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A direct predictive DC-link voltage control with warm-starting iterative approach for three-level NPC BTB converter fed PMSG-based wind turbine systems

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Expert Systems with Applications 2025年 283卷

作者： Ganesh, Mayilsamy Jeong, Jae Hoon Research Center for Wind Energy systems School of IT Information and Control Engineering Kunsan National University 588 Daehak-ro Gunsan-si Jeonbuk54150 Korea Republic of College of Computer and Software Kunsan National University 588 Daehak-ro Gunsan-si Jeonbuk54150 Korea Republic of

This article presents a direct predictive DC-link voltage control (DPVC) scheme for a three-level (3L) neutral point clamped (NPC) back-to-back (BTB) converter fed permanent magnet synchronous generator (PMSG)-based wind turbine system (WTS). The conventional 3L NPC BTB converter employs an external PI controller to regulate the DC-link voltage at the set point by generating power or current references for the cascaded finite control set (FCS) predictive control. The presented strategy incorporates the DC-link voltage regulation term into the power regulation term in the grid-side converter (GSC) cost function, identifying the optimum switching state that directly regulates the DC-link voltage at the set point. Through this, the required active power is transferred to the grid to maintain power balance, and the conventional cascaded control structure is eliminated. In addition, a warm-starting iterative approach (WSIA) is introduced to derive the switching states employed for each switching instant. This reduces computation in both the GSC and machine-side converter (MSC) control and inherently incorporates switching frequency optimization. The presented scheme is validated in 3 MW rated WTS simulations under various operating conditions, with comparative studies performed to demonstrate its significance. © 2025 Elsevier Ltd

关键词： Predictive control systems

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Finite Sample Analysis of Subspace Identification Methods

arXiv

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arXiv 2025年

作者： He, Jiabao Ziemann, Ingvar Rojas, Cristian R. Qin, S. Joe Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden University of Pennsylvania PhiladelphiaPA19104 United States Institute of Data Science Lingnan University Hong Kong

As one of the mainstream approaches in system identification, subspace identification methods (SIMs) are known for their simple parameterization for MIMO systems and robust numerical properties. However, a comprehensive statistical analysis of SIMs remains an open problem. Amid renewed focus on identifying state-space models in the non-asymptotic regime, this work presents a finite sample analysis for a large class of open-loop SIMs. It establishes high-probability upper bounds for system matrices obtained via SIMs, and reveals that convergence rates for estimating Markov parameters and system matrices are O(1/√N) up to logarithmic terms, in line with classical asymptotic results. Following the key steps of SIMs, we arrive at the above results by a three-step procedure. In Step 1, we begin with a parsimonious SIM (PARSIM) that uses least-squares regression to estimate multiple high-order ARX models in parallel. Leveraging a recent analysis of an individual ARX model, we obtain a union error bound for a bank of ARX models. Step 2 involves model reduction via weighted singular value decomposition (SVD), where we consider different data-dependent weighting matrices and use robustness results for SVD to obtain error bounds on extended controllability and observability matrices, respectively. The final Step 3 focuses on deriving error bounds for system matrices, where two different realization algorithms, the MOESP type and the Larimore type, are considered. Although our study initially focuses on PARSIM, the methodologies apply broadly across many variants of SIMs. Copyright © 2025, The Authors. All rights reserved.

关键词： MIMO systems

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A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing

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Tsinghua Science and Technology 2016年第1期21卷 71-79页

作者： Rui Wang Jinglei Zhang Suli Ren Qingjuan Li School of Computer and Communication Engineering University of Science and Technology Beijing School of Computer and Control Engineering University of Chinese Academy of Sciences

In recent years, Compressed Sensing（CS） has been a hot research topic. It has a wide range of applications, such as image processing and speech signal processing owing to its characteristic of removing redundant information by reducing the sampling rate. The disadvantage of CS is that the number of iterations in a greedy algorithm such as Orthogonal Matching Pursuit（OMP） is fixed, thus limiting reconstruction ***, in this study, we present a novel Reducing Iteration Orthogonal Matching Pursuit（RIOMP） algorithm that calculates the correlation of the residual value and measurement matrix to reduce the number of *** conditions for successful signal reconstruction are derived on the basis of detailed mathematical *** compared with the OMP algorithm, the RIOMP algorithm has a smaller reconstruction error. Moreover, the proposed algorithm can accurately reconstruct signals in a shorter running time.

