bluuebunny/crossref_metadata_2025 huggingface dataset:
The design scheme of a 31.5 MW mountain photovoltaic power station: a case study
Authors: Tang Jianhua , Wang Senyao
Published: December 2022

The development of photovoltaic power generation is of great significance to the realization of double carbon goals. The construction of photovoltaic power stations in mountain areas can save land resources. In this paper, the construction of a 31.5 MW photovoltaic power station in the mountainous area of Yunnan Province, China is analyzed in detail from the aspects of solar energy resource evaluation, and photovoltaic system design, power generation estimation, as well as economic and environmental benefits. The results show that the average sunshine radiation in this area is 5764.3 MJ/m2, which is suitable for building photovoltaic power stations. 24 units are designed with a total of 86320 photovoltaic modules, and the capacity of each module is 365 Wp. Fixed installation is adopted, and the most suitable inclination angle is 28° (south area) and 15° (north area). Correspondingly, the center distance of the front and rear arrays in the south area and north areas is 6 m and 12 m, respectively. The project is economically feasible and has significant environmental benefits.
An Experimental Study on Multiscale Conductivity of Shale Fracturing
Authors: Wang Junchao , Wang Lei , Li Jiacheng , Ma Haiyang , Ma Mingwei , Chen Chong
Published: December 2022

Volumetric fracturing is an effective way to develop shale oil. Fracturing forms a complex system of fractures of various scales that can be divided into micro-fractures, secondary fractures, and primary fractures. To improve the conductivity of multiscale fractures in volumetric fracturing of Jimsar shale oil, conductivity experiments were performed under such conditions as rough fractures without proppant placement, rough fractures with proppant placement, and straight fractures with discontinuous sanding, thereby studying the change law of fracture conductivity at various scales. According to the results, micro-fracture conductivity is largely affected by the roughness of the fracture face; placing proppants can significantly increase the conductivity of rough fractures; the conductivity under the condition of discontinuous sanding is highly sensitive to stress; fractures with discontinuous sanding of an area of 40% show the best conductivity.
Electric Energy Monitoring Node Design Based on NodeMCU and MQTT Protocol
Authors: Sun Jinhua , Lin Jiayuan
Published: December 2022

Intelligent electricity management systems can be widely used for monitoring the regional power consumption of enterprises, factories, campuses, properties, etc. To meet the current demand of electricity consumers for monitoring the power data of each node, a node design method based on Modbus and MQTT protocols for monitoring power consumption has been proposed. It can monitor the power, current, voltage, electricity, and other related parameters of the power nodes so that the electricity consumers can control the electric energy data of each power node in real time. The test results show that the node scheme can effectively monitor the data of each power node and realize the real-time monitoring of power consumption data, which shows that the design scheme is effective and feasible.
Characteristic Analysis of Irregular Workpiece Based on Electromagnetic Induction Heating Technology
Authors: Wang Qiuqiang , Wang Liping , Song Xingen , Qi Zhiqiang
Published: December 2022

Simulation of coil electromagnetic heating provides an effective model basis for a temperature control system. However, due to the irregularity of industrial heating molds, they cannot meet the model requirements. In this paper, based on the research of electromagnetic heating of thin plates, hypothesis research is carried out. Bony plates and cylinders in fixed positions on thin plates are defined as irregular workpieces. Taking the influence of cylinder height on temperature as the research direction, the temperature distribution on the surface of workpieces is explored, and the influence of different parameters on the simulation results is analyzed, which provides a simulation analysis method and data support for the electromagnetic heating system of irregular workpieces under this model and follow-up researchers.
Research on Distributed Resource Aggregation Technology Based on Hierarchical Agglomerative Clustering Analysis
Authors: Ling Xiaobo , Tu Qi , Jin Minjie , Wang Weihong , Cui Yong , Zhu Jianan
Published: December 2022

With the rapid development of distributed resources such as wind turbines and photovoltaics in the power system, intermittency and volatility will directly impact the security and stability of the power grid and power transactions. This paper combines the principal component analysis method and hierarchical agglomerative clustering algorithm to analyze the output characteristics of distributed resources. According to the Davies-Bouldin index to evaluate the clustering results, the typical characteristic output curves of distributed resources are obtained. Then the typical curve is used to represent the distributed resources for aggregation. Based on daily peak-valley difference and power fluctuation index, the comprehensive results are judged and the optimal solution is selected. Finally, an example with eight distributed resources is given to verify the effectiveness of the proposed method.
An Analysis of Li-ion Traction Battery System Thermal Simulation Based on Simplified Pipeline Fluid Model and Liquid Cooling Enhancement
Authors: Wang Bing , Ou Yang , Wang Peng , Li Zhengbo , Zhang Yuanwei , Zhang Xujian , He Xiangming
Published: December 2022

