检索结果-内蒙古大学图书馆

Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1752-1762页

作者： Xianglei Zhu Wen Hu Zejian Deng Jinwei Zhang Fengqing Hu Rui Zhou Keqiu Li Fei-Yue Wang the College of Intelligence and Computing Tianjin UniversityTianjin 300350 the China Automotive Technology and Research Center Co.Ltd. Tianjin 300300China the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterlooON N2L3G1Canada the School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China the Macao University of Science and Technology MacaoChina the Waytous Inc. Qingdao 266000China IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by *** improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic *** integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and *** dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference *** on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax ***,the comparative analysis is performed to validate the proposed method using the naturalistic driving *** results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

关键词： Cut-in behavior interaction-aware mixed traffic risk assessment trajectory prediction

来源：评论

学校读者我要写书评

暂无评论

Improved Q-Learning Algorithm for Path Planning of an Automated Guided Vehicle (AGV)

Improved Q-Learning Algorithm for Path Planning of an Automa...

引用

IEEE International Conference on Artificial Intelligence in engineering and Technology (IICAIET)

作者： Hao Guo Min Keng Tan Kit Guan Lim Helen Sin Ee Chuo Baojian Yang Kenneth Tze Kin Teo Modelling Simulation & Computing Laboratory Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia Department of Automation Baotou Light Industry Vocational and Technical College Baotou City China Department of Intelligent Manufacturing Wuyi University Jiangmen China

With the development of modern industry 4.0, intelligent path planning is an essential research direction of schedule systems for an automated guided vehicle (AGV), which has been widely used in logistics distribution centers of enterprises. This work implements the improved Q-learning algorithm to solve the typical obstacle avoidance problems in path planning. Specifically, the conventional Q-learning algorithm has shortcomings including low operational efficiency and slow learning speed. The improved Q-learning algorithm is successively proposed by adding a learning process based on the original Q-learning algorithm, which enables AGV to find obstacles and target locations within the shortest time, thus improving the efficiency of path planning. Finally, the simulation experiments are carried out in the grid environment with MATLAB. In comparison to the conventional Q-learning algorithm, the improved Q-learning algorithm has faster convergence and higher learning efficiency, improved by 20%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Rectified Flow-Based Generative Transfer Learning for System Fault Diagnosis

Rectified Flow-Based Generative Transfer Learning for System...

引用

International Conference on Vision, Image and Signal Processing (ICVISP)

作者： Ruonan Liu Shuai Wu Qiming Liu Dongsheng Guo Weidong Zhang Department of Automation Shanghai Jiao Tong University Shanghai China Tianjin University College of Intelligence and Computing Tianjin China Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Mechanical Engineering Tianjin China Hainan University School of Information and Communication Engineering Hainan China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

With the rapid development of new energy sources under the “dual carbon” background, the safety and stability of energy storage and power generation systems have attracted widespread attention. This paper investigates the challenges of insufficient training data and the limitations of existing generative transfer learning methods in the field of system fault detection. To address these issues, we propose a graph neural network model based on a generative transfer learning method utilizing the Rectified Flow algorithm for system fault detection. The Rectified Flow algorithm is applied to transfer learning to enhance the efficiency and effectiveness of system fault diagnosis, especially when training data is limited. The model leverages source domain data to train the target domain data, overcoming the data scarcity issue in the target domain. Our experimental results demonstrate that the proposed model outperforms traditional methods in terms of computational efficiency and model conciseness, showing remarkable performance in system fault detection. This study not only provides an effective solution to the data insufficiency problem in system fault detection but also offers valuable insights for further enhancing the performance of system fault diagnosis.

关键词： Fault diagnosis Fault detection Computational modeling Transfer learning Signal processing algorithms Training data Signal processing Stability analysis Safety Power generation

