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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Xie, Jietao Xie, Yuxiang Wu, Juan Zhai, Jiangtao National University of Defense Technology College of Systems Engineering Changsha China Baicheng Ordnance Test Center of China Test and Training Research Center Jinzhou China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing China

ISBN: (纸本)9798350356083

Time censoring test statistical schemes are widely utilized in engineering practice for reliability testing and are essential for the design and evaluation of reliability tests. However, the existing literature lacks rigorous mathematical proof regarding the correctness of the calculation methods employed in these schemes, and they often prove inadequate for scenarios with predetermined test durations. In this paper, we provide a comprehensive mathematical derivation to validate the calculation methods specified in GJB 899A for the time censoring test statistical schemes. Furthermore, we propose an optimization scheme specifically designed for fixed test durations, supported by a strict proof and a calculation example to demonstrate its correctness. This work serves as a valuable reference for advancing reliability testing methodologies. © 2024 IEEE.

关键词： Reliability analysis

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Application of Permanent Magnet Synchronous Motors in Electric Drive systems

Application of Permanent Magnet Synchronous Motors in Electr...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Mehta, Deepak Kumar, Ritesh Shah, Pradeep Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Everlasting Magnet Synchronous vehicles (PMSMs) are becoming increasingly famous in electric-powered power structures due to their great efficiency and overall performance traits. PMSMs are synchronous motors that use everlasting magnets to supply torque. They provide easy and coffee-cost control answers and can be used for a spread of programs along with electric-powered motors, domestic appliances, and commercial automation. PMSMs are, in particular, appealing to electric pressure system programs due to their better electricity density, higher efficiency, better velocity variety, and less complicated manipulation than different synchronous vehicles. The precept in the back of the PMSM is that the rotor, which contains everlasting magnets, interacts with the rotating magnetic area created via the stator windings. The stator windings are energized via an outside strength digital supply, and the ensuing alternating cutting-edge creates a magnetic area, which in flip produces torque on the rotor. This torque causes the rotor to rotate and produce mechanical power. The torque of the PMSM can be managed through various amplitudes and segments of the stator modern-day. This adaptability makes PMSMs perfect for use in variable-speed power applications consisting of electric automobiles. © 2024 IEEE.

关键词： Everlasting Magnetic Synchronous Windings

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Ergodic Capacity Analysis for a STAR-RIS-Segmented Symbiotic Backscatter NOMA System 35

Ergodic Capacity Analysis for a STAR-RIS-Segmented Symbiotic...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Wen, Zhen Ding, Haiyang Elkashlan, Maged Yuen, Chau Moualeu, Jules M. National University of Defense Technology School of Information and Communications Wuhan China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Nanyang Technological University School of Electrical and Electronic Engineering Singapore University of the Witwatersrand School of Electrical and Information Engineering Johannesburg South Africa

ISBN: (纸本)9798350362244

This paper proposes a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) segmented symbiotic backscatter non-orthogonal multiple access (NOMA) system. Specifically, the STAR-RIS is divided into an enchancing primary signal (EP) zone and a backscatter device (BD) zone. To characterize the overall transmission effectiveness, the metric of sum ergodic capacity (EC) of the considered symbiotic system is established and the corresponding suboptimal approximation solutions are derived in closed-form for three typical NOMA channel conditions. Our results show that the sum EC obeys the scaling law of (Ps) where Ps is the total transmit power, and is dominated by the weaker one of the transmission and reflection channels. Moreover, our simulation results show that both the instantaneous sum rate and the sum EC derived by the proposed suboptimal solution are very close to the optimal one obtained through exhaustive search. More importantly, the transmission effectiveness of the proposed system is superior to the STAR-RIS-assisted NOMA system and the STAR-RIS-segmented symbiotic backscatter orthogonal multiple access (OMA) system. Additionally, it is shown that as the quantification order of the imperfect channel state information (ipCSI) increases, the sum EC performance gradually improves. © 2024 IEEE.

关键词： Channel state information

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Real-time SLAM Algorithm for Efficient Association Prediction Introducing Residual Connection 7

Real-time SLAM Algorithm for Efficient Association Predictio...

