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2024 International Conference on computational intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Ustun, Taha Selim Fukushima Japan

ISBN: (纸本)9798350381887

Grid Forming Inverters (GFIs) are becoming more popular with their ability to support frequency in a grid. This helps overcome the natural limitation of renewable energy deployments. More companies are looking at using SIs in their networks and closely study their impacts of these devices on the power system operation. Carefully controlled test environments show that necessary virtual inertia support can be received from GFIs. However, these modes actively try to change the system frequency in the grid and may cause unknown problems. Especially if the GFI control block is compromised by a hacker, these problems may have disastrous consequences. In order to fill this gap and investigate these points, a GFI model is used to investigate impacts of cyberattacks on its operation as well as power system operation at large. With the integration of Information Technologies (IT) and automation in power systems, cybersecurity has become a real threat and a concern. Most of the cybersecurity research focuses on large scale power plants that are connected at transmission level. This work analyzes the impact of such attacks on small-scale inverters that are connected to distribution networks. Based on these findings, cybersecurity measures can be developed to secure GFI operation. © 2024 ieee.

关键词： Authentication Cybersecurity IEC 61850 IEC 62351 Message Integrity Power System automation

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Performance-based Fault-tolerant control for Uncertain Nonlinear Systems with Actuator Failures 7

Performance-based Fault-tolerant Control for Uncertain Nonli...

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7th International symposium on Autonomous Systems, ISAS 2024

作者： Liu, Yuqi Gao, Yu Wu, Yue Cao, Ye Xi'an Jiaotong University School of Automation Science and Engineering Xi'an China Shaanxi Railway Institute Department of Basic Courses Weinan China Co. Ltd. School of Automation Science and Engineering Urumqi China

ISBN: (纸本)9798350363173

In this paper, a performance-based fault-tolerant control method is proposed, whose purpose is to ensure stable operation even at the failure situation. A prescribed performance function-based supervision mechanism is proposed, which can detect the occurrence of an extreme failure. Also, a control strategy with low computational complexity is proposed, which can recover the system performance to its original level within a preset time after an extreme failure. Furthermore, its effectiveness is verified through the simulation. © 2024 ieee.

关键词： Actuators

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Development of a User-Friendly and Efficient control System for Smart Home 17

Development of a User-Friendly and Efficient Control System ...

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ieee Congress on Cybermatics: 17th ieee International Conference on Internet of Things, iThings 2024, 20th ieee International Conference on Green Computing and Communications, GreenCom 2024, 17th ieee International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th ieee International Conference on Smart Data, SmartData 2024

作者： Kar, Pushpendu Ingkasit, Puttipatt University of Nottingham Ningbo China School of Computer Science China University of Nottingham Ningbo China Next Generation Internet of Everything Laboratory China

ISBN: (纸本)9798350351637

This paper presents a comprehensive approach to the development of an efficient control system for smart homes, addressing the issues of previous smart home systems, including automation, compatibility, and user-friendliness. Here, we design a smart home control system based on the principles of usercentric design and adaptive smart home systems. After identifying the gaps in previous smart home dashboards, it introduces the floating panel concept and recommender systems in the proposed smart home dashboard. Web development technologies such as JavaScript, HTML, and CSS are also used for seamless user experiences across different operating systems and platforms. The implementation of the proposed smart home system control is expected to improve the overall user experience and encourage more potential smart home users. Performance metrics in the proposed dashboard were substantially improved, with reduced loading times and higher performance scores;First Contentful Paint (FCP) reduced from 3.9 seconds to 1.2 seconds and Largest Contentful Paint (LCP) from 5 seconds to 1.3 seconds. The Total Blocking Time (TBT) for the proposed dashboard is 0 ms, improving by 310 ms over the benchmark. © 2024 ieee.

关键词： Smart homes

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Type 1 Diabetes Blood Glucose Management Based on Fuzzy Logic and Reinforcement Learning 4

Type 1 Diabetes Blood Glucose Management Based on Fuzzy Logi...