In recent years, while the “decarbonized economy” is being practiced globally, the ownership of new energy vehicles has been increasing rapidly. During the development of new energy vehicles, the design of thermal management systems has been underlined. Developing an effective and energy-saving battery thermal management system is an important factor in guaranteeing the high performance of new energy vehicles. This article dissects the electrothermal coupling performance of the battery pack based on finite element methodology and proposes a liquid cooling layout of the battery cooling system with an optimized cooling effect. The research reveals that: (1) By constructing a multi-dimensional electrothermal coupling model consisting of a one-dimensional electrochemical model, one-dimensional pipeline fluid model, and three-dimensional heat transfer model, the electrothermal performance of the battery pack can be simulated efficiently and further improvement can be established on its liquid-cooling system. (2) During the discharging process, the central temperature of the battery pack is significantly higher which reaches 45°C with a 2C discharging rate. (3) The liquid-cooling pipelines on the side can efficiently reduce the temperature of the battery pack. With an increasing flow rate of the coolant, the average temperature of the battery pack is reduced while the maximum temperature difference is increasing instead; with a certain flow rate, increasing the radius of the pipe would lead to a decrease in maximum temperature and the maximum temperature difference within the battery pack.
Research on high-proportion distributed photovoltaic access planning method considering voltage over limit risk
Authors: Zhu Dandan , Zhou Qian , Wang Chenggen
Published: December 2022

The current distributed PV access planning method mainly controls the access capacity, ignoring the impact of different access locations on the distribution network operation, which leads to the inability of PV to be fully consumed and generates large disturbances. To improve the utilization efficiency of PV power generation and solve the above-mentioned shortcomings, a high-proportion distributed PV access planning method that considers the risk of voltage crossing limits is studied. After analyzing the changes in distribution network operation characteristics after accessing a high-proportion distributed PV, the voltage crossing risk is estimated by calculating the network current. Multiple optimization objectives including voltage crossing risk are established to build the access planning model. The access planning results are obtained by solving the model with the multi-intelligence algorithm. After the distribution network experiment, the method considering the voltage crossing risk improves the PV consumption by about 18.3%, the connected PV generates less disturbance to the distribution network, and the access point is better.
Anti-interference detection and operation state identification of transformer acoustic characteristics based on Conv-Tas-ResNet
Authors: Lin Yuyin , Lin Yuzi , Wu Qingyu , Wu Xinhai , Tu Jiayi , Ren Weisheng , Lu Yu , Zhang Yaoyin , Cheng Engang , Guan Xiangyu
Published: December 2022

There is a limitation in the process of acoustic signal detection, which lies in serious background noise interference and the complex correlation between acoustic signal characteristics and operation states. Integrating the denoising model and feature classification model, a method of transformer acoustic signal anti-interference detection and operation state detection based on deep learning is proposed in this paper. Through tests in anechoic rooms, acoustic signals of transformers in the normal state or under harmonic load are acquired. Combining these signals with the background noise, a dataset containing 12000 samples of acoustic signals is constructed. To implement anti-interference detection, Conv-TasNet is utilized to get the transformer acoustic signal and environmental noise separated; then, ResNet is utilized to classify the operation states of the transformer accurately. Results show that compared with the blind source separation method through RNN and FastICA, the denoising model established in this paper improves Si-SDRi parameters by 37.4dB and 17.53dB respectively, and the transformer operation state classification model established in this paper classifies the test dataset with an accuracy of 97.7%, thus providing an effective method for the extraction of transformer acoustic signal and diagnosis of transformer operation states in complex environments.
Geometry Optimization for Increasing Fatigue Life of Pressing Packaged IGBT with SiC Chips and Silver Sintering Layers
Authors: Zheng Han , Cui Wei
Published: December 2022

A novel SiC IGBT module packaging structure is proposed. Pressing packaging with silver sintering is adopted. The thermal analysis is made with FEM. A maximum temperature of 137°C is obtained when the module is working, which leads to significant fatigue problem. The minimum fatigue life of the fundamental model is 106, and it occurs in the upper silver layer nearby the SiC. Five improved models with different silver layer edge geometry are analyzed. It turns out that the best geometry optimization in this study can lead to a fatigue life of 141, that is, 33% higher than the fundamental model. This shows that a very subtle change in the geometry of the silver layer edge can lead to significant improvement of the fatigue life of the module. This result can provide useful reference for the design the third generation IGBT device.
New indoor main transformer ventilation design modification and simulation
Authors: Liu Guojian , Liu Xianze , Chen Dekai , Li Qian , Zhang Yuefeng
Published: December 2022

The main transformer is the heart of the transmission and substation system, and its safe and stable operation is crucial. The heat dissipation and cooling effect of the main transformer are directly related to its working performance. This project takes the 110 kV Xiba substation of Kunming Power Supply Bureau of Southern Power Grid Company as an example, simulates the change of indoor main transformer heat source by Fluent software, and uses an indoor substation transformer auxiliary heat dissipation device, which is expected to achieve rapid heat dissipation of the transformer, even when the transformer is under high load and the transformer generates large heat. The ventilation of the main transformer can effectively reduce the operating temperature of the transformer load, effectively improve the indoor environment temperature of the main transformer room of the substation, and eliminate the equipment hidden danger that the excessive temperature of the transformer affecting the insulation life of the transformer.
An Identification Method of Metering Anomaly Based on Line Loss Analysis of Low Voltage Station
Authors: Qin Yuewei , Cui He , Zhang Muxin
Published: December 2022

