Hongyi , Liu Xueying , Jiang Youhua
Published: December 2022

With the rapid growth of urban power transmission cable scale, the harmonic pollution in the high-voltage power grid is more transmitted to other power grids of different voltage levels, which makes the harmonic transmission more complicated, increases the harmonic emission level, and makes it difficult to quantitatively analyze and study the allowable value of users’ electrical pollution emission. Therefore, in this paper, by establishing the harmonic conduction model of the urban high-voltage power grid and considering the equivalent circuit of transformer and transmission cable in the urban power grid, the parameters of transmission cable harmonic transfer coefficient and the allowable emission value of power quality pollution of the urban power grid are deduced and quantitatively analyzed. Then the harmonic transmission and mutual influence of urban power network harmonics in power transmission cable are analyzed through examples, which is helpful to determine the allowable value of harmonic pollution emission scientifically and reasonably.
A Study on Energy Saving of Condensate Pump Speed Regulation improvement for CPR1000 Nuclear Power Unit
Authors: Gan Changxian , Li Huahuan , Hu Yanjun , Yang Jinqiu , Li Zhenghui , Yang Yonghong
Published: December 2022

To reduce the energy consumption of nuclear power plants, this paper studies the control mode of changing the condensate pump frequency to variable speed regulation, using permanent magnet flexible transmission technology to change the speed of the condensate pump, and the control strategy of deaerator water level and the outlet pressure of condensate pump is redesigned to achieve the purpose of energy saving. Through the study of control strategy, process design analysis, theoretical calculation, and process simulation of the full-range simulator, the feasibility of the proposed method is confirmed, which provides an important reference for the improvement of energy saving and speed regulation of auxiliary machines in nuclear power plants.
Research on the Influence of Ecological Flow on Cascade Short-term Power Generation Operation in the Baishui River Basin
Authors: Zhou Jia , Liang Chusheng , Tao Xiangming
Published: December 2022

Cascade hydropower station uses natural runoff and outflow from upstream reservoirs as the “fuel” for power generation, while the discharge of ecological flow directly affects the storage capacity. For hydropower stations without ecological units, the discharge of ecological flow will inevitably affect the power generation capacity of the cascade hydropower stations. The design of the Baishuijiang cascade hydropower station has requirements for ecological flow discharge, but the requirements and standards of ecological flow in actual operation and management are not uniform, resulting in the unclear implementation of ecological flow. By investigating the construction and solution of the short-term optimization scheduling model, this paper analyzes the impact of ecological flow discharge on the power generation of the Baishuijiang cascade hydropower station. The research results can provide a reference for further clarifying the ecological flow standard of the Baishuijiang cascade hydropower station and guiding its production and operation.
An Experimental Study on Propped Fracture Conductivity in Tight Reservoir
Authors: Zhao Jinsheng , Li Yongming , Chang Du , Hui Bo , Li Xianwen
Published: December 2022

Propped fracture conductivity is a major element which will influence the stimulation effect after fracturing. To reveal the major factors affecting the propped fracture conductivity, the propped fracture conductivity with 20-40 mesh sand and ceramics of different sanding concentrations was experimentally studied. It can be concluded that, the propped fracture conductivity decreases as the closure pressure increases. For the silica sand proppant, when the closure pressure increased to 41.4 MPa, the downward trend of the conductivity became slower. For silica sand proppant, when the closure pressure is small, with the increase of sanding concentration, the fracture conductivity increases. When the closure pressure is greater than 41.4 MPa, the conductivity almost does not increase. Considering proppant costs, ceramic proppant can be used in low-closure pressure formations and silica sand proppant can be used in high-closure pressure formations.
Research and practice on the intelligent transformation of the gate control system in a large hydropower station
Authors: Lu Shuai
Published: December 2022

With the development of information technology, the research on the hydropower stations intelligence has gradually become an important indicator for the evaluation of the hydropower industry. Therefore, it is of great significance to carry out the intelligent transformation of relevant equipment and systems in hydropower stations by integrating emerging information technology. In view of the implementation content of the intelligent project transformation of the gate control system in a large hydropower station, this research studies the structural design of the control system, the power supply design, the component selection, the multiple landing gate function, the intelligent protection and monitoring function, and the intelligent network architecture design of IEC61850, and analyses the practice on the intelligent transformation. Therefore, this paper can provide a certain reference for the intelligent transformation, research, and development of the gate control system of hydropower stations.
Application of the unmanned inspection system in power generation enterprises
Authors: hao-Li Jing
Published: December 2022

