检索结果-内蒙古大学图书馆

Immoral post detection using a one-dimensional convolutional neural network-based LSTM network

Multimedia Tools and Applications 2025年 1-20页

作者： Saqia, Bibi Khan, Khairullah Rahman, Atta Ur Khan, Aurangzeb Jain, Deepak Kumar Department of Computer Science University of Science and Technology Bannu28100 Pakistan Riphah Institute of Systems Engineering Riphah International University Islamabad46000 Pakistan Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune India

In text mining and Natural Language Processing (NLP), extracting emotions from textual data is gaining rapid attraction. The proliferation of online content and the freedom of expression on social media platforms has compelled the advancement of more sophisticated methods for the automatic identification of unwanted content. The One-Dimensional Convolutional Neural Network (1D-CNN) performs best for feature extraction, while the Long Short-Term Memory Network (LSTM) is well-known for extracting sequential features and classification of textual data. This study proposes a novel solution by combining 1D-CNN with LSTM. Utilizing a context-aware model, this study aims to enhance the detection of immoral posts, including hate speech and other forms of immoral content on social media platforms. Various posts are extracted from social media, which are then processed using Glove word embedding models. It depicts the word representations in the text, which yields the form of a real-valued vector determining the words' meaning. The 1D convolution utilizes a filter window over the input time-series data to extract features. Relying on the acquired parameters of the filters, they act as feature extractors. This feature extraction technique allows the model to distinguish detailed patterns and nuances from the textual contents of posts. The sequential processing capabilities of the LSTM network enable an in-depth examination of the text, capturing temporal relationships and context, which is critical for successful content categorization. To validate the proposed model, we utilized a benchmark dataset, called the Twitter dataset. The proposed model achieved the highest accuracy of 95.37% and outperforms state-of-the-art works. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Improved Hybrid Gaussian Process Regression Model for state-of-Health Estimation of Li-ion Batteries

Improved Hybrid Gaussian Process Regression Model for State-...

引用

Chinese Automation Congress (CAC)

作者： Tao An Zhitao Liu Chizhou Yan Ke Xu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Wanxiang A123 Systems Corp. Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate state-of-health (SOH) estimation con-tributes to the healthy and stable operation of the Li-ion batteries. This paper proposes an improved hybrid Gaussian process regression (GPR) method incorporating physical information for the high-accuracy estimation of battery SOH. The estimation process considers the effects of temperature, discharge rate, and depth of discharge (DOD) on the battery health, and the relevant features of the electrode degradation process are extracted for battery SOH estimation. The kernel function of GPR comprehensively considers the mechanical characteristics of battery degradation and can effectively track the battery degra-dation process. The validation experiments were conducted on the A123 LFP/Graphite battery dataset. Sufficient comparative experiments have validated the accuracy and robustness of the proposed method.

关键词： Lithium-ion batteries Electrodes Degradation Accuracy Estimation Gaussian processes Feature extraction Robustness Batteries Kernel

来源：评论

学校读者我要写书评

暂无评论

Battery Charge Dispatching in Multi-robot systems 18

Battery Charge Dispatching in Multi-robot Systems

引用

18th IEEE International Conference on Networking, Sensing and control, ICNSC 2021

作者： Xing, Zichao Wu, Weimin Niu, Haoyi Hu, Ruifen Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China

ISBN: (纸本)9781665440486

Multi-robot systems (MRSs) attract more and more attention due to their efficiency and safety. In a practical scenario, robots need to work for a long time without human intervention, which requires the robots do not run out of power. The battery charging strategy of most systems uses a fixed threshold or fixed periods to determine when robots should be charged. It does not consider the system task situation, nor the situation of charging station (CS) competition. This paper proposes an efficient charge dispatching strategy, which can ensure that a robot firstly completes a task quickly when it is scheduled to work and firstly be charged to be ready for a subsequent task when it is idle. For the case that multiple robots compete for the same CS, we designe a bidding strategy to determine which robot should be serviced. A rule-based charging strategy is used to compare with the proposed charge dispatching strategy in this paper. The results show that our method performs better in terms of stability and efficiency. © 2021 IEEE.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

A PREA and OODA Computing Framework of Unmanned systems to Perform Complex Tasks

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-18页

作者： Yang, Dongsheng Yu, Lei Zhang, Yi Zhu, Fenghua Li, Qiang Cui, Huachao Ma, Qiongmin Fan, Lili Tian, Yonglin Jinan University Guangzhou China Intelligent Science & Technology Academy Limited of CASIC Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China 28th Research Institute of China Electronics Technology Group Corporation Nanjing China National Key Laboratory for Complex Systems Simulation National University of Defense Technology Changsha China School of Information and Electronics Beijing Institute of Technology Beijing China

A C2 computing framework for unmanned systems to perform complex tasks is proposed using methods of system of systems engineering, which is formed by combining the macro-scale command and control (C2) process mechanism model (PREA loop) and micro-scale C2 process mechanism model (OODA loop). Guided by PREA loop and OODA loop, the computing framework is divided into four steps and three kinds of transformations. The four steps are design, construction, operation monitoring, and assessment. The three transformations are tactical feedback, campaign feedback, and strategic feedback. A C2 organization model of joint landing combat force is established against the background of a typical complex task of joint landing operation, and the C2 computing framework based on the PREA &OODA is used to implement the C2 activities of the joint landing combat force. The influence of the computing framework on the performance of unmanned systems under different task conditions is verified, including task coordination load, task efficiency and sensitivity of unmanned systems response. Authors

关键词： Feedback

来源：评论

学校读者我要写书评

暂无评论

Bayesian CNN-GRU with Attention Mechanism for Lithium-ion Battery state of Health Estimation

Bayesian CNN-GRU with Attention Mechanism for Lithium-ion Ba...

