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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Andreea Elena Țîru Iulian Vasiliev Larisa Diaconu Ramon Vilanova Daniel Voipan Harsha Ratnaweera Department of Automatic Control and Electrical Engineering "Dunărea de Jos" University of Galaţi Galaţi Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain Department of Computer Science and Information Technology "Dunărea de Jos" University of Galaţi Galaţi Romania Faculty of Science and Technology (Realtek) Norwegian University of Life Sciences Ås Norway

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so.

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Joint Parking and Power Management for Electric Vehicle Edge Computing: A Bilevel Optimization Approach

Joint Parking and Power Management for Electric Vehicle Edge...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Xumin Huang Weifeng Zhong Jiangtian Nie Jiawen Kang Zehui Xiong Yuan Wu Mohsen Guizani School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China School of Computer Science and Engineering Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Computer and Information Science University of Macau Macau China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

With the vehicle-to-grid and computing capabilities, a parked electric vehicle (EV) has a dual role, namely being an energy prosumer as well as a computing node for accommodating computation-offloading services. This dual-role feature of EVs yields a new computing paradigm named Electric Vehicle Edge Computing (EVEC). To ease the implementation of EVEC, we propose a fine-grained EV management approach to jointly provide parking guidance for EVs and control their charging/discharging power in parking lots. We formulate a bilevel optimization problem where the top-level problem optimizes the matching between EVs and parking lots from the perspective of computation offloading, and the bottom-level problem optimizes the control of EV charging/discharging power from the view of power networks. We transform the bilevel optimization problem into a single-level form, which is a nonconvex mixed-integer nonlinear programming problem, and we further tackle it by linearization techniques. Finally, we provide numerical results to demonstrate the efficiency and effectiveness of our approach.

关键词： Wireless communication Vehicle-to-grid Systematics Power system management Computational modeling Transforms Relaxation methods

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Logical-combinatorial approaches in dynamic recognition problems

arXiv

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arXiv 2021年

作者： Aslanyan, Levon H. Krasnoproshin, Viktor V. Ryazanov, Vladimir V. Sahakyan, Hasmik A. Institute for Informatics and Automation Problems NAS RA Armenia Belarusian State University Department of Information Management Systems Belarus Dorodnitsyn Computing Centre Federal Research Center Computer Science and Control RAS Russia

A pattern recognition scenario, where instead of object classification into the classes by the learning set, the algorithm aims to allocate all objects to the same, the so-called "normal" class, is the research objective. Given the learning set L;the class K! is called "normal", and the reminder l classes K", K#,..., K$ from the environment K are "deviated". The classification algorithm is for a recurrent use in a "classification, action" format. Actions A% are defined for each "deviated" class K%. Applied to an object X ∈ K%, the action delivers update A%(x) of the object. The goal is in constructing a classification algorithm that applied repeatedly (small number of times) to the objects of L, moves the objects (correspondingly, the elements of K) to the "normal" class. In this way, the static recognition action is transferred to a dynamic domain. This paper is continuing the discussion on the "normal" class classification problem, its theoretical postulations, possible use cases, and advantages of using logical-combinatorial approaches in solving these dynamic recognition problems. Some light relation to the topics like reinforcement learning, and recurrent neural networks are also provided. Copyright © 2021, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Implementation of a Human-Robot Collaboration System Based On Smart Hand Gesture and Speech Recognition

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Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao 2020年第6期41卷 755-762页

作者： Chen, Shang-Liang Huang, Li-Wu Huang, Chung-Chi Lee, Feng-Chi Huang, Ho-Chuan Chen, Chien-Yu Institute of Manufacturing Information and Systems National Cheng Kung University Tainan70101 Taiwan Department of Automation and Control Engineering Far East University Tainan74448 Taiwan Department of Mechatronics Control Industrial Technology Research Institute Hsinchu310 Taiwan Department of Intelligent Commerce National Kaohsiung University of Applied Sciences Kaohsiung824 Taiwan Computer Science and Information Engineering National University of Tainan Tainan70005 Taiwan