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7th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2022

作者： Wenju, Li Mengying, Li Shanghai Institute of Technology University School of Computer Science and Information Engineering Shanghai China

ISBN: (数字)9781665482295

ISBN: (纸本)9781665482295

In view of the problems of blurred images and insufficient feature points caused by the structured environment or too fast camera movement in the current slam (Simultaneous Localization and Mapping) system, this paper proposes a real-time slam algorithm for efficient association prediction introducing residual connection. The algorithm introduced residual connection on the basis of an efficient correspondence prediction network to make up for the loss of information in the downsampling process, and form a new feature point extraction and matching method, which can replace the ORB (Oriented FAST and Rotated BRIEF) feature points in ORB-SLAM2. This paper conducted experiments on the TUM dataset and compared it with the experimental results of ORB-SLAM2 and a smaller version of efficient correspondence prediction network. The results show that this method can enhance map readability and effectively improve the accuracy of camera positioning while ensuring real-time performance. © 2022 IEEE.

关键词： Forecasting

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An Encoder-Sequencer-Decoder Network for Lane Detection to Facilitate Autonomous Driving 23

An Encoder-Sequencer-Decoder Network for Lane Detection to F...

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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Hussain, Muhammad Ishfaq Rafique, Muhammad Aasim Ko, Yeongmin Khan, Zafran Olimov, Farrukh Naz, Zubia Kim, Jeongbae Jeon, Moongu School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Korea Republic of College of Computer Sciences and Information Technology King Faisal University Department of Information Systems Al Ahsa31982 Saudi Arabia Monitorapp Threat Intelligence Team Seoul Korea Republic of Pusan National University Pusan Korea Republic of

ISBN: (纸本)9788993215274

Lane detection in all weather conditions is a pressing necessity for autonomous driving. Accurate lane detection ensures the safe operation of autonomous vehicles, enabling advanced driver assistance systems to effectively track and maintain the vehicle within the lanes. Traditional lane detection techniques heavily rely on a single image frame captured by the camera, posing limitations. Moreover, these conventional methods demand a constant stream of pristine images for uninterrupted lane detection, resulting in degraded performance when faced with challenges such as low brightness, shadows, occlusions, and deteriorating environmental conditions. Recognizing that continuous sequence patterns on the road represent lanes, our approach leverages a sequential model to process multiple images for lane detection. In this study, we propose a deep neural network model to extract crucial lane information from a sequence of images. Our model adopts a convolutional neural network in an encoder/decoder architecture and incorporates an extended short-term memory model for sequential feature extraction. We evaluate the performance of our proposed model using the TuSimple and CuLane datasets, showcasing its superiority across various lane detection scenarios. Comparative analysis with state-of-the-art lane detection methods further substantiates our model's effectiveness. © 2023 ICROS.

关键词： Advanced driver assistance systems

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Robust Practical Stabilization for Complex Dynamical Networks With DoS Attacks and Actuator Saturation

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3108-3118页

作者： Shi, Xueya Peng, Zhinan Yu, Junzhi Li, Hongfei Southwest University College of Electronic and Information Engineering Chongqing400715 China Sichuan Artificial Intelligence Research Institute Yibin644000 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China

This article addresses the problem of designing an attack-resilient adaptive event-triggered (AET) controller for complex dynamical networks (CDNs) under DoS attacks and actuator saturation, with a focus on robust practical stability (RPS). First, considering the impact of DoS attacks on closed-loop systems, an AET controller against DoS attacks is designed. Unlike other event-triggered controllers, the complete timeline is divided, and the AET controller is built with two switching modes based on the intervals of dormant and active periods of DoS attacks in which the system is located. Second, to reconcile AET controller with actuator saturation, a switched system modeling approach is established that explicitly incorporates saturation constraints into the coupled network dynamics. Third, a switched Lyapunov-Krasovskii functional (LKF) is proposed, with which sufficient conditions are provided to ensured the RPS, and a joint design strategy is developed for the desired triggered matrix and feedback gain using linear matrix inequalities (LMIs). Moreover, the results are generalized to the case of actuator faults, indicating that the system is able to achieve RPS with actuator faults. Finally, the proposed method is verified through an example. © 2013 IEEE.

关键词： Lyapunov functions

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An Analysis of Machine Learning Algorithms for Smart Healthcare systems 1

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International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Alduailij, Mai Mishra, Anupama Zamzami, Ikhlas Fuad Psannis, Konstantinos Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Dehradun India Faculty of Business King Abdulaziz University Rabigh Saudi Arabia University of Macedonia Thessaloniki Greece

ISBN: (数字)9783031220180

ISBN: (纸本)9783031220173

The epidemic taught us to keep digital health records. It also showed us how wearable observing gear, video conferences, and AI-powered chatbots may give good treatment remotely. Real-time data from health care devices across the globe helped attack and track the infection. Biomedical imaging, sensors, and machine learning have improved health in recent years. Medical care and biomedical sciences are now information science sectors that demand enhanced data mining approaches. Biomedical data have high dimensionality, class irregularity, and few tests. AI uses information and computations to imitate how people learn, continually improving its accuracy. ML is an important part of information science. Calculations utilising measurable methods reveal essential information about information mining operations. This paper explains and compares ML algorithms that can detect diseases sooner. We summarise ML’s algorithms and processes to extract information for a data-driven society. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Image Recognition based on Multi-scale Feature Fusion Transformer

Image Recognition based on Multi-scale Feature Fusion Transf...