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4th International symposium on Artificial intelligence and Intelligent Manufacturing, AIIM 2024

作者： Liu, Xuan Hu, Feng Su, Zuqiang Yang, Fusong Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798331541729

Developing a personalized insulin regimen for the treatment of Type 1 Diabetes (T1D) is a healthcare task with very high safety requirements. Compared to traditional control algorithms, current reinforcement learning-based methods for blood glucose control can increase the duration of blood glucose within the target range while reducing risks. However, they do not account for the uncertainties of factors affecting blood glucose levels, such as diet, age, weight, etc. These factors do not have a simple linear relationship with blood glucose changes. For example, consuming a certain amount of carbohydrates does not always immediately result in the same degree of blood glucose increase, and this variability is precisely the key to personalized insulin regimens. To address this issue, this work proposes a personalized insulin regimen based on fuzzy logic reasoning and reinforcement learning, namely the Fuzzy Rule-Based TD3-BC model (FRBTD3-BC). This regimen learns from real-world treatment data of T1D patients, uses membership functions to obtain the initial conditions and outcome categories of the data, and then calculates the weight of each rule based on confidence levels. Subsequently, a fuzzy rule base is obtained and embedded into the Actor module of the TD3-BC algorithm in the form of a fuzzy reasoning logic layer. By interacting with the FDA-approved UVA/Padova glucose kinetics simulator, the utility of FRBTD3-BC in blood glucose control was studied in 30 virtual patients. The results show that the FRBTD3-BC model outperforms other comparable methods, with the TIR indicator increasing by 5.53 ± 0.64% compared to the original TD3-BC. © 2024 ieee.

关键词： Reinforcement learning

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Design of Sliding Mode Extended State Observer for Active disturbance rejection control of Air Intake System 4

Design of Sliding Mode Extended State Observer for Active di...

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4th International symposium on Artificial intelligence and Intelligent Manufacturing, AIIM 2024

作者： Zhou, Zezhen Rong, Haina Bi, Tong Zhang, Gexiang Dan, Zhihong Zhang, Song School of Electrical Engineering Southwest Jiaotong University Chengdu611756 China School of Automation Chengdu University of Information Technology Chengdu610225 China Science and Technology on Altitude Simulation Laboratory AECC Sichuan Gas Turbine Establishment Mianyang621703 China

ISBN: (纸本)9798331541729

The air intake control system is a key part of a type of propulsion system test bed. However, the existence of disturbances and random measurement noise in the control system, directly affect the reliability of the engine transition state test results. In this paper, a novel sliding mode extended state observer is designed to improve the anti-disturbance performance of the intake pressure control system. First, a sliding-mode convergence law is designed to improve the convergence speed and accuracy of the system state, and then a sliding mode surface is designed to reduce tracking error. Based on the designed sliding-mode convergence law and sliding mode surface, a nonlinear convergence function is designed to modify the structure of traditional extended state observer, improving tracking accuracy and speed. Simulation experiment results show that the proposed sliding mode extended state observer reduces the overshoot by 32.7% when tracking the intake pressure and 48.9% when tracking the intake temperature, indicating its effective anti-disturbance performance while ignoring the temperature feedback delay. © 2024 ieee.

关键词： Disturbance rejection

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Bipedal Robot Gait control Strategies Based on Reinforcement Learning 6

Bipedal Robot Gait Control Strategies Based on Reinforcement...

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6th International symposium on Robotics and Intelligent Manufacturing Technology, ISRIMT 2024

作者： Mao, Zhengquan Li, Jing Shen, Nanyan Ding, Jiawen Zhou, Bin Li, Chong School of Mechatronic Engineering and Automation Shanghai University Shanghai China Fourier Intelligence Shanghai China

ISBN: (纸本)9798331528942

This paper presents a reinforcement learning-based gait control strategy for bipedal robots. The work focuses on enabling the robot to adapt to complex terrains without relying on visual information. By leveraging the neural network's fitting capability in a simulated environment, the robot estimates privileged information from limited proprioceptive data. The approach is based on reinforcement learning fundamentals and includes a teacher-student policy architecture. We design a series of reward functions to achieve velocity tracking, periodic gait phases, accurate foot traj ectories, and smooth locomotion. Domain randomization is employed to bridge the gap between simulation and reality. Experiments using Proximal Policy Optimization show promising results, and the future work aims to deploy the policy on a real humanoid robot and explore full-body walking strategies. © 2024 ieee.

关键词： Anthropomorphic robots

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Memristive Circuit Design of Quantized Convolutional Auto-Encoder

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2023年第4期7卷 1301-1313页

作者： Zhang, Yaozhong Wang, Xiaoping Yang, Chao Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Guangxi Univ Sci & Technol Sch Comp Sci & Technol Liuzhou 545006 Peoples R China

This work proposes a memristor-based quantized convolutional auto-encoder (MQCAE) and applies it in an image denoising application. The pulse width or amplitude is gradually tuned by incremental steps in current memristive programming methods, which can be extremely time-consuming. In this work, a multi-level programming method without incremental steps is proposed. MQCAE is composed of five kinds of network layers: convolution layers, deconvolution layers, activation function layers, batch normalization layers and max-pooling layers. We design a memristive circuit for realizing convolution and deconvolution with different kernel parameters. The proposed circuit generates one output feature row every cycle. Analog data buffers are designed to store the intermediate data among network layers. In addition, a reconfigurable analog circuit for realizing activation functions and batch normalization is presented. By using analog temp modules, convolution layers and max-pooling layers are able to compute simultaneously. We construct the MQCAE and introduce the pipeline technique among network layers based on the circuit modules. As a result, MQCAE is able to process one FashionMNIST image in 36 cycles with clock frequency 20 kHz. Finally, we verify the effectiveness of MQCAE in an image denoising task. The results show that the denoising performance of proposed scheme is close to software model while the processing speed is faster.

