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Journal of Network Intelligence 2024年第2期9卷 1046-1055页

作者： Zhang, Fu-Quan Ma, Lin-Juan Zhou, Jun-Xian Wang, Run-Hong Zhang, Guang-Zhen College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University Fuzhou China Fujian Province University Fuzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Minjiang University Fuzhou China Information Media School of Tianjin Technician Institute of Mechanical & Electrical Technology Tianjin China

The purpose of this paper is to present a new GAN-ID algorithm to gener-ate interior scene intelligently based on Artificial Intelligence of Things (AIoT). Although great progress has been made when GAN algorithm applied in smart homes and smart cities, the issue of the trade-off between the accuracy and the efficiency in interior scene has been overlooked. A number of experiments were performed to check the performance and it is indicated that the results of the proposed GAN-ID has great advantages in model sample generation than other common methods when generating interior scene intelligently, which is similar to state-of-the-art. It is meaningful when it can be applied in smart cities and smart homes filed based on AIoT in the future. © 2024, J. Network Intell. All rights reserved.

关键词： Automation

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Intelligent Technologies for Designing Information Systems for controlling the Polymer Waste Processing into Useful Technical Products

Intelligent Technologies for Designing Information Systems f...

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2023 Seminar on Information Systems Theory and Practice, ISTP 2023

作者： Chistyakova, Tamara B. Paukin, Oleg A. Novozhilova, Inna V. Razygraev, Aleksandr S. Goncharova, Tatyana S. Department of Computer-Aided Design and Control Systems St. Petersburg State Institute of Technology Saint Petersburg Russia

ISBN: (纸本)9798350345193

The article describes intelligent technologies used in the design of an information system for controlling the polymer waste processing into useful technical products. The basis of the system is a configurable information model that includes a database of characteristics of polymer waste and a knowledge base of the processes of their recycling into technical products (containers, packing materials, etc.). The knowledge base is built using a production-frame model for the representation of unformalized knowledge about the polymers compatibility, the quantitative ratio of the components of a composite mixture, waste processing methods to obtain a homogeneous mixture with specified properties (strength, resistance to aggressive substance, etc.). The result of the functioning of the system is the formation of a technological map of the production of a technical product, including information about the formulation and synthesis process of the polymer composition, specifications of the hardware design and the values of the technological modes of the production stages. Testing of the system according to the LLC "Plastic processing plant named after "Komsomolskaya Pravda" on the basis of the Polymer Ecology Training Center of SPSIT confirmed its operability and the possibility of use at polymer industry enterprises. The use of the system allows you to reduce the time spent on solving the problem of reconfiguration of production and selection of compositions, reduce defects and improve the consumer characteristics of products. ©2023 IEEE.

关键词： Intelligent systems

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Low-disturbance Nonlinear control of TBM Cutterhead Speed in Coal Mine Tunneling 50

Low-disturbance Nonlinear Control of TBM Cutterhead Speed in...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Liu, Nannan Wang, Tong Wang, Jianmin Fan, Yingda School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Department of Computer Science University of Pittsburgh Pittsburgh United States

ISBN: (纸本)9781665464543

To minimize the disturbance of the Tunnel Boring Machine (TBM) cutterhead on the surrounding rock during the coal mine roadway excavation process and ensure that the cutterhead rotation speed achieves fast tracking performance with minimal overshoot, we propose a direct adaptive robust control method for the cutterhead rotation speed hydraulic system based on inversion design. This method considers the strong disturbances such as loads and motion affecting the cutterhead hydraulic drive system, as well as the uncertainties in the cutterhead model. We establish the nonlinear model of the cutterhead hydraulic drive system and employ virtual control to reduce the model order. Using Lyapunov functions, we ensure the stability of the entire system and derive the control law for the cutterhead rotation speed controller, along with parameter-adaptive laws acting as parameter estimators. We validate the effectiveness of the proposed control strategy through joint simulation using AMESim and Simulink. The results show that the designed cutterhead rotation speed controller achieves high tracking accuracy and good adaptability. © 2024 IEEE.

关键词： Hydraulic drives

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Single Image Super Resolution Based on Dual-Path Large Kernel Learning 1

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2nd International Conference on Internet of Things, Communication and Intelligent technology, IoTCIT 2023

作者： Jiang, He Liu, Gui Cao, Gaoting Zheng, Ping Zhang, Haoxiang Kou, Qiqi Xu, Feixiang Cheng, Deqiang Department of Information and Control China University of Mining and Technology Xuzhou China Department of Computer Science China University of Mining and Technology Xuzhou China Department of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9789819727575

ISBN: (纸本)9789819727568

To excel in image super-resolution, deep neural network-based models often employ a stacking strategy of network modules. However, this approach leads to parameter explosion and information redundancy, thereby restricting the feasibility of deploying these models on mobile devices. To tackle this challenge, this study introduces a lightweight Dual-path Large Kernel Learning approach, namely DLKL, and applies it to image super-resolution. In DLKL, the first step involves the utilization of multi-scale large kernel decomposition to effectively establish long-range dependencies between pixels and successfully preserve local information. Subsequently, DLKL enhances the feature expression ability and achieves effective feature fusion through its dual-path network architecture. Furthermore, DLKL reduces the number of parameters while maintaining performance, thus striking a balance between network performance and efficiency. The remarkable performance of DLKL has been validated through a multitude of experiments by quantitative metric tests and visual evaluations. Comparative analysis, conducted against other prevailing algorithms, provides evidence of the DLKL method’s consistent proficiency in generating images featuring enhanced texture clarity and more faithful representation of natural structures. These findings serve to reinforce the method’s inherent superiority and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network architecture

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On the relationship between the activity at point of interests and road traffic

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Communications in Transportation Research 2023年第1期3卷 150-160页

