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Model and Data Hybrid Reinforcement Learning for Optimal Voltage-Current Control of Hybrid Active Power Filter

IEEE Transactions on Power Electronics 2025年

作者： Gong, Cheng Lam, Chi-Seng University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI 999078 China University of Macau Institute of Microelectronics 999078 China University of Alberta Department of Electrical and Computer Engineering EdmontonAB Canada University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology 999078 China

In this paper, a novel design for optimal voltage and current control (OVCC) is introduced, employing a hybrid model-data reinforcement learning (MDRL) approach for hybrid active power filters (HAPF). High steady-state accuracy and rapid dynamic response are achieved by the MDRL-based OVCC, which also maintains low sampling and computational demands. The limitations of dynamic response, inherent in traditional control methods that require significantly lower bandwidth for DC voltage control compared to AC current control, are effectively addressed. Initially, a circuit configuration and mathematical model for the HAPF are proposed. Subsequently, an MDRL-OVCC structure for optimal control is established. The training of the MDRL-OVCC is conducted using an online robust policy iteration method, and the safety of the system is ensured through a proposed robust stability analysis. Comprehensive simulation and experimental results are presented, demonstrating the superior performance of the MDRL-OVCC compared to existing state-of-the-art methods. © 1986-2012 IEEE.

关键词： Optimal control systems

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

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Mediterranean Conference on Control and Automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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Three-Dimensional Spatial-Temporal Near-Field Passive Localization Based on an Exact Spatial Propagation Model

Three-Dimensional Spatial-Temporal Near-Field Passive Locali...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiaxiong Fang Juan Liu Hua Chen Wei Liu Ye Tian Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

Based on the exact source-sensor spatial geometry, a three-dimensional (3-D) spatial-temporal localization algorithm for multiple near-field (NF) sources is proposed without adopting the Fresnel approximation, which simplifies the spatial phase difference by Taylors polynomial. In addition, considering the propagation attenuation which varies from different sensors, the spatial and temporal information can be exploited to construct a third-order parallel factor (PARAFAC) data model and the array manifold matrices can be extracted by trilinear decomposition; then, estimation of the unambiguous range and angle parameters of the NF sources is achieved from the spatial amplitude-phase factors by the least squares method. The obtained 3-D parameters associated with each source require no additional pairing process, as also demonstrated by simulation results.

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Hybrid polynomial-based trajectory planning for lower limb exoskeleton robots 18

Hybrid polynomial-based trajectory planning for lower limb e...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Li, Shupei Zhang, Chi Yi, Feng Lv, Pingping Yuan, Ting Zhang, Meitong University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315201 China Hebei University of Science & Technology School of Electrical Engineering Ningbo315201 China

ISBN: (纸本)9798350312201

To ensure smooth, continuous, and precise motion of lower limb exoskeleton robots, this paper proposes a 6-5-6 polynomial trajectory planning method (6-5-6 PTPM) that combines 5th and 6th polynomials. By imposing corresponding kinematic constraints, this method achieves the trajectory plan of seven sub-phases of the gait cycle on the kinematic model of the lower limb exoskeleton robot. The simulation is conducted and compared to the 4-3-4 polynomial trajectory planning method (4-3-4 PTPM). The comparison results indicate that the 6-5-6 PTPM is shown to have a lower error between the planned trajectory and the actual trajectory. Additionally, it avoids spikes of the acceleration curve that existed in the 4-3-4 polynomial method. The proposed method enables the acceleration curve smoother and mechanical impact lessened, which demonstrates the feasibility and stability of the proposed method. © 2023 IEEE.

关键词： Trajectories

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Energy Joint Scheduling of Isolated Islands Considering Low-Density Production Period of Renewable Energy

SSRN

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SSRN 2024年

作者： Sun, Chengcheng Bai, Chunhan Wei, Fanrong Lin, Xiangning Khalid, Muhammad S. Dawoud, Samir M. State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Hubei Province Wuhan430074 China Department of Electrical Engineering National University of Computer and Emerging Sciences Faisalabad Pakistan Department of Electrical Power and Machines Engineering Faculty of Engineering Tanta University Tanta Egypt

In view of the special dispatching demand of isolated islands in the low-density period of renewable energy power generation, the defects of traditional dispatching mode applying in isolated power generation system is analyzed, and the idea of reasonably extending the daily scheduling cycle is proposed to adapt to the application of flexible energy resources in the form of energy package under various uncertain scenarios. Under the multi-party cooperative power supply strategy of isolated islands, we analyze the shortcomings of key element modeling. The global optimal model of energy scheduling of isolated islands considering low-density energy output period is constructed based on refined element model, and the corresponding solution method is proposed for the nonlinear constraints. The reasonability and effectiveness of the refined model, global optimal model and the assumption of extended scheduling cycle are verified by theoretical analysis and case simulation. © 2024, The Authors. All rights reserved.

关键词： Energy resources

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Experimental Study on Motion Controller based on NN-ARX and ARMAX Actuator Identification for 3-DoF Delta Parallel Robot

Experimental Study on Motion Controller based on NN-ARX and ...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Hasan Jalali Hossein Damavandi Ahmad Kalhor Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers.

