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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Xingya Wang Shiyu Zhang Fangxiao Liu Lichao Feng Zhihong Zhao School of Computer Science and Technology Nanjing Tech University Nanjing China Command and Control Engineering College Army Engineering University of PLA Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781665478373

Mutation testing simulates typical software defects by executing mutation operations. It can effectively measure the defect detection capabilities of a test-suite. However, in the mutation testing, we must generate, compile, and execute plenty of mutants, which causes substantial computing costs. Mutants selection can reduce the number of mutants but cannot avoid compiling and executing all mutants. Therefore, mutation testing suffers an efficiency problem, making it hard to use in practice. In this paper, we propose a Mutants Quality Prediction method, MQP, which relies on the static features of the source program, test-suite, and mutants to predict the quality of a test-suite. MQP avoids compiling and executing all mutants during mutants selection and thus can save expensive computing costs. Then, we use MQP as the fitness function and select the evolutionary optimization strategy, Genetic Algorithm, to generate a high-quality mutant subset. We empirically studied MQP on nine widely used items. The experimental results show that MQP achieves a good predictive effect. Its accuracy rate can reach 98.30%, while the average absolute error is only 0.74%. Moreover, the reduction efficiency of the MQP-based mutation testing is about 20 times that of the random-based selective mutation testing.

关键词： Software testing Costs Static analysis Software quality Predictive models Software reliability Security

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Deep Feature Pyramid Network for Eeg Emotion Recognition

SSRN

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SSRN 2022年

作者： Hou, Fazheng Gao, Qiang Song, Yu Wang, Zhe Bai, Zhongli Yang, Yi Tian, Zekun School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University of Technology Tianjin300384 China TUT Maritime College Tianjin University of Technology Tianjin300384 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

In this work, the Feature Pyramid Network (FPN) is proposed for improving the performance of electroencephalography (EEG) emotion recognition. Specifically, the Differential Entropy (DE) is extracted as a basic feature, and we adopt bi-harmonic spline interpolation to interpolate the features maps according to real 3D electrode spatial information. The spatial information is obtained to avoid the loss of effective information. Next, the proposed FPN is applied to fuse multiscale EEG spatial information for obtaining the deeper semantic features, which avoid blind self-learning of deep convolutional neural networks. Finally, a Support Vector Machine (SVM) is utilized to predict the emotional state. The experiment results of our method has a state-of-the-art performance by 70-30 cross-validation with the mean accuracy of 96.97% for arousal and 94.29% for valence on the DEAP database. And the accuracy rate of 97.12% on the SEED database also proves the generalization of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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NAO Robot Limb control Method Based on Motor Imagery EEG

NAO Robot Limb Control Method Based on Motor Imagery EEG

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International Symposium on computer, Consumer and control (IS3C)

作者： Yuan Guo Mei Wang Tianwei Zheng Yuancheng Li Pai Wang Xuebin Qin College of Electrical and Control Engineering Xi'an University of Science and Technology Xi’an China College of Computer Science Xi'an University of Science and Technology Xi’an China

ISBN: (数字)9781728193625

ISBN: (纸本)9781728193632

Brain computer interface (BCI) technology refers to the technology that helps establish direct communication and control channels between the human brain and the computer. This technology can meet the control requirements of external equipment without passing through the patient's damaged nerves and declining muscle system. Electroencephalogram (EEG) contains a variety of information, including motor imagination information, steady-state visually evoked EEG information, emotional information, and fatigue information, etc. This paper designs a brain-computer interface system based on motor imagination and uses it to control the body movements of the NAO robot. The system is mainly divided into three modules: signal acquisition, signal processing and robot control. In terms of signal acquisition, electrode caps, conductive glue, EEG amplifiers and Curry7 software are used. In this paper, we design a frequency band feature selection algorithm based on an improved sub-band filter bank (FB)-Common Space Pattern (CSP). These features are classified using Bagging ensemble classification algorithm and converted to use Commands for controlling the NAO robot. In terms of communication control, the instruction codes are preset, and the classification results are corresponding to instructions for controlling different actions of the robot. It is transmitted to the robot by using wireless communication technology. The Bagging ensemble classification method proposed in this paper classifies EEG features. The accuracy of the four types of motor imaging EEG classification is as high as 78.29%, and the accuracy of NAO robot action execution experiments is up to 86.33%, which proves the correctness of the algorithm studied in this paper. The reliability and feasibility of the NAO robot brain control system designed in this paper.