In the power generation company, it is the unmanned inspection system that can through visual image recognition, infrared monitoring, and fixed-point sensor complete the real-time monitoring of the plant area and equipment, through the system building perception layer, network layer, and application layer realizes the remote control command issued, and complete the equipment running status and working parameters of the real-time monitoring and remote control. Through the design of the bearing state feature extraction method and the fusion of machine mechanism, and artificial intelligence data processing method, the intelligent diagnosis and early warning of the whole plant equipment are realized, and the one-click inspection of power generation enterprises is achieved.
An Electromagnetic Environment Monitoring Platform for AC-DC Parallel Line
Authors: Wang Guangzhou , Tian Xu , Liu Weipo , Li Yuan , Guo Xing , Li Haifeng
Published: December 2022

With the rapid development of China's economy, the electricity demand is increasing and the scale of power grid construction is expanding. Large-scale construction of transmission and substation projects leads to an increasing shortage of transmission corridors, which inevitably leads to the formation of parallel erection of AC and DC transmission patterns. When the AC and DC transmission lines are erected in parallel, the coupling effect makes the hybrid electromagnetic environment of the AC and DC parallel lines more complicated. In this paper, with the track-supported intelligent robot as the hardware and the AC-DC mixed electromagnetic environment data analysis management system as the software, an automated synchronous measurement system integrating the measurement equipment of frequency electric field, radio interference, synthetic electric field and meteorological monitoring equipment is established. It can realize mobile multi-point time-divisional measurement and dynamic management of data with one set of equipment, carry out hybrid electromagnetic environment monitoring for the typical AC-DC parallel line sections that have been put into operation, and provide technical support for the rational design of subsequent large-scale UHV projects and the evaluation of the AC-DC hybrid electromagnetic environment.
Low-Latency Autonomous Service Orchestration for the Power Communication Network
Authors: Liang Wenjuan , Li Yijie , Li Bo , Liu Pengju , Kong Deyu
Published: December 2022

Power communication network based on optical technology can support the operation, production and scheduling of power systems. However, due to the dynamic and information uncertainty factors in the network environment, the independent service arrangement of the power communication network is faced with many challenges. To address these challenges, a software-defined network (SDN)-empowered the power communication network architecture. In this paper, we proposed a routing algorithm based on ε-greed to minimize total transmission delay of service data flow, which can support the autonomous, intelligent, and flexible orchestration for the power communication network. Simulation verify the superiority of the proposed ε-greedy algorithm in the aspect of delay and optimal path selection.
A rapid detection and sorting system for seed maturity
Authors: Zhang Yuhan , yu Lintao , Zhang Shida
Published: December 2022

To solve the problems of low speed and low efficiency of seed sorting, this study designs an efficient seed separation method. In this method, a vibrator is used to scatter the seeds and make them fall rapidly through parallel multi-tracks. The seeds are irradiated by a light source with a wavelength of 670 nm after passing through the identification area. The high-speed linear array camera takes photos of the irradiated seeds. The convolution neural network algorithm combining residual learning and attention mechanism is used to analyze and judge the maturity of the seeds. The algorithm model can effectively capture the characteristics of the seed photos and conduct efficient processing analysis and judgment, Finally, the low maturity seeds are blown away by an air compressor and blower to achieve rapid and efficient seed sorting. The test shows that the sorting speed can reach 50 particles/s and above, and the sorting accuracy can reach 96%.
A Lightweight Two-Stream Model for Driver Emotion Recognition
Authors: Hu He-xuan , Zhang Lu-feng , Yang Tian-jin , Hu Qiang
Published: December 2022

