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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Iure Rosa L. De Oliveira Kevin B. De Carvalho Alexandre S. Brandão Graduate Program in Computer Science Universidade Federal de Viçosa Viçosa Brazil Electrical Engineering Department Universidade Federal de Vicosa Viçosa Brazil

Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveillance, object transportation as well as search-and-rescue. A particular case that can highly benefit from the employment of multiple agents is the logistics in a warehouse scenario. This work proposes an multi-agent Q-learning based algorithm with curriculum learning and transfer learning to perform the path planning process. With progressively more complex stages of training as well as knowledge transfer from one stage to another, the algorithm is capable of achieve high success rates. In order to validate the proposed method, simulations were done to compare it to other combinations of the used techniques, as well as using Q-learning only. Scalability tests were also performed. The proposed method achieved up to 94% success rate after training.

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Ai-Driven Thermography-Based Fault Diagnosis in Single-Phase Induction Motor

SSRN

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SSRN 2024年

作者： Atif, Muhammad Azmat, Shoaib Khan, Faisal Albogamy, Fahad R. Khan, Adam COMSATS Abbottabad22060 Pakistan Computer Sciences Program Turabah University College Taif University Taif21944 Saudi Arabia University of Engineering and Technology Abbottabad22044 Pakistan

Single-phase induction motors (SIMs) are commonly used in industrial applications. The extensive industrial usage of SIMs requires proper maintenance and fault detection. Among various faults, the most common mechanical fault in SIMs is bearing failure. Thus, detecting this fault during motor operation is essential for preventing damage. Many fault diagnosis methods have been proposed to detect bearing faults based on contact sensors, but they have accessibility problems. This paper presents a novel Infrared Thermography (IRT) based fault diagnosis method, leveraging both conventional machine learning (CML) and deep learning (DL) techniques for motor condition classification. In CML, Statistical and Gray Level Co-occurrence Matrix (GLCM) features are extracted from thermal images. The support vector machine recursive feature elimination (SVM-RFE) method is used to select the most relevant high-score features from the extracted features. Support vector machine (SVM) and K-nearest neighbours (KNN) classifiers are applied to the selected interpretable features to classify healthy and faulty bearings. Furthermore, in DL, a convolutional neural network (CNN) characterized by a single convolutional layer with several filters is also used as a healthy and faulty bearings classifier. The proposed method achieves classification accuracy between 97.87% - 100% using both CML and DL. Moreover, our IRT-based SIMs bearing faults diagnosis methods demonstrate a reduction in architectural complexity compared to prevailing state-of-the-art approaches. © 2024, The Authors. All rights reserved.

关键词： Support vector machines

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Machine Learning based Multiscale Reduced Kernel PCA for Nonlinear Process Monitoring

Machine Learning based Multiscale Reduced Kernel PCA for Non...

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

作者： Khaled Dhibi Radhia Fezai Kais Bouzrara Majdi Mansouri Abdelmalek Kouadri Mohamed-Faouzi Harkat Laboratory of Automatic Signal and Image Processing School of Engineers of Monastir University of Monastir Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

Fault detection (FD) is fundamental for monitoring several chemical processes. Thus, this paper introduces a novel structure multiscale reduced kernel principal component analysis (MS-RKPCA). The proposed FD method aims to address the problem of great computation time and significant storage memory space by using a data reduction structure based on the Euclidean distance metric. Additionally, to further enhance the RKPCA method, a multiscale representation of data will be used. The enhanced MS-RKPCA method uses the wavelet coefficients of the reduced data at each scale to enhance the fault detection results. The detection performance of the proposed MS-RKPCA method is evaluated using the Tennessee Eastman Process (TEP). The effectiveness of the enhanced method is evaluated in terms of the missed detection rates (MDR), false alarms rates (FAR) and computation time (CT). The results demonstrate that the developed technique is more effective for fault detection mostly in terms of computation time and memory storage space.