关键词： Robot control Signal processing algorithms Electroencephalography Brain-computer interfaces Classification algorithms Robots Bagging

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Industrial Scene Text Detection with Refined Feature-attentive Network

arXiv

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arXiv 2021年

作者： Guan, Tongkun Gu, Chaochen Lu, Changsheng Tu, Jingzheng Feng, Qi Wu, Kaijie Guan, Xinping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The College of Engineering and Computer Science The Australian National University CanberraACT2600 Australia

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at: https://***/TongkunGuan/RFN. © 2021, CC BY.

关键词： Character recognition

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Automatic Detection of Lumbar Disc Herniation Using YOLOv7

Automatic Detection of Lumbar Disc Herniation Using YOLOv7

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ardha Ardea Prisilla Yori Pusparani Wen-Thong Chang Ben-Yi Liau Yih-Kuen Jan Peter Ardhianto Chih-Yang Lin Chi-Wen Lung Department of Digital Media Design Asia University Taichung Taiwan Department of Fashion Design LaSalle College Jakarta Jakarta Indonesia Department of Visual Communication Design Budi Luhur University Jakarta Indonesia Department of Computer Science and Information Engineering Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Creative Product Design Asia University Taichung Taiwan Department of Visual Communication Design Soegijapranata Catholic University Semarang Indonesia Department of Mechanical Engineering National Central University Taoyuan Taiwan

The detection of lumbar disc herniation (LDH) through magnetic resonance imaging (MRI) poses a challenge due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. One potential solution is using deep learning methods to identify lumbar abnormalities in MRI images automatically. The YOU ONLY LOOK ONCE (YOLO) model series has gained popularity for training deep learning algorithms for real-time biomedical image detection. This study aims to assess the performance of the latest YOLOv7 in detecting LDH across different regions of the lumbar intervertebral disc. The analysis revealed that YOLOv7 exhibits a poor performance and low detection rate of LDH across the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 regions.

关键词：

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QoTa-MPR: QoS-oriented and Traffic-aware Multi-path Routing Protocol for Internet of Remote Things

Research Square

引用

Research Square 2021年

作者： Ma, Zhongyu Liu, Yanxing Guo, Qun Dang, Xiaochao Hao, Zhanjun Tian, Ran College of Computer Science and Engineering Northwest Normal University China Gansu Internet of Things Engineering Research Center Northwest Normal University China College of Electrical and Information Engineering Lanzhou University of Technology China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou University of Technology China National Demonstration Center for Experimental Electrical and Control Engineering Education Lanzhou University of Technology China

Internet of Remote Things (IoRT) is widely used in both military and civilian applications. However, due to the unique characteristics, which is characterized as long-distance and high-latency, the design of the routing protocol is a great challenge of IoRT. In this paper, a QoS-oriented and traffic-aware multi-path routing protocol (QoTa-MRP) is proposed for IoRT. QoTa-MRP is composed of two parts. The first part is the link traffic-aware based muti-paths source route discovery mechanism, which is used to establish multi-paths with lower link disjoint degree. The second part is path similarity and traffic priority based multi-path selection mechanism, which is used to determine the transmission mode of the traffic flows to enhance the reliability or effectiveness of the transmission. Simultaneously, the protocol is theoretically analyzed in terms of the successful transmission rate of routing transmission data packet. Finally, the dynamic source routing, which is a very representative protocol and is usually used as the baseline comparison protocol, is revisited for the performance verification of the QoTa-MRP in IoRT. It is shown in the simulation results that there are significant superiorities of QoTa-MRP than that of DSR in terms of network throughput, packet loss rate and routing packet header overhead in IoRT. © 2021, CC BY.