In recent years, more and more attention has been paid to the traffic safety of drivers. Studies show that the motor vehicle speed will affect the incidence of traffic accidents and traffic accident casualty rate. In this paper, machine learning vision technology is used for real-time recognition of driver emotions, and a lightweight SIC-NET+RC-NET dual-stream recognition model is designed to cope with different scenarios of vehicle speed during driving. SIC-NET is a lightweight CNN network model, which greatly reduces the number of model parameters and computational power requirements, sacrifices part of accuracy, and greatly improves the real-time performance of model recognition. To improve the residual convolutional network model of inception blocks, RC-NET nested multiple micro-convolutional neural networks are used to identity mapping between different inception blocks to enhance the network training effect, expand network width and convolutional depth, and alleviate network degradation. This article stipulates that when the vehicle speed is greater than 60km/h, SIC-NET is used. When the vehicle speed is less than or equal to 60km/h, SIC-NET+RC-NRT is used. The FER2013 dataset was used for testing and validation in this paper. When SIC-NET is used, the model has better real-time performance and can provide a more timely warning when the vehicle speed is higher. When SIC-NET+RC-NET is used, the accuracy of the model in the FER2013 dataset is 69.65%, and the model can better cope with the scenario of a low vehicle speed. The experiment shows that the lightweight dual-flow model has good robustness and identification effect, which solves the problem of using the same model for different vehicle speeds and can effectively ensure the safety of the driver’s life and property.
Design and Implementation of Wastewater Pollutant Data Analysis and Visualization System for Vessels
Authors: Lv Taizhi , Tang Peiyi , Zou Yujuan
Published: December 2022

To monitor the discharge of water pollutants from vessels, a monitoring system for the discharge of water pollutants from vessels has been established on the Yangtze River. The system generates tens of thousands of ship wastewater pollutant data every day. The analysis and visualization of wastewater pollutant discharge data can help the monitoring department to grasp the discharge situation and improve the accuracy of supervision. This system extracts the data to the data warehouse in real-time based on the data extraction technology, improves the query efficiency based on the three-level storage architecture, makes real-time pollutant data statistics based on the stream computing technology, and realizes the visual display of the data based on the front-end and back end-separation technology. The system has been applied in the maritime supervision department to provide a data basis for vessel wastewater pollutant supervision and decision-making.
Research on PM2.5 Pollution Prediction Method in Hefei City Based on CNN-LSTM Hybrid Model
Authors: Wang Qiang , Zhang Yiwen
Published: December 2022

The air quality data has the characteristics of large scale and high time sequence. In the deep learning model, CNN has good feature extraction and expression ability through convolution operation. LSTM has strong time series data processing ability, which effectively solves the long-term dependence problem in the time series data that CNN cannot process. However, a single feature extraction or time series processing method will affect the accuracy of the model. This paper proposes a PM2.5 pollution prediction method based on the CNN-LSTM hybrid model, which can fuse the time series and nonlinear features of the data. By selecting the air quality and meteorological data of Hefei City in Anhui Province from 2018 to 2021 for prediction research, the experimental results show that the CNN-LSTM hybrid model is better than the LSTM model in terms of RMSE, MAE, and R2 evaluation indexes, which reflects higher prediction accuracy. It can not only be used for real-time prediction of PM2.5 pollution, but also for the prediction of other pollutants in the atmosphere. It also provides a reference for the application of CNN-LSTM model in solving other time series big data.
Classification of epileptic EEG based on improved empirical wavelet transform
Authors: Lv Chao , Song Bo
Published: December 2022

Electroencephalography (EEG) is the most commonly used method in the diagnosis of epilepsy diseases. In order to identify epilepsy EEG signals more effectively, an automatic identification method of epilepsy EEG signals based on improved empirical wavelet transform (EWT) is proposed. Firstly, in view of the difficulty of spectral division in the EEG signal processing of epilepsy by empirical wavelet transform, an improvement measure is proposed, that is, the average difference spectrum of the signal is obtained to replace the signal spectrum in the empirical wavelet transform, and then a number of component signal with epileptic characteristic can be obtained from the original signal. Afterward, feature extraction and classification are completed through a common spatial pattern and AdaBoost algorithm. Simulation analysis was carried out on the Bonn epilepsy EEG data set, and the EEG signals of healthy people and epilepsy patients were identified and classified in the interictal and ictal periods, and high classification accuracy was achieved.
Runoff Prediction Based on Deep Residual Shrinkage Long Short-term Memory Network
Authors: Guan Zhuang , Hu He-xuan , Hu Qiang , Lu Jia-min
Published: December 2022

