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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Guangchen Lu, Guangquan Wang, Jinghua Liu, Miaomiao School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Accurately analyzing and predicting driver lane-changing intentions is of paramount importance, as it significantly enhances the safety of self-driving vehicles in their decision-making processes, holding great promise for the future of intelligent transportation systems. This study extracts fundamental lane-changing characteristic parameters from the Next Generation Simulation (NGSIM) trajectory data set and employs Structural Equation Modeling (SEM) to construct a model that elucidates the factors influencing drivers’ lane-changing intentions. Furthermore, it provides insight into how lane-changing intentions are influenced by lane-changing behavior and explores the intricate connection between intention and associated risks. The results emphasize the substantial negative impact of the longitudinal speed difference between the self-vehicle and the preceding vehicle on lane-changing intentions. Additionally, the consideration of lane-changing risk plays a pivotal role in drivers’ decisions to engage in lane-changing behavior. These findings have significant implications for a deeper comprehension of lane-changing intentions, guiding lane-changing decisions, and enhancing overall traffic safety. © ASCE.

关键词： Autonomous vehicles

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MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert 39

MapExpert: Online HD Map Construction with Simple and Effici...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Dapeng Chen, Dayu Zhi, Peng Chen, Yinda Yuan, Zhenlong Li, Chenyang Sunjing Zhou, Rui Zhou, Qingguo School of Information Science & Engineering Lanzhou University China Robotics Technology Co. Ltd. China School of Information Science and Technology University of Science and Technology of China China Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)157735897X

Constructing online High-Definition (HD) maps is crucial for the static environment perception of autonomous driving systems (ADS). Existing solutions typically attempt to detect vectorized HD map elements with unified models;however, these methods often overlook the distinct characteristics of different non-cubic map elements, making accurate distinction challenging. To address these issues, we introduce an expert-based online HD map method, termed MapExpert. MapExpert utilizes sparse experts, distributed by our routers, to describe various non-cubic map elements accurately. Additionally, we propose an auxiliary balance loss function to distribute the load evenly across experts. Furthermore, we theoretically analyze the limitations of prevalent bird’s-eye view (BEV) feature temporal fusion methods and introduce an efficient temporal fusion module called Learnable Weighted Moving Descent. This module effectively integrates relevant historical information into the final BEV features. Combined with an enhanced slice head branch, the proposed MapExpert achieves state-of-the-art performance and maintains good efficiency on both nuScenes and Argoverse2 datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Expert systems

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Buck Boost Converter Control in an IoT-Based Wind System Using Supervised Learning Technique 1

Buck Boost Converter Control in an IoT-Based Wind System Usi...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Anbuselvan, N. Hemalatha, S. Lakshminarayanan, Suresh Sivanantham, E. Mohankumar, N. Thamizhamuthu, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India University of Technology and Applied Sciences - Almusanna Oman Department of Engineering and Electronics South Al Batina Oman Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B.V Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Srm Institute of Science and Technology Kattankulathur Campus-Chengalpattu Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

In recent years, distributed energy resources (DERs) have gained significance as a way of introducing a substantial quantity of renewable energy sources (RESs) into power grids. Wind turbines are accessible in windy areas, giving wind energy systems (WES) a realistic alternative for clients. This work attempts to increase the effectiveness and adaptability of WES by presenting a novel way of managing DC-DC buck-boost converters. The suggested method combines an Internet of Things (IoT) system for monitoring with supervised learning algorithms to make energy conversion more stable, efficient, and able to adapt in real time to changing wind conditions. In this research, creating a reliable supervised learning control method is the primary goal. The second goal is to optimize the most important parameters of the converter. Using the new supervised learning algorithm, the study changes variables like duty cycle, switching frequency, and inductance to get the best results based on past performance data and how the system is working now. It investigates the influence of these parameters on overall system efficiency and stability. The incorporation of an IoT monitoring system into the WES is an essential component of the study. Various sensors monitor variables such as wind speed, converter input and output voltages, and currents in real-time and are shown as results. © 2024 IEEE.

关键词： Buck-Boost converter

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Security of Medical Multimodality Images for Telemedicine Using PDESNet Encryption Standard

Security of Medical Multimodality Images for Telemedicine Us...

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International Conference on Futuristic Technologies (INCOFT)

作者： P. Shyamala Madhuri D. J. Nagendra Kumar B. Amutha Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India Department of Information Technology Vishnu Institute of Technology Bhimavaram Andhra Pradesh India

The telemedicine approach is a famous medical assistance service in worldwide, in this process diagnosis records like Scan reports, X-ray, CT and all other radiology reports analysis has been done with doctors. Many times, sending and receiving of these images from one point to many points, due to this security has been reduced. The multimodality images are major key diagnosis elements to detect disease of patients, these multimodality images security also been reduced due to low encryption techniques. In this work an advanced cryptographic encryption technique i.e Parallel Data encryption Standard (PDESNet) algorithm. The PDES is a modern novel algorithm can provide security to multimodal images from transmission to storage part. Finally summarize the proposed algorithm with existed models in terms of PSNR, BER, NC, MSE, accuracy and recall.