关键词： Routing protocols

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State Estimation for Integrated Energy Systems: Motivations, Advances, and Future Work

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IEEE Transactions on Power Systems 2024年

作者： Zhao, Junbo Srivastava, Ankur Guo, Ye Cetenovic, Dragan Lin, Yuzhang Levi, Victor Yin, Guanxiong Huang, Manyun Zhang, Tingting Li, Zhigang Chen, Yanbo Exposito, Antonio G. Zhang, Wen Zheng, Weiye Chakrabarti, Saikat Li, Yang Sharma, Ankush Kamwa, Innocent Guo, Qinglai Terzija, Vladimir Mili, Lamine University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States Tsinghua University Tsinghua-Berkeley Shenzhen Institute Shenzhen518055 China The University of Manchester Department of Eee ManchesterM13 9PL United Kingdom University of Kragujevac Faculty of Technical Sciences Čačak Čačak32000 Serbia New York University Department of Electrical and Computer Engineering NY11201 United States Tsinghua University Department of Electrical Engineering Beijing100084 China Hohai University College of Energy and Electrical Engineering Nanjing210098 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China The Chinese University of Hong Kong-Shenzhen School of Science and Engineering Shenzhen518172 China North China Electric Power University School of Electrical and Electronic Engineering Beijing102206 China University of Sevilla Spain Sevilla41092 Spain South China University of Technology School of Electrical Power Engineering Guangzhou510641 China Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Northeast Electric Power University School of Electrical Engineering Jilin132012 China Laval University Department of Electrical and Computer Engineering QuebecQCG1V 0A6 Canada Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy Systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power Systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy Systems-State Estimation (IES-SE) and more established Electric Power System-State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE. © 1969-2012 IEEE.

关键词： Power system analysis

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Asymmetric CPW-Fed Multistubs Loaded Compact Printed Multiband Antenna for Wireless Applications 3rd

Asymmetric CPW-Fed Multistubs Loaded Compact Printed Multiba...

引用

3rd International Conference on Optical and Wireless Technologies, OWT 2019

作者： Kumar, Ashok Deegwal, Jitendra Kumar Kumar, Arjun Verma, Karan Department of Electronics and Communication Engineering Govt. Mahila Engineering College AjmerRajasthan305002 India Department of Electronics Instrumentation and Control Engineering Govt. Engineering College AjmerRajasthan305025 India Department of Electronics and Communication Engineering Bennett University Greater NoidaUttar Pradesh201310 India Department of Computer Science and Engineering National Institute of Technology Delhi New Delhi110040 India

ISBN: (纸本)9789811529252

An asymmetric coplanar waveguide-fed branched multistubs resonators loaded printed antenna is presented for multiband operation. By incorporating an asymmetrical long and short L-shaped branched stubs, the lower resonances for 2.4 and 3.5 GHz bands are achieved while the higher resonance for 5.5 GHz band is achieved by embedding horizontal stub on asymmetric CPW-fed printed antenna. By placing multistubs resonators at suitable location on asymmetric CPW-fed printed antenna, it is likely to excite the triple resonances at 2.44/3.66/5.25 GHz frequencies and antenna exhibits the bandwidths of 5.73% (140 MHz, 2.36–2.50 GHz), 16.98% (630 MHz, 3.40–4.03 GHz), and 14.65% (800 MHz, 5.06–5.86 GHz), respectively. The design procedure and antenna characteristics of the proposed multistubs loaded multiband antenna are presented. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Microstrip antennas

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MUFFIN-HGCN: Multi-feature Fusion Hierarchical-GCN for 3D Object Detection

