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Finite-Time Sliding Mode Control of Vehicle Formation with Specified Performance

Engineering Letters 2025年第3期33卷 620-629页

作者： Guo, Dongyang Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

The current urban intelligent transportation is in a rapid development stage, and coherence control of vehicle formations has important implications in urban intelligent transportation research. This article focuses on the problem of urban intelligent transportation planning, and proposes a new control method for vehicle formation regulation tracking performance control with bounded disturbances and model uncertainty, combining the multiple advantages of sliding mode control and finite time control and utilizing the universal approximation of neural network. This method can ensure that the vehicle formation can achieve the specified tracking performance and be stable in finite time instead of asymptotically stable. First, the third-order dynamics model and spacing strategy of the vehicle are given. The tracking performance of the vehicle formation is specified. Then, the control objective of the system is transformed by using the error transformation to obtain a new system. An improved sliding mode surface is designed for the transformed system. Furthermore, the universal approximation property of the neural network is utilised to overcome the parameter uncertainty in the system. The reconstruction error of neural network is handled by a robust term. In addition, the jittery vibration phenomenon of sliding mode control has been overcome. Finally, the finite-time stability of the system is analysed by constructing the Lyapunov function, simulations were performed to validate and compare with conventional control methods, simulation results show that the novel control method proposed in this paper is significantly faster than the conventional control method, validated the effectiveness of the method proposed in this paper. © 2025, International Association of Engineers. All rights reserved.

关键词： Lyapunov functions

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Coarse-to-Fine Video Instance Segmentation With Factorized Conditional Appearance Flows

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1192-1208页

作者： Zheyun Qin Xiankai Lu Xiushan Nie Dongfang Liu Yilong Yin Wenguan Wang the School of Software Shandong UniversityJinan 250101China the School of Computer Science and Technology Shandong Jianzhu UniversityJinan 250101China the College of Computer and Information Science Southwest UniversityChongqing 400715China the College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect,segment and track each instance in a video *** from current discriminative tracking-by-detection solutions,our proposed hierarchical structural embedding learning can predict more highquality masks with accurate boundary details over spatio-temporal space via the normalizing *** formulate the instance inference procedure as a hierarchical spatio-temporal embedded learning across time and *** the video clip,our method first coarsely locates pixels belonging to a particular instance with Gaussian distribution and then builds a novel mixing distribution to promote the instance boundary by fusing hierarchical appearance embedding information in a coarse-to-fine *** the mixing distribution,we utilize a factorization condition normalized flow fashion to estimate the distribution parameters to improve the segmentation *** qualitative,quantitative,and ablation experiments are performed on three representative video instance segmentation benchmarks(i.e.,YouTube-VIS19,YouTube-VIS21,and OVIS)and the effectiveness of the proposed method is *** impressively,the superior performance of our model on an unsupervised video object segmentation dataset(i.e.,DAVIS19)proves its *** algorithm implementations are publicly available at https://***/zyqin19/HEVis.

关键词： Embedding learning generative model normalizing flows video instance segmentation(VIS)

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Clustering and Artificial Intelligence-based Prediction of Ecologically Sustainable Species Introductions

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第4期52卷 1159-1168页

作者： Liu, Shuqiao Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient survey data in many current development areas, development progress is delayed and stagnant. To address this situation, this paper proposes a SWOT-TOPSIS-K-Means (STK) data analysis and evaluation model to analyze ecological factors, which can realize a comprehensive and complete data analysis with fewer samples. Decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural network models were constructed from the results of this analysis, and statistical tests such as r-squared, mean absolute error, and cross-validation are used to further confirm the performance efficiency of the computational prediction models to provide real-time prediction research solutions. For this purpose, data from research scholars on species introduction in ecosystem development were selected for testing. The results show that the proposed assessment model and modeling results satisfy all accuracy-related acceptance requirements. Among them, MLP is better than DT and RF. In summary, the STK assessment model and the MLP prediction model can provide a basis for the selection and development of ecological factors. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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ER-Net:Efficient Recalibration Network for Multi-ViewMulti-Person 3D Pose Estimation

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computer Modeling in Engineering & Sciences 2023年第8期136卷 2093-2109页

作者： Mi Zhou Rui Liu Pengfei Yi Dongsheng Zhou National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian116622China School of Computer Science and Technology Dalian University of TechnologyDalian116024China

Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application *** the introduction of end-to-end direct regression methods,the field has entered a new stage of ***,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal *** this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external ***,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding *** call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.

关键词： Multi-view multi-person pose estimation attention mechanism computer vision

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DTCC:Multi-level dilated convolution with transformer for weakly-supervised crowd counting

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Computational Visual Media 2023年第4期9卷 859-873页

作者： Zhuangzhuang Miao Yong Zhang Yuan Peng Haocheng Peng Baocai Yin Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial IntelligenceFaculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Taiji Computer Corporation Ltd. China

Crowd counting provides an important foundation for public security and urban *** to the existence of small targets and large density variations in crowd images,crowd counting is a challenging *** methods usually apply convolution neural networks(CNNs)to regress a density map,which requires annotations of individual persons and ***-supervised methods can avoid detailed labeling and only require counts as annotations of images,but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually *** propose a weakly-supervised method,DTCC,which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd *** main components include a recursive swin transformer and a multi-level dilated convolution regression *** recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features,including global *** multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction *** module can capture both low-and high-level features simultaneously to enhance the receptive *** addition,two regression head fusion mechanisms realize dynamic and mean fusion *** on four well-known benchmark crowd counting datasets(UCF_CC_50,ShanghaiTech,UCF_QNRF,and JHU-Crowd++)show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods.

