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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Hu, Hongsheng Xu, Hao Huang, Shipei Zhuhai519082 China National-Local Joint Engineering Laboratory for Digitalize Electrical Design Technology Wenzhou University Wenzhou325035 China

ISBN: (纸本)9789819616930

A finite-time boundedness (FTB) round-robin protocol (RRP) control problem for the 2-D continuous-discrete system described by Roesser model is investigated in the work. Firstly, a novel 2-D RRP is developed to reduce network data congestion and data collisions between controllers and actuators. Under the RRP, a finite-time controller is designed for considered systems. By resorting to the Lyapunov function, deriving finite-time bounded sufficient conditions guarantees that the state is finite-time bounded in a finite-time interval. Finally, the correctness of the proposed result is proved via a simulation example under the RRP in this paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete time control systems

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Dynamic Event-Triggered Control Under the Round-Robin Protocol for 2-D Continuous System in Roesser Model

Dynamic Event-Triggered Control Under the Round-Robin Protoc...

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2023 China Automation Congress, CAC 2023

作者： Hu, Hongsheng Xu, Hao Luo, Yufei Meng, Yunhe Huang, Shipei Huang, Junhua Zhuhai China Wenzhou University National-Local Joint Engineering Laboratory for Digitalize Electrical Design Technology Wenzhou China

ISBN: (纸本)9798350303759

In this work, we address the issue of event-triggered control under the Round-Robin protocol (RRP) for a specific class of 2-D continuous system in Roesser model that is subject to communication constraints. Our approach not only considers network congestion but also takes into account the problem of data collision. To reduce network overloads and save bandwidth, we propose a dynamic event-triggered scheme that determines whether a datum should be transmitted or not. Additionally, RRP is utilized to assign priority to the actuator at a given moment. Sufficient conditions of performance are established to ensure the considered system is stable. Moreover, an event-based control law under the RRP is constructed. The effective of the proposed methods are demonstrated by a numerical example in the end. © 2023 IEEE.

关键词： Numerical methods

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Optimal Planning of a Standalone Microgrid by Constrained Multi-objective Population Extremal Optimization

Optimal Planning of a Standalone Microgrid by Constrained Mu...

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International Conference of electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Qin, Zhen Zeng, Guo-Qiang Lu, Kang-Di Wang, Rong Wu, Jun-Yi National-Local Joint Engineering Laboratory of Digitalize Electrical Design Technology Wenzhou University Wenzhou325035 China College of Information Science and Technology Donghua University Shanghai201620 China

ISBN: (纸本)9789819618637

A standalone microgrid plays an important role in supplying power to residential and remote areas by integrating different types of distributed generation. One of the key open issues in both academic and engineering fields is how to provide an optimal planning strategy for distributed generations and storage systems in a standalone microgrid, considering multi-objective performance indices such as reliability and cost. In this paper, a novel constrained multi-objective population extremal optimization (CMOPEO) is proposed for standalone microgrid optimal planning. The fundamental concept of the suggested approach is to frame the problem as a standard constrained multi-objective problem, where power loss probability, fuel emission, and power cost of the system are considered as the objectives, and the maximum amount of extra power and the availability of renewable resources are the constraints. The superiority of the CMOPEO algorithm is demonstrated through simulation results on a standalone microgrid case over multi-objective particle swarm optimization. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Constrained optimization

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MO-Transformer-IDS: Multi-objective Optimization-based Transformer for Multi-Classification Intrusion Detection of Cyber-Physical Power Systems 8

MO-Transformer-IDS: Multi-objective Optimization-based Trans...

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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Wang, Rong Zeng, Guo-Qiang Lu, Kang-Di Wenzhou University National-Local Joint Engineering Research Center of Digitalize Electrical Design Technology Wenzhou China Wenzhou University Jinan University National-Local Joint Engineering Research Center of Digitalize Electrical Design Technology Collge of Cyber-Security Guangzhou Wenzhou China Donghua University College of Information Science and Technology Shanghai China

