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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

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

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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

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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

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Computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

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OPS-YOLO: A Lightweight Foreign Objectors Detection Model for Overhead Power System 43

OPS-YOLO: A Lightweight Foreign Objectors Detection Model fo...

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43rd Chinese control Conference, CCC 2024

作者： Liu, Boning Shi, Rui Deng, Heng Zhang, Liguo Beijing University of Technology Faculty of Information Technology Beijing100124 China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing100124 China

ISBN: (纸本)9789887581581

The performance of modern railway transit systems heavily relies on the overhead power system (OPS). However, OPSs built on the ground are vulnerable to foreign objects that can pose a threat to railway safety. In this paper, an improved lightweight detection method called OPS-YOLO, which is based on YOLO-v5, is proposed to detect foreign objects in OPSs. First, the Ghostnet-v2 is improved to compress the lightweight backbone model to slim down the original YOLO-v5 and reduce the complexity and computation. Then, a parameter-free attention mechanism SimAM is introduced to enhance the detection precision of OPS-YOLO. Furthermore, the Focal-EIoU is chosen as the loss function of the OPS-YOLO to improve object border localization precision while accelerating bounding box regression speed. To evaluate the performance of OPS-YOLO, we generate a customized dataset of OPS scenarios by using augmentation methods to expand the existing samples and improve training results. Finally, we extensive experimental tests on the dataset are conducted to verify the proposed method. Results show that the proposed OPS-YOLO improves the mAP by 0.5% compared to the classical YOLO-v5s network. Additionally, OPS-YOLO compresses the model parameters by 37.9% and reduces the model size by 37.5%. It is demonstrated that OPSYOLO can achieve a balance between computational efficiency and detection accuracy, making it suitable for automating OPS supervision and reducing reliance on human inspection. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolutional neural networks

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Improving Knowledge Tracing Through Learning Processes and Concept Similarity Map

Improving Knowledge Tracing Through Learning Processes and C...

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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

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Scene image recognition with knowledge transfer for drone navigation

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Journal of Systems Engineering and Electronics 2023年第5期34卷 1309-1318页

作者： DU Hao WANG Wei WANG Xuerao ZUO Jingqiu WANG Yuanda School of Automation Southeast UniversityNanjing 210096China Autonomous Control Robot Laboratory Jiangsu Zhongke Institute of Applied Research on Intelligent Science and TechnologyChangzhou 213164China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing 210044China

In this paper,we study scene image recognition with knowledge transfer for drone *** divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and ***,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the ***,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction *** results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.

关键词： scene recognition convolutional neural network knowledge transfer global navigation satellite systems(GNSS)-aided

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autonomous landing scene recognition based on transfer learning for drones

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Journal of Systems Engineering and Electronics 2023年第1期34卷 28-35页

作者： DU Hao WANG Wei WANG Xuerao WANG Yuanda School of Automation Southeast UniversityNanjing 210096China Autonomous Control Robot Laboratory Jiangsu Zhongke Institute of Applied Research on Intelligent Science and TechnologyChangzhou 213164China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing 210044China

In this paper, we study autonomous landing scene recognition with knowledge transfer for drones. Considering the difficulties in aerial remote sensing, especially that some scenes are extremely similar, or the same scene has different representations in different altitudes, we employ a deep convolutional neural network(CNN) based on knowledge transfer and fine-tuning to solve the problem. Then, LandingScenes-7 dataset is established and divided into seven classes. Moreover, there is still a novelty detection problem in the classifier, and we address this by excluding other landing scenes using the approach of thresholding in the prediction stage. We employ the transfer learning method based on ResNeXt-50 backbone with the adaptive momentum(ADAM) optimization algorithm. We also compare ResNet-50 backbone and the momentum stochastic gradient descent(SGD) optimizer. Experiment results show that ResNeXt-50 based on the ADAM optimization algorithm has better performance. With a pre-trained model and fine-tuning, it can achieve 97.845 0% top-1 accuracy on the LandingScenes-7dataset, paving the way for drones to autonomously learn landing scenes.

关键词： landing scene recognition convolutional neural network(CNN) transfer learning remote sensing image

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Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

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Science China(Technological Sciences) 2023年第4期66卷 1096-1109页

作者： HE HaiJun MENG Xi TANG Jian QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBejing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Bejing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Bejing 100124China Xi'an Institute of Electromechanical Information Technology Xi'an 710065China

Due to the large uncertainty in the municipal solid waste incineration(MSWI)process,the furnace temperature of the MSWI process is difficult to control and the controller is updated *** improve the accuracy and reduce the number of controller updates,a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network(ETCSTSFNN)is ***,the neurons of the rule layer are grown or pruned adaptively based on activation intensity and control error to meet the dynamic change of the actual operating ***,the performance index is designed based on the correntropy of tracking errors,and the parameters of the controller are adjusted by gradient descent ***,a fixed threshold event-triggered condition is designed to determine whether the current controller is updated or *** stability of the control system is proved based on the Lyapunov stability ***,the furnace temperature control experiments are conducted based on the actual data of a municipal solid waste incineration plant in *** experimental results show that the proposed ET-CSTSFNN controller shows a better control performance,which can reduce the number of the controller update significantly while achieving accurate furnace temperature control compared with other traditional control methods.

关键词： municipal solid waste incineration furnace temperature fuzzy control event-triggered

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

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

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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

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

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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

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