引用

Chinese control and Decision Conference, CCDC

作者： Tao An Zhitao Liu Chizhou Yan Ke Xu Shuxing Zhang Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Wanxiang A123 Systems Corp. Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Accurate estimation of the state of health (SOH) is crucial for ensuring the safe and stable operation of lithium-ion (Li-ion) batteries. This paper presents an approach called Bayesian CNN-GRU with Attention Mechanism (AM-BCG) to estimate the entire lifecycle health state of the battery. Effective feature extraction is performed during the SOH estimation to ensure accurate prediction. Specifically, domain knowledge- related temporal features of Li-ion battery degradation processes are extracted, while capacity difference curves are utilized as spatially relevant features for model inputs. The AM-BCG approach comprises parallel CNN-GRU networks with attention mechanism and bayesian neural networks (BNN). The parallel CNN-GRU networks extract generalized information from prior data, the attention mechanism increases the weight of essential features and reduces overfitting, and BNN provides uncertainty estimates. Experimental validation on the A123 67Ah LFP battery dataset confirms that the proposed method exhibits high robustness and accuracy in estimating battery SOH.

关键词： Lithium-ion batteries Attention mechanisms Accuracy Uncertainty Neural networks Estimation Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation

引用

IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 2011-2026页

作者： Xiaolong Chen Biao Xu Manjiang Hu Yougang Bian Yang Li Xin Xu State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body the College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body the College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082 Wuxi Intelligent Control Research Institute(WICRI)of Hunan University Wuxi 214115China College of Intelligence Science and Technology Institute of Unmanned SystemsNational University of Defense TechnologyChangsha 410073China

Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and *** paper proposes a reinforcement learning(RL)method for autonomous vehicles to navigate unsignalized intersections safely and *** method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning.A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them.A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at *** algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization *** proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning *** intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic *** algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.

关键词： Autonomous driving decision-making reinforcement learning(RL) unsignalized intersection

来源：评论

学校读者我要写书评

暂无评论

Comparison of Three Data-Driven Networked Predictive control Methods for a Class of Nonlinear systems

引用

IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1714-1716页

作者： Zhong-Hua Pang Xue-Ying Zhao Jian Sun Yuntao Shi Guo-Ping Liu the Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of TechnologyBeijing 100144China the State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China the Center for Control Science and Technology Southern University of Science and TechnologyShenzhen 518055China

Dear Editor,In this letter,in order to deal with random network delays and packet losses in a class of networked nonlinear systems,three data-driven networked predictive control methods are *** closed-loop systems and... 详细信息

关键词： letter Nonlinear nonlinear

来源：评论

学校读者我要写书评

暂无评论

Parallel Vision ■ Image Synthesis/Augmentation

引用

IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 782-784页

作者： Wenwen Zhang Wenbo Zheng Qiang Li Fei-Yue Wang the School of Computer Science Shaanxi Normal UniversityXi’an 710119China the School of Computer Science and Technology Wuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,Scene understanding is an essential task in computer *** ultimate objective of scene understanding is to instruct computers to understand and reason about the scenes as humans *** vision is a research fram... 详细信息

关键词： instru explanation computer

来源：评论

学校读者我要写书评

暂无评论

Can ChatGPT Boost Artistic Creation:The Need of Imaginative Intelligence for Parallel Art

引用

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 835-838页

作者： Chao Guo Yue Lu Yong Dou Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

CHATGPT,one of the leading Large Language Models(LLMs),has acquired linguistic capabilities such as text comprehension and logical reasoning,enabling it to engage in natural conversations with humans.

关键词： such has reasoning

来源：评论

学校读者我要写书评

暂无评论

Global-state-Free Obstacle Avoidance for Quadrotor control in Air-Ground Cooperation

引用

IEEE Robotics and Automation Letters 2025年第7期10卷 6688-6695页

作者： Zhang, Baozhe Chen, Xinwei Chen, Qingcheng Xu, Chao Gao, Fei Cao, Yanjun Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou310027 China Zhejiang University Huzhou Institute Huzhou313000 China Shanghai Institute of Special Equipment Inspection and Technical Research Shanghai200062 China

CoNi-MPC [Zhang et al. (2023)] provides an efficient framework for UAV control in air-ground cooperative tasks by relying exclusively on relative states, eliminating the need for global state estimation. However, its lack of environmental information poses significant challenges for obstacle avoidance. To address this issue, we propose a novel obstacle avoidance algorithm, Cooperative Non-inertial frame-based Obstacle Avoidance (CoNi-OA), designed explicitly for UAV-UGV cooperative scenarios without reliance on global state estimation or obstacle prediction. CoNi-OA uniquely utilizes a single frame of raw LiDAR data from the UAV to generate a modulation matrix, which directly adjusts the quadrotor's velocity to achieve obstacle avoidance. This modulation-based method enables real-time generation of collision-free trajectories within the UGV's non-inertial frame, significantly reducing computational demands (less than 5 ms per iteration) while maintaining safety in dynamic and unpredictable environments. The key contributions of this work include: 1) a modulation-based obstacle avoidance algorithm specifically tailored for UAV-UGV cooperation in non-inertial frames without global states;2) rapid, real-time trajectory generation based solely on single-frame LiDAR data, removing the need for obstacle modeling or prediction;and 3) adaptability to both static and dynamic environments, thus extending applicability to featureless or unknown scenarios. © 2016 IEEE.

关键词： Quadrotors Collision avoidance Dynamics Autonomous aerial vehicles Trajectory Laser radar Air to ground communication Real-time systems Modulation Dynamical systems

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：