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International Conference on Industrial Artificial Intelligence (IAI)

作者： Jingyu Hu Zhongyang Han Jun Zhao Wei Wang School of Control Science and Engineering Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Liaoning China

With the increase of the proportion of new energy sources connected to the grid, challenges are also emerged for modeling and control of the conventional coal-fired power units to increase their operational flexibility of deep peak-regulation. Traditional data-driven modelling (DDM) requires a large amount of training data containing a variety of operating conditions, which are hard to be fully satisfied in engineering practice. This paper investigates the mechanics and dynamic characteristics of boiler-turbine systems in the coal-fired power unit, and proposes a mechanistic data-driven combined modelling (M-DDM) method. It not only requires less amount of data comparing with the DDM based method, but also covers a wide range of operating conditions by considering the dynamic characteristics of the coal-fired power unit. A model predictive control (MPC) based method was subsequently designed as the control vehicle. In order to improve the computing efficiency and reduce the probability of model mismatch considering the involved ordinary differential equations (ODEs) and nonlinearities. reinforcement learning is embedded into the MPC. Finally, simulations are conducted for demonstrating the superior performance of the proposed methods comparing with other commonly deployed modeling and control methods.

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Prediction Method of Mixed Gas Consumption in Hot Rolling Based on Improved K-Medoids Working Condition Division

Prediction Method of Mixed Gas Consumption in Hot Rolling Ba...

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Chinese Automation Congress (CAC)

作者： Hao Li Demin Xu Fan Zhou Jun Zhao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

The prediction of mixed gas consumption in hot rolling production is of great significance in gas production-consumption balance and energy saving. However, in the actual production process of hot rolling, the complexity of production environment and the strong coupling of production processes lead to the complexity of working conditions, which brings great challenges to the prediction of mixed gas consumption. A prediction method of hot rolling mixed gas consumption based on improved K-Medoids working condition division is proposed in this paper. Based on Chebyshev's theorem, the method uses more robust median and median absolute deviation to evaluate the outliers in the cluster at each iteration in terms of distance, and further verifies from the perspective of density to complete the division of normal working conditions and abnormal working conditions. On this basis, considering the time dependence of hot rolling data, the prediction model of mixed gas consumption under corresponding working conditions was established by using the Long Short-Term Memory (LSTM) neural network. In order to verify the effectiveness of the proposed method, the actual industrial field data are used. The simulation results show that the proposed method has high prediction accuracy and is suitable for the prediction of mixed gas consumption in hot rolling.

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Time-Triggered control of Double Channel Transmission Networked Switched Systems under DoS Attacks

Time-Triggered Control of Double Channel Transmission Networ...

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Chinese control Conference (CCC)

作者： Jie Lian Hao Li Feiyue Wu Dong Wang The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

This paper investigates the time-triggered control for double channel transmission networked switched systems (NSSs) under denial-of-service (DoS) attacks. For NSSs, the information of the sensor and controller needs to be transmitted through the network, which increases the risk of packet loss due to network attacks. The resulting packet loss will greatly degrade the system performance. To address this problem, this paper proposes a double channel transmission scheme and a novel control strategy. Firstly, based on the traditional time-triggered control framework, the transmission link is extended to two channels. At each triggered instant, the sensor selects one of the channels to transmit and the attacker can only select one of the channels to attack. In this way, the packet loss rate caused by DoS attacks can be effectively reduced. Secondly, the control strategy is designed to combine hold-input and zero-input according to the actual packet loss situation. Under this control strategy, sufficient conditions for the asymptotically mean-square stability of the NSSs are obtained by dwell time switching, and the solution of controller gain is solved. Finally, the feasibility of this study is verified by aeroengine model.

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Cost Effectivities Analysis of Perovskite Solar Cells: Will it Outperform Crystalline Silicon Ones?

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Nano-Micro Letters 2025年第09期 345-355页

作者： Yingming Liu Ziyang Zhang Tianhao Wu Wenxiang Xiang Zhenzhen Qin Xiangqian Shen Yong Peng Wenzhong Shen Yongfang Li Liyuan Han State Key Laboratory of Metal Matrix Composites Shanghai Jiao Tong University Materials Genome Institute Shanghai University Xinjiang Key Laboratory of Solid State Physics and Devices School of Physical Science and Technology Xinjiang University State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Institute of Solar Energy Key Laboratory of Artificial Structures and Quantum Control (Ministry of Education) School of Physics and AstronomyShanghai Jiao Tong University Beijing National Laboratory for Molecular Sciences CAS Key Laboratory of Organic Solids Institute of Chemistry Chinese Academy of Sciences