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Integrating Sustainability and Industry 4.0: A Framework for Sustainable Logistics 4.0

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Circular Economy and Sustainability 2025年 1-39页

作者： Di Nardo, Mario Gallab, Maryam Murino, Teresa Wu, Jing Pandey, Shatrudhan Facoltàdi Ingegneria Università Telematica Pegaso Rome 00186 Italy MIS-LISTD Laboratory Computer Science Department Mines-Rabat School (ENSMR) Rabat 10000 Morocco Department of Chemical Materials and Industrial Production Engineering University of Naples “Federico II” Naples Italy Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. Lyngby 2800 Denmark Marwadi University Research Center Faculty of Management Studies Marwadi University Gujarat Rajkot 360003 India

As society becomes more aware of environmental and social issues, supply chain stakeholders increasingly consider these factors alongside efficiency, cost, and time. The logistics industry must adapt by integrating Industry 4.0 technologies and sustainability practices. Sustainable Logistics 4.0, though nascent, is gaining traction for enhancing sustainability through digital interventions. This study conducted a comprehensive literature review and bibliometric network analysis using data extracted from the Scopus and Google Scholar databases, covering the period from 2013 to 2023, focusing on the integration of sustainability, Industry 4.0, and logistics. Noting a gap in conceptual frameworks, the paper performs a SWOT analysis to identify key factors and proposes a framework for digitalizing sustainable logistics and addressing emerging challenges. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Circular Economy Industry 4.0 Logistics Sustainability TBL

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Multiplication Complexity Optimization based on Quantified Boolean Formulas

Multiplication Complexity Optimization based on Quantified B...

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Electronics Design Automation (ISEDA), International Symposium of

作者： Jun Zhu Hongyang Pan Zhufei Chu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China State Key Laboratory of Integrated Chips and System School of Microelectronics Fudan University Shanghai China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

In the field of cryptography and security, minimizing the number of AND gates within logic networks is crucial. Such optimization significantly influences the multiplication complexity of circuits. This paper proposes a Quantified Boolean Formulas (QBF) based resynthesis to dynamically construct local circuits from sub-circuits, with the goal of minimizing the number of AND gates and optimizing the number of XOR gate numbers in well-optimized networks. Experimental results on parts of the EPFL benchmarks suite indicate that the proposed method effectively reduces the number of AND gates by 15.51 % and the number of XOR gates by 3.34%.

关键词： Design automation Circuits Logic gates Benchmark testing Complexity theory Logic Cryptography

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Cloud and Edge based Smart Agriculture: A Real-World Deployment

Cloud and Edge based Smart Agriculture: A Real-World Deploym...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Et-Taibi, Bouali Abid, Mohamed Riduan Boufounas, El-Mahjoub Benhaddou, Driss Moulay Ismail University of Meknès Reipt Laboratory Faculty of Sciences and Technology of Errachidia Morocco Al Akhawayn University in Ifrane School of Science and Engineering Ifrane53000 Morocco Columbus State University Tsys School of Computer Science ColumbusGA31907 United States University of Houston Department of Engineering Technology 4800 Calhoun Rd HoustonTX77004 United States Alfaisal University College of Engineering Electrical Engineering Department Riyadh Saudi Arabia

ISBN: (纸本)9798350365306

Smart Agriculture presents significant opportunities for transforming farming methods, although its widespread adoption remains constrained by cost barriers, especially for small and medium-sized farmers. To bridge this divide, we propose a cost-effective solution tailored specifically for soilless cultivation within drip irrigation systems. Our approach harnesses the power of IoT, cloud computing, and edge computing to bolster connectivity and streamline data management. Central to our solution is a three-tiered open-source software architecture, which encompasses local sensors, edge computing, and cloud computing layers. Locally deployed Wireless Sensor Networks facilitate real-time monitoring and data acquisition, while the edge tier orchestrates irrigation tasks in the field using Fuzzy Logic algorithms. The cloud layer serves as the backbone for data analytics, storage, and visualization, ensuring optimal resource management. Through the utilization of IoT protocols, seamless communication is established between sensors, edge devices, and the cloud, enabling efficient data exchange and analysis. Our tangible prototype validates the effectiveness of this platform in optimizing drip irrigation systems. This offering provides an accessible solution for small and medium-scale farmers, empowering them to enhance farming productivity and sustainability. © 2024 IEEE.

关键词： Smart agriculture

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An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

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Automotive Innovation 2022年第2期5卷 134-145页

作者： Huanyang Huang Jinhao Meng Yuhong Wang Lei Cai Jichang Peng Ji Wu Qian Xiao Tianqi Liu Remus Teodorescu College of Electrical Engineering Sichuan UniversityChengduChina Faculty of Computer Science and Engineering Shanxi Key Laboratory for Network Computing and Security TechnologyXi’an University of TechnologyXi’anChina Smart Grid Research Institute Nanjing Institute of TechnologyNanjingChina Department of Automation University of Science and Technology of ChinaHefeiChina Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjinChina Department of Energy Technology Aalborg University9220 AalborgDenmark

In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management *** paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized *** Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the *** for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high *** the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing *** of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.

关键词： Li-ion battery State of health Gaussian process regression Kernel function Feature optimization

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