Abnormal measurement on the power side is an important reason for abnormal statistical line loss. Considering that there are many abnormal measurement reasons, it is very difficult to rely on on-site investigation alone. To overcome this problem, a method based on line loss analysis in low voltage stations is proposed. In order to ensure the true and reliable analysis results, this paper combines the management standard of the line loss of the low-voltage platform area and the actual operation situation of the platform area, analyzes the measurement factors affecting the line loss of the low-voltage platform area, and extracts the analysis rules and calculation model of the abnormal measurement factors. In order to improve the efficiency of on-site investigation, this paper combines data analysis and proposes abnormal identification method with fault prediction. This method can analyze the possible abnormal causes before on-site investigation and improve the accuracy of on-site line loss verification. Finally, the accuracy and effectiveness of the calculation anomaly analysis model and the identification method are verified.
TPE-based Short-Term Load Forecasting Neural Network Structure Design and Parameter Optimization Method
Authors: Ling Wuneng , Li Ling , Li Li , Huang Kui , Wu Yin , Zhuo Yixin , Wu Jianfeng
Published: December 2022

Short-term load forecasting is generally applied in power system real-time dispatching and day-ahead generation planning, which is of great significance for power system economic dispatching and safe operation of the system. Much research on short-term load forecasting using neural networks has been carried out at home and abroad. However, how to obtain the optimal network and structure evolution accurately and quickly has evolved into a key challenge for short-term load forecasting, because the prediction effect of the neural network method is more easily affected by the existing network structure and parameters, and the personality difference of the prediction object itself makes it difficult for the parameters to be reused. Aiming at this problem, this paper proposes a TPE-based neural network structure design and parameter optimization method for short-term load forecasting. This method realizes fast optimization of parameters of different types, structures, parameters, and input and output of deep neural networks, and is widely applied in Guangxi power grid. It is verified by an actual example that the method can effectively improve prediction accuracy.
An Ancient Chinese Painting Restoration Method Based on Improved Generative Adversarial Network
Authors: Luo Renquan , Luo Renze , Guo Liang , Yu Hong
Published: December 2022

During the long-term preservation process of ancient Chinese paintings, there are various degrees of damage. Manual repair methods are inefficient, professional, and prone to secondary damage. Aiming at the above problems, a UGAN ancient painting restoration model based on an improved Generative Adversarial Network (GAN) is proposed. The model uses GAN as the overall framework. The generator in the framework adopts a U-shaped network (U-Net), and adds a dilated convolution-gated residual block (DCGR-Block) in the middle, which enhances the model’s ability to extract the shallow and deep feature information of the image. In the experiment, the test results are compared with the current mainstream methods. The comparison results show that when repairing the overall deletion, the peak signal-to-noise ratio (SPNR) and structural similarity (SSIM) of UGAN are improved by 8.14% and 4.79% on average compared with the global and local consistency image inpainting (GLCIC). When the rate is higher, the performance of the network model in this paper is also better than that of similar mainstream algorithms.
3D human pose estimation based on negative exponential reduction Gaussian kernel
Authors: Gu Lanqing , Wang Yu
Published: December 2022

Human pose estimation has become an important research direction in the field of motion recognition. 3D human pose estimation adds depth information to 2D pose estimation, which is more widely used. In this paper, the weight of each voxel is calculated in the 3D discrete space by projecting the joint point heatmap to directly estimate the 3D human pose. To improve the accuracy of 3D human pose estimation, the Gaussian kernel of heatmap with the variable size is reduced by a negative exponent in the process of training. The dilated convolution of a small convolution kernel is used to replace the large convolution kernel to solve the problem of large computation overhead when detecting key points in discrete 3D space. Experimental results show that this method is effective and can accurately estimate the 3D pose in multi view images.
Fault Rate Estimation of Electric Energy Meter Based on WDPOD-NHBM
Authors: Zhang Wei , Li Ning , Guo Zhiming , Kang Lei , Zhu Xingyu , Qiu Jun
Published: December 2022

