检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes an O(T max{c,1−c})network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further reduced to O)(log(T)), and the network cumulative constraint violation bound is reduced to O(log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Fast Coding Mode Decision for Intra Prediction in VVC SCC

引用

IEEE Transactions on Broadcasting 2025年第2期71卷 506-516页

作者： Dayong Wang Weihong Liu Zeyu Zhou Xin Lu Jinhua Liu Hui Guo Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity Chengdu University of Information Technology Chengdu China School of Computer Science and Informatics De Montfort University Leicester U.K. School of Digital Technology Industry Shangrao Normal University Shangrao China Glasgow College and the School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

Currently, screen content video applications are widely used in our daily lives. As the latest Screen Content Coding (SCC) standard, Versatile Video Coding (VVC) SCC employs a quad-tree plus nested multi-type tree (QTMT) coding structure and various screen content coding modes (CMs). This design enhances the coding efficiency of VVC SCC but also results in a highly complex coding process, which significantly hinders the broader adoption of screen content video technology. Consequently, improving the coding speed of VVC SCC is highly desirable. In this paper, we propose a fast CM and transform decision algorithm for Intra prediction in VVC SCC. Specifically, we initially use Convolutional Neural Networks (CNNs) to predict content types for all Coding Units (CUs). Subsequently, we predict candidate CMs for CUs based on the CM distributions of different content types. We then select the Sum of Absolute Transformed Difference (SATD) as a feature and use a naive Bayes classifier to skip unlikely Intra mode early. Finally, we terminate Block-based Differential Pulse-Code Modulation (BDPCM) early and then select the best transform type in Intra mode prediction to improve coding speed. Experimental results demonstrate that the proposed algorithm improves coding speed by an average of 39.28%, with the BDBR increasing by 0.80%.

关键词： Encoding Copper Prediction algorithms Classification algorithms Transforms Electronic mail Standards Complexity theory Training Partitioning algorithms

来源：评论

学校读者我要写书评

暂无评论

STFormer: A dual-stage transformer model utilizing spatio-temporal graph embedding for multivariate time series forecasting

引用

Journal of Intelligent and Fuzzy Systems 2024年第3期46卷 6951-6967页

作者： Xiao, Yuteng Liu, Zhaoyang Yin, Hongsheng Wang, Xingang Zhang, Yudong Jinan China Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Xuzhou University of Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing China

Multivariate Time Series (MTS) forecasting has gained significant importance in diverse domains. Although Recurrent Neural Network (RNN)-based approaches have made notable advancements in MTS forecasting, they do not effectively tackle the challenges posed by noise and unordered data. Drawing inspiration from advancing the Transformer model, we introduce a transformer-based method called STFormer to address this predicament. The STFormer utilizes a two-stage Transformer to capture spatio-temporal relationships and tackle the issue of noise. Furthermore, the MTS incorporates adaptive spatio-temporal graph structures to tackle the issue of unordered data specifically. The Transformer incorporates graph embedding to combine spatial position information with long-term temporal connections. Experimental results based on typical finance and environment datasets demonstrate that STFormer surpasses alternative baseline forecasting models and achieves state-of-the-art results for single-step horizon and multistep horizon forecasting. © 2024 - IOS Press. All rights reserved.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Track Prediction Based on Spatio-Temporal Attention 10th

Track Prediction Based on Spatio-Temporal Attention

引用

10th China Conference on Command and control, C2 2022

作者： Wang, Peng Zhang, Junyi Zhang, Lu Jin, Jianglin Fan, Yuqi The 54th Research Institute of China Electronics Technology Group Corporation Hubei Shijiazhuang050080 China Hebei Key Laboratory of Electromagnetic Spectrum Cognition and Control Hubei Shijiazhuang050080 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789811960512

