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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Gao, Xianghong Cao, Yan Xi'an Technological University School of Mechanical and Electrical Engineering Xi'an China Xi'an Technological University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350305388

As the basic parts of workshop processing, the life of the tool will affect the processing efficiency and processing quality, and the life of the tool will be affected by a variety of uncertain factors in the processing process, which makes it difficult to accurately predict. In order to improve the prediction accuracy, an improved IPSO algorithm was proposed to optimize the penalty factor c and kernel parameter g of SVM based on particle swarm optimization (PSO). Firstly, the influencing parameters of tool life are analyzed and the experimental data are extracted, and then the data is brought into the IPSO-SVM model to obtain the predicted data. IPSO algorithm can realize the adaptive change of inertia weight and introduce asynchronous learning formula to dynamically adjust the learning factor, which can effectively improve the prediction accuracy of tool life. Compared with the traditional PSO-SVM prediction model, the average relative error and average absolute error of IPSO-SVM are reduced by 5.275% and 5.006% respectively, which indicates that the SVM model optimized by IPSO algorithm has higher prediction accuracy. © 2023 IEEE.

关键词： Forecasting

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Near-sensor computing-assisted simultaneous viral antigen and antibody detection via integrated label-free biosensors with microfluidics

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InfoMat 2023年第10期5卷 80-95页

作者： Byungjoon Bae Yongmin Baek Jeongyong Yang Heesung Lee Charana S.S.Sonnadara Sangeun Jung Minseong Park Doeon Lee Sihwan Kim Gaurav Giri Sahil Shah Geonwook Yoo William APetri Kyusang Lee Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVirginiaUSA School of Electronic Engineering Soongsil UniversitySeoulRepublic of Korea Department of Electrical and Computer Engineering University of MarylandCollege ParkMarylandUSA Department of Chemical Engineering University of VirginiaCharlottesvilleVirginiaUSA Division of Infectious Diseases and International Health Department of MedicineSchool of MedicineUniversity of VirginiaCharlottesvilleVirginiaUSA Department of Material Science and Engineering University of VirginiaCharlottesvilleVirginiaUSA Department of Intelligent Semiconductors Soongsil UniversitySeoulRepublic of Korea

Precise diagnosis and immunity to viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and Middle East respiratory syndrome coronavirus(MERS-CoV)is achieved by the detection of the viral antigens and/or corresponding antibodies,***,a widely used antigen detection methods,such as polymerase chain reaction(PCR),are complex,expensive,and time-consuming Furthermore,the antibody test that detects an asymptomatic infection and immunity is usually performed separately and exhibits relatively low *** achieve a simplified,rapid,and accurate diagnosis,we have demonstrated an indium gallium zinc oxide(IGZO)-based biosensor field-effect transistor(bio-FET)that can simultaneously detect spike proteins and antibodies with a limit of detection(LOD)of 1 pg mL–1 and 200 ng mL–1,respectively using a single assay in less than 20 min by integrat-ing microfluidic channels and artificial neural networks(ANNs).The near-sensor ANN-aided classification provides high diagnosis accuracy(>93%)with significantly reduced processing time(0.62%)and energy consumption(5.64%)compared to the software-based *** believe that the development of rapid and accurate diagnosis system for the viral antigens and antibodies detec-tion will play a crucial role in preventing global viral outbreaks.

关键词： artificial intelligence biosensor edge-computing integration microfluidic

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Mean-field chaos diffusion models 24

Mean-field chaos diffusion models

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Proceedings of the 41st International Conference on Machine Learning

作者： Sungwoo Park Dongjun Kim Ahmed M. Alaa Department of Electrical Engineering and Computer Sciences UC Berkeley Department of Computer Science Stanford Department of Electrical Engineering and Computer Sciences UC Berkeley and UCSF

In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory. We present mean-field chaos diffusion models (MF-CDMs), which address the curse of dimensionality inherent in high-cardinality data by utilizing the propagation of chaos property of interacting particles. By treating high-cardinality data as a large stochastic system of interacting particles, we develop a novel score-matching method for infinitedimensional chaotic particle systems and propose an approximation scheme that employs a subdivision strategy for efficient training. Our theoretical and empirical results demonstrate the scalability and effectiveness of MF-CDMs for managing large high-cardinality data structures, such as 3D point clouds.