To solve the problems of the small amount of sample information in the original failure rate data of smart energy meters and the difficulty of mining under typical environmental stress, a smart energy meter failure rate prediction and reliability evaluation model based on double precision combined with nonlinear hierarchical Bayes is proposed. First, the KNN algorithm is used to test the outliers in the original data, and a double-precision outlier detection algorithm is further established to assign the weight to outliers. Then, a nonlinear hierarchical Bayesian model is built based on Weibull distribution, and couples the failure rate data of smart energy meters with various typical environmental stresses. Through multi-dimensional analysis of the data distribution rule, the most prior distribution is selected. Finally, the Markov Chain Monte Carlo method is used to sample and solve the posterior condition distribution of the model parameters. The posterior parameters and confidence interval estimation are obtained, and the reliability of the smart energy meters is calculated as 95%. The experimental results show that the method can accurately predict the trend of the failure rate of smart energy meters over time in a typical environment.
A Graph Convolutional Network-based Method for Drug-Target Interaction Prediction Running title: GCNDTI
Authors: Li Yexuan , Jin Li
Published: December 2022

Studying the interaction between drugs and targets is the key step of drug repositioning. Through machine learning methods, we can provide reliable drug-target pairs for drug-target interaction (DTI) identification for wet-lab experiments and improve its efficiency. Previous methods did not combine node attributes and relationships of drug and target, which limited the performance of those methods. To this end, we propose a prediction method that takes into account both node attributes and topology information and named it GCNDTI. Using a graph neural network, the low-dimensional feature vectors of drugs and targets are obtained. Utilizing the nonlinear graph neural network model, the drugs and targets’s abstract feature are obtained. The experimental results confirm that compared with added methods, our method shows superior achievement in DTI prediction.
An Improved Gesture Recognition Model Based on Mini-Xception
Authors: Yu Cun-jiang , Zhou Guo-bao , Yan Cheng-wei , Ding xiao-ying , Li Cheng-shuo
Published: December 2022

With the rapid development of artificial intelligence technology, gestures have become the mainstream in the field of human-computer interaction because of their simplicity, easy understanding and non-contact. Compared with the early data gloves, the vision-based non-contact gesture recognition interaction method has obvious advantages. However, the variability of the gesture itself, the complexity of the background and the influence of different lighting conditions have impacted the accuracy of gesture recognition. With the rapid development of deep learning technology, gesture recognition has achieved amazing results in accuracy. To improve gesture recognition accuracy in complex background, this paper optimizes the mini-xception network model of lightweight convolutional neural network algorithm, introduce the transfer learner and integrate YOLOV4-tiny into the mini-xception model, generate a new network model YT_ mini-Xception. After experimental verification, by YT_ Mini-xception network model, the average accuracy of 0-9 gesture recognition on complex background data set is 96.64%, the average recognition time was 39.8 milliseconds, and the expected goal was achieved.
Research on improved Unet in microscopic image segmentation of esophageal cancer
Authors: Xu Jing , Qu Yingmin , Xu Mingzhi , Chen Mingxin
Published: December 2022

Timely diagnosis can greatly reduce the mortality of esophageal cancer. However, the existing diagnostic methods can only rely on pathologists to observe and estimate through stained images of naked eye slices because of lacking advanced analysis methods. These methods will cause a long testing cycle and will have different degrees of error in the results of testing. Therefore, we put forward a cell segmentation model based on the basic framework of Unet network and introduced an attention mechanism to the model. First, the Unet with encoder-decoder structure is used for initial segmentation based on esophageal cancer cell images. Then, the attention mechanism is used to heavy the weight of the cell region to reduce the interference of the model caused by the low contrast between esophageal cancer living cells under an optical microscope image and the background. As a result, the recognition accuracy of the improved model for esophageal cancer cells can reach 96%. The experimental results show that the improved model reaches an accuracy of 96% in recognizing esophageal cancer cells. The proposed model can provide a real-time, objective, and accurate solution to the diagnosis of esophageal cancer.
Network Meta-analysis of Oral Chinese Patent Medicine in Treatment of Primary Osteoporosis
Authors: Yang Jingjing , Zhang Qing , Zhang Hao , Xiang Yi , Fu Tingshuai , Ding Jiajia
Published: December 2022

The efficacy of oral proprietary Chinese medicine in the prevention and treatment of primary osteoporosis was systematically evaluated through a network meta-analysis. The computer retrieved the four major Chinese databases of CNKI, VIP, Wang Fang, CBM in China and abroad, as well as the three English databases and Web of Science of Medline, EMbase and Cochrane Library. The search cycle was designed as the database was established until February 15, 2022, and screened randomized controlled trials of export taking proprietary Chinese medicine combined with conventional western medicine for the prevention and treatment of primary osteoporosis according to the system evaluation pre-formulated exclusion criteria. Literature screening and data extraction are completed by at least 2 clinical investigators alone. Bias assessment and mapping of included studies using the Revman software, using the Cochrane Bias Risk Assessment tool, using Stata 16. 0 Software for data acquisition and analysis. In the end, 19 RCTS were included, involving 8 kinds of oral proprietary Chinese medicines (You Gui Pills, Jin Tiange Capsules, Gu Shu Kang Granules, Qiang Bone Capsules, Gu Songbao Granules, Xianling Gu Bao Capsules, Hu Gu Capsules, Zuo Gui Pills). The results of the network meta-study are as follows: (1) in terms of improving clinical efficiency, conventional western medicine combined with Hu Gu Capsules has the best effect; (2) in terms of improving bone density, conventional western medicine combined with Xianling Gu Bao has the best effect. This network meta-analysis provides evidence for the efficacy of the above eight proprietary Chinese medicines in the treatment of patients with POP.
Defect Detection Image Processing Technology Based on Swarm Intelligence Optimization Algorithm
Authors: Zhang Kui , Zhu Shan
Published: December 2022

