bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.