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Dataset Ratio Influence on kNN Classification Results 33rd

Dataset Ratio Influence on kNN Classification Results

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Gaće, Marin Galba, Tomislav Baumgartner, Alfonzo Livada, Časlav Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031805967

Classification is used in many fields today, and for most of them machine learning algorithms can be used to make a decision. This article investigates the effects of different sizes of training and test datasets on the accuracy of classification using both classical k-nearest neighbors (kNN) and weighted k-nearest neighbors (wkNN) methods. This study systematically evaluates how different ratios of training to test data affect classification performance and provides a comprehensive analysis of the interplay between the ratios of the datasets and the parameter k. The results show that the ratio of training to test datasets exerts a more significant influence on classification accuracy than the choice of the parameter k. In particular, extreme ratios, where the training dataset is either much larger or much smaller than the test dataset, lead to suboptimal classification results. This emphasizes the importance of carefully selecting an appropriate balance between training and test data to ensure robust and reliable classification results. By highlighting the critical role of the ratio between datasets, this paper provides valuable insights for optimizing kNN and weighted kNN methods in practical applications. The results have far-reaching implications for the design and implementation of machine learning models and emphasize the need for a balanced data distribution to achieve high classification accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： k-nearest neighbors

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Smart Roads: Lighting the Way to Safety and Efficiency 26

Smart Roads: Lighting the Way to Safety and Efficiency

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Hasan, Shakibul Raxit, Srabone Jahan, Israt Hossain, Md Sajid Ramisa Samiha, Kazi Zayad, Ahnaf Abdullah Hossain, Rifat American International University-Bangladesh Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

The "Smart Roads: Lighting the Way to Safety and Efficiency"project introduces an innovative solution to improve the efficiency and sustainability of outdoor lighting systems. This research focuses on developing automated streetlights and introduces the indication concept for over-speeding drivers. The primary objectives of this design are to conserve energy, improve road traffic safety, and lower casualties from road traffic collisions. By harnessing LDR, Ultrasonic sensor, and Arduino board, the proposed streetlights can automatically detect ambient lighting conditions and respond accordingly. These smart streetlights are programmed to activate during nighttime (or overcast days) and remain off during daylight hours, thereby optimizing energy usage. An Ultrasonic sensor is introduced that detects any vehicle from a distance and activates the Streetlights as the vehicle approaches. Even so, using the data from the Ultrasonic sensor, the speed of vehicles is measured, and drivers are warned using additional lights at the mid-bottom of the lamp post. The significance of this project lies in its potential to revolutionize urban lighting infrastructure. The technology's accessibility and affordability can lead to a widespread adaptation, contributing to broader societal benefits. © 2023 IEEE.

关键词： Arduino LDR Speed Streetlights Ultrasonic

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A Framework for Optimizing Multilevel AC Battery Energy Storage Systems 24

A Framework for Optimizing Multilevel AC Battery Energy Stor...

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24th IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2023

作者： Ramyar, Alireza Siegel, Jason B. Avestruz, Al-Thaddeus University of Michigan Electrical Engineering and Computer Science Ann Arbor United States University of Michigan Mechanical Engineering Ann Arbor United States

ISBN: (纸本)9798350316186

Battery energy storage systems (BESS) play an essential role in modern grids by supporting renewable power systems, improving grid power quality through voltage and frequency regulation, and supporting electric vehicle (EV) charging stations. At the same time, and with the rapid growth of EVs, an enormous number of EV batteries will be retired soon. These second-use EV batteries still have approximately 80% capacity and can be utilized in stationary applications like grid-connected BESSs to reduce the emissions from producing new batteries for energy storage systems. Directly producing multilevel AC from batteries reduces cost by eliminating the need for an explicit conventional inverter. In this paper, a framework is presented for optimizing the multilevel integration of power processing in BESSs, which is particularly applicable to BESSs with heterogeneous second-use batteries. © 2023 IEEE.

关键词： Power quality

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Near-end Intelligibility Improvement Through Voice Transformation in Transfer Learning Framework 31

Near-end Intelligibility Improvement Through Voice Transform...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Biswas, Ritujoy Nathwani, Karan Abrol, Vinayak Dept. of Electrical Engineering IIT Jammu India Infosys Centre for AI Dept. of Computer Science and Engineering IIIT Delhi India

ISBN: (纸本)9789464593600

In recent works, using voice transformation functions (VTF) in optimal shifting of formants has improved near-end speech intelligibility. Though these VTFs are promising, they are computationally expensive to optimize and generate unwanted artifacts during voice modification. Additionally, they were specific to the environmental condition they were optimized for. For the applicability of this approach to different languages without re-optimization, transfer learning (TL) was used to shape the parameters of VTF to accommodate the target language [1]. However, TL across noises and TL across languages and noises (simultaneously) was not viable due to the dependency on pitch information of source and target noises. Hence in this work, a statistical Gaussian Transformation Function (GTF) is developed with parameters optimized for specific environmental conditions. Defined by just three parameters, the optimization time came down, and the intelligibility surpassed the previously used VTF. Additionally, GTF allows TL across both noises and languages simultaneously, with fewer artifacts while shifting the formants. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Speech intelligibility

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An Innovative Next Activity Prediction Approach Using Process Entropy and DAW-Transformer

arXiv

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arXiv 2025年

作者： Zare, Hadi Abbasi, Mostafa Ahang, Maryam Najjaran, Homayoun Department of Electrical and Computer Engineering Faculty of Engineering and Computer Science University of Victoria Victoria Canada Department of Mechanical Engineering Faculty of Engineering and Computer Science University of Victoria Victoria Canada

