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Mixed Driven Iterative Adaptive Critic Control Design Towards Nonaffine Discrete-Time Plants ⁎

IFAC-PapersOnLine 2020年第2期53卷 3803-3808页

作者： Ding Wang Mingming Ha Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing 100124 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China

In this paper, an effective mixed driven framework is constructed involving both data and event considerations. The primary purpose lies in that the mixed driven iterative adaptive critic method is established to address approximate optimal control towards discrete-time nonlinear dynamics. The neural dynamic programming technique is inventively integrated with the mixed driven architecture, such that the knowledge of the controlled plant is needless and the number for updating control inputs is prominently reduced. A triggering threshold is also designed with theoretical guarantee, which renders that the control signals can be updated conditionally. Through carrying out simulation studies with comparisons, the superiority of the present near-optimal regulation approach is confirmed at last.

关键词： Discrete-time nonlinear dynamics iterative adaptive critic algorithm mixed driven design neural optimal control triggering condition

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Review of Used Mobile Phone Recognition Method for Recycling Equipment

Review of Used Mobile Phone Recognition Method for Recycling...

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第三十九届中国控制会议

作者： Zixuan Wang Jian Tang Chengyu Cui Weitao Li Zhe Xu Honggui Han Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System School of Electrical Engineering and Automation Hefei University of Technology

With the increase of the replacement frequency of mobile phones,the recycling of used mobile phones（UMPs） has gradually become a hot *** recycling equipment based on the internet of things technology has become one type of the main devices to recycle UMPs,but there are still some problems such that low recognition success rates and tedious recycling *** key to solving these problems is to improve the accuracy of UMP recognition（UMPR）.In view of the above problems,this paper reviews the methods of UMPR for UMP recycling equipment,which provides support for improving recycling ***,we briefly introduce the structure and recycling process of UMP recycling ***,the four typical methods of UMPR are addressed in detail and their respective shortcomings are ***,these methods are summarized and analyzed,and the shortcomings and deficiencies of the current UMPR methods based on artificial intelligence,especially the image recognition method are ***,the directions for future research on UMPR are given out.

关键词： Used mobile phone（UMP） Used mobile phone recognition（UMPR） Recycling equipment Image recognition Artificial intelligence

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Dioxin Emission Concentration Forecasting Model for MSWI Process with Random Forest-Based Transfer Learning

Dioxin Emission Concentration Forecasting Model for MSWI Pro...

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第三十九届中国控制会议

作者： Heng Xia Jian Tang Qiumei Cong Junfei Qiao Zhe Xu Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System College of Information Engineering Shenyang University of Chemical Technology

The dioxin（DXN） emission concentration is an important indicator related to the stability and harmless operation of municipal solid waste incinerator（MSWI） process, and also one of the key parameters for MSWI process to realize optimal operation control. In the actual MSWI process, the process variables such as furnace temperature, grate speed and air pressure are hundreds. However, the modeling samples containing DXN emission concentrations are small, which makes it impossible to establish accurate DXN emission concentration forecasting model. In this study, a forecasting model construction method by using random forest（RF）-based transfer learning was proposed. At first, initial weights are assigned to the source domain and target domain samples. Then, an RF-based DXN emission concentration transfer learning model is established, and the prediction errors are used as the indicator to calculate the weight adjustment coefficients. Finally, the sample weights are adjusted through iteration loop in terms of increase the source domain instance weights that related to the target domain. The method proposed in this paper can transfer the source domain sample to enhance the prediction performance of the DXN emission concentration forecasting model. The experimental results based on the actual industrial data show the effectiveness of the proposed method.

