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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Wang, Zibo Li, Honghui Zhang, Junwen Yang, Xiaorui Shen, Nahao School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway of MOE Beijing China

ISBN: (纸本)9798350354010

The structure of railway systems is complex, and accurately and rapidly identifying the root cause of faults post-occurrence is an emerging challenge in the industry. However, railway fault tracing mainly relies on manual experience and traditional data analysis techniques, with most tracing methods suffering from low efficiency and insufficient accuracy. To address these issues, this paper proposes a railway safety equipment fault tracing method based on knowledge graphs and weighted path ranking algorithm. The approach integrates knowledge graphs with Bayesian networks, attention mechanisms, and graph computation, utilizing a weighted calculation to run the weighted path ranking algorithm. Experimental results demonstrate that the proposed method achieves excellent tracing performance in the field of railway equipment. © 2024 IEEE.

关键词： Knowledge graph

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PCA Dimensionality Reduction Algorithm Based on Ordered Pair of Normalized Real Numbers 17

PCA Dimensionality Reduction Algorithm Based on Ordered Pair...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Zhao, Xiang Zheng, Yi Pan, Xiaoqin Zhou, Lei School of Computer Science and Technology Southwest University of Science and Technology China Engineering Technology Center Southwest University of Science and Technology China School of Mathematics and Physics Southwest University of Science and Technology Sichuan621010 China

ISBN: (纸本)9798331507398

This paper presents a PCA (Principal Component Analysis) data dimensionality reduction algorithm based on OPNs (Ordered Pair of Normalized Real Numbers), referred to as OPNs-PCA. This algorithm aims to improve the dimensionality reduction performance of data. Especially, it shows significant advantages in removing feature redundancy and noise, providing a clearer and more effective data basis for subsequent machine learning or data mining tasks. The algorithm proposed in this paper introduces the concept of OPNs into the PCA algorithm and performs PCA dimensionality reduction operations under the framework of OPNs. Through the characteristic of OPNs' operation in processing feature relationships in non-real number spaces, it can effectively reduce data noise, enhance the processing ability of nonlinear data, and improve the performance of subsequent classification algorithms. We have conducted detailed experimental verification on multiple public data sets. The results show that unlike classic PCA, LLE, KPCA, SPCA and other algorithms that require more principal components to retain sufficient information, OPNs-PCA can achieve a similar or even better level of information retention with fewer principal components, and has a significant improvement in the processing ability of nonlinear data. © 2024 IEEE.

关键词： Dimensionality reduction

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Social engineering attack types and prevention techniques- A survey 1

Social engineering attack types and prevention techniques- A...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bhatia, Shruti Yadav, Satya Prakash Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

The term "Cyber Security"is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim's social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article. © 2023 IEEE.

关键词： Crime

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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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Real-Time Lightweight Convolutional Neural Network for Polyp Detection in Endoscope Images

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Journal of Shanghai Jiaotong University (science) 2025年第3期30卷 521-534页

作者： Si, Bingqi Pang, Chenxi Wang, Zhiwu Jiang, Pingping Yan, Guozheng School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Drug Detoxification and Rehabilitation Shanghai200240 China College of Computer Science and Technology Jilin University Changchun130012 China

Colorectal cancer is the most common cancer with a second mortality rate. Polyp lesion is a precursor symptom of colorectal cancer. Detection and removal of polyps can effectively reduce the mortality of patients in the early period. However, mass images will be generated during an endoscopy, which will greatly increase the workload of doctors, and long-term mechanical screening of endoscopy images will also lead to a high misdiagnosis rate. Aiming at the problem that computer-aided diagnosis models deeply depend on the computational power in the polyp detection task, we propose a lightweight model, coordinate attention-YOLOv5-Lite-Prune, based on the YOLOv5 algorithm, which is different from state-of-the-art methods proposed by the existing research that applied object detection models or their variants directly to prediction task without any lightweight processing, such as faster region-based convolutional neural networks, YOLOv3, YOLOv4, and single shot multibox detector. The innovations of our model are as follows: First, the lightweight EfficientNetLite network is introduced as the new feature extraction network. Second, the depthwise separable convolution and its improved modules with different attention mechanisms are used to replace the standard convolution in the detection head structure. Then, the α-intersection over union loss function is applied to improve the precision and convergence speed of the model. Finally, the model size is compressed with a pruning algorithm. Our model effectively reduces parameter amount and computational complexity without significant accuracy loss. Therefore, the model can be successfully deployed on the embedded deep learning platform, and detect polyps with a speed above 30 frames per second, which means the model gets rid of the limitation that deep learning models must rely on high-performance servers. © Shanghai Jiao Tong University 2023.