作者： MátéKolat Tamás Tettamanti TamásBécsi Domokos Esztergár-Kiss Department of Control for Transportation and Vehicle Systems Faculty of Transportation Eng.and Vehicle Eng.Budapest University of Technology and EconomicsBudapest1111PestHungary Systems and Control Laboratory Institute for Computer Science and Control of the Eötvös Lorand Research NetworkBudapest1053PestHungary Department of Transport Technology and Economics Faculty of Transportation Eng.and Vehicle Eng.Budapest University of Technology and EconomicsBudapest1111PestHungary

The estimation and analysis of road traffic represent the preliminary steps towards satisfying the current needs for smooth,safe,and green ***,effective traffic monitoring is an essential topic alongside the planning of sustainable transportation systems and the development of new traffic management *** contrast to classical traffic detection solutions,this study investigates the correlation between travelers'social activities and road *** s's primary goal is to investigate the presence of the relationship between social activity and road traffic,which might allow an infrastructure-independent traffic monitoring technique as ***'s general activities at Point of Interest(POI)locations(measured as occupancy parameter)are correlated with traffic data so that,finally,proper proxys can be defined for link-level average traffic speed *** method is tested and evaluated using real-world traffic and POI occupancy data from Budapest(District XI.).The results of the correlation investigation justify an indirect relationship between activity at POIs and road traffic,which holds promise for future practical applicability.

关键词： Road traffic estimation Google popular times(GPT) Point of interest(POI) POI occupancy Genetic algorithm(GA)

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Leveraging Single and Multi-task Reinforcement Learning Algorithms for Autonomous Mobile Aloha Robot 1

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8th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2024

作者： Narendra, Aditya Makarov, Dmitry Panov, Aleksander I. Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control RAS Moscow117312 Russia Artificial Intelligence Research Institute Moscow105064 Russia

ISBN: (数字)9783031776885

ISBN: (纸本)9783031776878

Traditional Reinforcement Learning (RL) methods often require careful algorithm design, hyper-parameter tuning, and experimentation to perform optimally across multiple tasks. Multi-task models, however, offer increased efficiency, better generalization, and improved resource utilization, which are crucial for robots performing diverse autonomous tasks. On the other hand, single-task models often demonstrate better results and more robust task-specific policies. In this paper, we demonstrate the versatility of these models through experiments on the Mobile Aloha robot, which has both manipulation and navigation capabilities. The main idea behind our work is to demonstrate the use of various types of RL algorithms (single and multi-task) for multi-control robots (in our case, Mobile Aloha) which has not been explored much in the past. We conclude the paper with comparative results of experiments with state-of-the-art RL algorithms across different types of tasks including navigation, manipulation, and a combination of both. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Accelerating Data Transfer in Dataflow Architectures Through a Look-Ahead Acknowledgment Mechanism

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Journal of computer Science & technology 2022年第4期37卷 942-959页

作者： Yu-Jing Feng De-Jian Li Xu Tan Xiao-Chun Ye Dong-Rui Fan Wen-Ming Li Da Wang Hao Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Beijing Smartchip Microelectronics Technology Company Limited Beijing 100000China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100190China

The dataflow architecture,which is characterized by a lack of a redundant unified control logic,has been shown to have an advantage over the control-flow architecture as it improves the computational performance and power efficiency,especially of applications used in high-performance computing(HPC).Importantly,the high computational efficiency of systems using the dataflow architecture is achieved by allowing program kernels to be activated in a simultaneous ***,a proper acknowledgment mechanism is required to distinguish the data that logically belongs to different *** solutions include the tagged-token matching mechanism in which the data is sent before acknowledgments are received but retried after rejection,or a handshake mechanism in which the data is only sent after acknowledgments are ***,these mechanisms are characterized by both inefficient data transfer and increased area *** performance of the dataflow architecture depends on the efficiency of data *** order to optimize the efficiency of data transfer in existing dataflow architectures with a minimal increase in area and power cost,we propose a Look-Ahead Acknowledgment(LAA)*** accelerates the execution flow by speculatively acknowledging ahead without *** simulation analysis based on a handshake mechanism shows that our LAA increases the average utilization of computational units by 23.9%,with a reduction in the average execution time by 17.4%and an increase in the average power efficiency of dataflow processors by 22.4%.Crucially,our novel approach results in a relatively small increase in the area and power consumption of the on-chip logic of less than 0.9%.In conclusion,the evaluation results suggest that Look-Ahead Acknowledgment is an effective improvement for data transfer in existing dataflow architectures.

关键词： dataflow model control-flow model high-performance computing application data transfer power efficiency

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of Systems Science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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Space-Filling Input Design for Nonlinear State-Space Identification ⁎

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IFAC-PapersOnLine 2024年第15期58卷 562-567页

作者： Máté Kiss Roland Tóth Maarten Schoukens Control System group Eindhoven University of Technology Eindhoven the Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing experiments for linear time-invariant systems is well understood and mainly focuses on the power spectrum of the input signal. Performing experiment design for nonlinear system identification on the other hand remains an open challenge as informativity of the data depends both on the frequency-domain content and on the time-domain evolution of the input signal. Furthermore, as nonlinear system identification is much more sensitive to modeling and extrapolation errors, having experiments that explore the considered operation range of interest is of high importance. Hence, this paper focuses on designing space-filling experiments i.e., experiments that cover the full operation range of interest, for nonlinear dynamical systems that can be represented in a state-space form using a broad set of input signals. The presented experiment design approach can straightforwardly be extended to a wider range of system classes (e.g., NARMAX). The effectiveness of the proposed approach is illustrated on the experiment design for a nonlinear mass-spring-damper system, using a multisine input signal.

关键词： Experiment Design Nonlinear System Identification State-Space

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