关键词： Municipal solid waste incineration（MSWI） dioxins（DXN） Random forest（RF） Transfer learning

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Two-Way Feature-Aligned And Attention-Rectified Adversarial Training

Two-Way Feature-Aligned And Attention-Rectified Adversarial ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Haitao Zhang Fan Jia Quanxin Zhang Yahong Han Xiaohui Kuang Yu-an Tan College of Intelligence and Computing Tianjin University Tianjin China Beijing Institute of Technology National Key Laboratory of Science and Technology on Information System Security

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

Adversarial training increases robustness by augmenting training data with adversarial examples. However, vanilla adversarial training may be overfitting to certain adversarial attacks. Small perturbations in images bring in error which is gradually amplified when forwarded through the model so that the error leads to wrong classification. Besides, small perturbations will also distract classifier's attention to significant features that are relevant to the true label. In this paper, we propose a novel two-way feature-aligned and attention-rectified adversarial training (FAAR) to improve adversarial training (AT). FAAR utilizes two-way feature alignment and attention rectification to mitigate the problems mentioned above. FAAR effectively suppresses perturbations in lowlevel, high-level and global features by moving features of perturbed images towards those of clean images with twoway feature alignment. It also leads the model into focusing more on useful features which are correlated with true label through rectifying gradient-weighted attention. Besides, feature alignment activates attention rectification by reducing perturbations in high-level feature. Our proposed method FAAR surpasses other existing AT methods in three aspects. First, it pushes the model to keep invariant when dealing with different adversarial attacks and different magnitude of perturbations. Second, it can be applied to any convolution neural networks. Third, the training process is end-to-end. For experiments, FAAR shows promising defense performance on CIFAR-10 and ImageNet.

关键词： Training Perturbation methods Robustness Mathematical model Convolution Image color analysis Computational modeling

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Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

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IEEE/CAA Journal of Automatica Sinica 2018年第5期5卷 968-976页

作者： Junfei Qiao Hongbiao Zhou beijing university of technology and beijing key laboratory of computational intelligence and intelligent system Beijing l00l24，China

Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process *** parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.

关键词： Density peaks clustering effluent quality （EQ） energy consumption （EC） fuzzy neural network improved Levenberg-Marquardt algorithm wastewater treatment process （WWTP）.

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Collaborative Scheduling of computing Tasks for Edge computing

Collaborative Scheduling of Computing Tasks for Edge Computi...

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Chinese Automation Congress (CAC)

作者： Yue Liu Gang Xiong Fenghua Zhu Shichao Chen Liguo Zhang Xudong Liu School of Artificial Intelligence and Automation Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation and the Cloud Computing Center Chinese Academy of Sciences Beijng China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation and Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China Faculty of Information and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781665426480

In the face of the rapid development of the IoT, the traditional cloud computing, while providing services for mobile intelligent devices, also brings problems such as network bandwidth occupation and communication delay. Edge computing can solve these problems to some extent. But too many offloaded tasks may result in an edge server becoming overloaded and execution delay of tasks increasing, the computing resources of idle edge devices are also not fully utilized. In order to make full use of the computing resources of idle edge devices in the local network and reduce the execution delay for computing tasks, this paper takes the average delay time for all end devices as the optimization objective and proposes an end-edge cooperative scheduling (EECS) model to schedule tasks for edge computing. The scheduling algorithm used in EECS model is improved based on MCT algorithm. We use the EdgeCloudSim to verily the EECS model and scheduling algorithm. The simulation results show that the average completion time of computing tasks in EECS model, compared with only single edge server (SEE) task computing model, can be reduced by up to three times. In addition, the system load can be balanced to a certain extent in local network.

关键词： Analytical models Scheduling algorithms Computational modeling Collaboration Scheduling Delays Servers

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xiao, Feng Shen, Cong Chen, Yu Yang, Tian Chen, Shengyong Liao, Zhijun Tang, Jijun Tianjin University of Technology School of Computer Science and Engineering Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China School of Basic Medical Sciences Fujian Medical University Fujian Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States

ISBN: (纸本)9781665401265

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score. © 2021 IEEE.