The swarm intelligence optimization algorithm has obtained good results in practical application in the field of image processing with defect detection, and it has become the focus and hot spot of attention and research in the field of image processing. In this paper, the application of ALO as the representative of the relevant swarm intelligence optimization algorithm is studied to address the problems and shortcomings of image processing technology in the field of object defect detection. By extracting typical defect detection image samples, the effect of the application of the algorithm in sample processing is systematically studied. In addition, the introduction of perturbation strategy and inertia weights in ALO effectively improves the search performance of the algorithm. Finally, this paper analyzes the performance comparison between the commonly used defect detection image processing techniques and the algorithm in this paper by establishing comparative verification experiments. The experimental results show that the image processing strategy constructed in this paper has significant application advantages in the dimensions of image enhancement and image processing applicability.
Land cover classification of remote sensing images based on improved DeeplabV3+ network
Authors: Lu Jin , Zhou Bin , Wang Ben , Zhao Qihang
Published: December 2022

For land survey and land planning, the precise segmentation of land cover based on remote sensing images is extremely important. Based on the high-resolution satellite remote sensing images and the corresponding land cover GIS vector data in Hangzhou, for the categorization and change detection of urban land cover, a unique convolutional neural network is suggested. The data source is from high-resolution satellite remote sensing images, the semantic segmentation model DeeplabV3+ is improved. The original backbone network Xception is replaced by MobileNetV2 with smaller parameters, and add the channel attention module after the multi-scale module. In addition, to deal with the effect of category imbalance on model results, the focus loss function is introduced for network training. The pixel accuracy and average intersection ratio of 77.63% and 67.46% respectively, which are better than the commonly used semantic segmentation models DeeplabV3 +, UNet, and PSPNet models, and the model size is only 20.1MB. The experimental results show that the model is effective in redecing the number of model paramenters and improving the segmentation effect.
Semantic segmentation of buildings in high-resolution remote sensing images based on DeepLabV3+ algorithm
Authors: Li Wenbo , Zhao Shuang
Published: December 2022

With the satellite remote sensing technology ushered in a leap of development, the resolution and clarity of satellite images have also been substantially improved, and high-resolution images depict the features more finely and provide more spectral information and texture contour information. The semantic segmentation of remote sensing image is one of the focuses of remote sensing technology research, which is very important for the development of remote sensing technology. To address the problems of imprecise target segmentation and low boundary segmentation accuracy in remote sensing image segmentation, a high-precision segmentation algorithm is proposed which based on DeepLabV3+. The algorithm optimizes the decoding region structure of the original network, adds the attention mechanism module, and improves the segmentation accuracy of remote sensing image.
A deep learning method for X-ray image safety detection: YOLO-T
Authors: Wang Mingxun , Yuan Zhe , Li Yanyi
Published: December 2022

There are many problems in X-ray image dangerous goods target recognition with existing technology, such as low degree of automation, slow detection time, easy to misjudge under occlusion interference, etc. Based on the above problems, this paper proposes a multi-objective intelligent security inspection method for X-ray images based on the YOLO-T deep learning network. By adding the optimized Transformer structure to the YOLO architecture, this method can better solve the above problems. In order to better carry out the experiment, we proposed a set of X-ray safety detection data set GDXray-Expanded containing multiple categories of dangerous goods, and tested several versions of the deep learning network model of the YOLO series on this basis. Experiments show that the existing YOLO series algorithms still cannot solve the problem that dangerous goods in X-ray images are easy to be misjudged under occlusion interference. The YOLO-T method proposed in this paper solves this problem well, and in the big data set test, the maximum mAP can reach 97.73%, which is 7.66%, 16.47%, and 7.11% higher than the three methods of YOLO v2, YOLO v3, and YOLO v4 respectively, and has achieved the most competitive performance in the detection of seven categories of dangerous goods. To sum up, the YOLO-T network proposed in this paper mainly solves a series of problems in the field of dangerous goods target recognition and detection in X-ray security inspection images and has a high engineering application prospect in the field of X-ray security inspection.
Video Sequence Image Matching Algorithm Based on Adaptive Gamma Correction
Authors: Wang Jiaqi , Li Yang , Wang Yifan , Lv Haofeng , Lu Xiaohan
Published: December 2022