With the rapid development of aviation, the air traffic flow is increasing exponentially, leading to increasingly serious route congestion and route conflicts. Accurate prediction of aircraft trajectories is essential to ensure the safety of airspace. Trajectory data is a time-series sequence, and most of the existing methods for trajectory prediction are based on recurrent neural networks and attention mechanisms. However, these methods fail to effectively accumulate the correlation between the attributes of trajectory points and the time varying attribute features, and suffer from slow model training. In this paper, we propose STA-STCN, a novel track prediction model based on spatio-temporal dual attention (STA) and stacked temporal convolutional network (STCN). STA extracts the spatial and temporal features in parallel based on self-attention. The spatial feature characterizes the relationships between attributes of a track point, and the temporal feature represents the time varying characteristics of an attribute in track points. STA then fuses the extracted features to describe the accumulated time varying attributes correlation. STCN takes the fused features as the input to predict target track point attributes. We conduct experiments on the flight data provided by global flight data services website VariFight. Experimental results show that the model can reduce the root mean square error, mean absolute error, and track prediction model training time, indicating that model STA-STCN can achieve effective track prediction. © 2022, Chinese Institute of Command and control.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Text Generation of Speech Imagery Based on an Enhanced CTA-BiLSTM Model Utilizing EEG Signals

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Pan, Hongguang Chu, Xin Miao, Rui Wang, Mei Wang, Yiran Li, Zhuoyi College of Electrical and Control Engineering Xi’an University of Science and Technology Xi’an710054 China Xi’an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security Xi’an710054 China LTD Company of Shenzhen HQVT Technology Co Shenzhen518100 China College of Computer Science and Technology Xi’an University of Science and Technology Xi’an710054 China Northwestern Polytechnic University School of Automation Xi’an710072 China

Recent studies have demonstrated the potential application of speech imagery neural signals in brain–computer interface (BCI) technology. Text generation based on speech imagery offers a natural communication method for individuals with speech disabilities. However, the limitations in imagined content and the immaturity of text generation technology currently constitute an obstacle to its applications. Therefore, this study proposes an enhanced CTA-BiLSTM model for efficient text generation utilizing speech imagery electroencephalography (EEG) signals, significantly enhancing the accuracy and fluency of text generation. Firstly, distinct from the prevailing imagination of characters and words, this study has assembled a sentence-level EEG dataset from ten subjects to facilitate communication. Subsequently, addressing the temporal dynamics characteristics and sequence dependencies of sentence signals, we employ dynamic time warping (DTW) and hidden Markov models (HMM) for accurate temporal alignment and signal annotation to generate fine-grained sentence labels. Finally, the proposed CTA-BiLSTM model leverages channel-time attention mechanism to dynamically adjust weights across channels and time, emphasizing critical features. Concurrently, the bidirectional long short-term memory (BiLSTM) network captures and utilizes long-term dependencies in the EEG signals, thereby enhancing the accuracy of the model in decoding complex temporal patterns. The experimental results demonstrate that the average sentence decoding accuracy can reach 67.50% on the self-built dataset, realizing a better evaluation accuracy and validating its potential for application. © 1975-2011 IEEE.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

A Survey of Fault Tolerance in Cloud Computing

A Survey of Fault Tolerance in Cloud Computing

引用

Systems, Advanced Technologies and Knowledge (ISSATK), International Symposium of

作者： Om Kolthoum Elhajjej Nouha Alyaoui Samia Dardouri Karim Chabir Research Laboratory Modeling Aanalysis Control of System (MACS) LR16ES22 National engineering school of Gabes (ENIG) University of Gabes Tunisia Computer Science and Telecommunications Department Laboratory of Modeling Analysis and Control of Systems (MACS) Higher Institute of Applied Sciences and Technology Zarroug Gafsa Gafsa University Tunisia Department of Computer Science College of Computing and Information Technology Shaqra University Saudi Arabia InnoV’COM Laboratory-Sup’Com University of Carthage Tunisia

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault tolerance. This paper explores fault tolerance in the context of cloud computing and discusses recent challenges and innovations in the field. Moreover, it examines the ongoing research efforts to improve fault-tolerance architectures. At the end of the paper, the paper presents system-level metrics that are relevant to fault tolerance.