关键词： Object detection

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High Performance Soft Electrochemical Actuators Based on Hierarchical Conductive Polymer Ionogels

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Journal of Bionic engineering 2023年第6期20卷 2755-2763页

作者： Hongwei Hu Shengtao Zhang Yan Li Xinghao Hu Lin Xu Aixin Feng Guanggui Cheng Jianning Ding Zhejiang Provincial Key Laboratory of Laser Processing Robotics College of Mechanical and Electrical EngineeringWenzhou UniversityWenzhou325035China Institute of Intelligent Flexible Mechatronics Jiangsu UniversityZhenjiang212013China School of Mechanical Engineering Jiangsu University of Science and TechnologyZhenjiang212100China School of Mechanical Engineering Yangzhou UniversityYangzhou225127China Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering Changzhou UniversityChangzhou213164China Ruian Graduate School of Wenzhou University Ruian325207ZhejiangChina

Electrochemical actuators based on conductive polymers are emerging as a strong competitive in the field of soft actuators because of their intrinsically conformable/elastic nature,low cost,low operating voltage and air-working *** development has shown that adding electroactive materials,such as CNT and graphene,can improve their actuation *** the complex material systems used,their output strains(one of the key factors)are generally lower than 1%,which limited further applications of them in multiple ***,we report soft electrochemical actuators based on conductive polymer ionogels by embedding polyaniline particles between the PEDOT:PSS *** show that such a hierarchical structure not only leads to a high conductivity(1250 S/cm)but also improved electrochemical *** a low operating voltage of 1 V,the maximum strain of these soft actuators reaches an exceptional value of 1.5%,with a high blocking force of 1.3 *** these high-performance electrochemical actuators,we demonstrate soft grippers for manipulating object and a bionic flower stimulated by an electrical *** work sets an important step towards enabling the enhanced performance of electrochemical actuators based on conductive polymers with designed microstructures.

关键词： Conductive polymer Soft actuator Polyaniline PEDOT:PSS Bioinspired actuator

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Design and Experimental Validation of a Worm-Like Tensegrity Robot for In-Pipe Locomotion

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Journal of Bionic engineering 2023年第2期20卷 515-529页

作者： Xiaolin Dai Yixiang Liu Wei Wang Rui Song Yibin Li Jie Zhao School of Control Science and Engineering Shandong UniversityJinan250061China Engineering Research Center of Intelligent Unmanned System of Ministry of Education Jinan250061China Shandong Research Institute of Industrial Technology Jinan250061China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin150080China

Traditional rigid-body in-pipe robots usually have complex and heavy structures with limited flexibility and *** soft in-pipe robots have great improvements in flexibility,they still have manufacturing difficulties due to their reliance on high-performance soft *** structure is a kind of self-stressed spatial structure consisting discrete rigid struts connected by a continuous net of tensional flexible strings,which combines the advantages of both rigid structures and soft *** applying tensegrity structures into robotics,this paper proposes a novel worm-like tensegrity robot for moving inside ***,a robot module capable of body deformation is designed based on the concept of tensegrity and its deformation performance is ***,the optimal parameters of the module are obtained based on the tensegrity *** deformation ability of the tensegrity module is tested ***,the worm-like tensegrity robot that can crawl inside pipes is developed by connecting three modules in *** performance and load capacity are tested on the prototype of the worm-like tensegrity robot by experiments of moving in horizontal pipe,vertical pipe,and elbow *** results demonstrate the effectiveness of the proposed design and suggest that the robot has high compliance,mobility,and adaptability although with simple structure and low cost.

关键词： Bio-inspired robot Soft robot application Tensegrity structure Pipe crawling

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A fast anomaly network traffic detection method based on the constrained k-nearest neighbor 13

A fast anomaly network traffic detection method based on the...

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13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Zhu, Zhiguang Xie, Ying Yang, Xu Hu, Wei University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Southwest Minzu University School of Computer Science and Engineering Chengdu China National Engineering Research Center of Classified Protection Safeguard Technology for Cybersecurity Beijing China

ISBN: (纸本)9781665462631

The k-nearest neighbor algorithm has been widely used in network anomaly detection works, but its query efficiency decreases significantly when the number of samples and feature dimensions increase. To meet the demand for real-time detection, an accurate and timely anomaly detection solution is particularly important. This paper proposes a fast anomaly traffic detection method based on the constrained k-nearest neighbor (CKNN) algorithm. The method uses equilibrium modified k-means and randomized incremental method to optimize the ball tree construction scheme. Specifically, randomized incremental method is used to solve the minimum coverage ball, which optimizes the selection process of the centeroid and radius while reducing the depth of the ball tree. And the equilibrium modified k-means method replaces the original k-center principle used in the division of subtrees, which solves the problem of unbalanced search binary tree division. Meanwhile, by reducing the required number of backtracking to search the K nearest neighbors, which reduces the classification time overhead of the algorithm. We validate the effectiveness of the method on several benchmark datasets. The experimental results show that the CKNN maintains a higher query rate without loss of detection accuracy when dealing with high-dimensional, massive sample data compared with the traditional KNN algorithm. And with the growth ratio of up to 99.68% for some samples, our method also exhibits higher detection accuracy and less time consumption compared with other machine learning algorithms. © 2023 IEEE.

关键词： Anomaly detection

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Pairwise tagging framework for end-to-end emotion-cause pair extraction

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Frontiers of computer science 2023年第2期17卷 111-120页

作者： Zhen WU Xinyu DAI Rui XIA National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210023China

Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a *** generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and *** works adopt pipelined approaches or multi-task learning to address the ECPE ***,the pipelined approaches easily suffer from error propagation in real-world *** multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction *** address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging *** prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging *** this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause *** validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark *** experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.

关键词： emotion-cause pair extraction pairwise tagging framework end-to-end neural network

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