Aiming at the low matching accuracy and poor robustness in video sequence image matching, a video sequence image matching algorithm based on Adaptive Gamma Correction is proposed in the paper. Firstly, Adaptive Gamma Correction is used to enhance the contrast of video sequence images. Then feature extraction and description are realized by the ORB algorithm. The RANSAC algorithm is optimized in matching by improving the interior point ratio after Brute Force Match. The experimental results show that the algorithm proposed improves the correct matching rate by 19.51%, 12.53%, and 4.87% respectively compared with the SIFT, ORB, and ORB+RANSAC algorithms without increasing the running time. Meanwhile, the algorithm in this paper has good robustness in video sequence image matching.
Secondary Modeling of Air Quality Based on LSTM Cycle Neural Network
Authors: Xie Yannan , Chen Mengxiang
Published: December 2022

Based on the “primary prediction data” generated by the operation of the commonly used air quality prediction model WRF-CMAQ and the “measured data” obtained from the actual monitoring of the air quality monitoring stations, this paper establishes the air quality prediction constant value by comprehensively applying the long short-term memory (LSTM) neural network algorithm and other methods. Secondly, the meteorological conditions are reasonably classified, and the K-Means algorithm is used to eliminate the influence of the dimensional difference between the data. The original data is missing or abnormal. In this paper, the mixed interpolation method is proposed to deal with the missing data, and the isolated forest algorithm is used to deal with the abnormal data. Thirdly, three possible classification schemes are found by using the method of Elbow Method, and the correlation analysis and data visualization methods are used respectively. After discussing the three meteorological classification conditions, the optimal classification number is determined to be 4 categories. Finally, a model is established which is applicable to A, B and C, three monitoring points, and verify the rationality of the modeling and forecasting process and the robustness of the model.
Res2-YOLOV4 Object Detection Algorithm for Snakeheads
Authors: Zhang Yifei , Yue Jun
Published: December 2022

Fast and accurate detection of snakeheads is crucial for their aquaculture and behavior analysis. While farming, intra-class overlap causes wrong detection and omission, undermining the accuracy. And at present, there are few effective detection methods for snakehead aquaculture. To address this problem, this paper proposes the Res2-YOLOV4 snakehead object detection method based on the dataset of 2000 snakehead images. Firstly, the Res2net is integrated into the CSPDarkNet53 backbone network based on the YOLOV4 model to enhance the feature extraction capability. Secondly, IOU Soft-NMS, which regards the degree of coincidence between the prediction box and the actual object position as its screening criterion, is put forward to solve the problem that the conventional NMS incorrectly excludes correct prediction boxes in case of intra-class overlap. The experimental results show that AP50 and AP75 reach 97.7% and 94.77% respectively, increasing by 3.9% and 5.61%, 5.6% and 5.87%, and 4.6% and 4.76% respectively compared with YOLOF, VFNET, and YOLOX, which proves the effectiveness of this method.
Mongolian-Chinese Machine Translation Based on Graph Neural Network
Authors: Hao Jia , Tian Yonghong , Zhang Qingqing , Zhang Genmao
Published: December 2022

Most of the current Mongolian-Chinese neural machine translation relies on a sequence-to-sequence-based encoder-decoder, which does not effectively utilize the syntactic information of sentences as well as the hierarchical information of the language. To solve this problem, a graph-to-sequence-based encoder-decoder is constructed by introducing graph neural networks, making better use of syntactic information and semantic knowledge of sentences. We will build a Mongolian dependency tree library and design a densely connected graph convolutional neural network (D-GCN) based on GCN combined with a densely connected network (DenseNet). Experiments are conducted to compare with the sequence-to-sequence based Mongolian-Chinese neural machine translation model and the variant model of graph neural network, and the results verify the advantages of the D-GCN-based Mongolian-Chinese neural machine translation model in translation performance.
Multi-objective Optimal Scheduling of Small Integrated Energy System Based on Improved Particle Swarm Optimization Algorithm
Authors: Huang Fangchen , Chen Yuhan , Lu Wenzhou
Published: December 2022

In order to explore a low-carbon and economical way of supplying energy for buildings and promote the transformation of the energy system, a small integrated energy system including energy supply equipment, energy conversion equipment and energy storage equipment is designed. After comprehensively considering operating cost and carbon emission, a multi-objective optimal scheduling model of the system is constructed. A genetic particle swarm optimization algorithm based on random dynamic inertia weight is proposed to solve the model and analyze the output of each piece of equipment in the system under the optimal operating strategy. An example of a building in a region with hot summer and cold winter is introduced for verification, and the simulation results show that the proposed small integrated energy system model is economical and environmentally-friendly, and the improved particle swarm optimization algorithm has a better optimization effect.
Research on Vehicle Detection Algorithm Based on Improved YOLOv4
Authors: Xu Mingzhi , Cui Wei , Xu Jing , Zhang Wenhan
Published: December 2022