Purpose - In Business Process Management (BPM), accurate prediction of the next activities is vital for operational efficiency and decision-making. Current Artificial Intelligence (AI)/Machine Learning (ML) models struggle with the complexity and evolving nature of business process event logs, balancing accuracy and interpretability. This paper proposes an entropy-driven model selection approach and DAW-Transformer, which stands for Dynamic Attribute-Aware Transformer, to integrate all attributes with a dynamic window for better accuracy. Design/methodology/approach - This paper introduces a novel next-activity prediction approach that uses process entropy to assess the complexity of event logs and dynamically select the most suitable ML model. A new transformer-based architecture with multi-head attention and dynamic windowing mechanism, DAW-Transformer, is proposed to capture long-range dependencies and utilize all relevant event log attributes. Experiments were conducted on six public datasets, and the performance was evaluated with process entropy. Finding - The results demonstrate the effectiveness of the approach across these publicly available datasets. DAW-Transformer achieved superior performance, especially on high-entropy datasets such as Sepsis exceeding Limited window Multi-Transformers by 4.69% and a benchmark CNN-LSTM-SAtt model by 3.07%. For low-entropy datasets like Road Traffic Fine, simpler, more interpretable algorithms like Random Forest performed nearly as well as the more complex DAW-Transformer and offered better handling of imbalanced data and improved explainability. Originality/ value - This work’s novelty lies in the proposed DAW-Transformer, with a dynamic window and considering all relevant attributes. Also, entropy-driven selection methods offer a robust, accurate, and interpretable solution for next-activity prediction. © 2025, CC BY-NC-SA.

关键词： Administrative data processing

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Advanced Frequency Control for Renewable and Electric Vehicle Integrated Systems

Advanced Frequency Control for Renewable and Electric Vehicl...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Pandit, Deepak Saket, Ram K. Cai, Niannian Nguyen, Nga University of Wyoming Electrical Engineering and Computer Science LaramieWY United States Department of Electrical Engineering Uttar Pradesh Varanasi India Google AustinTX United States

ISBN: (纸本)9798350331790

Modern power systems are experiencing a rapid movement from fossil-based generations to renewable energy resources (RERs) due to concerns about the environment and the dependence on fossil fuel sources. However, the replacement of conventional generation with intermittent and low inertia RERs in the power grid can negatively affect system regulation capability. Also, the increasing integration of electric vehicles (EVs) presents concerns over power system stability and reliability. To maintain the frequency of the system within stable limits, load frequency control (LFC) regulates the power output of the generating units in response to the changes in load demand. Therefore, appropriate control strategies and optimization algorithms are required for effective LFC while RER/EV penetration increases. In this paper, an advanced LFC model of a multi-area interconnected system with EV and RER integration is developed which considers the change in system inertia that arises from generator outages or the replacement of conventional units with RERs. A non-sequential Monte Carlo simulation (MCS) is implemented to calculate the system inertia and regulation capacity of each area considering the generator failure rates and load changes. The contribution of inertial response and primary frequency regulation from EVs to the system is assessed using a simulation-based approach. A particle swarm optimization (PSO) algorithm is used to tune the parameters of the PID controllers. The efficacy of the developed algorithm is demonstrated on a two-area model of the IEEE RTS-96 system with varying RER penetration levels. © 2023 IEEE.

关键词： Renewable energy resources

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Multiscale aggregation and illumination-aware attention network for infrared and visible image fusion

Multiscale aggregation and illumination-aware attention netw...

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作者： Song, Wenhao Zhai, Wenzhe Gao, Mingliang Li, Qilei Chehri, Abdellah Jeon, Gwanggil School of Electrical and Electronic Engineering Shandong University of Technology Zibo China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Image fusion plays a significant role in computer vision since numerous applications benefit from the fusion results. The existing image fusion methods are incapable of perceiving the most discriminative regions under varying illumination circumstances and thus fail to emphasize the salient targets and ignore the abundant texture details of the infrared and visible images. To address this problem, a multiscale aggregation and illumination-aware attention network (MAIANet) is proposed for infrared and visible image fusion. Specifically, the MAIANet consists of four modules, namely multiscale feature extraction module, lightweight channel attention module, image reconstruction module, and illumination-aware module. The multiscale feature extraction module attempts to extract multiscale features in the images. The role of the lightweight channel attention module is to assign different weights to each channel so as to focus on the essential regions in the infrared and visible images. An illumination-aware module is employed to assess the probability distribution regarding the illumination factor. Meanwhile, an illumination perception loss is formulated by the illumination probabilities to enable the proposed MAIANet to better adjust to the changes in illumination. Experimental results on three datasets, that is, MSRS, TNO, and RoadSence, verify the effectiveness of the MAIANet in both qualitative and quantitative evaluations. © 2023 John Wiley & Sons, Ltd.

关键词： Image fusion

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