With the development of artificial intelligence technology, the robot arm will be applied in more fields, undertake more work, and become an important assistant of humans. Especially in the manufacturing industry, if a perfect human-robot cooperation (HRC) model can be developed, in which the engineer is responsible for complex operations and monitoring the production process and the robot arm is responsible for highly repetitive and tiring work, the flexibility of the manufacturing process can be improved, the risk of personnel injury can be reduced, and the production line productivity can be improved. To realize the human-robot collaboration system that can be applied in various fields, the technologies of robot arm control, human-robot interaction (HRI), Radio Frequency Identification, machine-to-machine communication, and automatic speech recognition (ASR), etc. are combined in this research. Besides, the HRC in intelligent manufacturing is taken as the demonstration result of this research. In this application example, the human-robot interaction function is realized through smart hand gesture and speech recognition technology, which enables the user to communicate commands to the robot arm most intuitively and easily. The robot arm can execute specific actions, provide processing prompts, or read corresponding motion control command macros according to different commands, to realize various processing contents required by users. © 2020, Chinese Mechanical Engineering Society. All right reserved.

关键词： Radio frequency identification (RFID)

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Designing planar cubic B-spline curves with monotonic curvature for curve interpolation

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Computational Visual Media 2020年第3期6卷 349-354页

作者： Aizeng Wang Chuan He Fei Hou Zhanchuan Cai Gang Zhao School of Mechanical Engineering&Automation Beihang UniversityBeijing 100191China State Key Laboratory of Virtual Reality Technology&Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

Monotonic curvature plays an important role in industrial design and styling of curves with aesthetic shapes,e.g.,in automobile and aircraft design[1].Used in conventional parametric CAD/CAM systems,general B-splines are not adequate for aesthetic *** for the straight line and circle,monotonic curvature distribution,associated with pleasing shape,is very difficult to achieve.

关键词： curvature monotonic curves

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A review of Implementation and Challenges of Unmanned Aerial Vehicles for Spraying Applications and Crop Monitoring in Indonesia

arXiv

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arXiv 2023年

作者： Fikri, Muhamad Rausyan Candra, Taufiq Saptaji, Kushendarsyah Noviarini, Ajeng Nindi Wardani, Dilla Ayu Automation Technology and Mechanical Engineering Faculty of Engineering and Science Tampere University Tampere33720 Finland Information Systems Faculty of Engineering and Technology Sampoerna University Jakarta12780 Indonesia Industrial Engineering Faculty of Engineering and Technology Sampoerna University Jakarta12780 Indonesia Mechanical Engineering Faculty of Engineering and Technology Sampoerna University Jakarta12780 Indonesia Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta12780 Indonesia

The rapid development of technology has brought unmanned aerial vehicles (UAVs) to become widely known in the current era. The market of UAVs is also predicted to continue growing with related technologies in the future. UAVs have been used in various sectors, including livestock, forestry, and agriculture. In agricultural applications, UAVs are highly capable of increasing the productivity of the farm and reducing farmers' workload. This paper discusses the application of UAVs in agriculture, particularly in spraying and crop monitoring. This study examines the urgency of UAV implementation in the agriculture sector. A short history of UAVs is provided in this paper to portray the development of UAVs from time to time. The classification of UAVs is also discussed to differentiate various types of UAVs. The application of UAVs in spraying and crop monitoring is based on the previous studies that have been done by many scientific groups and researchers who are working closely to propose solutions for agriculture-related issues. Furthermore, the limitations of UAV applications are also identified. The challenges in implementing agricultural UAVs in Indonesia are also presented. Copyright © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO systems Under High-Mobility Scenarios

arXiv

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arXiv 2024年

作者： Zhou, Binggui Yang, Xi Ma, Shaodan Gao, Feifei Yang, Guanghua School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China Department of Automation Tsinghua University Beijing100084 China School of Intelligent Systems Science and Engineering Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics Jinan University Zhuhai519070 China