Aiming at the lack of sufficient meteorological data for runoff prediction in areas with few data, the accuracy of the data is improved by using soft thresholding under the deep attention mechanism, and a deep residual shrinkage long-short memory network prediction model is constructed combined with the watershed characteristic data. The model is validated by using 134 watersheds with spatial correlations in the CAMELS dataset. According to the experimental findings, the model is more accurate than the LSTM model, HIV model, and mHM model in predicting runoff in regions with little data, with an accuracy of 86%.
Dynamic Load Regulation of Robots with Multi-sensor Fusion
Authors: Cao Xinqian , Tan Benying , Li Yujie , Ding Shuxue
Published: December 2022

Multi-sensor fusion technology has been extensively deployed for the autonomous navigation and control of robots. Theoretically, by increasing the number of sensors, more effective data on the surrounding environment and obstacle information can be obtained. However, in practical applications, a massive number of sensors will not only increase the cost of using sensors in the system but also add to the difficulty and cost of data processing, leading to unnecessary resource consumption. To solve this problem, first, the robot navigation area is divided according to the importance of the navigation area and the characteristics of sensor parameters. The navigation non-core area adopts the single lidar sensor, while the core area adopts the multi-sensor fusion of “lidar +” to reduce the number of sensors used in the fusion and improve the performance of multi-sensor fusion after the division. Then, in consideration of the higher requirement for computing capacity of the device imposed by deep learning, the multi-sensor fusion algorithm is introduced into the dynamic load regulation mechanism to solve the problem from the algorithm level so that the outperformance of deep learning in feature extraction and other aspects can be utilized to reduce the performance requirements of the device and the overall power consumption of the system. Finally, by building the ROS experimental platform, it is verified that the robot can use autonomous navigation in complex and diversified home scenes.
Endoscopic Image Denoising Algorithm Based on Spatial Attention UNet
Authors: Zhang Biao , Li Mianhao , Zhou Changjiang , Yang Qing , Wang Liqiang , Yuan Bo
Published: December 2022

Endoscopic image has complex backgrounds and spatially different noise, bringing mainstream denoising methods to the problem of incomplete noise removal and the loss of image detail. Thus, an endoscopic image denoising algorithm based on spatial attention UNet network is proposed in this paper. UNet based on residual learning is utilized as the backbone network. Spatial attention modules based on noise intensity estimation and edge feature extraction modules are used to remove noise better while preserving the image details and improving generalization ability. We take endoscopic images of real scenes using gastroscopy and compare our method with mainstream methods. Experimental results show that our approach improves PSNR by 3.51 or 2.93 and SSIM by 0.03 or 0.015 compared with CBDNet or EDCNN, respectively. Our method can effectively improve the impact of noise on the image quality of endoscopic images, thus better assisting doctors in diagnosis and treatment.
Reservoirs Optimal Operation Based on Reinforcement Learning
Authors: Hu He-xuan , Qian Ze-yu , Hu Qiang , Zhang Ye
Published: December 2022

Reinforcement learning has been widely used to solve the problem of optimal operation of a single reservoir. However, the research on the combination of reinforcement learning and optimal operation of reservoir groups is still blank. The complexity of reservoir groups brings difficulties to the application of reinforcement learning. In order to solve the complex problem of optimal operation of the reservoir group, Q-learning combined with the penalty method is used to generate the optimal operation scheme of the reservoir group by exploring the optimization Q value of the operation sequence. The experimental results show that this method can achieve the optimal solution in theory. Furthermore, Q-learning combined with a feasible direction method is used to establish time feasible state table and state-feasible action hash table according to the constraints of the discrete four-reservoirs problem, so that the Q-learning algorithm can shorten the optimization time and obtain the optimal solution.
Vehicle Route Planning Using A* Algorithm in Real-world Roads
Authors: Liu Xingyu
Published: December 2022

With the technical progress in the fields of robotics as well as artificial intelligence, many intelligent algorithms which focus on robot path planning problems have been developed so far. A* algorithm is an efficient and technically mature path planning algorithm that can efficiently solve robot path planning problems. In this context, this paper proposes an improved A* algorithm to plan roads for automobiles in urban areas, introduce time factors into the cost function, select an urban area, use grid map modeling, and simulate in the MATLAB environment. The final result shows that this kind of algorithm can efficiently solve the problem of automobile road planning.
Study on the UWB location algorithm in the NLOS environment
Authors: Li Jinwang , Gao Tongyue , Wang Xiaobing , Guo Weiping , Bai Daizhuang
Published: December 2022

