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IEEE International Conference on Industrial technology (ICIT)

作者： Shuo Tian Helin Wang Hailong Zhang Department of Information and Control Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

A constant current (CC) and constant voltage (CV) charging topology applied to the bidirectional wireless power transfer system for electric vehicles (EVs) is presented in this paper. It only needs to adjust the switching state of a relay to realize the CC output of inductor-capacitor-capacitor - inductor-capacitor-capacitor (LCC-LCC) and CV output of inductor-capacitor-capacitor-series (LCC-S) and series- inductor-capacitor-capacitor (S-LCC). Compared with the other topologies, redundant components are not required, and the switching frequency is fixed under the operating process. Under the misalignment condition, the CC and CV output can be regulated by applying the phase-shift control. The proposed reconfigurable topology can realize bidirectional CC and CV charging without communication between primary and secondary side. A 6-KW prototype is built in the laboratory to verify the effectiveness of the topology, and the maximum effective is 94%.

关键词： Analytical models Voltage measurement Switching frequency Current measurement Inductive charging Prototypes Switches Wireless power transfer Topology Relays

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Distributional Uncertainty Propagation via Optimal Transport

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IEEE Transactions on Automatic control 2025年

作者： Aolaritei, Liviu Lanzetti, Nicolas Chen, Hongruyu Dorfler, Florian Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Switzerland

This paper addresses the limitations of standard uncertainty models, i.e., robust (norm-bounded) and stochastic (one fixed distribution, e.g., Gaussian), and proposes to model uncertainty via Optimal Transport (OT) ambiguity sets. These constitute a very rich uncertainty model, which enjoys many desirable geometrical, statistical, and computational properties, and which: (1) naturally generalizes both robust and stochastic models, and (2) captures many additional real-world uncertainty phenomena (e.g., black swan events). Our contributions show that OT ambiguity sets are also analytically tractable: they propagate easily and intuitively through linear and nonlinear (possibly corrupted by noise) transformations, and the result of the propagation is again an OT ambiguity set or can be upper bounded by an OT ambiguity set. In the context of dynamical systems, our results allow us to consider multiple sources of uncertainty (e.g., initial condition, additive noise, multiplicative noise) and to capture in closed form, via an OT ambiguity set, the resulting uncertainty in the state at any future time. Our results are actionable, interpretable, and readily employable in a great variety of computationally tractable control and estimation formulations. To highlight this, we study three applications in trajectory planning, consensus algorithms, and least squares estimation. © 1963-2012 IEEE.

关键词： Stochastic models

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels

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IEEE Internet of Things Journal 2025年第11期12卷 17123-17136页

作者： Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal Peng Cheng Laboratory Shenzhen518066 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi'an710071 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multi-user interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multi-user interference. More importantly, SFDMA can protect the privacy of users' semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta-Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme. © 2014 IEEE.

关键词： Image reconstruction

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Significant changes in the physicochemical properties of BC-containing particles during the cold season in Beijing

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Journal of Environmental Sciences 2025年第5期151卷 667-676页

作者： Shuya Hu Linghan Zeng Gang Zhao Shiyi Chen Chunsheng Zhao Weilun Zhao Min Hu State Key Joint Laboratory of Environmental Simulation and Pollution Control International Joint Research Center for Atmospheric Research(IJRC)College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China Department of Atmospheric and Oceanic Sciences School of PhysicsPeking UniversityBeijing 100871China Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing 210044China