The commercialization of perovskite solar cells（PSCs） has garnered worldwide attention and many efforts were devoted on the improvement of efficiency and stability. Here,we estimated the cost effectivities of PSCs based on the current industrial condition. Through the analysis of current process, the manufacturing cost and the levelized cost of electricity（LCOE）of PSCs is estimated as 0.57 $ W-1and 18–22 US cents（kWh）-1, respectively, and we demonstrate the materials cost shares 70% of the total *** analysis indicates that the improve- ment of efficiency, yield and decrease in materials cost significantly reduce the cost of the modules. Analysis of the module cost and LCOE indicates that the PSCs have the potential to outperform the silicon solar cells in the condition of over 25% efficiency and 25-year lifetime in future. To achieve this target, it is essential to further refine the fabrication processes of each layer in the module, develop stable inorganic transport materials, and precisely control material formation and processing at the microscale and nanoscale to enhance charge transport.

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A Genome-Wide Prediction Method of Biological Multiple Traits Using Feedback Stacking Deep Networks

A Genome-Wide Prediction Method of Biological Multiple Trait...

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IEEE International Conference on Bioinformatics and Computational Biology (ICBCB)

作者： Wenqi Zhu Junzhe Cao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

Genome-wide prediction methods establish a genetic statistical model using molecular marker genotypes distributed throughout the whole genome and biological trait data to predict phenotype values, in order to providing a guide for individual selection breeding. Most of the existing genome-wide selection methods focus on the prediction of a single trait, which can only examine each trait one by one when predicting multiple traits that would translate into neglect of the correlation among traits. In this paper, we proposed a joint prediction method for multiple biological traits based on feedback stacking deep networks, which stack orderly the output of the model to the non-shared layer so as to introduce the correlation among traits into the learning network. The method selected the single nucleotide polymorphism (SNP) loci obtained from whole genome sequencing as molecular markers, extracted the genotype of each SNP allele to constitute sample features, and adopted a two-stage feature selection method to pick up the SNP loci highly correlated with target, and then constructed a deep network model with feedback stacking for joint prediction of multiple traits. The proposed approach was compared with other models by using oyster and corn datasets, and the results showed that the method on this work could achieve better prediction effect than the others.

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Multisensor Deep Clustering Networks With Joint Learning Strategy for Fault Diagnosis Considering Consensus-Specific Constraint

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IEEE Sensors Journal 2025年第11期25卷 19921-19936页

作者： Xu, Yihao Lu, Mingfeng Wang, Gang Fang, Fang University of Science and Technology of China School of Management Hefei230026 China Hefei University of Technology School of Management Hefei230009 China Hefei University of Technology School of Management Key Laboratory of Process Optimization and Intelligent Decision-Making of the Ministry of Education Ministry of Education Engineering Research Center for Intelligent Decision-Making and Information System Technologies Hefei230009 China

The deep clustering method has been successfully employed for fault diagnosis (FD) due to its remarkable ability to extract deep representation features, particularly in scenarios where labeled data is unavailable. However, the separate learning strategy utilized in deep clustering-based FD methods leads to suboptimal FD results, and the consensus representation feature present in multisensor industrial data is not fully explored to improve the effectiveness of FD. To address the above problems, a novel deep clustering method named multisensor deep clustering networks (MsDCNs) with joint learning strategy is proposed for FD, which can comprehensively utilize industrial multisensor data and enable joint optimization. First, the Commonality-net uses a sensor-aware attentive encoder (SaAE) to integrate multisensor industrial data and extract the consensus representation feature. Second, the Individuality-net is established to extract the specific representation features of multisensor industrial data. Finally, a joint multisensor deep clustering learning strategy, including consensus-specific constraints, is designed to optimize the networks jointly and learn the consensus representation feature with consistent and complementary information. Extensive experiments on the gearbox and bearing datasets validate the proposed MsDCN. The results indicate that the proposed MsDCN can generate a reliable consensus representation feature to achieve better FD performance than other benchmark methods. Moreover, its capability to enhance multisensor FD with unlabeled data highlights its potential for real-world applications. © 2001-2012 IEEE.

关键词： Labeled data

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Optimal denial-of-service attack scheduling for remote state estimation via sequential observations

Optimal denial-of-service attack scheduling for remote state...

