作者机构：
[Deng, Jie; Chen, Baohui; Zhou, Tiannian; Chen, Jie; Liang, Ping] State Key Laboratory of Disaster Prevention & Reduction for Power, Grid Transmission and Distribution Equipment, Changsha, China;[Zhu, Hongzhang] School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha, China
会议名称：
10th China International Conference on Electricity Distribution, CICED 2022
作者机构：
[He, Yuqing; Zhou, Shengyu; Jiang, Zhuohan] State Grid Hunan Electric Power Company Limited Economic & Technical Research Institute, Hunan Key Laboratory of Energy Internet Supply-demand and Operation, Changsha, China;[Zhou, Renjun; Sun, Chenhao] School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha, China
会议名称：
10th China International Conference on Electricity Distribution, CICED 2022
作者机构：
[Liu, Shengpeng; Chen, Chun] School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha;410114, China;[An, Yi] State Grid Jiangxi Electric Power Research Institute, Nanchang;330096, China;[Liu, Shengpeng; Chen, Chun] 410114, China
会议名称：
10th China International Conference on Electricity Distribution, CICED 2022
作者机构：
[Zeng, Haiyan; Chen, Shuang; Zhang, Zhongyu; Li, Hang] Wuhan Power Supply Company of State Grid Hubei Electric Power Co., Ltd, Wuhan, China;[He, Shigeng; Wu, Chenyu] Changsha University of Science and Technology, Changsha, China
会议名称：
10th China International Conference on Electricity Distribution, CICED 2022
作者机构：
[Zhou, Xiu; Tian, Tian; Bai, Jin; Luo, Yan] Power Research Institute of State Grid State Grid Ningxia Power Co., Ltd, Ningxia, 750002, China;[Yang, Xin; Yang, Tai] College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha;410114, China;[Yang, Xin; Yang, Tai] 410114, China
会议名称：
10th China International Conference on Electricity Distribution, CICED 2022
作者机构：
[Zhou, Tiannian; Wu, Chuanping] State Key Laboratory of Disaster Prevention and Reduction for Power Grid Transmission and Distribution Equipment, State Grid Hunan Electric Company Limited, Disaster Prevention and Reduction Center, Changsha, China;[Zhu, Hongzhang] State Key Laboratory of Disaster Prevention and Reduction for Power Grid Transmission and Distribution Equipment, Changsha University of Science and Technology, Changsha, China
会议名称：
10th China International Conference on Electricity Distribution, CICED 2022
作者：
Zhang, J. I. N.;Luo, Meng;Sun, Cheng;Qu, Peiqi
作者机构：
[Qu, Peiqi; Zhang, J. I. N.] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha, Peoples R China.;[Luo, Meng; Sun, Cheng] Hunan Normal Univ, Coll Math & Stat, Changsha, Peoples R China.;[Zhang, J. I. N.] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha, Peoples R China.
会议名称：
21st International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)
会议时间：
DEC 03-05, 2021
会议地点：
ELECTR NETWORK
会议主办单位：
[Zhang, J. I. N.;Qu, Peiqi] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha, Peoples R China.^[Luo, Meng;Sun, Cheng] Hunan Normal Univ, Coll Math & Stat, Changsha, Peoples R China.^[Zhang, J. I. N.] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha, Peoples R China.
摘要：
Vehicle detection in aerial images has been applied in many fields and attracted more and more scholars' attention. In the task, the objects are multidirectional and arranged densely, the background information is complex, and the scale of the object is different. To achieve better detection performance, an improved detection model BFR-RetinaNet is proposed, which is based on the single-stage object detection model RetinaNet. BFR-RetinaNet optimizes vehicle positioning by adding a directional anchor box regression branch. Simultaneously, the model introduces a balanced feature pyramid structure to enhance the extraction of features and reduce the interference of complex backgrounds. The experimental results on the aerial dataset show that the precision, recall, and average precision of the proposed model have been improved to varying degrees. It achieves 86.2% Precision, 98.4% Recall, and 90.8% Average Precision (AP), which is 7.96, 0.45, and 0.58 points higher than R-RetinaNet.
