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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Design and Implementation of an IoT based Smart Door Lock system 2

Design and Implementation of an IoT based Smart Door Lock Sy...

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2nd International Conference on Multidisciplinary Engineering and Applied science, ICMEAS 2023

作者： Ahmed, Abdullahi Abdulkadir, Said Mohamed, Jama Ali, Suleiman Abdullahi Abdi, Mowlid Kahie, Sharmake Ali Jamhuriya University of Science and Technology Faculty of Computer and Information Technology Mogadishu Somalia Jamhuriya University of Science and Technology Jamhuuriya Research Centre Mogadishu Somalia Jamhuriya University of Science and Technology Faculty of Computer & It Mogadishu Somalia

ISBN: (纸本)9798350358834

The Internet of thing devices are interconnected physical objects that collect and share data over the internet. IoT has the potential to revolutionize many industries, including security. Traditional door lock systems have several limitations. They are often inconvenient to use, as they require users to carry keys or enter passcodes. They are also vulnerable to physical attacks, such as lockpicking or drilling. Additionally, traditional door lock systems cannot be controlled remotely, which can be a problem if a user is away from home or needs to grant access to someone else. The proposed IoT-based smart voice and keypad door lock system using Arduino overcomes the limitations of traditional door lock systems by providing a convenient, secure, and flexible way to unlock doors. The system uses three unlocking methods: voice control, fingerprint recognition, and keypad entry. Users can choose the unlocking method that best suits their needs and preferences. Voice control enables hands-free and efficient unlocking, while fingerprint recognition offers reliable and accurate access. Keypad entry provides a balance between accuracy and ease of use. The system successfully integrates these methods, offering a robust and flexible door lock system. The proposed system has the potential to significantly improve security in a variety of settings, including homes, offices, and public spaces. It offers a convenient, secure, and accessible way to unlock doors, meeting the diverse needs of users. © 2023 IEEE.

关键词： Locks (fasteners)

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A model of service-oriented architecture of e-certification system to support boat registration and site visit inspection to support maritime safety and crew health inspection

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Marine systems and Ocean technology 2023年第1-2期18卷 45-63页

作者： Saputra, Diaz Gaol, Ford Lumban Abdurachman, Edi Sensuse, Dana Indra Matsuo, Tokuro Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Binus Graduate Program - Master in Information System Bina Nusantara University Jakarta11480 Indonesia Department of Research in Management Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan City University of Macau China Asia University Taichung Taiwan

This article describes the architecture of software services that provide registration of small boat data to build maritime safety. The proposed system architecture which so-called SIMKAPEL aims to support small boat registration for maritime safety. It is developed from a conventional service-oriented architecture (SOA) with an open standard for data interoperability between elements. The goal of this research is to implement and evaluate the proposed architecture to support small boat registration and e-certification. Several parts of the architecture have been implemented as a platform that provides core services and data sources to be used by Ministry of Transportation, Directorate General of Sea Transportation, and other government agencies. Finally, the proposed SIMKAPEL platform with the SOA architecture is evaluated for its benefits to users through the expert opinion survey to measure its efficiency. The analysis results showed that SOA-based SIMKAPEL is feasible to use as a boat e-certification system. Furthermore, experiment results provide three parameters (e.g., maritime safety, namely, cargo/passengers, boat safety facilities, and maritime safety systems) with efficiency values of 42%, 46%, and 42%, respectively. The parameter with the highest efficiency shows a big effect on the level of safety of the boat and passengers. Thus, the parameters that need to be prioritized and need boat owners’ attention are cargo/passenger, boat safety equipment, and maritime safety system. © 2023, The Author(s).

关键词： Boat e-certification Boat online certification Cloud-based service Integrated system SOA architecture

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Skin Cancer Detection using Machine Learning Framework with Mobile Application 7

Skin Cancer Detection using Machine Learning Framework with ...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Emam Ananna, Mariam Nayeem, Jannatul Jahangir Alam, Mohammad Islam, Saiful Daffodil International University Computer Science and Engineering Faculty of Science and Information Technology Bangladesh

ISBN: (纸本)9798350397284

A notable increase in skin cancer mortality, one of the most lethal kinds of cancer, has been caused by a lack of awareness of warning signals and preventative measures. The need for early skin cancer diagnosis has increased because of the fast development rate of melanoma skin cancer, its high cost of treatment, and its mortality risk. Treatment of cancer cells usually requires perseverance and manual identification. This study recommends using image synthesis and machine learning techniques to develop a system for diagnosing skin cancer. Thermoscopic pictures are the input for the pre-processing phase. Following the segmentation of the thermoscopic images, the attributes of the injured skin cells are obtained using a feature extraction approach. Utilizing a convolutional neural network classifier with deep learning, the collected characteristics are stratified. An accuracy of 89% has been discovered using the publicly available dataset. © 2023 IEEE.