ISBN: (纸本)9798331523558

Deep neural networks have been widely applied in intrusion detection systems (IDSs), but most of the existing models are designed manually and it is difficult to obtain a lightweight and high-performance model, especially for multi-classification IDS. This work presents an automatic deep learning-based IDS method, i.e., a multi-objective optimization-based transformer for multi-classification intrusion detection of cyber-physical power systems (CPPS), termed MO-Transformer-IDS. This method considers both the architectures such as the number of attention heads in the Transformer-Encoder, the type of activation function, and the number of layers in the Encoder, and the hyperparameters such as the learning rate, batch size, and dropout rate as the decision variables. The two optimization objectives are to minimize the detection error rate and the number of transformer model parameters. The evolutionary operations under the framework of multi-objective differential evolution (MODE) are designed elaborately to search for a Pareto-optimal transformer model. The experimental results on the thirty-seven-classification IDS issue of Power System Attack Datasets have demonstrated that the proposed MO-Transformer-IDS outperforms three state-of-the-art automatic deep learning (ADL) methods including SOPA-GA-CNN, WOA-ANN, GA-CNN, and an advanced LSTM in terms of Accuracy, Precision, Recall, F1-Score, and the number of transformer model parameters. © 2024 IEEE.

关键词： Network intrusion

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SOPA-GA-CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet-of-Things

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IET Cyber-Systems and Robotics 2023年第1期5卷 51-66页

作者： Jia-Cheng Huang Guo-Qiang Zeng Guang-Gang Geng Jian Weng Kang-Di Lu College of Cyber Security and the National Joint Engineering Research Center of Network Security Detection and Protection Technology Jinan UniversityGuangzhouChina National-Local Joint Engineering Research Center of Digitalize Electrical Design Technology Wenzhou UniversityWenzhouChina National Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhouChina

In recent years,deep learning has been applied to a variety of scenarios in Industrial Internet of Things(IIoT),including enhancing the security of ***,the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the *** authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT.A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks(SOPA-GA-CNN)is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks(CNNs)by genetic algorithms(GA)for the intrusion detection issue of *** efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters,including batch size,learning rate,weight optimiser and weight regularisation,and the architectures,such as the block-based network topology and the parameters of each CNN *** experimental results on five intrusion detection datasets in IIoT,including secure water treatment,water distribution,Gas Pipeline,Botnet in Internet of Things and Power System Attack Dataset,have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy,precision,recall,F1-score,and the number of parameters of the deep learning models.

关键词： cyber-systems deep learning deep neural network Internet of things swarm intelligence

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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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Dual modality prompt learning for visual question-grounded answering in robotic surgery

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 316-328页

作者： Yue Zhang Wanshu Fan Peixi Peng Xin Yang Dongsheng Zhou Xiaopeng Wei National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian 116622LiaoningChina School of Computer Science and Technology Dalian University of TechnologyDalian 116081LiaoningChina

With recent advancements in robotic surgery,notable strides have been made in visual question answering(VQA).Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the *** limitation restricts the interpretative capacity of the VQA models and their abil-ity to explore specific image *** address this issue,this study proposes a grounded VQA model for robotic surgery,capable of localizing a specific region during answer *** inspiration from prompt learning in language models,a dual-modality prompt model was developed to enhance precise multimodal information ***,two complementary prompters were introduced to effectively integrate visual and textual prompts into the encoding process of the model.A visual complementary prompter merges visual prompt knowl-edge with visual information features to guide accurate *** textual complementary prompter aligns vis-ual information with textual prompt knowledge and textual information,guiding textual information towards a more accurate inference of the ***,a multiple iterative fusion strategy was adopted for comprehensive answer reasoning,to ensure high-quality generation of textual and grounded *** experimental results vali-date the effectiveness of the model,demonstrating its superiority over existing methods on the EndoVis-18 and End-oVis-17 datasets.

关键词： Prompt learning Visual prompt Textual prompt Grounding-answering Visual question answering

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Dynamic Data Reconciliation for Improving the Forecasting Performance of Wind Power based on Long Short-term Memory

Dynamic Data Reconciliation for Improving the Forecasting Pe...

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2023 China Automation Congress, CAC 2023

作者： Zhu, Wangwang Zhang, Ying Zhou, Weihua Zhang, Zhengjiang Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Taizhou Vocational and Technical College Department of Mechanical and Electrical Engineering Taizhou318000 China Wenzhou University National-Local Joint Engineering Laboratory for Digitalize Electrical Design Technology Wenzhou325035 China

ISBN: (纸本)9798350303759

As one of the most promising power generation methods, effective forecast of wind power generation for uncertain environment contributes to the planning and operation of power systems. In this work, the long short-term memory (LSTM) network is developed for wind power forecasting. Then, a dynamic data reconciliation (DDR) method is proposed for suppressing sensor noise and improve the forecasting performance. LSTM extends the recurrent neural networks (RNN) through gate functions, which is a popular RNN variant with state memory. However, the modeling datasets with Gaussian (or non-Gaussian) sensor noise may degrade the expected performance of LSTM. The proposed DDR performs Bayesian estimation of the real information by extracting useful parts of the two process-related information, and the relatively accurate data obtained from DDR is used to improve the accuracy of modeling. Experimental results verify that the LSTM model with DDR is less affected by Gaussian or non-Gaussian sensor noise, showing a better wind power forecasting performance. © 2023 IEEE.