关键词： compressed sensing signal processing wireless sensor networks

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A novel digital twin-enabled three-stage feature imputation framework for non-contact intelligent fault diagnosis

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Advanced engineering Informatics 2025年 66卷

作者： Yu, Yue Karimi, Hamid Reza Gelman, Len Liu, Xin Department of Mechanical Engineering Politecnico di Milano via La Masa 1 Milan20156 Italy School of Computing and Engineering University of Huddersfield Queensgate HuddersfieldHD1 3DH United Kingdom Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao06600 China

Vibration-based fault diagnosis methods are widely used in industrial applications due to their high accuracy and reliability. However, their implementation is often hindered by challenges such as confined spaces, harsh environmental conditions, and cost limitations. Moreover, traditional deep learning-based approaches frequently overlook the critical relationship between virtual and physical signals, which can limit their diagnostic performance. To overcome these challenges, this study introduces a novel digital twin-driven feature imputation framework (NIFD-Net) for non-contact intelligent fault diagnosis. The NIFD-Net framework consists of three key stages: (1) codebook generation, where features from simulated vibration signals (SVS) and non-contact signals (NCS) are compressed and regularized using diagnosis-oriented codebooks;(2) mapping construction, where a Transformer is employed to establish the relationship between SVS and NCS in a compressed feature space;and (3) diagnostic enhancement, where imputed SVS features are integrated with original NCS features to improve diagnostic performance. First, digital twin (DT)-enabled models, developed using structural parameters and fault characteristics, dynamically simulate the operating conditions of rolling bearings to generate high-quality SVS samples. The relationship between the two feature spaces of SVS and NCS is then learned by a transformer to impute the SVS features. Finally, the imputed SVS features are integrated with the original NCS features to enhance fault diagnostic performance. Extensive experiments on three case studies demonstrate that NIFD-Net outperforms state-of-the-art methods, achieving an average accuracy of 98.34% across diverse tasks. Notably, the framework exhibits strong noise robustness, maintaining high diagnostic performance even under adverse conditions (e.g., SNR = -10 dB). The results highlight the effectiveness of integrating simulated and real signals for non-contact fault diag

关键词： Vibrations (mechanical)

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Joint Sensing, Communication and Computation for Status Update in Mobile Edge Computing with Non-Orthogonal Multiple Access

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IEEE Internet of Things Journal 2025年

作者： Zhang, Jianfei Wang, Yufan Hu, Yun Chang, Zheng Qiqihar University College of Computer and Control Engineering Qiqihar 161003 China Xidian University National Demonstration Center for Experimental Electronic Information and Communications Engineering Education Xi’an 710071 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu 611731 China University of Jyväskylä Faculty of Information Technology Jyväskylä 40014 Finland

Mobile Edge Computing (MEC) is considered as a promising solution for augmenting the computational capabilities of Internet of Things (IoT) devices by offloading tasks to nearby edge servers (ESs). However, finite communication and computational resources at both IoT devices and ESs, coupled with escalating congestion as more devices connect, present critical challenges in maintaining low latency and high efficiency. In this work, we jointly design the task sensing, communication, and resource allocation for MEC with Non-Orthogonal Multiple Access (NOMA). To address the urgent need for timely data processing in IoT applications, we utilize the Age of Information (AoI) metric as a measure of data freshness. With the objective to minimize the system cost, we propose to jointly optimize sensing sampling intervals, sensing frequencies, offloading decision, and power allocation. Recognizing that this problem is NP-hard, we decompose it incrementally and propose a High-Dimensional Progressive Cost Optimization (HDPCO) algorithm to reduce overall system cost. Simulation results confirm the effectiveness of HDPCO, showing significant improvements in minimizing overall system cost compared to other proposed schemes. © 2014 IEEE.