关键词： Acceleration memristor crossbar array convolutional auto-encoder image denoising quantization

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Fourier U-Shaped Network for Multi-Variate Time Series Forecasting

Fourier U-Shaped Network for Multi-Variate Time Series Forec...

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Xu, Baowen Wang, Xuelei Liu, Chengbao Li, Shuo Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665430654

Multi-variate time series forecasting plays a crucial role in addressing key tasks across various domains, such as early warning, pre-planning, resource scheduling, and other critical tasks. Thus, accurate multi-variate time series forecasting is of significant importance in guiding practical applications and facilitating these essential tasks. Recently, Transformer-based multi-variate time series forecasting models have demonstrated tremendous potential due to their outstanding performance in long-term time predictions. However, Transformer-based models for multi-variate time series forecasting often come with high time complexity and computational costs. Therefore, we propose a low time complexity model called Fourier U-shaped Network (F-UNet) for multi-variate time series forecasting, which is non-Transformer based. Specifically, F-UNet is composed of low time complexity neural network components, such as Fourier neural operator and feed-forward neural network, arranged in a U-shaped architecture. F-UNet conducts channel and temporal modeling separately for the multi-variate time series. The U-Net constructed based on Fourier neural operators is employed to achieve channel interactions, while linear layers are used to realize temporal interactions. Experimental results on several real-world datasets demonstrate that F-UNet outperforms existing Transformer-based models with higher efficiency in multi-variate time series forecasting. © 2023 ieee.

关键词： fourier transform Multivariate time series forecasting U-Net

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Research on multi-modal data fusion technology based on automotive spot welding process 3

Research on multi-modal data fusion technology based on auto...

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3rd International symposium on Artificial intelligence and Intelligent Manufacturing, AIIM 2023

作者： Cong, Zhuang Wang, Ting Bi, Meng Zhu, Jingyang Zhang, Yuzheng Xing, Pengju Yang, Ying Shenyang University of Technology School of Software Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Shanghai Ballsnow Intelligent Technology Co. Ltd Shanghai China Liaoshen Industries Group Co.Ltd Shenyang China

ISBN: (纸本)9798350393835

With the wide application of automotive welding automation, the fusion of multimodal data has become an important research direction. In this paper, a multi-modal data fusion technology based on automotive spot welding process is proposed, including management configuration service, equipment data service, database data service, data fusion service, gateway service and public class module. Through the selection of microservice framework Spring Cloud, message queue, front-end framework, and other technologies, different types of data sources are accessed, managed, processed, and stored. Experimental results show that this technology can effectively improve the efficiency of data integration, sharing and application, and provides strong technical support for the intelligent construction of automotive welding automation. © 2023 ieee.

关键词： Data integration

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Real-Time Neural control for Discrete Nonlinear Systems Under Unknown Input and State Disturbances

Real-Time Neural Control for Discrete Nonlinear Systems Unde...

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Alanis, Alma Y. Alvarez, Jesus G. Sanchez, Oscar D. Zuniga, Pavel Munoz-Gomez, Gustavo University of Guadalajara Cucei Guadalajara Mexico Tecnologico Nacional de Mexico Nochistlan Mexico

ISBN: (纸本)9781665430654

This paper presents the design of an intelligent controller for uncertain discrete-time nonlinear systems. The proposed controller is resilient to external unknown disturbances as well as state and input uncertainties, even though the model of the system is considered unknown. An intelligent controller is designed for an unknown discrete-time nonlinear system, this controller is model-free and it is based on sensor measurements and therefore including unknown system dynamics, actuator nonlinearities, measurement errors, noise, uncertainties, external disturbances and other phenomena associated to real-world applications. Then, using the neural model, a backstepping controller is designed to ensure a resilient performance. Finally, realtime results are included to demonstrate the effectiveness of the proposed approach using a three-phase induction motor. © 2023 ieee.

关键词： Experimental results Induction motor Intelligent control Neural control Resilience Uncertain discrete-time nonlinear system

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