关键词： Fault detection Computational modeling Euclidean distance Wavelet analysis Kernel Principal component analysis Wavelet coefficients

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Customer Segmentation and Churn Prediction via Customer Metrics

Customer Segmentation and Churn Prediction via Customer Metr...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Tunahan Bozkan Tuna Ç akar Alperen Sayar Seyit Ertuğ rul MEF University Information Technology Graduate Program Istanbul Turkey Computer Engineering Department MEF University Istanbul Turkey Department of Information Technology MEF University Istanbul Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

In this study, it is aimed to predict whether customers operating in the factoring sector will continue to trade in the next three months after the last transaction date, using data- driven machine learning models, based on their past transaction movements and their risk, limit and company data. As a result of the models established, Loss Analysis (Churn) of two different customer groups (Real and Legal factory) wascarried out. It was estimated by the XGBoost model with anF1 Score of 74% and 77%. Thanks to this modeling, it was aimed to increase the retention rate of customers through special promotions and campaigns to be made to these customer groups, together with the prediction of the customerswho will leave. Thanks to the increase in retention rates, a direct contribution to the transaction volume on a company basis was ensured.

关键词： Measurement Analytical models Law Companies Machine learning Predictive models Signal processing

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Microsphere-assisted quantitative phase microscopy:a review

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Light(Advanced Manufacturing) 2024年第1期5卷 131-150页

作者： Vahid Abbasian Tobias Pahl Lucie Hüser Sylvain Lecler Paul Montgomery Peter Lehmann Arash Darafsheh Department of Radiation Oncology Washington University School of Medicine in St.LouisSt.LouisMO 63110USA Imaging Science Program McKelvey School of EngineeringWashington University in St.LouisSt.LouisMO 63130USA Measurement Technology Group Faculty of Electrical Engineering and Computer ScienceUniversity of KasselWilhelmshöher Allee 7134121 KasselGermany ICube Research Institute INSA StrasbourgUniversitéde StrasbourgCNRS67000 StrasbourgFrance

Light microscopes are the most widely used devices in life and material sciences that allow the study of the interaction of light with matter at a resolution better than that of the naked *** microscopes translate the spatial differences in the intensity of the reflected or transmitted light from an object to pixel brightness differences in the digital ***,a phase microscope converts the spatial differences in the phase of the light from or through an object to differences in pixel *** microscopy,a phase-based approach,has found application in various *** interferometry has brought nanometric axial resolution,the lateral resolution in quantitative phase microscopy(QPM)has still remained limited by diffraction,similar to other traditional microscopy *** the resolution has been the subject of intense investigation since the invention of the microscope in the 17th *** the past decade,microsphere-assisted microscopy(MAM)has emerged as a simple and effective approach to enhance the resolution in light *** can be integrated with QPM for 3D label-free imaging with enhanced ***,we review the integration of microspheres with coherence scanning interference and digital holographic microscopies,discussing the associated open questions,challenges,and opportunities.

关键词： Phase microscopy Digital holographic microscopy Microsphere Resolution

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Probabilistic structure from sound

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Advanced Robotics 2009年第12-13期23卷 1687-1702页

作者： Wang, Chieh-Chih Lin, Chi-Hao Hu, Jwu-Sheng Department of Computer Science and Information Engineering Graduate Institute of Networking and Multimedia National Taiwan University Taipei 10617 Taiwan Intelligent Robotics Program of Industrial Technology Research Institute Department of Electrical and Control Engineering National Chiao Tung University HsinChu 310 Taiwan

Auditory perception is one of the most important functions for robotics applications. Microphone arrays are widely used for auditory perception in which the spatial structure of microphones is usually known. In practice, microphone array calibration can be tedious and other devices or means are required. The structure from sound (SFS) approach addresses the problem of simultaneously localizing a set of microphones and a set of acoustic events that provides a great flexibility to calibrate different setups of microphone arrays. However, the existing method does not take measurement uncertainty into account and does not provide uncertainty estimates of the SFS results. In this paper, we propose a probabilistic structure from sound (PSFS) approach using the unscented transform in which the uncertainties of the PSFS results are also available. In addition, a probabilistic sound source localization approach using the PSFS results is provided to improve sound source localization accuracy. The ample results of simulation and experiments using low-cost, off-the-shelf microphones demonstrate the feasibility and performance of the proposed PSFS approach. © 2009 VSP.