关键词： computation offloading HDPCO joint optimization mobile edge compute Non-Orthogonal Multiple Access

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Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Design of Nanorobots for an Early Detection of Alzhimer's Disease

Design of Nanorobots for an Early Detection of Alzhimer's Di...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Yosra Ben Fadhel Khawla Kachroudi Achraf Derbel Hedi Trabelsi Nabil Derbel University of Tunis El Manar Higher Institute of Medical Technologies Tunisia Departement of Computer Systems Eng. Mediterranean Institute of Technology South Mediterranean University Tunisia University of Sfax National Engineering School of Sfax Control & Energy Management Laboratory Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

In this study, we propose a novel nanorobot model for the early detection of Alzheimer's disease. As a first step, we explored the polymorphism of the TREM2 gene's exons to assess its association with Alzheimer's. Our case-control study involved 124 Tunisian participants-64 Alzheimer's patients and 60 age-matched controls, averaging 67 years. Each participant underwent neurological evaluation, neuropsychological testing, brain imaging, and molecular analysis. Using Polymerase Chain Reaction (PCR) and sequencing techniques, we identified the rs2234253 variant in exon 2 and a novel intronic variant in intron 4, both of which were significantly correlated with Alzheimer's risk. Based on these genetic findings, we designed a nanorobot capable of detecting early biomarkers of Alzheimer's disease, such as amyloid plaques and tau tangles. The nanorobots are approximately 100 nm in size, enabling them to navigate the blood-brain barrier and specifically target regions of the brain affected by Alzheimer's pathology. They are constructed from biocompatible materials, such as gold nanoparticles and silica, and functionalized with surface receptors that selectively bind to Alzheimer's-related biomarkers. Equipped with nanosensors, the nanorobots are designed to detect binding events and transmit data to an external monitoring system in real time, facilitating early diagnosis and continuous monitoring of disease progression. These technological advancements offer a promising approach to enhancing Alzheimer's detection and improving patient out-comes.

关键词： Pathology Sequential analysis Biomarkers Real-time systems Safety Polymers Alzheimer's disease Monitoring Silicon dioxide Testing

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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Blockage-Resilient Integrated Sensing and Communication in mmWave Networks: Multi-View Collaboration and Efficient Task Allocation

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IEEE Transactions on Mobile Computing 2025年

作者： Cui, Yue Ding, Haichuan Ma, Ying Li, Xuanheng Zhang, Haixia Fang, Yuguang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Dalian University of Technology School of Information and Communication Engineering Dalian116023 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Shandong University Shandong Provincial Key Laboratory of Wireless Communication Technologies School of Control Science and Engineering Jinan250061 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Integrated sensing and communication (ISAC) has emerged as a promising technology for future millimeter wave (mmWave) networks. However, the susceptibility of mmWave signals to blockages poses considerable challenges for ISAC as it can result in unreliable links and disrupted sensing. As a result, this paper investigates the blockage-resilient ISAC design that leverages the robustness offered by multi-base station (BS) collaboration. Given the dynamic blockages and the fluctuation in the targets' radar cross section (RCS), the blockage-resilient multiBS collaborative ISAC design is cast as a chance constrained integer programming (CCIP) by jointly considering the diverse deadlines of different sensing tasks and the spatial/temporal user-target pairing for dual-functional radar and communication (DFRC) waveform scheduling. To facilitate efficient solution finding, we develop a group concatenating assisted reinforcement learning (GCRL) algorithm, where we linearize the chance constraints via variable grouping and concatenation, enabling the RL agent to understand the problem structure with bipartite graphs so as to develop an efficient branching policy. Extensive experiments demonstrate the resilience of the obtained ISAC scheme to dynamic blockages. © 2025 IEEE. All rights reserved.

关键词： Integer programming

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Cloud-based parallel power flow calculation using resilient distributed datasets and directed acyclic graph

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Journal of Modern Power Systems and Clean Energy 2019年第1期7卷 65-77页

作者： Dewen WANG Fangfang ZHOU Jiangman LI School of Control and Computer Engineering North China Electric Power University

With the integration of distributed generation and the construction of cross-regional long-distance power grids, power systems become larger and more *** require faster computing speed and better scalability for power flow calculations to support unit *** on the analysis of a variety of parallelization methods, this paper deploys the large-scale power flow calculation task on a cloud computing platform using resilient distributed datasets(RDDs).It optimizes a directed acyclic graph that is stored in the RDDs to solve the low performance problem of the MapReduce *** paper constructs and simulates a power flow calculation on a large-scale power system based on standard IEEE test *** are conducted on Spark cluster which is deployed as a cloud computing *** show that the advantages of this method are not obvious at small scale, but the performance is superior to the stand-alone model and the MapReduce model for large-scale *** addition, running time will be reduced when adding cluster *** not tested under practical conditions, this paper provides a new way of thinking about parallel power flow calculations in large-scale power systems.

关键词： Power flow calculation Parallel programming model Distributed memory-shared model Resilient distributed datasets(RDDs) Directed acyclic graph(DAG)

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