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作者： Soares, Emerson L. Jacobina, Cursino B. De Freitas, Nayara B. Rocha, Nady Maia, Ayslan C. N. Lima, Antonio M. N. Federal University of Campina Grande Department of Electrical Engineering Paraiba Campina Grande58429-900 Brazil Institute for Systems and Computer Engineering Porto4200-465 Portugal Federal University of Paraíba Department of Electrical Engineering JoÃ£o Pessoa Paraíba58051-900 Brazil Federal Institute of Ceará Department of Electrical Engineering CearÃ¡ Cedro63400-000 Brazil

This article presents a nine-leg (9L) multilevel inverter to drive an asymmetrical six-phase induction machine in an open-end winding (OEW) configuration. The system is based on three conventional two-level three-phase voltage source inverters (VSIs) with three, two, or one isolated dc links. A machine with two sets of 30 degrees shifted three-phase stator windings is considered. The article discusses the converters operating principles and proposes a simple space-vector pulse-width modulation (SV-PWM) and its implementation through equivalent level-shifted PWM (LS-PWM). Also, a comparative study is presented regarding the discussed 9L-OEW systems and two conventional drives: the six-leg two-level (6L), and the 12-leg OEW (12L-OEW) systems. The figures of merit were the number of components, required dc-link voltage, voltage and current harmonic distortions, torque ripple, and semiconductor losses. Simulations and experiments show results for a 1 HP machine. Finally, the article concludes that the characteristics of the 9L-OEW system using three isolated dc links fit between the 6L and 12L-OEW conventional systems. © 1972-2012 IEEE.

关键词： Winding

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

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Journal of Modern Power systems and Clean Energy 2022年第5期10卷 1184-1193页

作者： Yunxuan Dong Shaodan Ma Hongcai Zhang Guanghua Yang IEEE State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao S.A.R.999078China Institute of Physical Internet and the School of Intelligent Systems Scienceand Engineering Jinan UniversityZhuhai 519070China

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

关键词： Wind power prediction wind generation time series analysis logistic function based classification

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An effective approach for detecting deepfake videos using Long Short-Term Memory and ResNet

An effective approach for detecting deepfake videos using Lo...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Keerthana, S. Deepika, N. Pooja, E. Nandhini, I. Shanthalakshmi, M. Khanaghavalle, G.R. Sri Venkateswara College Of Engineering Department Of Computer Science And Engineering Pennalur Tamilnadu Sriperumbudur602117 India Rajalakshmi Institute Of Technology Department Of Computer Science And Business Systems Tamilnadu Kuthambakkam600124 India School Of Electrical And Communication Vel Tech Rangarajan Dr. Sakunthala R&d Institute Of Science And Technology Department Of Ece Avadi India

ISBN: (纸本)9798350352689

Deep learning algorithms are becoming more potent and producing human-synthesized, undifferentiated footage is a simple process thanks to advances in computing power. A method of synthesizing human images called deep fakes is built on neural network techniques like GAN. These technologies use deep learning algorithms to overlay target pictures onto the source films, producing realistic-looking deep-fake photos and videos. In this work, we provide a novel deep-learning approach that can reliably discriminate between actual and AI-generated fake videos. In this work, we test our method on a large number of balanced and mixed datasets created by mixing various publicly available datasets, such as Face-Forensic++, Deepfake detection, Celeb DF, and other publicly available datasets as images and videos, to emulate real-time scenarios and improve model performance with real-time data by using LSTM and ResNet. © 2024 IEEE.

关键词： computer vision

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Underwater Robot-To-Human Communication Via Motion: Implementation and Full-Loop Human Interface Evaluation 18th

Underwater Robot-To-Human Communication Via Motion: Implemen...

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18th robotics: Science and systems, RSS 2022

作者： Fulton, Michael Mehtaz, Muntaqim Sattar, Junaed Queeglay, Owen Deptartment of Computer Science & Engineering Minnesota Robotics Institute University of Minnesota Twin Cities United States Department of Electrical Engineering Stanford University United States

ISBN: (纸本)9780992374785

Autonomous underwater vehicles (AUVs) have long lagged behind other types of robots in supporting natural communication modes for human-robot interaction. Due to the limitations of the environment, most AUVs use digital displays or topside human-in-the-loop communications as their primary or only communication vectors. Natural methods for robotto-human communication such as robot "gestures" have been proposed, but never evaluated on non-simulated AUVs. In this paper, we enhance, implement and evaluate a robot-to-human communication system for AUVs called Robot Communication Via Motion (RCVM), which utilizes explicit motion phrases (kinemes) to communicate with a dive partner. We present a small pilot study that shows our implementation to be reasonably effective in person followed by a large-population study, comparing the communication effectiveness of our RCVM implementation to three baseline systems. Our results establish RCVM as an effective method of robot-to-human communication underwater and reveal the differences with more traditional communication vectors in how accurately communication is achieved at different viewpoints and types of information payloads. © 2022, MIT Press Journals. All rights reserved.