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge- and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency estimation

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Privacy-preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet

arXiv

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arXiv 2022年

作者： Xu, Minrui Niyato, Dusit Yang, Zhaohui Xiong, Zehui Kang, Jiawen Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China Zhejiang Laboratory Hangzhou31121 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore 487372 Singapore The School of Automation Guangdong University of Technology China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL is still facing model confidentiality issues due to eavesdropping risks of exchanging cryptographic keys through traditional encryption schemes. Therefore, in this paper, we propose a hierarchical architecture for quantum-secured FEL systems with ideal security based on the quantum key distribution (QKD) to facilitate public key and model encryption against eavesdropping attacks. Specifically, we propose a stochastic resource allocation model for efficient QKD to encrypt FEL keys and models. In FEL systems, remote FEL workers are connected to cluster heads via quantum-secured channels to train an aggregated global model collaboratively. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is unpredictable. The proposed systems need to efficiently allocate limited QKD resources (i.e., wavelengths) such that the total cost is minimized in the presence of stochastic demand by formulating the optimization problem for the proposed architecture as a stochastic programming model. To this end, we propose a federated reinforcement learning-based resource allocation scheme to solve the proposed model without complete state information. The proposed scheme enables QKD managers and controllers to train a global QKD resource allocation policy while keeping their private experiences local. Numerical results demonstrate that the proposed schemes can successfully achieve the cost-minimizing objective under uncertain demand while improving the training efficiency by about 50% compared to state-of-the-art schemes. © 2022, CC BY.

关键词： Stochastic systems

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A Novel Scalable Framework For Constructing Dynamic Multi-objective Optimization Problems

A Novel Scalable Framework For Constructing Dynamic Multi-ob...

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Congress on Evolutionary Computation

作者： Qingshan Tan Changhe Li Hai Xia Sanyou Zeng Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Modeling dynamic multi-objective optimization problems (DMOPs) has been one of the most challenging tasks in the field of dynamic evolutionary optimization. Based on the analysis of the existing DMOPs, several features widely existed in real-world applications are not taken into account: different objectives may have different function models and variables to be optimized; and the number of conflicting variables should be independent from the number of objectives; the time-linkage property is not considered. In order to overcome the above issues, a novel framework for constructing DMOPs is proposed, where all objectives can be designed independently, and the number of the conflicting variables can be tuned by users. Moreover, it is easy to add new dynamic features to this framework. Several classical dynamic multi-objective optimization algorithms are tested on four scenarios, results show that these characteristics are challenging for the existing algorithms.

关键词： Schedules Analytical models Heuristic algorithms Object oriented modeling Evolutionary computation Benchmark testing Dynamic scheduling

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A Reinforcement-Learning-Based Evolutionary Algorithm Using Solution Space Clustering For Multimodal Optimization Problems

A Reinforcement-Learning-Based Evolutionary Algorithm Using ...

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Congress on Evolutionary Computation

作者： Hai Xia Changhe Li Sanyou Zeng Qingshan Tan Junchen Wang Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

In evolutionary algorithms, how to effectively select interactive solutions for generating offspring is a challenging problem. Though many operators are proposed, most of them select interactive solutions (parents) randomly, having no specificity for the features of landscapes in various problems. To address this issue, this paper proposes a reinforcement-learning-based evolutionary algorithm to select solutions within the approximated basin of attraction. In the algorithm, the solution space is partitioned by the k-dimensional tree, and features of subspaces are approximated with respect to two aspects: objective values and uncertainties. Accordingly, two reinforcement learning (RL) systems are constructed to determine where to search: the objective-based RL exploits basins of attraction (clustered subspaces) and the uncertainty-based RL explores subspaces that have been searched comparatively less. Experiments are conducted on widely used benchmark functions, demonstrating that the algorithm outperforms three other popular multimodal optimization algorithms.

关键词： Uncertainty Clustering algorithms Evolutionary computation Reinforcement learning Benchmark testing Search problems Feature extraction

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