关键词： Microphone array

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Dataset Condensation with Contrastive Signals

arXiv

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arXiv 2022年

作者： Lee, Saehyung Chun, Sanghyuk Jung, Sangwon Yun, Sangdoo Yoon, Sungroh Department of Electric and Computer Engineering Seoul National University Korea Republic of NAVER AI Lab. Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation (DC), methods can achieve state-of-the-art performance when applied to data-efficient learning tasks. However, in this study, we prove that the existing DC methods can perform worse than the random selection method when task-irrelevant information forms a significant part of the training dataset. We attribute this to the lack of participation of the contrastive signals between the classes resulting from the class-wise gradient matching strategy. To address this problem, we propose Dataset Condensation with Contrastive signals (DCC) by modifying the loss function to enable the DC methods to effectively capture the differences between classes. In addition, we analyze the new loss function in terms of training dynamics by tracking the kernel velocity. Furthermore, we introduce a bi-level warm-up strategy to stabilize the optimization. Our experimental results indicate that while the existing methods are ineffective for fine-grained image classification tasks, the proposed method can successfully generate informative synthetic datasets for the same tasks. Moreover, we demonstrate that the proposed method outperforms the baselines even on benchmark datasets such as SVHN, CIFAR-10, and CIFAR-100. Finally, we demonstrate the high applicability of the proposed method by applying it to continual learning tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Condensation

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Integrated 4H-silicon carbide diode bridge rectifier for high temperature (773 K) environment

Integrated 4H-silicon carbide diode bridge rectifier for hig...

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European Conference on Solid-State Device Research (ESSDERC)

作者： Shiqian Shao Wei-Cheng Lien Ayden Maralani Albert P. Pisano Department of Mechanical Engineering University of California at Berkeley USA Applied Science and Technology Program University of California at Berkeley USA Department of Electrical Engineering and Computer Science University of California at Berkeley USA Jacobs School of Engineering University of California at San Diego USA

In this paper, we demonstrate the stable operation of integrated 4H-silicon carbide (SiC) diode bridge rectifier circuits at high temperature up to 773 K for the first time. The turn-on voltages of the fabricated 4H-SiC pn diode are 2.6 V and 1.4 V at room temperature and 773 K, respectively, with a low shifting rate of 2.2 mV/K. The integration of the 4H-SiC diode bridge rectifier circuit was achieved with contact and interconnect metallization technique for high temperature. The demonstration of the extremely high temperature operation of the integrated 4H-SiC diode bridge rectifier circuits brings promising applications in harsh environment electronics and sensing.

关键词： Bridge circuits Rectifiers Silicon carbide Temperature sensors Plasma temperature Temperature measurement Fabrication

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Towards Precision Cancer Therapy: Prottrans and Multi-Window Scanning Convolutional Neural Networks Transforming Protein-Peptide Interaction Predictions

SSRN

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SSRN 2024年

作者： Van Le, The Zi Zhan, Jun Yu Ou, Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model achieves an impressive AUC of 0.855 and 0.822 on Set_1 and Set_2 datasets, outperforming previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Minimum-Time Trajectory Planning for Helicopter UAVs using Computational Dynamic Optimization

Minimum-Time Trajectory Planning for Helicopter UAVs using C...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Nathan Xu Wei Kang Guowei Cai Ben M. Chen Summer Intern Program Department of Applied Mathematics Naval Postgraduate School Department of Applied Mathematics Naval Postgraduate School Department of Electrical and Computer Engineering National University of Singapore

ISBN: (纸本)9781467317139

In this paper, we apply pseudospectral optimal control, a computational algorithm of dynamic optimization, to the problem of helicopter UAV for minimum-time trajectory planning in the presence of obstacles. The problem is formulated as a nonlinear optimal control subject to the dynamics and limitations of helicopter UAVs, in which the obstacles are formulated as inequality constraints by using p-norms. The dynamical system is defined by a set of fifteen states nonlinear differential equations developed for HeLion, a helicopter UAV constructed in National University of Singapore (NUS). The problem does not have an analytic solution. We numerically solve the problem using a pseudospectral method. Various terrain scenarios were tested, from a single obstacle to multiple obstacles. We found bifurcation points of minimum-time trajectories near obstacles. The bifurcation points and their relationship with the distance to the obstacle are analyzed.

关键词： Minimum-time control Helicopter UAV Computational optimal control Obstacle avoidance Unmanned Aerial Vehicle Helicopters trajectory planning barriers BIFURCATION nonlinear differential equations inequality constraint drone honey bees

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