关键词： Diagnosis

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A Physics-guided Multimodal Transformer Path to Weather and Climate Sciences

arXiv

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arXiv 2025年

作者： Han, Jing Chen, Hanting Han, Kai Huang, Xiaomeng Hu, Yongyun Xu, Wenjun Tao, Dacheng Zhang, Ping School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Huawei Noah’s Ark Lab Beijing China Department of Earth System Science Tsinghua University Beijing China Institute of Ocean Research Peking University Beijing China College of Computing & Data Science Nanyang Technological University Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

With the rapid development of machine learning in recent years, many problems in meteorology can now be addressed using AI models. In particular, data-driven algorithms have significantly improved accuracy compared to traditional methods. Meteorological data is often transformed into 2D images or 3D videos, which are then fed into AI models for learning. Additionally, these models often incorporate physical signals, such as temperature, pressure, and wind speed, to further enhance accuracy and interpretability. In this paper, we review several representative AI + Weather/Climate algorithms and propose a new paradigm where observational data from different perspectives, each with distinct physical meanings, are treated as multimodal data and integrated via transformers. Furthermore, key weather and climate knowledge can be incorporated through regularization techniques to further strengthen the model’s capabilities. This new paradigm is versatile and can address a variety of tasks, offering strong generalizability. We also discuss future directions for improving model accuracy and interpretability. Copyright © 2025, The Authors. All rights reserved.

关键词： Engineering education

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Air Quality Controlling-Oriented Highly Efficient Method for Monitoring Particulate Matters

Air Quality Controlling-Oriented Highly Efficient Method for...

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第三十九届中国控制会议

作者： Yonghui Zhang Ke Gu Zhifang Xia Junfei Qiao Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System State Information Center

Since Particulate Matters（PMs） are closely related to people’s living and health, it has become one of the most important indicator of air quality monitoring around the world. But the existing sensor-based methods for PMs monitoring have remarkable disadvantages, such as low-density monitoring stations and high-requirement monitoring conditions. It is highly desired to devise a method that can obtain the PMs concentration at any location for the following air quality control in *** prior works indicate that the PMs concentration can be monitored by using ubiquitous photos. To further investigate such issue, we gathered 1,500 photos in big cities to establish a new AQPDCITY dataset. Experiments conducted to check nine stateof-the-art methods on this dataset show that the performance of those above methods perform poorly in the AQPDCITY *** address the above issue, we propose a new photo-based model for PMs monitoring. To be specific, we use the Support Vector Regression（SVR） to incorporate four types of 18 features to obtain a high-density PMs monitoring map. Experiments show that the newly proposed model has achieved superior performance than recently developed methods in the AQPDCITY dataset.

关键词： Particulate Matters（PMs） Monitoring AQPDCITY Dataset Feature Extrastion and Fusion

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Role of strategy update rules in the spatial memory-based mixed strategy games

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The European Physical Journal B 2021年第1期94卷 1-11页

作者： Zhang, Fan Wang, Juan Gao, Hongyu Li, Xiaopeng Xia, Chengyi Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology Tianjin People’s Republic of China School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin People’s Republic of China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin People’s Republic of China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin People’s Republic of China

Based on a continuous mixed strategy game model considering the memory mechanism and the uncertainty of individual decision-making, we explore the influence of several different update rules on the spatial cooperation behavior. Here, we mainly investigate the role of the Moran-like process, Fermi rule and replicator dynamics in the evolution of cooperation. Meanwhile, we further compare the evolution of cooperation when the memory mechanism and individual uncertainty are taken into account. In particular, memory length M and strategy adjustment factor $$\delta $$ can promote the cooperation behavior in different ways. Extensive Monte Carlo simulations indicate that the Moran-like process can generally drive the expansion of cooperative clusters faster and finally achieve the highest frequency of cooperation although the Fermi update rule performs better under the same condition. Moreover, the replicator dynamics create the worst scenario as far as the evolution of cooperation is concerned. Therefore, the level of cooperation strongly depends on the strategy update rules; these findings may be helpful to understand and analyze the evolutionary process of cooperation in many real-world natural and social systems.

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