关键词： Feature extraction

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Optimizing the Tobacco Leaf Quality Assessment system via Grey Relational and Circle Ill-Condition Index Analysis 24

Optimizing the Tobacco Leaf Quality Assessment System via Gr...

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7th International Conference on information Management and Management science, IMMS 2024

作者： Cheng, Xijie Feng, Weiqing Pang, Su Hu, Gang Cai, Shimin College of Management Science Chengdu University of Technology China Faculty of Computer Science & Information Technology Universiti Malaya Malaysia Sichuan Tobacco Company Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9798400716997

Traditional methods for assessing tobacco leaf quality rely heavily on experience and subjective judgment, resulting in inefficiency, and a lack of accuracy and reproducibility. To address these challenges, there is an urgent need for a tobacco leaf quality assessment system capable of assessing various aspects of leaf quality in a systematic and quantitative manner. In this paper, we quantitatively analyze the tobacco leaf quality assessment system for Sichuan tobacco, specifically focusing on the Yunyan 87 variety. In further, we employ Grey Relational Analysis and Circle Ill-Condition Index Analysis for indicator selection and optimization. Through quantitative analysis, key indicators that significantly impact the assessment results and exhibit low information redundancy are retained, and weights are reallocated based on correlation analysis. The optimized system retains four core indicators: Oil Content, Delicacy, Total Nicotine, and Sugar-to-Nicotine Ratio, significantly enhancing the scientific rigor and practical utility of the assessment process. This optimized assessment tool provides a scientific basis for quality control in Sichuan tobacco production, ultimately enhancing production efficiency and product quality. © 2024 ACM.

关键词： Quality control

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Motorbike Helmet Usage Detection in Foggy Weather Conditions with Dark Channel Prior and YOLOV8

Motorbike Helmet Usage Detection in Foggy Weather Conditions...

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2024 International of Seminar on Application for technology of information and Communication, iSemantic 2024

作者： Ridho, Ihda Innar Andono, Pulung Nurtantio Purwanto Soeleman, Moch Arief Noersasongko, Edi Dian Nuswantoro University Department of Computer Science Semarang Indonesia Islamic University of Kalimantan Faculty of Information Technology Semarang Indonesia

ISBN: (纸本)9798350366648

Driving safety is crucial for maintaining road safety, and helmet use by motorcyclists is essential for reducing serious head injuries during accidents. However, compliance with helmet use remains challenging, especially in developing countries. Automatic detection of helmetless motorcyclists is, therefore, a highly relevant topic for traffic surveillance and law enforcement. One significant challenge is detecting helmetless motorcyclists in non-ideal weather conditions, such as fog, which reduces visibility and hinders visual detection. The Dark Channel Prior (DCP) technique enhances visibility in foggy conditions by exploiting the nature of foggy images, where at least one color channel (red, green, or blue) at each pixel has very low intensity. This dehazing process removes fog effects and improves image contrast. YOLOv8, an advanced object detection algorithm, utilizes an optimized convolutional neural network for real-time, high-accuracy detection of various objects, including motorcyclists and helmets. Combining DCP and YOLOv8 can create a robust system for detecting helmetless motorcyclists in various weather conditions, providing a reliable solution for law enforcement and traffic safety. This motorcycle helmet use detection system can be applied to traffic Closed-Circuit Television (CCTV) for Electronic Traffic Law Enforcement (ETLE), to facilitate authorities in enforcing applicable traffic laws. © 2024 IEEE.

关键词： Motorcycles

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Growing Greener: Exploring the Creative Potential of Deep Learning in Agricultural Disease Control

Growing Greener: Exploring the Creative Potential of Deep Le...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Rahman, Syed Ziaur Sreelatha, P. Dhanasekaran, S. Ayyasamy, Ramesh Kumar Swetha, Devulapally MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Majan University College Faculty of Information Technology Oman A.P Tirupati India Kalasalingam Academy of Research and Education Deemed to be University Department of Information Technology Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bolli Kunta India

ISBN: (纸本)9798331540685

According to estimates, by mid-2030, there will be 8.6 billion people on the planet. The human population has been growing exponentially. There will be a rise in food production and consumption due to the massive population growth. Crop disease is the primary danger to food security, which damages agricultural goods while growing. Due to the limited availability of infrastructure, it remains challenging to recognize crop-damaging diseases in various parts of the world. Deep learning methods and computer vision technologies are used in this work to help identify agricultural illnesses. The train set is used to train the model, and the data split is used for validation. The farmer can use the app (Identifying and Resolving Crop Diseases using Deep Learning) to take pictures of the crops and examine whether or not there are any infections, proving the solution's viability. © 2024 IEEE.

关键词： Plant diseases

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6G Non-Terrestrial Networks for Intelligent IoT Services

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IEEE Network 2024年第4期38卷 6-8页

作者： Jia, Min Chen, Hsiao-Hwa Chang, Zheng Zhang, Ning Wu, Zhibin Harbin Institute of Technology School of Electronics and Information Engineering National Cheng Kung University Department of Engineering Science University of Jyvaskyla Faculty of Information Technology University of Windsor Department of Electrical and Computer Engineering CETC Potevio Science & Technology Company Ltd.