关键词： Long short-term memory

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electrical stimulation for brighter persistent luminescence

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Light(Science & Applications) 2024年第8期13卷 1590-1601页

作者： Xilin Ma Yuhua Wang Takatoshi Seto Key Laboratory for Special Function Materials and Structural Design of the Ministry of Education National&Local Joint Engineering Laboratory for Optical Conversion Materials and Technology of National Development and Reform CommissionDepartment of Materials ScienceSchool of Materials and EnergyLanzhou UniversityNo.222South Tianshui RoadLanzhouGansu 730000China

An immature understanding of the mechanisms of persistent luminescence(PersL)has hindered the development of new persistent luminescent materials(PersLMs)with increased *** this regard,in-situ direct current(DC)electric field measurements were conducted on a layered structure composed of the SrAl_(2)O_(4):Eu^(2+),Dy^(3+)phosphor,and an *** this study,the photoluminescence(PL)and afterglow properties were investigated with respect to voltage by analyzing the current signal and thermoluminescence(TL)*** intensity of PersL increased due to a novel phenomenon known as“external electric field stimulated enhancement of initial brightness of afterglow”.This dynamic process was illustrated via the use of a rate equation approach,where the electrons trapped by the ultra-shallow trap at 0.022 eV could be transferred through the conduction band during long *** afterglow intensity could reach 0.538 cdm^(-2)at a 6 V electric *** design of an electric field stimulation technique enables the enhancement of the intensity of PersLMs and provides a new perspective for exploring the fundamental mechanics of certain established PersLMs.

关键词： spectroscopy luminescence conduction

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BPSO-AHDL-IDS: Binary Particle Swarm Optimization-Based Automated Hybrid Deep Learning Model for Intrusion Detection of Internet of Things

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15859-15877页

作者： Lu, Kang-Di Yang, Yao-Wei Zeng, Guo-Qiang Peng, Chen Geng, Guang-Gang Weng, Jian Donghua University College of Information Sciences and Technology Shanghai201620 China Jinan University College of Cyber Security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangzhou510632 China Wenzhou University National-Local Joint Engineering Research Center of Digitalized Electrical Design Technology Wenzhou325035 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China

The pervasive adoption of Internet-of-Things (IoT) systems has exposed critical vulnerabilities in cyber-security frameworks due to their decentralized deployment in unattended environments. While deep learning-based intrusion detection systems (IDSs) offer promising solutions, the design of hyper-parameters and neural architectures in existing models imposes prohibitive computational costs and expert dependency. To address these limitations, this work proposes an innovative automated hybrid deep learning method for IDS by employing a binary particle swarm optimization (BPSO) algorithm called BPSO-AHDL-IDS to effectively address the intrusion detection tasks of IoT. In BPSO-AHDL-IDS, the combination of convolutional neural network and recurrent neural network is considered as the hybrid deep learning model to extract features of the IoT dataset for accurate detection of intrusions. First, an efficient binary encoding mechanism is developed to describe the hyper-parameters and neural architectures of the hybrid deep learning model. Then, an efficient BPSO-based evolutionary operation is introduced to evolve the hyper-parameters and neural architectures to discover the optimized hybrid deep learning model. The performance of the proposed BPSO-AHDL-IDS method is testified by employing four datasets gathered from different IoT scenarios. It achieves accuracy of 0.9832, 0.9959, and 0.9897, precision of 0.9700, 0.9788, and 0.9843, recall of 0.9724, 0.9822, and 0.965, and F1 -score of 0.9712, 0.9804, and 0.9744 on Bot-IoT, ToN-IoT, and Gas Pipeline datasets, respectively. On SWaT dataset, it achieves a precision of 0.9962, a recall of 0.9969, and an F1 -score of 0.9965, respectively. The experimental results show the superiority of the proposed BPSO-AHDL-IDS method to machine learning methods, state-of-the-art manually designed and automated deep learning-based IDSs in terms of accuracy, precision, recall, and F1 -score. © 2004-2012 IEEE.

关键词： Particle swarm optimization (PSO)

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