关键词： crowd counting transformer dilated convolution global perspective field pyramid

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Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

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computer Modeling in Engineering & Sciences 2024年第6期139卷 3011-3027页

作者： Rui Wang Yao Zhou Guangchun Luo Peng Chen Dezhong Peng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer Science Sichuan UniversityChengdu610065China National Innovation Center for UHD Video Technology Chengdu610095China

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

关键词： Time series anomaly detection unsupervised feature learning feature fusion

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A user requirements-oriented privacy policy self-adaption scheme in cloud computing

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Frontiers of computer Science 2023年第2期17卷 33-44页

作者： Changbo KE Fu XIAO Zhiqiu HUANG Fangxiong XIAO School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210003China College of computer science and technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Software Engineering Jinling Institute of TechnologyNanjing 210000China

In an ever-changing environment,software as a Service(SaaS)can rarely protect users'*** able to manage and control the privacy is therefore an important goal for *** the participant of composite service is substituted,it is unclear whether the composite service satisfy user privacy requirement or *** this paper,we propose a privacy policies automatic update method to enhance user privacy when a service participant change in the composite ***,we model the privacy policies and service variation ***,according to the service variation rules,the privacy policies are automatically generated through the negotiation between user and service ***,we prove the feasibility and applicability of our method with the *** the service quantity is 50,ratio that the services variations are successfully checked by monitor is 81%.Moreover,ratio that the privacy policies are correctly updated is 93.6%.

关键词： cloud computing SaaS service privacy protec-tion privacy policies update

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QAAS:quick accurate auto-scaling for streaming processing

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Frontiers of computer Science 2024年第1期18卷 5-15页

作者： Shiyuan LIU Yunchun LI Hailong YANG Ming DUN Chen CHEN Huaitao ZHANG Wei LI State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyber Science and Technology Beihang UniversityBeijing 100191China School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China

In recent years,the demand for real-time data processing has been increasing,and various stream processing systems have *** the amount of data input to the stream processing system fluctuates,the computing resources required by the stream processing job will also *** resources used by stream processing jobs need to be adjusted according to load changes,avoiding the waste of computing *** present,existing works adjust stream processing jobs based on the assumption that there is a linear relationship between the operator parallelism and operator resource consumption(e.g.,throughput),which makes a significant deviation when the operator parallelism *** paper proposes a nonlinear model to represent operator *** divide the operator performance into three stages,the Non-competition stage,the Non-full competition stage,and the Full competition *** our proposed performance model,given the parallelism of the operator,we can accurately predict the CPU utilization and operator *** with actual experiments,the prediction error of our model is below 5%.We also propose a quick accurate auto-scaling(QAAS)method that uses the operator performance model to implement the auto-scaling of the operator parallelism of the Flink *** to previous work,QAAS is able to maintain stable job performance under load changes,minimizing the number of job adjustments and reducing data backlogs by 50%.

关键词： stream processing performance model auto-scaling

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Towards Enhancing Aviation Safety Through Advanced Incident Analysis Using Large Language Models 34

Towards Enhancing Aviation Safety Through Advanced Incident ...

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34th Annual International Conference on Collaborative Advances in software and Computing, CASCON 2024

作者： Siddeshwar, Vaishali Azim, Akramul Alwidian, Sanaa Makrehchi, Masoud University of Ontario Institute of Technology Department of Electrical Computer and Software Engineering Canada

ISBN: (纸本)9798331504830

Aerospace incident reports, crucial for identifying abnormal aircraft occurrences, are submitted to regulatory authorities like FAA or EASA. These reports, often in natural language, offer clarity and context. Existing literature proposes incident-analysis tools utilizing manual rules and NLP techniques like Part-of-Speech Tagging and dependency parsers to extract entities from logs. However, these approaches may miss nuances, leading to incomplete or inaccurate extractions and potential mis-interpretations. This study aims to automate initial extraction using Large Language Models, extracting Aircraft Number/Model, contributing and human factors, primary root cause, and incident summaries. Automating this phase can expedite investigations and improve expert efficiency during review processes. © 2024 IEEE.

关键词： Aircraft accidents

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Competition for gradient-free tuning of large language models:approaches, results, current challenges and future directions

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National Science Review 2023年第6期10卷 16-19页

作者： Tingfeng Cao Liang Chen Dixiang Zhang Tianxiang Sun Zhengfu He Xipeng Qiu Xing Xu Hai Zhang School of Software Engineering South China University of Technology School of Computer Science Fudan University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

PROBLEM Recent years have witnessed the rapid progress of self-supervised language models (LMs)[1],especially large language models (LLMs)[2].LLMs not only achieved state-of-the-art performance on many natural language processing tasks,but also captured widespread attention from the public due to their great potential in a variety of real-world applications (***,search engines,writing assistants,etc.)through providing general-purpose intelligent services.A few of the LLMs are becoming foundation models,an analogy to infrastructure,that empower hundreds of downstream applications.

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