来源：评论

学校读者我要写书评

暂无评论

Next-Gen IoT: 5G Realities and 6G Possibilities

Next-Gen IoT: 5G Realities and 6G Possibilities

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Amrita Ticku Sagar Sidana Anurag Sinha S Sathesh Manas Uniyal Biresh Kumar Raj Shekhar Sinha Petheru Raj Ahmed Al-Khayyat Mansi Sikarwar Department of computer science and engineering Bharati Vidyapeeth’s College of Engineering India Department of computer science and engineering Maharshi Dayanand University Rohtak Haryana India School of Computing and information Science IGNOU New Delhi India India Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Tamil Nadu India Department of computer science and engineering Graphic Era Hill University India Department of computer science and IT Amity University Jharkhand India Reliance Jio Platforms Ltd Bangalore India Department of computer science and engineering Islamic University Iraq Department Of Mechanical And Automation Indira Gandhi Technical University For Women India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In a world where technology reigns supreme, the Internet of Things has taken center stage, revolutionizing our lives in countless ways. Augmented reality, crystal-clear video streaming, self-driving cars, intelligent environments, and advanced healthcare are just a few examples of the IoT’s widespread impact. With these groundbreaking ideas in mind, the IoT demands faster data rates, wider bandwidth, increased capacity, swift data transmission, and minimal delays. Addressing these requirements head-on, this paper introduces 5G as the leading-edge technology capable of meeting these challenges. The fifth-generation cellular networks, known as 5G, offer a host of vital features that facilitate the widespread adoption of IoT. These features include device-to-device communication, where devices can directly exchange information without relying on a central server, coordinated multipoint processing to enhance network efficiency, massive machine type communication that enables seamless connectivity for countless IoT devices, ultra-reliable low latency communication to ensure critical data is transmitted swiftly and reliably, and extreme mobile broadband to support high-speed, uninterrupted connectivity on the go. It has paved ways for innovative wireless architecture and intelligent services and 6 G which will be the next step in this evolutionary cycle of wireless networks is expected to amplify this development to new and unimaginable levels. This article gives an extensive overview of the IoT technology, outlining its statistical and architectural trends, application scenarios, challenges and its potential. The research also offers a thorough and in-depth analysis of the current 5G-IoT environment. The use cases for 5 G in IoT systems are also emphasized. As a result, we discuss some of the key 5G IoT research directions and trends. After which we highlight the challenging areas of 5G-IoT and where special emphasis will be a necessity in paving the way for 6 G enabled

关键词： 6G mobile communication 5G mobile communication Wireless networks Transforms Ultra reliable low latency communication Streaming media Market research Telecommunications Internet of Things Wireless fidelity

来源：评论

学校读者我要写书评

暂无评论

A multi-view comparative learning method for spatial transcriptomics data clustering

A multi-view comparative learning method for spatial transcr...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Peng Zhihao Zhang Wei Dai Xiaodong Fu Li Liu Lijun Liu Ning Yu Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Department of Computing Sciences The College at Brockport State University of New York Brockport NY

Clustering individual cells or spots based on their gene expression profiles in a spatial context is a powerful approach to uncovering the underlying biological diversity and relationships among cells. The intricate information within spatial transcriptomics data demands sophisticated algorithms that effectively integrate gene expression, cell position, and tissue image data for accurate cell or spot clustering. This work proposes a Multi-View Comparative Learning method for clustering Spatial Transcriptomics data (MVCLST). MVCLST first builds on two data views using gene expression profiles, cell space coordinates, and image features. Then it employs four different encoders to capture the common and private features of the two views. The model employs a contrastive learning loss to encourage effective interaction between the two views and ensure feature consistency. The shared and private features from both views are fused using corresponding decoders. Finally, the model employs the Leiden algorithm for downstream clustering of the learned features. We test the MVCLST method on a human dorsolateral prefrontal cortex dataset. The results show that MVCLST outperforms other state-of-the-art methods in most cases. Additionally, the clusters identified by MVCLST align closely with manual annotations and established neuroscience definitions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Neural Rendering and Flow-assisted Unsupervised Multi-view Stereo for Real-time Monocular Tracking and Scene Perception

引用

IEEE Transactions on automation Science and engineering 2025年

作者： Tong, Kevin W. Cai, Yandong Jie, Yu-Wen Duan, Ya Hou, Yuhong Wu, Edmond Q. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210023 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian116024 China Shanxi University School of Automation and Software Engineering Shanxi 237016 China Flight Test Technology and Engineering Center of the Chinese Flight Test Establishment Xian710089 China China Civil Aviation Shanghai Aircraft Airworthiness Certification Center Shanghai200335 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

The existing camera tracking and perception methods mainly rely on sparse SLAM, which limits the dense perception ability of the scene and affects the reliability of auxiliary decision-making. Different from this, this work proposes a real-time tracking and unsupervised dense sensing framework. Firstly, the dense depth value of the scene is predicted by unsupervised multi-view stereo to remove the dependence on labeled data. Then, the quality of synthetic pseudo-reference image is quantified according to the predicted depth map and used as a weighted guidance to train the unsupervised model, thus reducing the ambiguity of feature matching in areas such as specular reflection. Moreover, the sparse optical flow of the keyframes is solved by real-time and robust ORB feature matching operator, which assists the high-precision training of unsupervised depth inference model. To increase the prediction accuracy of occluded area, a novel rendering consistency loss via neural radiance fields is designed to constrain the geometric characteristics of object surface. Finally, dense direct image alignment is performed from a global model to improve the tracking robustness, which is incrementally constructed from dense depth prediction. Extensive experiments on synthetic datasets and real datasets validate the effectiveness and practicability of the proposed work, which is an effective supplement to the existing SLAM work. © 2025 IEEE.

