检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2025年第3期6卷 533-548页

作者： Le, Siqi Lai, Yingxu Wang, Yipeng He, Huijie Beijing University of Technology Faculty of Information Technology Beijing100124 China Ministry of Education Engineering Research Center of Intelligent Perception and Autonomous Control Beijing100124 China

Real open network environments include the traffic generated by known applications or protocols, which have been previously identified and labeled, and unknown network traffic that cannot be identified based on existing knowledge. Accurately identifying unknown traffic is critical to network management and security, not only to help managers allocate bandwidth appropriately for all types of applications and ensure quality of service but also to prevent security breaches that may result from unknown applications or protocols. Notably, the unknown network traffic has been increasing with the emergence of new applications or protocols, which further increases the difficulty in identifying them. Existing unknown traffic classification methods based on Softmax output confidence values cause bias in the prediction probability due to overconfidence of the model during the training process, thus decreasing the identification accuracy. Thus, for unknown traffic identification, this study proposes a deep-learning-based uncertainty-estimation (EUE) approach. EUE introduces the theory of evidence to the task of identifying unknown traffic by inferring traffic uncertainty directly from traffic evidence without the need for a Softmax layer, thus avoiding overconfidence in the model. Thus, the EUE can accurately identify unknown traffic while classifying known traffic at the application level. We construct two experimental scenarios simulating the real network environments with different proportions of unknown traffic to evaluate EUE. The experimental results show that the proposed approach EUE exhibits excellent classification accuracy. © 2024 IEEE. All rights reserved.

关键词： Internet protocols

来源：评论

学校读者我要写书评

暂无评论

Counteracting New Attacks in CPS: A Few-shot Class-incremental Adaptation Strategy for Intrusion Detection System

引用

IEEE Transactions on Network and Service Management 2025年第3期22卷 2473-2488页

作者： Dong, Xinrui Lai, Yingxu Zhang, Xiao Xu, Xinyu Beijing University of Technology College of Computer Science Beijing100124 China Ministry of Education Engineering Research Center of Intelligent Perception and Autonomous Control Beijing100124 China

The deep integration of physical devices and communication networks has increased the security risks of cyber-physical systems (CPSs) compared to traditional control systems. Deep learning-based intrusion detection systems (IDSs) play a crucial role in ensuring CPSs security. However, the existing IDSs often rely on known attack features, rendering them unable to withstand emerging new attacks arising from the dynamic evolution of intrusion behaviors. This paper aims to develop an IDSs with high adaptability and strong generalization capabilities, which is capable of rapidly adapting to new attack classes with only a few new samples. To achieve this objective, we propose CAT-IDS, a few-shot class-incremental adaptation strategy for an IDS to counteract new attacks on CPSs. We design a highly symmetric classifier structure for CAT-IDS that can flexibly adjust the classification space to adapt to new attacks. Furthermore, we calibrate the biased distribution formed by a few training samples through statistical feature transfer. In order to prevent the model from forgetting old attack information during the adaptation process, we devise hybrid features for attack detection. These features contain essential information for both old and new class classifications. We demonstrate the effectiveness of CAT-IDS through multiple experiments on three CPSs datasets. The results show that CAT-IDS achieves an average accuracy improvement of approximately 4. 5% compared to the state-of-the-art methods, demonstrating its superior ability to adapt to new attacks while maintaining high performance in classifying existing attacks. © 2004-2012 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Dissolved oxygen concentration control in wastewater treatment process based on reinforcement learning

引用

Science China(Technological Sciences) 2023年第9期66卷 2549-2560页

作者： DU ShengLi CHEN PeiXi HAN HongGui QIAO JunFei School of Artificial Intelligence and Automation Beijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China

In this article, the dissolved oxygen(DO) concentration control problem in wastewater treatment process(WWTP) is *** existing control strategies that control DO concentration at a fixed value, here we develop a different control *** the proposed control framework, an intelligent control method of DO concentration based on reinforcement learning(RL)algorithm is presented to resolve the DO concentration control problem. By using the deep deterministic policy gradient(DDPG)algorithm, the DO concentration of the fifth tank in the activated sludge reactor can be adjusted dynamically. In addition, by designing two different reward functions and by analysing the relationships among effluent quality, energy consumption, and DO concentration, the target of energy-saving and emission-reducing is achieved. The simulation results indicate that the designed control method can reduce energy consumption while ensuring that the effluent quality meet the specified standards.