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chang Jiang, Aimin Zhou, Jia Li, Min Zhu, Yanping Kwan, Hon Keung Hohai University College of Information Science and Engineering Changzhou213200 China Changzhou University School of Microelectronics and Control Engineering Changzhou213164 China University of Windsor Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada

3D object detection play a pivotal role in various applications, such as autonomous driving and environmental perception. However, the challenging task of detecting targets (e.g., vehicles and pedestrians) in 3D point clouds obtained from LiDAR sensors is hindered by two primary factors. Firstly, point clouds are non-Euclidean data and lack rigid data properties. Secondly, these point clouds are sparse and deficient in semantic information. In this paper, we propose a novel deep learning framework for 3D object detection. The proposed approach involves two stages. Specifically, we first present an innovative fusion technique that combines images and point clouds using transformers. This technique enhances the integration of semantic and geometric features while accurately mapping points to pixels without relying on camera extrinsic parameters. Additionally, a multi-level graph convolutional network (GCN) architecture is further introduced for object detection. This architecture effectively handles the non-Euclidean characteristics of point clouds while achieving precise object identification within them. A comprehensive series of experiments on the KITTl and nuScenes datasets validate the effectiveness of the proposed framework. © 1967-2012 IEEE.

关键词： Object detection

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Exploring nontrivial topology at quantum criticality in a superconducting processor

arXiv

引用

arXiv 2025年

作者： Tan, Ziqi Wang, Ke Yang, Sheng Shen, Fanhao Jin, Feitong Zhu, Xuhao Ji, Yujie Xu, Shibo Chen, Jiachen Wu, Yaozu Zhang, Chuanyu Gao, Yu Wang, Ning Zou, Yiren Zhang, Aosai Li, Tingting Bao, Zehang Zhu, Zitian Zhong, Jiarun Cui, Zhengyi Han, Yihang He, Yiyang Wang, Han Yang, Jianan Wang, Yanzhe Shen, Jiayuan Liu, Gongyu Song, Zixuan Deng, Jinfeng Dong, Hang Zhang, Pengfei Jian, Shao-Kai Li, Hekang Wang, Zhen Guo, Qiujiang Lin, Hai-Qing Song, Chao Yu, Xue-Jia Wang, H. Wu, Fei College of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Physics ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou310000 China Institute for Advanced Study in Physics School of Physics Zhejiang University Hangzhou310058 China Department of Physics and Engineering Physics Tulane University New OrleansLA70118 United States Hefei National Laboratory Hefei230088 China Department of Physics Fuzhou University Fujian Fuzhou350116 China Fujian Key Laboratory of Quantum Information and Quantum Optics College of Physics and Information Engineering Fuzhou University Fujian Fuzhou350108 China

The discovery of nontrivial topology in quantum critical states has introduced a new paradigm for classifying quantum phase transitions and challenges the conventional belief that topological phases are typically associated with a bulk energy gap. However, realizing and characterizing such topologically nontrivial quantum critical states with large particle numbers remains an outstanding experimental challenge in statistical and condensed matter physics. Programmable quantum processors can directly prepare and manipulate exotic quantum many-body states, offering a powerful path for exploring the physics behind these states. Here, we present an experimental exploration of the critical cluster Ising model by preparing its low-lying critical states on a superconducting processor with up to 100 qubits. We develop an efficient method to probe the boundary g-function based on prepared low-energy states, which allows us to uniquely identify the nontrivial topology of the critical systems under study. Furthermore, by adapting the entanglement Hamiltonian tomography technique, we recognize two-fold topological degeneracy in the entanglement spectrum under periodic boundary condition, experimentally verifying the universal bulk-boundary correspondence in topological critical systems. Our results demonstrate the low-lying critical states as useful quantum resources for investigating the interplay between topology and quantum criticality. Copyright © 2025, The Authors. All rights reserved.

关键词： Topology

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