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Hand medical monitoring system based on machine learning and optimal EMG feature set

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Personal and Ubiquitous computing 2023年第6期27卷 1991-2007页

作者： Yu, Mingchao Li, Gongfa Jiang, Du Jiang, Guozhang Tao, Bo Chen, Disi Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Research Center of Biologic Manipulator and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China 3D Printing and Intelligent Manufacturing Engineering Institute Wuhan University of Science and Technology Wuhan430081 China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

Considering that serious hand function damage will greatly affect the daily life of patients, its recovery mainly depends on the regular inspection and manual training of medical staff, and medical monitoring based on bioelectric signals can largely replace manual re-examination as autonomous rehabilitation technology. So, for the rationality of feature selection and the diversity of classifier design in the gesture recognition process based on electromyography (EMG) signals, this paper proposes a hand medical monitoring system based on feature selection method of feature subset average recognition rate and optimal machine learning algorithm selection, which mainly depends on the prediction of hand movement. At the same time, since most experiments are conducted in different non-public proprietary databases, the comparison between various gesture recognition methods can only be analyzed to a certain extent. Therefore, this paper uses the DB1 dataset in the large publicly available NinaPro database and combines with presently well-known 11 time-domain (TD) features and 5 frequency domain (FD) features, then uses the support vector machine (SVM) classifier to comparative analysis total 136 feature combinations under various feature numbers. Under the premise of ensuring the overall recognition rate of electromyography gesture, this method will be able to reduce the number of features in feature set, according to the change of the average remove redundant features, and construct an optimal reduced EMG feature set. Finally, through the four common hand motion classifiers based on machine learning: SVM, back propagation neural network, linear discriminant analysis, and K-nearest neighbor, this paper tests and verifies the separability of the optimal reduced EMG feature set, and based on this, selects the optimal hand motion classifier to build the optimal hand motion recognition system, improve the hand medical monitoring system, and provide technical reference for the c

关键词： Discriminant analysis

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SeqAdver: Automatic Payload Construction and Injection in Sequence-based Android Adversarial Attack

SeqAdver: Automatic Payload Construction and Injection in Se...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Fei Zhang Ruitao Feng Xiaofei Xie Xiaohong Li Lianshuan Shi College of Intelligence and Computing Tianjin University Tianjin China School of Computing and Information Systems Singapore Management University Singapore School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China

Machine learning has achieved a great success in the field of Android malware detection. In order to avoid being caught by these ML-based Android malware detection, malware authors are inclined to initiate adversarial sample attacks by tampering with mobile applications. Although machine learning has high capability, it lacks robustness against adversarial attacks. Currently, many of the adversarial attacking tools not only inject dead code into target applications, which can never be executed, but also require the injection of many benign features into a malicious APK. This can be easily noticeable by program analysis techniques. In this paper, we propose SeqAdver, an automatic payload construction and injection tool, which aims to bring the adversarial attack to the next level by injecting a payload that allows execution without breaking the app’s original functionalities. These payloads are obtained from benign APKs at the Smali level and normalized into usable code snippets. The extracted Smali codes are carefully selected by filtering out ‘user-visible’ APIs and Intents. Therefore, payloads are able to be executed without any visible change noticed by the user. Besides, extracted payloads can be injected into different locations of the file based on sequence position or on the launcher class. Experiments were conducted to prove that randomly extracted payloads from benign apps are able to execute without causing any ‘user-visible’ behaviors or crashing the app when running the app in Android emulators.

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Design of an Efficient Deep Learning Framework for Covid-19 Image Classification

Design of an Efficient Deep Learning Framework for Covid-19 ...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： J Himasree K. Aravindhan K.P. Keerthana C Gobinath School of Computing and Information and Technology Reva University Bangalore Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The worldwide health crisis caused by COVID-19 and the subsequent control measures have had significant and widespread impacts on various aspects of human life. Detecting COVID-19 early is crucial for effective diagnosis, and ML algorithms have played a vital role in speeding up the process and improving efficiency. However, factors like delivery time and access to training data are also crucial considerations. Extensive analysis of lung ultrasound pictures using the COVID-19 database and deep learning algorithms show that the former is superior to the latter in terms of illness diagnosis. Without specialised testing, it might be difficult to tell COVID-19 from other viral fevers. The research suggests a method of COVID-19 image classification using Convolutional Neural Networks (CNNs). Further evidence of the superiority of the proposed model is provided by a loss and accuracy comparison study.

关键词： COVID-19 Deep learning Ultrasonic imaging Training data Lung Convolutional neural networks Security

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Enabling Efficient Large Recommendation Model Training with Near CXL Memory Processing

Enabling Efficient Large Recommendation Model Training with ...