关键词： dissolved oxygen concentration wastewater treatment process intelligent control reinforcement learning energy-saving and emission-reducing

来源：评论

学校读者我要写书评

暂无评论

Time-Varying Formation Tracking control of Heterogeneous Multi-Agent Systems With Intermittent Communications and Directed Switching Networks

引用

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems control theory autonomous systems Stability

来源：评论

学校读者我要写书评

暂无评论

Improving Knowledge Tracing Through Learning Processes and Concept Similarity Map

Improving Knowledge Tracing Through Learning Processes and C...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lai, Yingxu Xu, Xinyu Zhang, Xiao Dong, Xinrui Zhuang, Junxi Liu, Jing Beijing University of Technology Faculty of Information Technology Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China

ISBN: (纸本)9781665410205

Knowledge Tracing (KT) aims to track the evolving knowledge states of students based on their historical performance, playing a vital role in online intelligent education systems. While deep learning-based knowledge tracing achieves impressive predictive performance, existing methods suffer from two main shortcomings. Firstly, storing massive amounts of historical information introduces irrelevant noise during the training process. Additionally, individual differences may prevent the model from accurately capturing students comprehensive states. Secondly, deep learning models lack interpretability, failing to provide precise descriptions of students knowledge states. This paper proposes an improved knowledge tracing method through learning processes and concept similarity map (LCKT). We incorporate diverse features, including exercise, exercise difficulty, concept, response time, response, and interval time, to measure the diversity of exercise interactions. Additionally, we utilize a forgetting gate to simulate the decline of students knowledge over time during the learning process. Furthermore, we introduce a concept similarity map as a constraint for model training, which clearly delineates students mastery across different knowledge points. Extensive experiments on three real-world datasets demonstrate that LCKT outperforms state-of-the-art KT methods and exhibits interpretability to some extent. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Edge-Cloud Collaborative High-Quality Recommendation: A Meta-Learning Approach 18

Edge-Cloud Collaborative High-Quality Recommendation: A Meta...

引用

18th International Conference on intelligent Systems and Knowledge engineering, ISKE 2023

作者： Chen, Hao Yin, Ruiping Yang, Zhen Beijing University of Technology Faculty of Information Technology Beijing China The Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China

ISBN: (纸本)9798350318401

Under traditional recommendation frameworks based on a central server, recommender systems provide rec-ommendations for different users by training a global model for all users on the cloud. However, this method cannot provide high-quality recommendations for the majority of low-active users as well as those who are not willing to provide their personal data to the central server. Additionally, training solely on a user's device with local data may lead to overfitting due to the insufficiency of local data. In this work, we propose a two-stage process of coarse and fine-grained data augmentation using clustering and filtering to alleviate data insufficiency at the edge. Furthermore, we use meta-learning to formulate a two-stage training process from cloud to edge. The meta-training process of the global model is conducted on the cloud, and fast training on the edge is suitable for resource-constrained edge devices, enabling high-quality recommendations for each user. The extensive experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Average Consensus Algorithm Under Round-Robin Scheduling Protocol

引用

IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1705-1707页

作者： Yingjiang Guo Wenying Xu Haodong Wang Jianquan Lu Shengli Du the School of Mathematics Southeast UniversityNanjing 21189China the School of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106Chengdu 610106China the School of Artificial Intelligence and Automation Beijing University of TechnologyBeijing 100124 the Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China

Dear Editor,Over the past decades,cooperative control and distributed optimization have gained significant research attention due to their broad applications such as signal processing,robotics,and social networks[1],[2].As a fundamental component of distributed control and optimization,the issue of average consensus has become a recurring topic of interest[3],[4].