Recently, the Internet of Things (IoT) has been considered for its applications in diverse settings, such as intelligent urban environments, advanced manufacturing facilities, and environmental surveillance. However, significant obstacles exist in the implementation of these IoT solutions due to various issues, such as heterogeneity, diverse application requirements, restricted resources, and massive connectivity. To tackle these issues, merging satellite-based networks with conventional ground-based networks has been proposed as an effective and affordable option for enhancing IoT device connectivity.

关键词： Special issues and sections 6G mobile communication Internet of Things Security Sensors Semantics Resource management computer architecture Collaboration Intelligent systems

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Public Decision-Making Analysis in Urban Heat Islands Based on Socioeconomic Modelling by Deep Learning Techniques

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Remote Sensing in Earth systems sciences 2024年第1期8卷 108-117页

作者： Prakash, S. Wilson Kiruthigha, R. Yadav, Ajay Kumar Kanna, S. K. Rajesh Shaik, Khasimbee Krishna, B. Murali Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Kattankulathur India Department of Information Technology SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Application United Institute of Management UP Prayagraj 211010 India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat island (UHI) effects, especially in highly urbanised areas, and greenhouse gas emissions from human activity are two elements that accelerate global climate change (GCC). Sustainable city planning and modification may benefit from the quantification of the surface UHI (SUHI) effect utilising land surface temperature (LST), local climate zones (LCZ), and deep learning methods like convolutional neural networks (CNN) and pix2pix. The findings show that socioeconomic variables account for 12–20% of variability in UHI intensity. In general, the contribution rate of urban economic scale is higher than that of population and industrial structure variables. The increase in urban economic expansion increased summertime heat discomfort. This research propose novel method in socioeconomic analysis based on urban heat pattern analysis with public healthcare system using decision-making with DL (deep learning) model. Input is collected as urban heat pattern dataset and processed for noise removal and normalisation. This data has been features are extracted and classified using convolutional fuzzy Q-Gaussian neural network and variational K-C-means regressive learning. The simulation analysis has been carried out based on training accuracy, average precision, recall, and F-measure. The proposed model attained average precision was 94%, recall was 90%, training accuracy was 97%, and F1-score was 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Decision-making Deep learning model Public healthcare system Socioeconomic analysis Urban heat pattern

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LCEFL: A Lightweight Contribution Evaluation Approach for Federated Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6643-6657页

作者： Guo, Jingjing Li, Jiaxing Liu, Zhiquan Xiong, Yupeng Ma, Yong Vasilakos, Athanasios V. Li, Xinghua Ma, Jianfeng Xidian University School of Cyber Engineering Xi’an710071 China Jinan University College of Cyber Security Guangzhou510632 China Jiangxi Normal University Department of Computer Science and Technology Nanchang330022 China Department of Networks and Communications College of Computer Science and Information Technology Dammam31441 Saudi Arabia Grimstad4879 Norway

The prerequisite for implementing incentive mechanisms and reliable participant selection schemes in federated learning is to obtain the contribution of each participant. Available evaluation methods for participant contributions require the server to possess a test dataset, often impractical. Additionally, the excessively high complexity of these works is unacceptable when training complex models in large-scale federated learning system. To address these issues, we propose a lightweight contribution evaluation method for federated learning participants, named LCEFL, based on model projection theory, which does not require the server to provide a test dataset. In addition, a model compression method is designed to be used in LCEFL to reduce the computational complexity. Furthermore, a trusted aggregation method based on LCEFL is proposed, where the weight of each participant's local model is determined by its trust level, which can be calculated using its contribution evaluation result. Experimental results show that LCEFL can achieve nearly the same accuracy as schemes based on Shapley Value, while significantly reducing computational overhead by more than 50%. Compared to available aggregation methods, the proposed trusted aggregation scheme is able to accelerate the convergence speed of the global model and improve its accuracy by 2% to 45%. © 2002-2012 IEEE.

关键词： Federated Learning Computational Modeling Accuracy Servers Training Data Models Machine Learning Mobile Computing Particle Measurements Convergence Federated Learning Contribution Evaluation Trust Evaluation Model Projection Theory Secure Aggregation Federated Learning Computational Complexity Test Dataset Global Model Selection Of Participants Model Projections Aggregation Method Shapley Value Speed Of The Model Contributions Of Participants Number Of Participants Accuracy Of Model Computational Cost Local Data Learning Task Layer Model Participation Outcomes Updated Model Value Of Participation Model Aggregation Server Aggregates Global Convergence Iterative Rounds Trust Value Local Updates MNIST Dataset Total Amount Of Data Local Training Aggregation Algorithm Complex Model Structure

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