摘要：
In this paper, we propose a novel key-point detector with only one-level feature with the stride of 8, which is 75.0% less than methods with the stride of 4. Due to the reduction of the feature layers, firstly we adopt a new key-point labeling method, which can make full use of the detection points on the feature map. Secondly, we propose a U-shaped feature fusion module with group residual dense blocks, which works together with grouped convolutional and re-parameterization methods to bring significant improvements while reducing parameters. Thirdly, we use a soft non-key-point branch to re-weight the classification score. Using NVIDIA GeForce 3060 GPU and based on the VOC dataset, the proposed model with RepVGG-A0 runs about 51.4% faster than CenterNet with ResNet-18, runs 261.3% faster, and achieves higher accuracy than CenterNet with ResNet-101 under the resolution of 512 x 512.
通讯机构：
[Jiang-xia Li] S;School of Hydraulic and Environmental Engineering, Changsha University of Science and Technology, Changsha, China<&wdkj&>Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha, China
会议名称：
11th International Conference on Asian and Pacific Coasts (APAC)
会议时间：
NOV 14-17, 2023
会议地点：
Kyoto, JAPAN
会议主办单位：
[Yao, Yu;Wu, Ji;Li, Jiang-xia;Chen, Long] Changsha Univ Sci & Technol, Sch Hydraul & Environm Engn, Changsha 410114, Peoples R China.^[Yao, Yu;Li, Jiang-xia;Chen, Long] Key Lab Water Sediment Sci & Water Disaster Preven, Changsha 410114, Peoples R China.
会议论文集名称：
International Conference on Control and Robots
关键词：
Neural-network;tremor;surgical robots
摘要：
As a representation of teleoperated robots, surgical robots based on teleoperation are widely applied in the medical field. Generally, it can greatly guarantee the performance of microsurgery in terms of control and postoperative recovery by using a surgical robot in comparison with traditional surgical operation. However, the performance of surgical robots is greatly disturbed by the physiological tremor of surgeon in the process of operation. In order to cancel the impacts of tremor signal, a neural-network-based (NN-based) algorithm is developed in this paper. For the proposed NN-based approach, we develop a hybrid wavelet basis function to deal with the variable tremor signal. Additionally, the proposed method can cancel the tremor signals based on the excellent ability of nonlinear mapping and generalization and does not rely on a priori structural parameters. In order to evaluate the performance of the proposed method, comparative experiments of five different kinds of NN-based tremor filter are performed by using tremor signals with different frequencies and amplitudes. Experimental results validated that the proposed algorithm can achieve the performance of suppressing the tremor signal of the processing error. It is can be noted that the surgical robots can ensure the control performance of the surgical robots by using the developed NN-based filter. The developed method can also be applied as a filter for suppressing vibrations of processing operations in the future, such as chatter in micro-milling.
摘要：
With the connected vehicle (CV) can interact with the infrastructure and can be used as a moving sensor to bring more real-time and higher precision input for intersection signal control. However, such connectivity also brings network security risks.To protect the signal security of intersections, this paper designs a realistic signal attack model based on forged trajectory injection to simulate the potential attack that a smart attacker may launch, the key track points of queued vehicles were extracted, the traffic wave velocity of queued vehicles was used as the distance index to transform forged track recognition into an outlier detection problem, and a forged track detection algorithm based on hierarchical clustering was proposed. Experimental results show that under different attack targets and permeability, the highest detection rate is 95%, and the lowest is 67%. This method does not require training, learning and high computation power of edge equipment. Therefore, it has a certain potential for intersection signal timing using CV trajectory.