关键词： Autonomous underwater vehicles

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Advances of wearable silicone rubber-based triboelectric nanogenerators:from manufacturing to application

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International Journal of Extreme Manufacturing 2025年第3期7卷 126-158页

作者： Dianlong Shen Taili Du Fangyang Dong Hu Cai Aziz Noor Xiannan Du Yongjiu Zou Chengkuo Lee Minyi Xu Dalian Key Laboratory of Marine Micro/Nano Energy and Self-Powered Systems Marine Engineering CollegeDalian Maritime UniversityDalian 116026People’s Republic of China State Key Laboratory of Maritime Technology and Safety Dalian 116026People’s Republic of China Collaborative Innovation Research Institute of Autonomous Ship Dalian Maritime UniversityDalian 116026People’s Republic of China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore

Wearable bioelectronic devices are rapidly evolving towards miniaturization and multifunctionality,with remarkable features such as flexibility and ***,achieving a sustainable power supply for wearable bioelectronic devices is still a great *** nanogenerators(TENGs)provide an efficient solution by converting irregular,low-frequency bioenergy from the human body into electrical *** sustainably powering wearable bioelectronics,the harvested electrical energy also carries rich information for human body *** this conversion process,the choice of material plays a crucial role in affecting the output performance of the *** various materials,silicone rubber(SR)stands out due to its exceptional plasticity,flexibility,comfortability and other favorable ***,with appropriate treatment,SR can achieve extreme functionalities such as high robustness,good stability,self-healing capabilities,rapid response,and *** this review,recent advances in wearable SR-based TENGs(SR-TENGs)are systematically reviewed with a focus on their application in different parts of the human *** that the manufacturing method of SR-TENGs largely determines its output performance and sensitivity,this paper introduces the design of SR-TENGs,including material selection,process modulation,and structure ***,this article discusses the current challenges in the SR-TENG fabrication technology and potential future directions,aiming to promote the effective development of SR-TENGs in biomechanical energy harvesting and self-powered sensing applications.

关键词： extreme function silicone rubber triboelectric nanogenerators wearable bioelectronics energy harvesting self-powered sensing fabrication methods

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A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection

arXiv

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arXiv 2023年

作者： Conde, Pedro Lopes, Rui L. Premebida, Cristiano Institute os Systems and Robotics Department Of Electrical and Computer Engineering University of Coimbra Portugal Critical Software S.A. Portugal

The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasingly more present in various real-world applications. Consequently, there is a growing demand for highly reliable models in many domains, making the problem of uncertainty calibration pivotal when considering the future of deep learning. This is especially true when considering object detection systems, that are commonly present in safety-critical applications such as autonomous driving, robotics and medical diagnosis. For this reason, this work presents a novel theoretical and practical framework to evaluate object detection systems in the context of uncertainty calibration. This encompasses a new comprehensive formulation of this concept through distinct formal definitions, and also three novel evaluation metrics derived from such theoretical foundation. The robustness of the proposed uncertainty calibration metrics is shown through a series of representative experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling

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Complex System Modeling and Simulation 2023年第2期3卷 83-101页

作者： Zhen Shu Aijuan Song Guohua Wu Witold Pedrycz College of Systems Engineering National University of Defense TechnologyChangsha 410073China School of Traffic and Transportation Engineering Central South UniversityChangsha 410075China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canadaand also with the Systems Research InstitutePolish Academy of SciencesWarsaw 01447Poland

Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property ***-world emergency material scheduling(EMS)problems are typically large-scale and possess complex *** evolutionary algorithm(EA)is one of the effective methods for solving EMS ***,the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality *** handle the above challenges,we apply the idea of a variable reduction strategy(VRS)to an EMS problem,which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS ***,we define an emergency material allocation and route scheduling model,and a variable neighborhood search and NSGA-II hybrid algorithm(VNS-NSGAII)is designed to solve the ***,we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality ***,we integrate VRS with VNS-NSGAII to solve the reduced EMS *** prove the effectiveness of VRS on VNS-NSAGII,we construct two test cases,where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5∙12 Wenchuan earthquake emergency material support *** results show that VRS can improve the performance of the standard VNS-NSGAII,enabling better optimization efficiency and a higher-quality solution.

关键词： emergency material scheduling evolutionary algorithm variable reduction strategy

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Remote Aerial Vehicle Solutions for Weed Detection in Precision Agriculture

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IEEE Access 2025年 13卷 101440-101454页

作者： Shekhar S. Borah Aryan Anand Prabha Sundaravadivel Reginald Fletcher Krishna N. Reddy Department of Electrical and Computer Engineering Center for Robotics and Intelligent Systems The University of Texas at Tyler Tyler TX USA Crop Production Systems Research Unit USDA-Agricultural Research Service (ARS) Stoneville MS USA

This study presents a novel Remote Aerial Vehicle-based approach for detecting pigweeds in soybean (Glycine max) fields using a combination of deep learning and advanced image processing techniques. A custom, high-resolution dataset comprising RGB (red, green, and blue) and multispectral images was collected from USDA operated fields and manually annotated for target pigweed detection. Beyond incorporating YOLOv8 variants for real-time weed classification, this study integrates a comprehensive image processing pipeline, incorporating global thresholding, k-means clustering, 3D surface mapping, and spectral signature analysis to enhance interpretability and detection accuracy. A comparative evaluation of YOLOv8 nano, small, medium, and large models, along with Faster R-CNN, confirmed YOLOv8’s balance between detection accuracy and real-time performance for deployment in precision agriculture. The YOLOv8 nano model emerged as the most balanced in terms of precision (75.6%), recall (81.7%), and mAP@0.5 (81.5%), demonstrating effective weed detection performance under real field conditions. In addition, quadrant-level weed coverage and spatial heatmaps were generated to support targeted interventions. This study advances the current state of RAV based weed detection by providing a field-ready, explainable, and resource-efficient solution, contributing to sustainable farming and data-driven weed management practices.

关键词： Crops Deep learning Accuracy Autonomous aerial vehicles Agriculture Real-time systems Feature extraction Soil Precision agriculture Drones

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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