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Annual International Symposium on Computer Architecture, ISCA

作者： Haifeng Liu Long Zheng Yu Huang Jingyi Zhou Chaoqiang Liu Runze Wang Xiaofei Liaot Hai Jinf Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab Huazhong University of Science and Technology China School of Computer Science and Engineering University of New South Wales Australia Zhejiang Lab Hangzhou China

ISBN: (数字)9798350326581

ISBN: (纸本)9798350326598

Personalized recommendation systems have become one of the most important Internet services nowadays. A critical challenge of training and deploying the recommendation models is their high memory capacity and bandwidth demands, with the embedding layers occupying hundreds of GBs to TBs of storage. The advent of memory disaggregation technology and Compute Express Link (CXL) provides a promising solution for memory capacity scaling. However, relocating memory-intensive embedding layers to CXL memory incurs noticeable performance degradation due to its limited transmission bandwidth, which is significantly lower than the host memory bandwidth. To address this, we introduce ReCXL, a CXL memory disaggregation system that utilizes near-memory processing for scalable, efficient recommendation model training. ReCXL features a unified, hardwareefficient NMP architecture that processes the entire embedding training within CXL memory, minimizing data transfers over the bandwidth-limited CXL and enhancing internal bandwidth. To further improve the performance, ReCXL incorporates softwarehardware co-optimizations, including sophisticated dependencyfree prefetching and fine-grained update scheduling, to maximize hardware utilization. Evaluation results show that ReCXL outperforms the CPU-GPU baseline and the naïve CXL memory by $7.1 \times \sim 10.6 \times(9.4 \times$ on average) and $12.7 \times \sim 31.3 \times(22.6 \times$ on average), respectively.

关键词： Training Degradation Scalability Prefetching Memory management Web and internet services Bandwidth

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Algorithms for Probabilistic Hesitant Fuzzy Multiple Criteria Decision-Making Using Information Measures Derived from T-Norm

SSRN

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SSRN 2023年

作者： Farhadinia, Bahram Akhavan-Abdollahian, Malihe Aickelin, Uwe School of Science College of Sciences Technology Engineering Mathematics RMIT University Melbourne Australia School of Computing and Information Systems

Existing probabilistic hesitant fuzzy set (PHFS) measures are constructed using two information measures: hesitancy and unwrapped probabilities. We argue that it is not logical to unify these different semantic terms in PHFS information theory. We introduce a new class of information measures for PHFSs which address the logical wrapping of hesitant fuzzy sets (HFS) and probability;and propose a number of similarity measures for these sets which use the t-norm operator. Considering the relationship between measures of entropy and similary, we then represent the axiomatic definition of PHFS entropy measures. Finally, we use case studies to demonstrate applications of these information measures. We describe two multiple criteria decision making algorithms in which the last step is devoted to PHFS ranking procedures: one based on the score function of alternatives, and the other based on the relative closeness of alternatives. This contribution describes new information measures and uses case studies to illustrate how they can be applied to decision making processes. © 2023, The Authors. All rights reserved.

关键词： Information theory

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A Comparative Study of Clustering Distance Metrics for Personalized Federated Learning in Human Activity Recognition

A Comparative Study of Clustering Distance Metrics for Perso...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Xiaoxu Wen Chenggang Lu Ruixiang Hu Yingrui Geng Yan Wang Hongnian Yu Qiangsong Zhao School of Automation and Electrical Engineering Zhongyuan University of Technology Zhengzhou China School of Intelligent Sensing and Instrumentation Zhongyuan University of Technology Zhengzhou China Graduate School of Science and Engineering Ritsumeikan University Shiga Japan School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh UK

ISBN: (数字)9798350366204

ISBN: (纸本)9798350366211

In Federated Learning (FL), Non-Independent and Identically Distributed (Non-IID) data across different clients presents a significant challenge for a global model to effectively generalize to all clients, thus affecting fairness across the system. To address this, the Clustered Personalized Federated Learning (CPFL) framework groups clients with similar data distributions and trains models that are tailored to the characteristics within each cluster. In this study, we experimentally evaluate the performance of four distance metrics - Cosine distance, Euclidean distance, Manhattan distance, and Mahalanobis distance - across varying numbers numbers of clusters within the CPFL framework, focusing on their impact on model accuracy and fairness across client models. Our results demonstrate that Euclidean distance not only improves model accuracy but also significantly reduces performance disparities between clients, exhibiting the lowest variance and enhancing fairness. In contrast, the other distance metrics introduce larger variance in certain scenarios, leading to greater differences in client model performance. This study provides empirical support for the use of distance metrics in CPFL, showing that selecting appropriate clustering methods can enhance both overall performance and fairness across client model.

关键词： Measurement Accuracy Mechatronics Federated learning Shape Focusing Euclidean distance Data models Human activity recognition Principal component analysis

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