At present, people spend most of their time indoors, so it is necessary to study high-precision positioning. Ultra wide band (UWB) can obtain the ranging accuracy with centimeter-level error. However, since the indoor environment is more complex than the outdoor environment, positioning errors tend to be generated during the UWB positioning due to the influence of non-line of sight (NLOS). Therefore, this paper investigates how to identify the NLOS environment and reduce the NLOS error. This paper proposes a method to determine the line of sight (LOS) environment credibility and uses it to determine the NLOS environment. The data with NLOS error is determined according to the standard deviation of the residual between the square of the predicted ranging value and the square of the measured ranging value. Then the data with NLOS error is compensated by complementary filtering. Finally, the compensated measurement data and the optimal estimation of the state at the previous moment are imported into the Kalman filter (KF) to determine the optimal estimation at the current moment.
Research on Inverse Kinematics Solution of Manipulator Based on FGA Optimized BP Neural Network
Authors: Mao Liangan , Li Li
Published: December 2022

In view of the complexity of the traditional inverse kinematics solution, it is solved by the mapping of the BP neural network, but the precision is low and the network performance is not good. By using FGA to optimize BP neural network, the shortcomings of the neural network itself can be overcome. Taking the pose matrix and joint angle of the manipulator as the input and output variables of the prediction network, the inverse solution prediction BP network of the manipulator is constructed, and then samples are selected for training. Finally, the network test and simulation results show that the optimized BP neural network has higher prediction precision and improved network performance.
An Improved CLIQUE Algorithm
Authors: He Honglei , He Yuxuan , Zhu Chunjiang
Published: December 2022

CLIQUE is a clustering algorithm based on grid and density, which is sensitive to grid division parameter M and density threshold R, and has poor clustering accuracy. This paper proposes an improved algorithm, which is not sensitive to the initial grid division parameter M, and can automatically split the grid. Combined with the K-means algorithm, the clustering effect is improved. The F value on the S1 dataset is 36.9% higher than that of the classic CLIQUE algorithm.
Human Ear Recognition Algorithm of YOLOV3 Based on Attention Mechanism
Authors: Lei Yanmin , Pan Dong , Feng Zhibin , Qian Junru
Published: December 2022

To address the problems of false detection and the lack of accurate target bounding boxes in the process of detecting human ear recognition, a SE-YOLOv3 target detection algorithm with improved YOLOv3 is proposed. Based on the YOLOv3 algorithm, the effects of embedding the SE attention module into different positions of the model on the detection performance of the algorithm are studied and analyzed separately. It is proved that embedding the SE attention module in the parallel ResNet network in the backbone of the YOLOv3 model can effectively improve the detection accuracy of the algorithm. And it is experimentally demonstrated in CCU-DE and USTB human ear datasets.
Improving the performance of Uyghur speech recognition based on Factorized Time-Delay Neural Network
Authors: Zhang LuZhuo , Liu LanJiao , Huang GaoCe , Zhu HaiPeng , Zha HaiPeng , Qiu ZiCheng , Zhang Xia , Lu WenQiang
Published: December 2022

Speech recognition systems have low accuracy in recognizing the Uyghur language, a low-resource language, due to its strong language specificity and few public training datasets. Given this problem, considering the characteristics of Uyghur, we use morpheme units to build a language model and use mixture data augmentation methods to expand the training data. A 9-layer TDNN-F is applied, which can effectively utilize contextual information. An optimal 9.88% WER (Word Error Rate) is achieved in experiments on the open-source dataset THUYVG-20. Compared to the baseline system of this dataset, the WER is reduced by 6.7%, which significantly improves the accuracy of the Uyghur speech recognition, and provides a reference in other low-resource languages for speech recognization.
Research on the Tray-type Shuttle Crank Connecting Rod-parallelogram Mechanism Jacking System
Authors: Cao Chongzhen , Song Hong , Wang Fengqin , Xu Haohao , Feng Kecheng , Liang Fuxing
Published: December 2022

Pallet shuttle plays a key role in the efficient and stable operation of high-density automated storage systems. For tray type through the roof overloading its cargo, we design a crank connecting rod-combinations of parallelogram mechanism type jack-up system. This paper introduces the principle of the jack-up structure in detail, and part of the dynamics of jack-up institutions is analyzed, calculated, and simulated. The driving motor of the jacking mechanism is selected. The feasibility of the scheme is verified by a prototype test.
Galaxy Morphology Classification with DenseNet
Authors: Hui Wuyu , Robert Jia Zheng , Li Hansheng , Wang Zijian
Published: December 2022

