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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Sasaki, Reon Akima, Naoki Ito, Gimpei Fujiwara, Hiroshi Ooto, Hisashi Ohnishi, Wataru The University of Tokyo Department of Electrical Engineering and Information Systems Bunkyo-ku Tokyo Japan Panasonic Industry Osaka Japan

ISBN: (纸本)9798331533892

High-precision gantry stages are crucial in industrial equipment for fine processing. Achieving high-speed, accurate positioning is essential for improved throughput and product quality, particularly for large stages where low resonant modes and changes in transfer characteristics during operation present challenges. This paper introduces a method for real-time adjustment of transformation matrices for decoupling based on pre-acquired frequency response data and Gaussian process regression. The proposed approach allows for the implementation of adaptive, real-time control adjustments and pre-computed stability checks. The effectiveness of the method is demonstrated through experiments conducted with an XY gantry stage. © 2025 IEEE.

关键词： Robust control

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Methods for class-imbalanced learning with support vector machines: a review and an empirical evaluation

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Soft Computing 2024年第20期28卷 11873-11894页

作者： Rezvani, Salim Pourpanah, Farhad Lim, Chee Peng Wu, Q. M. Jonathan Department of Computer Science Ryerson University TorontoON Canada Department of Electrical and Computer Engineering University of Windsor WindsorON Canada Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC Australia

This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants and discuss their inefficiency in learning with class-imbalanced data sets. We introduce a hierarchical categorization of SVM-based models with respect to class-imbalanced learning. Specifically, we categorize SVM-based models into re-sampling, algorithmic, and fusion methods, and discuss the principles of the representative models in each category. In addition, we conduct a series of empirical evaluations to compare the performances of various representative SVM-based models in each category using benchmark imbalanced data sets, ranging from low to high imbalanced ratios. Our findings reveal that while algorithmic methods are less time-consuming owing to no data pre-processing requirements, fusion methods, which combine both re-sampling and algorithmic approaches, generally perform the best, but with a higher computational load. A discussion on research gaps and future research directions is provided. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Support vector machines

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Sustainable Teaching and Learning E-learning Model towards Redesigning Transformative Learning Model for Knowledge Sharing & Infrastructure Enhancements Post COVID-19

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Recent Patents on engineering 2025年第2期19卷 1-12页

作者： Veeramanickam, M.R.M. Kaur, Gaganpreet Acakpovi, Amevi Vadrevu, Pavan Kumar Ciro, Rodriguez R. Kalpana, A.V. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Software Engineering Universidad Nacional Mayor de San Marcos Lima Peru Electrical and Electronic Engineering Department Accra Technical University Accra Ghana Andhra Pradesh India Department of Software Engineering Universidad Nacional Mayor de San Marcos UNMSM Lima Peru Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India

Background: Recently, e-learning has become a very basic, integral part of technology-based learning. Wide trends are increasing day by day because of the demands and its usage based on working remotely due to highly penetrated mobile handheld devices and digital media. The smart campus infrastructure has played a vital role to its full extent towards Z millennium students in the 20th century. The teaching and learning accessibility depends on terms of various cost-based afforda-ble platforms, either with synchronous learning or asynchronous mode of learning. Methods: The current patent research explores the changeling leading to infrastructural reforms as per the need for digital media for e-learning during and after COVID-19 spreads. The perspectives in 2 forms of research study are: 1st working on infrastructural needs and demands for the smart campuses and online learning challenges and 2nd is working on platforms technology utilization for better accessible resources for all learners. This work studied different aspects during and after COVID-19, leading to the importance of uninterrupted internet access, phone, hardware and relia-bility, etc. In this work, the importance of gamification study and flipped classrooms for enhancing learner performance to highly engage them in learning environments focused research model on learner engagement on Gamified perceiving study with Smart PLS-SEM was investigated. Promoting sustainability in its entirety through knowledge transfer and contributions to address various challenges in the redesign of learners' syllabi to meet educational needs, emphasizing online learning to integrate various modes of learner platforms, personalized teaching and learn-ing, peer-to-peer communication for learner enhancement, and student engagement through gami-fication are studied. Results: Learners who are enrolled at the school, college, and university levels of education increased exponentially post-COVID-19. More than 90% responded to sch

关键词： Curricula

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Evaluation-free Time-series Forecasting Model Selection via Meta-learning

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ACM Transactions on Knowledge Discovery from Data 2025年第3期19卷 1-41页