关键词： optimization Round Robin

来源：评论

学校读者我要写书评

暂无评论

Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

引用

Chinese Journal of Chemical engineering 2023年第4期56卷 46-57页

作者： Haoshan Duan Xi Meng Jian Tang Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing 100124China

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission *** this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)***,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex ***,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the ***,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application ***,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI *** the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

关键词： Municipal solid waste incineration NO_(x)concentration prediction Modular neural network Model

来源：评论

学校读者我要写书评

暂无评论

Higher-order properties and extensions for indirect MRAC and APPC of linear systems

引用

Science China(Information Sciences) 2024年第4期67卷 218-238页

作者： Yanjun ZHANG Zhipeng ZHANG Jian SUN Lei WANG Kangkang ZHANG National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center College of Control Science and Engineering Zhejiang University Department of Electrical and Electronic Engineering Imperial College London KIOS Research and Innovation Center of Excellence University of Cyprus

Recently, a reference derived some new higher-order output tracking properties for direct model reference adaptive control（MRAC） of linear time-invariant（LTI） systems: limt→∞ e（i）（t） = 0, i = 1,..., n*-1, where n*and e（i）（t） denote the relative degree of the system and the i-th derivative of the output tracking error, respectively. However, a naturally arising question involves whether indirect adaptive control（including indirect MRAC and indirect adaptive pole placement control） of LTI systems still has higher-order tracking properties. Such properties have not been reported in the literature. Therefore, this paper provides an affirmative answer to this question. Such higher-order tracking properties are new discoveries since they hold without any additional design conditions and, in particular, without the persistent excitation condition. Given the higher-order properties, a new adaptive control system is developed with stronger tracking features.（1） It can track a reference signal with any order derivatives being unknown.（2） It has higher-order exponential or practical output tracking properties.（3） Finally, it is different from the usual MRAC system, whose reference signal's derivatives up to the n*order are assumed to be known. Finally, two simulation examples are provided to verify the theoretical results obtained in this paper.

关键词： higher-order tracking indirect adaptive control model reference adaptive control adaptive pole placement control

来源：评论

学校读者我要写书评

暂无评论

Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network

引用

Chinese Journal of Chemical engineering 2024年第12期76卷 318-326页

作者： Jiahui Liu Nan Guo Yixin Peng Wenlu Li Junfei Qiao Xiaolong Gao Wei Xiong Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Baotou Reclaimed Water Resources and Sewage Treatment Co. Ltd.Inner Mongolia 014000China Baotou Water Group Inner Mongolia 014000China

Steam-assisted combustion elevated flares are currently the most widely used type of petrochemical *** to the complex and variable composition of the waste gas they handle,the combustion environment is severely affected by meteorological *** process parameters such as intake composition,flow rate,and real-time data of post-combustion residues are difficult to measure or exhibit lag in data *** a result,the control methods for these flares are limited,leading to poor control *** address this issue,this paper proposes an adaptive sliding mode control method based on the radial basis function(RBF)***,the operational characteristics of the petrochemical flare combustion process are analyzed,and a control model for the combustion process is established based on carbon dioxide ***,an RBF neural network-based unknown function approximator is designed to identify the nonlinear part of the actual operating ***,by combining the control model of the petrochemical flare combustion and designing the RBF sliding mode controller with its adaptive control law,fast and stable control of the flare combustion state is *** results demonstrate that the designed control strategy can achieve tracking control of the petrochemical flare combustion state,and the adaptive law also accomplishes system identification.

关键词： Petrochemical torch combustion process Sliding mode control RBF neural network System identification Fast response

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：