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第43届中国控制会议

作者： Xinxin Yang Yuqing Ni Xiaomeng Chen Shan Chen Fei Liu The Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things EngineeringJiangnan University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This study investigates the optimal allocation problem of attack energy in multi-system remote state estimation for Cyber-Physical Systems(CPSs). We consider a scenario where there are M independent systems, each equipped with a remote sensor monitoring system. The local estimates from these systems are sent to a remote estimator through a packet loss channel. The objective of the attacker is to degrade the communication channel between the sensors and the remote estimator. Due to capacity limitations, the attacker is restricted to attacking at most one out of M channels. We incorporate interference from other sensors or interference sources into the communication model based on the SINR(Signal-to-Interference-plus-Noise Ratio)model. It should be noted that the same attack power can lead to different packet loss rates in different network *** the attacker's perspective, the goal is to find the optimal attack strategy that maximizes the average estimation error of the remote estimator. Given the uncertainties of wireless network environments, we employ the sequentially-observed Markov decision process(SOMDP) framework to solve the problem. By observing the wireless channels, we gather additional information and incorporate it into the decision-making policy, resulting in better performance than using a standard Markov decision process(MDP). The simulation results support our theoretical findings.

关键词： SOMDP remote state estimation packet loss channel CPS security

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Finite-time and bumpless transfer control of asynchronously switched systems: An output feedback control approach

arXiv

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arXiv 2024年

作者： Liu, Mo-Ran Wu, Zhen Du, Xian Fei, Zhongyang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

In this paper, the finite-time control and bumpless transfer control are investigated for switched systems under asynchronously switching. First, a class of dynamic output feedback controllers are designed to stabilize the switched system with measurable system outputs. Considering the improvement of transient performance, the bumpless transfer control and finite-time control are further studied in the controller design. To avoid the control bumps, a practical filter is introduced to make the control signal smoother and continuous. Furthermore, to derive a finite-time bounded system state over short-time intervals, the finite-time analysis is considered in managing the switching process with the average dwell time. New criteria are proposed to analyze the finite-time stability and finite-time boundedness for the closed-loop system and solvable conditions are newly proposed to optimize the controller gain. Finally, the superiorities of the proposed method are validated through an application to a boost converter. Copyright © 2024, The Authors. All rights reserved.

关键词： Closed loop systems

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Vision-based formation control of multiple UAVs with event-triggered integral sliding mode control

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Cyber-Physical Systems 2020年第4期6卷 231-251页

作者： Chen, Siqing Jin, Xin Zhang, Wenle Tang, Yang Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai China

Vision-based formation control offers an alternate solution for unmanned aerial vehicles (UAVs) to work together in the external position system denied environment. In this paper, we present a vision-based formation control strategy with the advantages of high tracking performance and low consumption of communication resources. A vision-based multi-robot localisation methods is applied to get precise position information, and the extended Kalman filter (EKF) is used to fuse measurement data from different sensors. The integral sliding mode controller (ISMC) is utilised to ensure the fast convergence and robustness to the desired formation, and the event-triggered communication scheme is employed to reduce redundant transmission of state information used in the formation controller. The simulation results in Gazebo with a team of three UAVs are given to illustrate the effectness of the proposed control strategy. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Extended Kalman filters

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Billet Number Recognition Based on ESRGAN and Improved YOLOv5 37

Billet Number Recognition Based on ESRGAN and Improved YOLOv...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Wang, Zijia Dong, Yichao Niu, Dan Liu, Minghao Li, Qi Chen, Xisong Ministry of Education School of Automation Nanjing China Ministry of Education School of Automation Key Laboratory of Measurement and Control of CSE Nanjing China Nanjing Sciyon Wisdom Technology Group Co. Ltd. Nanjing211102 China Jiangsu Province Key Laboratory of Thermal Process Intelligent Control Nanjing211102 China

ISBN: (纸本)9781665465366

Aiming at solving the problem of billet number recognition and further improve the rate of recognition, this paper proposes a billet number recognition algorithm based on ESRGAN and improved YOLOv5, which is referred to as YOLOv5-Billet in this paper. According to the actual situation, we propose a target recognition algorithm that uses the lightweight network MobileNetv3 to replace the backbone feature extraction network of YOLOv5. We also introduce ESRGAN to improve the quality of the input image and further improve the recognition effect. A series of rigorous experiments show that YOLOv5-Billet has a certain improvement in billet recognition speed and precision. Specifically, the average precision of YOLOv5-Billet is improved by 10.8%, and the detection speed is also improved by 5.12fps, reaching 40.35fps. Through the experimental verification of real-time collection of data sets, compared with a variety of classical target detection methods, the recognition precision and detection speed are improved to varying degrees. This model maintains the lightweight characteristics of YOLOv5 and meets the requirements of real-time monitoring of the number of blanks. © 2022 IEEE.

关键词： Image enhancement

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