作者： Abdallah, Mustafa Rossi, Ryan A. Mahadik, Kanak Kim, Sungchul Zhao, Handong Bagchi, Saurabh Purdue University in Indianapolis IndianapolisIN United States Adobe Systems Inc San JoseCA United States Department of Electrical and Computer Engineering Purdue Univ West LafayetteIN United States

Time-series forecasting models are invariably used in a variety of domains for crucial decision-making. Traditionally these models are constructed by experts with considerable manual effort. Unfortunately, this approach has poor scalability while generating accurate forecasts for new datasets belonging to diverse applications. Without access to skilled domain-knowledge, one approach is to train all the models on the new time-series data and then select the best one. However, this approach is nonviable in practice. In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one. In particular, we develop a forecasting meta-learning approach called AutoForecast that allows for the quick inference of the best time-series forecasting model for an unseen dataset. Our approach learns both forecasting models' performances over time horizon of the same dataset and task similarity across different datasets. The experiments demonstrate the effectiveness of the approach over state-of-the-art (SOTA) single and ensemble methods and several SOTA meta-learners (adapted to our problem) in terms of selecting better forecasting models (i.e., 2 × gain) for unseen tasks for univariate and multivariate testbeds. AutoForecast has also significant reduction in inference time compared to the naïve approach (doing inference using all possible models and then selecting the best one), with median of 42 × across the two testbeds. We release our meta-learning database corpus (348 datasets), performances of the 322 forecasting models on the database corpus, meta-features, and source codes for the community to access them for forecasting model selection and to build on them with new datasets and models which can help advance automating time-series forecasting problem. In our released database corpus, we unveil new traces

关键词： Prediction models

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Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm

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CSEE Journal of Power and Energy systems 2022年第4期8卷 1145-1156页

作者： Levent Yavuz Ahmet Soran AhmetÖnen Xiangjun Li S.M.Muyeen Electrical and Computer Engineering Department in Abdullah Gül University Kayseri 38080Turkey Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscat123Oman Department of Electrical Engineering Qatar UniversityDoha 2713Qatar State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems China Electric Power Rescarch InstituteBeijing 100192China

This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML *** the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)*** this purpose,power system failures are simulated by using the PSCA D-Python *** of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored ***,the proposed technique will be able to work on different systems,topologies,or data *** proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.

关键词： Decision tree(DT) ensemble machine learning algorithm fault detection islanding operation k-Nearest Neighbor(kNN) linear discriminant analysis(LDA) logistic regression(LR) Naive Bayes(NB) self-healing algorithm

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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8-Bit Precision 6T SRAM Compute-in-Memory Macro Using Global Bitline-Combining Scheme for Edge AI Chips

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IEEE Transactions on Circuits and systems II: Express Briefs 2024年第4期71卷 2304-2308页

作者： Su, Jian-Wei Lu, Pei-Jung Wu, Ping-Chun Chou, Yen-Chi Liu, Ta-Wei Chung, Yen-Lin Hung, Li-Yang Ren, Jin-Sheng Huang, Wei-Hsing Chien, Chih-Han Mei, Peng I. Li, Sih-Han Sheu, Shyh-Shyuan Lo, Wei-Chung Chang, Shih-Chieh Hong, Hao-Chiao Lo, Chung-Chuan Liu, Ren-Shuo Hsieh, Chih-Cheng Tang, Kea-Tiong Chang, Meng-Fan Hsinchu300044 Taiwan The Department of Electrical Engineering National Tsing Hua University Hsinchu300044 Taiwan The Institute of Electronic Engineering National Tsing Hua University Hsinchu300044 Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan The Institute of Systems Neuroscience National Tsing Hua University Hsinchu300044 Taiwan

Efforts to advance the use of analog SRAM compute-in-memory (SRAM-CIM) macros for high-precision multiply-and-accumulate (MAC) operations must deal with issues pertaining to energy efficiency, computing latency (TAC), and area overhead. This brief presents a novel SRAM-CIM structure that utilizes (1) a high input precision computing cell (HIPCC) to perform 8b-MAC operations with high multiplication throughput, and (2) a global bitline-combining (GBL-comb) scheme to improve energy efficiency by reducing the number of analog-to-digital converters (ADCs). A 28nm 384-kb SRAM-CIM macro with 20-bit output precision (near-full precision) was fabricated using a foundry-provided 28nm logic process for MAC operations with 8b-input, 8b-weight, and 16 accumulations. The resulting macro achieved a TAC of 3.6 ns with energy efficiency of 14.97 TOPS/W when applied to 8-bit MAC operations. © 2023 IEEE.