关键词： Stereo image processing

来源：评论

学校读者我要写书评

暂无评论

Fair rank aggregation 22

Fair rank aggregation

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Diptarka Chakraborty Syamantak Das Arindam Khan Aditya Subramanian School of Computing National University of Singapore Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi Department of Computer Science and Automation Indian Institute of Science Bengaluru

ISBN: (纸本)9781713871088

Ranking algorithms find extensive usage in diverse areas such as web search, employment, college admission, voting, etc. The related rank aggregation problem deals with combining multiple rankings into a single aggregate ranking. However, algorithms for both these problems might be biased against some individuals or groups due to implicit prejudice or marginalization in the historical data. We study ranking and rank aggregation problems from a fairness or diversity perspective, where the candidates (to be ranked) may belong to different groups and each group should have a fair representation in the final ranking. We allow the designer to set the parameters that define fair representation. These parameters specify the allowed range of the number of candidates from a particular group in the top-k positions of the ranking. Given any ranking, we provide a fast and exact algorithm for finding the closest fair ranking for the Kendall tau metric under strong fairness, i.e., when the final ranking is fair for all values of k. We also provide an exact algorithm for finding the closest fair ranking for the Ulam metric under strong fairness when there are only O(1) number of groups. Our algorithms are simple, fast, and might be extendable to other relevant metrics. We also give a novel meta-algorithm for the general rank aggregation problem under the fairness framework. Surprisingly, this meta-algorithm works for any generalized mean objective (including center and median problems) and any fairness criteria. As a byproduct, we obtain 3- approximation algorithms for both center and median problems, under both Kendall tau and Ulam metrics. Furthermore, using sophisticated techniques we obtain a (3 - ε)-approximation algorithm, for a constant ε > 0, for the Ulam metric under strong fairness.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Supporting Confidential Workloads in SPIRE

Supporting Confidential Workloads in SPIRE

引用

IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Eduardo Falcão Matteus Silva Ariel Luz Andrey Brito Department of Computer Engineering and Automation Federal University of Rio Grande do Norte Natal Brazil Department of Systems and Computing Federal University of Campina Grande Campina Grande Brazil

ISBN: (纸本)9781665463683

The migration of traditional deployment to clouds has driven the need for a more robust security model, the Zero-Trust model. The application of zero-trust principles addresses known security issues such as lateral movement attacks but adds extra identity management complexity. In addition, to cover a broader range of attacks, one must think of strategies to protect data, code, and credentials in such applications. Confidential computing aims to fulfill this goal. Nevertheless, confidential computing is even more complex to implement than Zero-Trust. In this work, we combine the Zero-Trust model with confidential computing by leveraging the SPIFFE standard through its reference implementation (SPIRE), and Intel SGX through the SCONE framework, to seamlessly supply software identities to confidential microservices. Furthermore, we also protected the whole identity-provisioning stack with Intel SGX and assessed the performance overhead. We believe this combination not only improves the security of SPIFFE deployments but also leverages SPIFFE to facilitate the integration between confidential computing components and native applications.

关键词： Computers Cloud computing Codes Computational modeling Microservice architectures Software Complexity theory

来源：评论

学校读者我要写书评

暂无评论

Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems

arXiv

引用

arXiv 2024年

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Auburn University AuburnAL United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review

arXiv

引用

arXiv 2024年

作者： Banos, Oresti Comas-González, Zhoe Medina, Javier Polo-Rodríguez, Aurora Gil, David Peral, Jesús Amador, Sandra Villalonga, Claudia Department of Computer Engineering Automation and Robotics University of Granada Granada Spain Department of Computer Science and Electronics Universidad de la Costa Barranquilla Colombia Department of Computer Science University of Jaén Jaén Spain Department of Computer Technology and Computation University of Alicante Alicante Spain Department of Sotware and Computing Systems University of Alicante Alicante Spain

Background: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. Objective: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. Methods: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. Results: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. Conclusions: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：