Galaxy classification is crucial in astronomy, as galaxy types reveal information on how the galaxy was formed and evolved. While manually conducting the classification task requires extensive background knowledge and is time-consuming, deep learning algorithms provide a time-efficient and expedient way of accomplishing this task. Hence, this paper utilizes transfer learning from pre-trained CNN models and compares their performances on the Galaxy10 DECals Dataset. This paper applies opening operation, data augmentation, class weights, and learning rate decay to further improve the models’ performance. In our experiments, DenseNet121 outperforms the other models and achieved approximately 89% test-set accuracy within 30 minutes. The second best-performing model, EfficientNetV2S, takes double the time achieving 2.43% lower test set accuracy.
Prediction of Output Force of Pneumatic System Using BP Neural Network
Authors: Jiang Fei , Zhang Wenqun , Chen Jia
Published: December 2022

The dynamics of complex pneumatic systems are highly non-linear. The traditional modeling method has many flaws, such as a large number of influencing parameters, difficulty solving expressions, and an easy accumulation of errors. The models obtained by the traditional method are often inaccurate and difficult to be adopted for accurate control. In this paper, the pneumatic system of a hybrid electric-pneumatic actuator was analyzed theoretically and tested experimentally. A BP neural network model for the pneumatic system inflation and deflation was developed by selecting appropriate state parameters, and a prediction algorithm was proposed to predict the output force of the system for a period of time during the process of inflation (deflation). The results showed that it was feasible to adopt this method to predict the output force of the pneumatic system containing the common switch valve. The prediction of output force variation is good within the range of experimental data. It provides an effective method for the dynamic modeling of complex pneumatic systems. The prediction results within the sample range were better than those outside the sample range. In the future, the training sample data can be increased to improve the prediction accuracy of the model.
Rapid Detection Method of Pipe Inner Diameter Based on Binocular Vision
Authors: Wang Yuzhang , Yang Guizhi , Gao Wenxiang , Ke Jiaxin , Huan Zhijie , Chen Lihua
Published: December 2022

With the rapid development of robotics, automatic pipeline detection has become a research hotspot in recent years. The existing pipeline diameter automatic detection method needs to calculate a large number of clouds, which is inefficient and difficult to play a role in real-time pipeline detection. Therefore, this paper proposes a fast detection method based on binocular vision to achieve real-time detection of the inner diameter of the pipeline. According to the gradient diagram of the color image after the down-sampling, adaptive double-threshold edge detection is proposed to calculate the edge points. Then an improved BM stereo matching algorithm is proposed to obtain the disparity map of edge points quickly. The least square method is finally used for fitting the edge point coordinates to calculate the inner diameter of the pipeline. The experimental results show that the system has strong accuracy and high efficiency, which can meet the needs of real-time detection of the inner diameter of the pipeline.
Design and kinematics analysis of AGV with unilateral climbing rack in the roadway
Authors: Cao Chongzhen , Xu Haohao , Wang Fengqin , Song Hong , Liang Fuxing
Published: December 2022

To improve the speed of intelligent transformation of warehousing and logistics, a unilateral climbing shelf-type AGV in warehouse aisles is proposed. The modular design is adopted, and the crawling drive module, the cargo module, and the telescopic rotating arm are combined to form an AGV that can walk in all directions on the ground, climb, and traverse along the shelf track to complete the storage and retrieval operations. After the scheme is determined, 3D modeling is carried out through SliodWorks, performing static stress analysis on key stressed components. The kinematic model of the transition stage between ground and climbing is established by analytical method, and the kinematics simulation of the transition stage is completed in the SliodWorks Motion module. The results show that the movement speed curve of the cargo module is smooth, and there is no rigid impact. The Lagrangian dynamics equation is established for the transition process of AGV ground and climb, which provides a theoretical basis for control design. The overall structure of the AGV is reasonable, and the transition stage between the ground and the climb runs smoothly, meeting the design requirements.
Research on Collision Detection Algorithm for Human-SRL Collaborative Motion Planning
Authors: Zhang Xiaobo , Liu Jinguo
Published: December 2022