关键词： Static random access storage

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Design and Analysis of Novel High-Gain Boost Converter for Renewable Energy systems (RES)

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IEEE Access 2024年 12卷 24262-24273页

作者： Lin, Chang-Hua Khan, Mohammad Suhail Ahmad, Javed Liu, Hwa-Dong Hsiao, Tung-Chun National Taiwan University of Science and Technology Department of Electrical Engineering Taipei 106 Taiwan National Taiwan Normal University Undergraduate Program of Vehicle and Energy Engineering Taipei 106 Taiwan

High-gain DC-DC converters are becoming increasingly popular in renewable energy applications and solar PV systems. This article introduces a non-isolated non-coupled inductor-based high-gain DC-DC boost converter with the desirable features of low voltage stress on controlled power switches and high voltage gain at lower duty ratios. The proposed converter is well suited for boosting the low-input DC voltage obtained from distributed generation units like photovoltaic (PV) or fuel cells to substantially higher DC voltage. The converter comprises only two switches, and a single PWM signal governs its operation. These characteristics result in a topology that is more compact, less costly, lighter weight, and easier to control. The proposed converter is compared with some previously developed topologies of high-gain boost converters across various performance parameters. This comparison illustrates that the proposed model surpasses them in every aspect. A 300 W hardware prototype of a proposed model is developed and tested in a laboratory environment to confirm the theoretical assertions of a proposed model. The proposed topology offers a promising solution and enables a high gain of approximately 12 times the input voltage at a smaller duty ratio, specifically 11.25 at a duty of 0.6 and 17.77 at a duty of 0.7. The efficiency of a proposed converter ranges between 92.5% to 94.5% making it appropriate for numerous medium-high power applications. The uniqueness of the suggested system is its simple design and greater efficiency, which provides a more considerable gain and higher output voltages for sustainable energy systems. © 2013 IEEE.

关键词： boost converter dc(!-!)dc converter high gain Renewable energy system (RES)

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A zeroth-order algorithm for distributed optimization with stochastic stripe observations

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Science China(Information Sciences) 2023年第9期66卷 297-298页

作者： Yinghui WANG Xianlin ZENG Wenxiao ZHAO Yiguang HONG School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Department of Control Science and Engineering Tongji University

Recent years have seen a rising interest in distributed optimization problems because of their widespread applications in power grids, multi-robot control, and regression *** the last few decades, many distributed algorithms have been developed for tackling distributed optimization problems. In these algorithms, agents over the network only have access to their own local functions and exchange information with their neighbors.

关键词：

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Soft Artificial Synapse Electronics

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Research 2024年第4期2025卷 101-118页

作者： Md.Rayid Hasan Mojumder Seongchan Kim Cunjiang Yu Department of Electrical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Materials Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Mechanical Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Materials Research Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Nick Holonyak Micro and Nanotechnology Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA

Soft electronics,known for their bendable,stretchable,and flexible properties,are revolutionizing fields such as biomedical sensing,consumer electronics,and robotics.A primary challenge in this domain is achieving low power consumption,often hampered by the limitations of the conventional von Neumann *** response,the development of soft artificial synapses(SASs)has gained substantial *** synapses seek to replicate the signal transmission properties of biological synapses,offering an innovative solution to this *** review explores the materials and device architectures integral to SAS fabrication,emphasizing flexibility and stability under mechanical *** architectures,including floating-gate dielectric,ferroelectric-gate dielectric,and electrolyte-gate dielectric,are analyzed for effective weight control in *** utilization of organic and low-dimensional materials is highlighted,showcasing their plasticity and energy-efficient ***,the paper investigates the integration of functionality into SASs,particularly focusing on devices that autonomously sense external *** SASs,capable of recognizing optical,mechanical,chemical,olfactory,and auditory cues,demonstrate promising applications in computing and sensing.A detailed examination of photo-functionalized,tactile-functionalized,and chemoreception-functionalized SASs reveals their potential in image recognition,tactile sensing,and chemosensory applications,*** study highlights that SASs and functionalized SAS devices hold transformative potential for bioelectronics and sensing for soft-robotics applications;however,further research is necessary to address scalability,longtime stability,and utilizing functionalized SASs for prosthetics and in vivo applications through clinical *** providing a comprehensive overview,this paper contributes to the understanding of SASs,bridging research gaps and paving the way tow

关键词： dielectric utilizing gained

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