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2022 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2022

作者： Zhu, Zhefeng Qi, Ke Chen, Wenbin Zhou, Yicong Li, Peiyue Liu, Zhenxian Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China University of Macau Department of Computer and Information Science China

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

Aiming at the problem that image recognition based on transformers has low image recognition rate due to ignoring local information of image blocks, an image recognition framework based on multi-scale Feature Fusion Transformer (FFT) is proposed, where the FFT block is designed to fuse feature information of different scales, and the residual attention module is introduced to emphasize feature channels and feature regions of interest. The FFT framework not only avoids the problem of vision transformer internal structure and local information loss of image feature blocks but also captures richer detailed features, which effectively improves the image recognition rate. A large number of experiments are performed on common image recognition datasets Tiny-ImageNet, CIFAR-10 and CIFAR-100, and the recognition accuracy can reach 57.81%, 82.04% and 56.98%, respectively, which are significantly higher than the mainstream image recognition algorithms. © 2022 IEEE.

关键词： Image recognition

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SIGVIC: Spatial Importance Guided Variable-Rate Image Compression 48

SIGVIC: Spatial Importance Guided Variable-Rate Image Compre...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liang, Jiaming Liu, Meiqin Yao, Chao Lin, Chunyu Zhao, Yao Beijing Jiaotong University Institute of Information Science Beijing100044 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

ISBN: (纸本)9781728163277

Variable-rate mechanism has improved the flexibility and efficiency of learning-based image compression that trains multiple models for different rate-distortion tradeoffs. One of the most common approaches for variable-rate is to channel- wisely or spatial-uniformly scale the internal features. However, the diversity of spatial importance is instructive for bit allocation of image compression. In this paper, we introduce a Spatial Importance Guided Variable-rate Image Compression (SigVIC), in which a spatial gating unit (SGU) is designed for adaptively learning a spatial importance mask. Then, a spatial scaling network (SSN) takes the spatial importance mask to guide the feature scaling and bit allocation for variablerate. Moreover, to improve the quality of decoded image, Top-K shallow features are selected to refine the decoded features through a shallow feature fusion module (SFFM). Experiments show that our method outperforms other learning- based methods (whether variable-rate or not) and traditional codecs, with storage saving and high flexibility. © 2023 IEEE.

关键词： image compression scale factor shallow feature spatial importance variable-rate

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Interpretable Medical Recommendations Based on SHAPs 6

Interpretable Medical Recommendations Based on SHAPs

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Wu, Yuanyuan Zhang, Linfei Bhatti, Uzair Aslam Huang, Mengxing Zhang, Yu School of Information and Communication Engineering Hainan University Haikou China Hainan University College of Computer Science and Technology Haikou570228 China

ISBN: (纸本)9798350325485

In the past few years, chronic diseases of the elderly have gradually become the killer of the elderly, seriously affecting their physical health of the elderly. Doctors can extract relevant information from the data to make patient treatment recommendations. This study proposed a smart recommendation system for doctors by using deep learning traditional methods. Proposed method firstly use six traditional deep learning methods such as naive Bayes, logistic regression, decision tree etc for recommendation purpose. Next step to get the better understanding of the recommendation we apply an interpretable recommendation system based on SHapley Additive exPlanations. (SHAP). Last time is provide recommendations based on better and weak recommenations by graphical way. The proposed methods were applied to two chronic diseases of old age, including heart disease and diabetes. After the data is preprocessed, naive Bayes, decision tree, and other machine learning operations are carried out on the data. SHAP was used to calculate the importance of each feature for randomly selected patients. Finally, the contribution coefficient of the feature to the result is presented, which impacts the output. By analyzing the influence of different features of users on recommendation results, the proposed system explains why such results are recommended to users to improve users' trust in the recommendation results, which is of great significance for medical recommendation. © 2023 IEEE.

关键词： Recommender systems

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