The existing collision detection algorithms are commonly based on triangle patch models, which leads to complex detection logic. Aiming at this problem, this paper proposes a new collision detection algorithm. It has simple detection logic and fast detection speed, and it is mainly used for human-SRL cooperative motion planning. The algorithm is based on a human-SRL skeleton model and a multi-sphere envelope method and includes four sub-algorithms, namely, a multi-sphere envelope sub-algorithm, an SRL collision detection sub-algorithm, a collision detection sub-algorithm between SRL and human, and a collision detection sub-algorithm between SRL and environment objects. This paper first introduces the pseudo-code of the collision detection algorithm. Secondly, the implementation principles of its four sub-algorithms are expounded. Finally, the correctness of the collision detection algorithm is verified by simulation.
Game Theory Methods for Pursuit-Evasion Problems
Authors: Li Yi , Liu Mingxin , Luan Peiyu , Zhou Junchi
Published: December 2022

In this article, two environment modeling methods and two methods are used to solve the pursuit-evasion game problem. It separately introduces several methods based on graph and geometry – all with the same goal of reducing the time for the pursuers to capture all the evaders. Corresponding experiment data and results are also listed. Furthermore, two methods, the differential game theory-based method and decentralization will also be introduced to solve the pursuit-evasion games. These two methods will be discussed under the condition of multiple pursuers and evaders. The number of pursuers is equal to the number of evaders. The experiment results, as well as the difference between the two methods, will be shown specifically in the following chapters.
Design of Orbital Mobile Robot System for Electroplating Production Line
Authors: Liu Jinan , Diao Han , Guo Ziyi , Zhu Xiaoxiao , Cao Qixin
Published: December 2022

For different electroplating processes, there are many automatic electroplating production lines at present, but there is still no long-distance electroplating production line for small batches and multiple varieties of plated parts. Therefore, this paper proposes a set of orbital mobile robot systems to improve the production efficiency and quality of electroplating and reduce the uncertainty of manual control. The hardware of the system is composed of an orbital car and a robotic arm. The orbital car includes mobile positioning and a holding brake mechanism and the robotic arm adopts KUKA kr20-r2100 industrial robot. According to the logic of moving and grabbing, the control framework of the system is determined. A lot of field experiments show that the designed system can adapt to the factory environment and automatically complete the scheduling tasks assigned by the system.
Adaptive regulation strategy of the soft deformable actuator based on angle self-sensing
Authors: Yao Haiying , Wen Yintang , Zhou Zixiang , Tian Hongmiao , Zhang Yuyan , Luo Xiaoyuan
Published: December 2022

Soft robots can adapt to various unstructured environments and have received extensive attention. The integrated structure of sensing and actuation makes soft robots more miniaturized and integrated, enriching their usage scenarios. The multiple degrees of freedom of soft actuators facilitate flexible deformation but also bring challenges to precisely control the deformation. This work presents a novel Liquid Crystal Elastomer-based soft actuator with self-sensing. The kinetic model of a soft finger structure made of the above soft actuator is proposed to facilitate the controller design. Considering the non-predetermined external excitation, a deformation regulation strategy based on a sliding mode controller is designed. Under the action of the controller, the bending angle of the finger joint can be stabilized in a smaller range around the desired value based on the feedback of the self-sensing information. This research realizes the integration of sensing, actuation, and control, enabling the flexible structure can organically adapt to the environmental dynamics and uncertainty in nature, which cannot be achieved by the existing flexible structure deformation control.
A Mobile Robot Mapping Method Integrating Lidar and Depth Camera
Authors: Jia Lu , Ma Zhengguang , Zhao Yongguo
Published: December 2022

For mobile robots in uncertain and complex environments, a single sensor obtains a single set of environmental information, creates maps with low accuracy, is easily disturbed by external factors, and easily collides with obstacles in the navigation process. In this paper, we propose a depth camera and LiDAR fusion map building method, which firstly collects data using the LiDAR and depth camera on board the mobile robot, and then preprocesses the collected radar data and depth data and builds a 2D local environment map using RTAB-MAP algorithm. Finally, the local maps are fused using a voxel filter to stitch together a complete global map to complete the map construction. Experimental validation on a ROS-based mobile robot platform shows that the map built using multi-sensor fusion is more complete, presenting more comprehensive environmental information and laying the foundation for subsequent navigation.
Configuration Enumeration of Multi-Limb Reconfigurable Modular Robots
Authors: Feng Jingkai , Liu Jinguo
Published: December 2022