The check of vehicle fact is a significant technology in the intelligent control of transport brightness. A vehicle diagnosis and identification algorithm based on Yolov4 is proposed to encounter the dare of flow vehicle information monitoring technology in exactness, rate and firmness. The K-means++ gathering manner is used to get the mooring frame organize points appropriate for the data set. The CIOU loss function is used to optimize the training process, and then it’s trained through the CSPDarkNet53 network framework to improve the YOLOv4 network structure. The DenseNet module is applied instead of the feature pyramid. The 5-time convolution module in this paper simplifies the feature network. It is found that the experimental results have achieved good results, which can be able to achieve the goal in the practice.
FEM-Based N-Net Control of a Flexible Dexterous Finger
Authors: Xu Jiawei , Li Lvzhou , Dong Xu , Yin Zijian , Jiang Xinyu , Hu Hongwei
Published: December 2022

Owing to their light weight, compactness, and good adaptability, soft robots are promising for applications with safe human-machine interactions. Compared with traditional rigid robots, the distributed actuation of soft robots with soft bodies leads to complex nonlinear deformation, making it challenging to model its dynamics. In this paper, a Finite Element Method (FEM)-based fast neural network open-loop control method is presented to control the flexible dexterous hands in their task-space using the finite element method. The main idea is to use a simple neural network to find the mapping between the end-effector control values and the displacement. In this way, the control of complex structures can be simplified. This model is especially suitable for complicated braking structures that cannot be accurately sensed. Finally, the effectiveness of the proposed method is verified by experiments, and the results are convincing.
Path Planning for AUVs with Energy-Optimal Velocity in Current Fields
Authors: Wang Feng , Chen Wenliang , Li Chenlong
Published: December 2022

This paper presents the energy-optimal velocity to solve the problem of energy-optimal path planning for autonomous underwater vehicles (AUVs) in current fields. In the proposed scheme, a sequence of thrust velocities and steering directions is predicted to enable vehicles to utilize/avoid currents and minimize their energy consumption. Each thrust velocity and steering direction are deduced through a vector analysis method under the expected displacement and corresponding current vector, which is the energy-optimal velocity. The proposed energy-optimal velocity combines the global optimization technique and guarantees the minimization of a vehicle’s energy consumption. Several experiments are performed on strong and turbulent current fields to validate the effectiveness of the proposed scheme.
Simulation and application of the electric power steering system simulation platform based on Simulink
Authors: Gao Senqi , Sun Rui , Fu Ying , Xu Jianguo
Published: December 2022

The principle of the electric power steering system is introduced in this paper, and the simulation platform based on Simulink/Simscape is established. The mechanical structure of the steering gear, the timing scheduling strategy, the hardware control circuit, and the integration of the software control algorithm are modeled and verified in detail. It is proved that the simulation model is reliable and highly precise. The simulation platform is used to analyze the actual problems, and propose an improvement plan. The results show that the platform can improve the intelligence of development and quickly locate, effectively reproduce and analyze actual problems, and reduce the trial-produce cost of the early production line, which has a certain guiding significance for software and hardware development.
Dynamic Trust Continuous Evaluation-based Zero-Trust Access Control for Power Grid Cloud Service
Authors: Lv Pang , Sun Xin , Huang Hui , Qian Jin , Sun Changhua , Dai Hua
Published: December 2022

The development of power grid cloud service puts forward higher requirements for remote secure access. To realize fine-grained access control of remote secure access of power grid cloud service, this paper proposes a zero-trust access control model for power grid cloud service based on dynamic trust continuous evaluation. The experimental results show that the model proposed in this paper can effectively implement continuous trust evaluation for remote access of PowerCloud services, and achieve zero trust access control, with an exception detection accuracy rate of 97.2% and the incremental learning accuracy rate of over 97% when the behavior model of remote access changes.
Workspace Mapping Method Based on Edge Drifting for the Teleoperation System
Authors: Gou Songlin , Zhou Jian , Zhang Hua , Liu Manlu , Yang Shunyao , Zhang Xinglang
Published: December 2022

The workspace mapping method for master-slave isomeric robots is a key research direction. In order to enhance the safety and stability of the operation of such robots, a position-joint hybrid mapping method based on edge drifting is proposed in this paper. First, teleoperation is divided into two phases, that is, task approach and task operation. During the task approach, the edge drifting algorithm is adopted to largely cover the workspace of a robot. Rate mapping is used to quickly control the movement of the slave robot to the target position. During task operation, a hybrid mapping algorithm integrating the Cartesian space and the joint space highly precisely maps both the position and pose of the slave robot to accurately follow the control command of the master end and achieve precise control. Lastly, the proposed method is validated on the experimental platform for master-slave isomeric teleoperation. The experimental results indicate that this mapping method allows the master-slave isomeric teleoperation system to cover a large workspace precisely with desirable precision in position and pose following.
Optimizing Non-diagonal Stiffness Matrix of Compliance Control for Robotic Assembly Using Deep Reinforcement Learning
Authors: Liu Gang , Yao Bitao , Xu Wenjun , Liu Xuedong
Published: December 2022