The ambient air quality has improved significantly under strict emission controls in Beijing,China over the last *** carbon(BC),as a short-lived climate forcer in ambient aerosols,profoundly impacts the air quality and *** studies have demonstrated a decline in the mass concentration of *** this study,we characterized the chemical compositions and size distributions of BC-containing particles during the cold season of 2022 in Beijing using state-of-the-art instruments capable of exclusively measuring BCcontaining *** optical properties of BC-containing particles were further calculated based on the Mie ***,we compared the properties of BC-containing particles in 2022 with the results of previous *** results showed that the diameters of BC cores became larger while the coating thickness of BC-containing particles became thinner in *** the coating materials,the mass fraction of nitrate increased obviously,and even replaced organic matter as the dominant component during the peak of the pollution *** in chemical compositions and size distributions resulted in lower mass absorption cross-sections(MAC)of BC-containing particles from 10.5±1.1 m^(2)/g in 2016 to 7.3±0.8 m^(2)/g in 2022,reduced by 30.5%.Our results demonstrate the synergistic benefits of air pollution control in improving air quality and mitigating climate ***,the MAC of BC adopted in climate models should vary with the changing air pollution *** study emphasizes that it is imperative to conduct long-term observations of BC-containing particles to better estimate BC’s climate effects.

关键词： Black carbon Chemical composition Size distribution Light absorption ability

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Synchronous Estimation Scheme of PMSM Mechanical Parameters: A Decoupled Extended Sliding Mode Observer

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IEEE Transactions on Power Electronics 2025年第8期40卷 10877-10888页

作者： Chang, He Wang, Zuo Yang, Jun Li, Shihua Lu, Shaowu Southeast University School of Automatin Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of CSE Nanjing210096 China Loughborough University Department of Aeronautical and Automotive Engineering LoughboroughLE11 3TU United Kingdom Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China

In the permanent magnet synchronous motor drives (PMSM), high-quality controller optimization and condition monitoring rely heavily on accurate knowledge of mechanical parameters. To address this need, this study presents a novel decoupled extended sliding mode observer (DESMO) capable of achieving high-quality synchronous estimation of the moment of inertia and the load disturbance. An extended state-space equation is, firstly, constructed by extending the mechanical equations. Subsequently, the novel DESMO is designed by introducing two new recursive equations. On this basis, the excitation weight on the moment of inertia is analyzed, resulting in the derivation of Lyapunov-mechanism-based decoupling updating conditions (DOCs) to further exploit the DESMO's estimation potential. Furthermore, when the DOCs are not all satisfied, the second observation equation is derived with the reaching law of the moment of inertia set to zero to achieve state decoupling during estimation process. With the switching excitation of DOCs, notably, the DESMO is capable of serving as a more competitive candidate for observing multi-mechanical parameters, as demonstrated through simulation and experimental results. © 1986-2012 IEEE.

关键词： Permanent magnets

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Semantic-aware dynamic scene tracking and reconstruction for mobile robots leveraging visual SLAM

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Journal of Supercomputing 2025年第7期81卷 1-39页

作者： Liu, Tianwei Luo, Jingwen School of Information Science and Technology Yunnan Normal University 768 Juxian Street Yunnan Kunming650500 China Engineering Research Center of Computer Vision and Intelligent Control Technology Department of Education of Yunnan Province 768 Juxian Street Yunnan Kunming650500 China

Mobile robots operating in dynamic environments often encounter challenges due to interference from moving objects, compromising the performance of their SLAM systems. To address this issue, we propose a depth camera based semantic-aware visual SLAM approach for mobile robots, aimed at dynamic scene tracking and reconstruction. By leveraging a lightweight Yolact++ network, we segment and detect objects in the scene, eliminate dynamic features through prior knowledge and epipolar constraints, and utilize high-confidence static features for robot’s pose estimation. Subsequently, the overall similarity between images is evaluated based on the similarity of the matched semantic object regions between images to select high-quality candidate keyframes, effectively enhancing the precision of loop closure detection. Furthermore, we employ Delta Generalized Labeled Multi-Bernoulli (Delta-GLMB) filtering to track the masks of prior dynamic objects provided by Yolact++. A global semantic octree map is then constructed, integrating both the semantic knowledge of the static scene and the tracking information of dynamic targets, which in turn realizes the tracking & reconstruction of the dynamic scene. This allows mobile robots to mimic human behavior and cognitive abilities, not merely to construct a map of unknown environments more efficiently, but also to track the state of moving targets in the field-of-view(FoV) in real-time, enabling self-localization and navigation functions. Experimental results demonstrate the effectiveness of our approach in improving the robustness, accuracy, and adaptability of mobile robots in dynamic environments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Mobile robots