By introducing modular and reconfigurable designs, the Multi-limb Reconfigurable Modular Robots (MLRMR) have many different configurations instead of a single fixed configuration, which greatly expands the boundaries of the operation. However, the number of non-isomorphic configurations increases exponentially with the number of modules, making it necessary to explore the enumeration method theoretically. Based on the group theory, the permutation groups of the Multi-limb (including three-limb, four-limb, and five-limb) Reconfigurable Modular Robots are established in this paper, and the theoretical formulas of configuration enumeration are derived by applying the Pólya enumeration theorem. This work can be used as the basis of configuration analysis.
Design and Realization of Mobile Robot Driven by Omnidirectional Wheels
Authors: Shi Qi , Wang Jucheng
Published: December 2022

Omnidirectional mobile robot refers to a robot technology that can move in any direction on the ground. Aiming at the problems of complex control, low driving efficiency, and poor carrying capacity of ordinary omnidirectional mobile robots, an omnidirectional wheel-driven mobile robot was proposed. The robot can achieve traverse characteristic motion with zero gyration radius without changing its attitude. Through the kinematic analysis of the mobile robot equipped with this wheelset, the omnidirectional motion function of efficient driving is verified. Prototype experiments show that the robot can effectively improve space utilization and intelligence in scenarios with limited space.
Design of an Underwater Vehicle for Adjusting Course by a Rudder Control Strategy
Authors: Li Te , Shao Hua , He Kai , Zhang Hua , Zuo Qiyang
Published: December 2022

Most underwater vehicles (UV) adjust their attitude depending on the direct thrust of their propellers at different parts or change their course through the rudder during moving at high speed. However, it is hard for the rudder to function at a low speed. This paper introduces the design and control of a UV that can work at various speeds, including the structure design and control method. This UV can adjust the attitude with three rudders. The top rudder controls the left-right heading in high-speed motion. The left and right rudders have two degrees of freedom (DOF), respectively, and the twisting degree of freedom, the same as the conventional rudder, is used to control the ascent and descent during high-speed motion. The unique feature is that the flapping degree of freedom allows the UV to adjust its heading at low speed or hovering, which is challenging with the conventional rudder control method. In this paper, we design an experiment to verify the function of the flapping steering, and the result shows the scheme’s feasibility.
The Research on the Mobile Measuring Robot Technology
Authors: Wang Kaiqiang , Li Di , Li Lifei , Chi Shengfeng
Published: December 2022

At present, the traditional construction method of engineering measurement relies on the cooperation of many people, who need to manually level the measuring instruments to measure and set them point by point, which has low construction efficiency and cannot meet the application requirements of modern engineering construction measurement. Therefore, it is necessary to develop mobile measuring robots. This paper mainly studies the model, operation flow, measurement accuracy, and data stability of the mobile measuring robot. The results show that the mobile measuring robot has high measurement efficiency, measurement accuracy, and data stability, and can replace the traditional survey for measurement construction.
Collision Avoidance for Multi-robot Navigation
Authors: Li Zimu
Published: December 2022

This paper introduces two representative distributed navigation methods with collision avoidance algorithms for multi-agent systems, the Reciprocal Velocity Obstacle method and the Buffered Voronoi Cell-based method. The definitions and principles of these two methods are introduced firstly by explaining the meaning of each function and making examples. Secondly, each method has a validation experiment to simulate them. By doing those simulations, the stability and performance are proven to fit the designers’ ideals. Finally, the result is shown as pictures to visualize the success of both methods. This paper may be seen as a reference to design a multi-robot system or develop a new approach to navigation algorithm.
Design of hydraulic pneumatic control system for rail grinding car
Authors: An Zhaozhe , Zhang Nan , Du Yujie
Published: December 2022

This paper comprehensively uses the knowledge of mechatronics, mechanical structure, etc. The grinding wheel and cradle frame of the rail grinding car is studied and designed. The grinding wheel is controlled by a pneumatic servo, and the cradle frame is controlled by a hydraulic system. According to the accuracy requirements of the rail grinding car, the pneumatic servo schematic diagram of the grinding wheel to achieve precise grinding, the schematic diagram of the hydraulic control system in which the cradle frame can turn accurately, the AMESim software working interface model is established, the actual working parameters of the system components are set. After the hydraulic profiling system is designed, simulation, test, and analysis are carried out.