With the advent of Industrial 4.0, industrial robots have been widely used in various sectors, e.g., in assembly. Peg-in-hole (PiH) assembly is the most typical one among assembly tasks. In PiH tasks, the robot transfers from a non-contact mode to a contact-rich mode. Instead of switching the position/force control mode with a pause when contact is detected, we deploy a non-diagonal stiffness compliance control for planning the adaptive trajectory to improve the task efficiency and ensure contact safety. In this paper, we proposed a deep reinforcement learning (DRL) method to achieve the above compliance. A compliance controller based on a virtual forward dynamic (FD) model is built. A DRL agent is deployed to optimize the parameters of the non-diagonal stiffness matrix for the built compliance controller to generate a trajectory that adapts to the changing contact condition. The experiment shows the proposed method can control the contact force within a safe range and improve the efficiency of assembly tasks.
MI-RRT-Connect Algorithm for Quadruped Robotics Navigation with Efficiently Path Planning
Authors: Zhang Yulong , Jiang Haoyu , Zhong Xungao , Zhong Xunyu , Zhao Jing
Published: December 2022

Autonomous navigation is playing an increasingly important role in quadruped robotic systems. However, providing safe and reliable path planning for robots is still an open problem. In this paper, we propose a sampling-based path planning algorithm fused with a dual-tree structure, here called Multiple Informed RRT-Connect (MI-RRT-Connect). The proposed MI-RRT-Connect can overcome the disadvantage that the Informed RRT* algorithm takes a long time in the initial search path, by using the RRT-Connect algorithm and the target deviation method, and the initial search path can be obtained quickly. Then, for the optimization of the initial search path, the multi-level parent node selection strategy and the method of calculating the path cost for the parent node are used. The simulation and quadruped robot experimental results show that our method can find an optimal path in a shorter time, and it has been well applied in a real quadruped robot.
Tracking of moving targets in complex environments
Authors: Ye Jianchuan , Wang Tingting
Published: December 2022

The tracking of a moving target algorithm based on the traditional visual servo can not introduce image and physical constraints, and does not consider the problems such as the features beyond the camera field of view, the robotic arm’s out-of-motion space, and the robotic arm reaching the joint limit. Moreover, the tracking accuracy and stability of the moving target continue to be poor in complex environments (such as the chaotic background, the illumination changes, and the target is partially occluded), and the recognition of the target image is extremely strict, In this paper, a new method of tracking and grasping moving targets is proposed by combining the pose estimation of targets with the tracking and grasping of industrial robots. Kalman filter is used to compensate for the time delay. This method avoids the influence of traditional methods on target depth estimation and image and physical constraints and has strong robustness.
Research on the Visual Servoing method based on semantic segmentation results of metal objects in a complex lighting environment
Authors: Shi Shunkai , Wang Tingting
Published: December 2022

The problem of feature recognition and servo tracking for semantic segmentation results of metal parts under complex and variable lighting conditions is addressed. In this paper, we propose an algorithm that incorporates template matching and image-based visual servoing based on semantic segmentation. The method adopts a geometry-based template matching method to stably identify the image features of the pixel region obtained from semantic segmentation, introduces a feature pyramid dimensionality reduction method to improve the processing speed, and then introduces the extracted image moment features into the image-based visual servoing to operate the robotic arm to perform the servo process on the target.
PRRT: An Intelligent Planning Algorithm with Memory
Authors: Chen Qinhu , Kang Meilin , Fan Zeming , Yu Xiaojun
Published: December 2022

This paper proposes a practically executable path planning method, namely, Pheromones-RRT(PRRT), for robots with a large joint range in a complex environment. To inter-activate with the real world, the point cloud is utilized as the scene information, while for sampling, the pheromones approach is designed to describe the pheromone content carried by each sampling point. During the sampling process, random sampling nodes are performed with a probability of ε, or those nodes with the highest pheromone content in the current sampling tree are selected with a probability of 1-ε and sampled in their neighborhood. To avoid the local minimum problem, the concept of pheromone volatile factor (PVF) is proposed, while in the expansion, double trees are also generated by PRRT in both cartesian and configuration spaces to improve the speed of the algorithm. The pheromone accumulation enables PRRT to certain learning abilities, reducing the randomness of the sampling process. Simulation results show that the proposed method can effectively plan an optimal obstacle avoidance path with satisfactory performances as compared with the RRT-Connect.
Design of Workshop Control System Based on Digital Twin
Authors: Zheng Liang , Yuan Minghai , Pei Fengque , Mao Kefu
Published: December 2022

For the problems of the traditional workshop control system, such as closed information and untimely delivery, the web-based digital twin workshop control system is designed from the concept of the digital twin. According to the physical entities in the workshop, the corresponding digital twin model is constructed, and the design of the whole process, from the collection of workshop data to the application, is completed. Finally, the system is applied to a small workshop, and the results show that the digital twin-based workshop control system could significantly improve the workshop’s productivity.