关键词： Measurement Surveys Cloud computing Fault tolerance Technological innovation Scalability Fault tolerant systems computer architecture Software Software reliability

来源：评论

学校读者我要写书评

暂无评论

Consistent and Asymptotically Efficient Localization from Range-Difference Measurements

arXiv

引用

arXiv 2023年

作者： Zeng, Guangyang Mu, Biqiang Shi, Ling Chen, Jiming Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen China The Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source's position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased (thus consistent) estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Predicted Extration Rate of Volatile Oil from Biomass Through Supercritical Fluid Extration technology

SSRN

引用

SSRN 2024年

作者： Shen, Boxiong Xiong, Lifu Tang, Cheng Liu, Shunyao Tianjin Key Laboratory of Clean Energy and Pollution Control School of Energy and Environmental Engineering Hebei University of Technology Tianjin300401 China School of Computer Science and Engineering Hunan University of Information Technology Changsha410151 China China Academic of Electronics and Information Technology Beijing100041 China

This paper utilizes modern artificial intelligence technology to evaluate and predict the practical application of supercritical fluid extraction technology. This method offers an appropriate opportunity to develop sustainable and environmentally friendly technologies, which hold valuable advantages in the industrial extraction sector. We simulated the extraction rate of biomass volatile oil using multiple machine learning models with supercritical carbon dioxide as solvent. The input was the pressure and temperature in the dataset used, and the single output was the extraction rate. Three well-known supervised learning models, Kernel Extreme Learning Machine, Long Short-Term Memory, and Support Vector Machine were selected for this study to model the process. Hyperparameters of these models were optimized using the Whale Optimization Algorithm and then the final models were evaluated using multiple metrics. In terms of the coefficient of determination, the scores of KELM, LSTM, and SVM models were 0.997, 0.982, and 0.963, respectively. In addition, their error rates were 1.75, 8.98, and 10.25 when using the Mean Absolute Error metric. Finally, the KELM model was selected as the best model, and the results of optimizing extraction parameters were discussed. Trend of how temperature and pressure affect the extraction rate was also discussed using the best model. © 2024, The Authors. All rights reserved.

关键词： Supercritical fluid extraction

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis based on Behavior and State-Awareness Digital Twin of Key Equipment in Power Supply System

Fault Diagnosis based on Behavior and State-Awareness Digita...

引用

Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Haifei Liu Qin Zhao Jie Hao Laifa Tao Chen Lu Institute of Reliability Engineering Beihang University Beijing China Science & Technology on Reliability & Environmental Engineering Laboratory Beijing China School of Reliability and Systems Engineering Beihang University Beijing China AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

With the rapid development of multi-electric and all-electric aircraft, the role of power supply systems in aircraft is becoming increasingly prominent. Traditional fault diagnosis methods have problems such as a single means of sensory modelling and unbalanced fault data. The rapid development of digital twin technology provides an opportunity to overcome these difficulties. However, how to achieve adaptive updating and how to improve the data-and-model-fusion capabilities are also urgent challenges to be solved. To address the lack of existing research, this paper combines time-frequency domain analysis, physical information neural network based on differential-algebraic equation (DAE-PINN) and transient stability analysis of the power supply system based on a digital twin model with dimensions including fault-behavior-twin and (FBT) fault-state-awareness-twin (FSAT). Ultimately, this paper achieves effective digital twin modelling, fault eigenfeature extraction and high accuracy fault diagnosis, reaching fault data balance and complete state characterization and adaptive update of the diagnostic model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

引用

computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xiuen Wu Tao Wang Yuanzheng Cai Lingyu Liang George Papageorgiou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians’ future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method.

关键词： Deep learning Pedestrians Computational modeling Predictive models Prediction algorithms Vectors Inference algorithms

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：