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Analysis of HVAC sensor characteristics for operation and maintenance of the indoor environment: A case research on public building HVAC

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Building Simulation 2025年 1-24页

作者： Zhang, Boyan Wang, Jiaming Shangguan, Liuyang Rezgui, Yacine Ghoroghi, Ali Cao, Ximing Song, Chengzhe Ding, Menglin Zhao, Tianyi Institute of Building Energy Department of Civil Engineering Dalian University of Technology Dalian China School of Engineering Cardiff University Cardiff United Kingdom School of instrument and electronics North University of China Taiyuan China School of Information Science and Technology Fudan University Shanghai China Landscape Architecture Rhode Island School of Design Providence02903 United States Dalian Shenghe Automatic Control Technology Co. Ltd Dalian China

Sensors are a crucial component in heating, ventilation, and air conditioning (HVAC) control systems, and the quality of them plays an important role in control accuracy. In the research of fault detection and control optimization, improving sensor data quality has inspiring potential in application. It has been largely limited to the application of signal processing methods in research focus, whereas a detailed analysis of the characteristics of signals from various sensors of the HVAC system has not been conducted. Therefore, this study analyzes the time-frequency domain characteristics of control sensors within HVAC systems through integrating the structural design and control logic of such systems. Additionally, the research examines the correlations between control sensors in HVAC systems. Based on statistical principles and the energy-mass dynamic laws of the equipment, this paper defines first-class (I) correlated sensors and second-class (II) correlated sensors. To sum up, the main contribution of this paper is conducting a fundamental study on the characteristics of control sensors within HVAC systems, providing theoretical reference for future research on HVAC system fault diagnosis and control optimization. © Tsinghua University Press 2025.

关键词： HVAC

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Evaluation of insulin sensitivity temporal prediction by using quantile regression combined with neural network model

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International Journal of Medical Informatics 2025年 202卷 105964-105964页

作者： Alkhafaf, Omer S. Chase, J.Geoffrey Benyó, Balázs Budapest University of Technology and Economics Faculty of Electrical Engineering and Information Technology Department of Control Engineering and Information Technology Budapest Hungary Department of Mechanical Engineering University of Canterbury Christchurch New Zealand College of Dentistry University of Mosul Mosul Iraq

Background: Stress-induced hyperglycemia, a pathologically high blood glucose level, is a frequent complication in intensive care units. Blood glucose (BG) level control is crucial but challenging due to patient variability. The Stochastic TARgeted (STAR) protocol is clinically used for blood glucose control, which uses the current and predicted future patient insulin sensitivity (SI) parameter to assess BG outcomes of alternative treatment options. Objective: Neural network (NN) models using quantile regression (QR) have enhanced SI prediction performance. However, remains a challenge in determining the optimal NN configuration to best predict SI. This study aims to find the NN configuration yielding the highest prediction accuracy to improve the STAR protocol and explores the behaviour of the QR method in predicting the percentiles of a non-Gaussian multi-mode distribution of a physiological parameter. Method: Alternative NN architectures combined with QR were implemented and trained on a large dataset comprising 1,897 patients collected between 2011 and 2023 using five-fold cross-validation ensuring model robustness. Prediction performance was evaluated among NN configurations and compared using case-specific metrics across the global SI domain as well as within subdomains to analyse the models’ local performance. Results: Outcomes indicate QR applied to simpler NN, consisting of one-hidden layer with four neurons, achieves a best prediction performance at a minimum network size. Using more complex NN did not improve the prediction performance significantly. However, at long prediction horizons, no compact network demonstrated improved outcomes. A more general methodological outcome of the study is that QR-based prediction does not need to be combined with complex NN to achieve the best prediction performance. Conclusion: The QR-based method was found to be appropriate for the SI prediction problem in short-term predictions which may improve the STAR protocol's c

关键词： Physiological models

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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