作者机构：
[Wang, Lina; Zhou, Kun; Tan, Xin; Wu, Changlong] Hunan Economic Institute Electric Power Design Co., Ltd, Hunan Engineering Research Center of Large-Scale Battery Energy Storage Application Technology, Changsha;410001, China;[Xu, Zhiqiang; Tan, Liping] Economical & Technical Research Institute of State Grid Hunan Electric Power Co., Ltd, Hunan Engineering Research Center of Large-Scale Battery Energy Storage Application Technology, Changsha;[Xia, Xiangyang; Zhang, Yuan; Jiang, Daiyu] Hunan Engineering Research Center of Large-Scale Battery Energy Storage Application Technology, School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha;410014, China
会议名称：
10th China International Conference on Electricity Distribution, CICED 2022
作者机构：
[Xie, Qiyue; Liang, Chao] Changsha University of Science and Technology, Hunan Province 2011 Collaborative Innovation Center of Clean Energy and Smart Grid, Changsha;410114, China;Changsha University of Science and Technology, School of Energy and Power Engineering, Changsha;[Xie, Qiyue; Liang, Chao] 410114, China;[Liang, Chao] 410114, China<&wdkj&>Changsha University of Science and Technology, School of Energy and Power Engineering, Changsha
摘要：
The most important factor in restricting safe and efficient is the unfavorable geological conditions ahead of the tunnel face during tunnel construction in karst area. To avoid the occurrence of geological disasters, advanced geological forecast technology is widely used to detect the development of unfavorable geological bodies. In order to better explain the time-frequency characteristics of GPR signals of tunnel cavern fillings, the Gaussian window function is selected according to the uncertainty principle, and the short-time Fourier transform is used to analyze and process the GPR signals of cavern fillings with different properties based on forward simulation. The results show that the time-frequency spectrum based on short-time Fourier transform can meticulously describe the process of change that takes places in time and frequency plane of GPR signals, which is beneficial to accurately extract the time-frequency characteristic parameters in GPR signals. When the electromagnetic wave wavelength of the GPR transmitting antenna is shorter than the detection depth of cavern fillings, two target reflectors which reflect the upper and lower reflecting interfaces of the fillings will appear in the spectrum of forward simulation signal. The relative errors of recognition results obtained according to the reflection time interval are all less than 5%, which provides a reference for the quantitative interpretation of GPR signals of tunnel cavern fillings.
摘要：
Tucker decomposition (TKD) has been utilized to identify functional connectivity patterns using processed fMRI data, but seldom focuses on originally acquired fMRI data. This study proposes to decompose multi-subject fMRI data in a natural three-way of voxel x time x subject via TKD. Different from existing tensor decomposition algorithms such as canonical polyadic decomposition (CPD) for extracting shared spatial maps (SMs), we propose to extract both shared and individual SMs by exploring spatial-temporal-subject relationship contained in the core tensor. We test the proposed method using multi-subject resting-state fMRI data with comparison to CPD for evaluating shared SMs and independent vector analysis (IVA) for assessing individual SMs under different model orders. The results show that the proposed method yields better and more robust shared SMs than CPD and more consistent individual SMs than IVA, indicating the potential of TKD in providing group and individual brain networks in a high-dimensional coupling way.
摘要：
COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). This paper proposes a deep learning model to assist medical imaging physicians in diagnosing COVID-19 cases. We designed the Parallel Channel Attention Feature Fusion Module (PCAF), and brand new structure of convolutional neural network MCFF-Net was put forward. The experimental results show that the overall accuracy of MCFF-Net66-Convl-GAP model is 96.79% for 3-class classification. Simultaneously, the precision, recall, specificity and the sensitivity for COVID-19 are both 100%. Compared with the latest state-of-art methods, the experimental results of our proposed method indicate its uniqueness.
摘要：
This paper presents a new closure method for closing the deck in the middle span of a cable-stayed bridge. It discusses the mechanism of the single joint method and develops a regression approach for fabrication dimensions of closure segment incorporating with temperature correction based on the field measurement. Following this, the authors propose the key erection procedures and the pulling system based on the erection of Jingyue Bridge in China. The application in this completed bridge demonstrates the proposed method, simple in operation and high in accuracy.
