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Binary feature mask optimization for feature selection

Neural Computing and Applications 2025年第6期37卷 5155-5167页

作者： Lorasdagi, Mehmet E. Turali, Mehmet Y. Kozat, Suleyman S. Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles United States

We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and multilayer perceptron as our machine learning models. Our results show that our methods outperform traditional feature selection techniques. Specifically, in experiments with the residential building dataset, our general binary mask optimization algorithm has reduced the mean squared error by up to 49% compared to conventional methods, achieving a mean squared error of 0.0044. The high performance of our general binary mask optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly s

关键词： Mean square error

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Accurate Representation of Distribution System Dynamics With DERs for Bulk System Studies

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IEEE Transactions on Industry Applications 2025年第3期61卷 5187-5197页

作者： Challa, Kiran Kumar Matavalam, Amarsagar Reddy Ramapuram Venkatraman, Ramakrishnan Ajjarapu, Venkataramana Iowa State University Department of Electrical and Computer Engineering Ames50011 United States Arizona State University School of Electrical Computer and Energy Engineering Tempe85282 United States

While there has beensignificant progress in recent years to incorporate the dynamics of distribution systems and industrial loads into the transmission using aggregated composite load models (CLM);there is no study that evaluates the accuracy of a well-tuned CLM model versus the true dynamics of a distribution system (DS) with 3-φ, 1-φ motors, ZIP loads, and distributed energy resources (DERs) connected to a transmission system (TS). This paper aims to address this gap by analyzing the limitations of the CLM and proposing physics-based approaches to address these drawbacks. We show that a single CLM is not flexible enough to represent the aggregated DS dynamics in situations when only a few motors in the DS stall or a few DERs trip. To address the drawback of the single CLM, we propose a DS structure preserving reduced order model (DS-ROM) that clusters nodes in the DS and represents each cluster using a CLM. We then present a Python-based automated approach to determine the parameters of the DS-ROM. Results on IEEE-14-bus TS connected to IEEE-37-node DS and a practical 240-node DS demonstrate that DS-ROM can capture the DS dynamics for various kinds of TS faults with an accuracy of ∼98%. © 2025 IEEE.

关键词： Problem oriented languages

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Power System Reliability Evaluation Based on Sequential Monte Carlo Simulation Considering Multiple Failure Modes of Components

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 202-214页

作者： Wei Huang Bo Hu Changzheng Shao Wei Li Xiaozhe Wang Kaigui Xie C.Y.Chung State Key Laboratory of Power Transmission Equipment Technology School of Electrical EngineeringChongqing UniversityChongqingChina China Southern Power Grid GuangzhouChina Department of Electrical and Computer Engineering McGill UniversityMontrealCanada Department of Electrical and Electronic Engineering Hong Kong Polytechnic UniversityHong KongChina

The component aging has become a significant concern worldwide,and the frequent failures pose a serious threat to the reliability of modern power *** light of this issue,this paper presents a power system reliability evaluation method based on sequential Monte Carlo simulation(SMCS)to quantify system reliability considering multiple failure modes of ***,a three-state component reliability model is established to explicitly describe the state transition process of the component subject to both aging failure and random failure *** this model,the impact of each failure mode is decoupled and characterized as the combination of two state duration variables,which are separately modeled using specific probability ***,SMCS is used to integrate the three-state component reliability model for state transition sequence generation and system reliability ***,various reliability metrics,including the probability of load curtailment(PLC),expected frequency of load curtailment(EFLC),and expected energy not supplied(EENS),can be *** ensure the applicability of the proposed method,Hash table grouping and the maximum feasible load level judgment techniques are jointly adopted to enhance its computational *** studies are conducted on different aging scenarios to illustrate and validate the effectiveness and practicality of the proposed method.

关键词： Power system reliability evaluation aging failure sequential Monte Carlo simulation

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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Alkali Metal Doping on Sol-Gel-Derived Copper Oxide for Performance Enhancement of Thin-Film Transistors

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第6期3卷 228-233页

作者： Baek, Seokhyeon Choi, Jun-Gyu Park, Sungjun Ajou University Department of Intelligence Semiconductor and Engineering Suwon16449 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16449 Korea Republic of Ajou University The Department of Electrical and Computer Engineering Suwon16449 Korea Republic of

In recent years, copper oxide (CuxO) has emerged as a promising p-type oxide semiconductor owing to its high Hall mobility. However, its inherent drawbacks, such as the substantial native defects and uncontrolled stoichiometry, limit its application in thin-film transistors (TFTs) for energy-efficient complementary devices. In this study, we employ the sol-gel synthetic approach for facile doping to investigate the doping effects of alkali metals (Li, Na, and K) on the electrical performance of CuxO TFTs. The results demonstrate that doping, particularly with lithium (Li), significantly improves the electrical performance of CuxO TFTs. The similarity in ionic radius facilitates efficient hole transport, which considerably enhances the field-effect mobility (4.7×10-3 cm2/Vs), subthreshold swing (SS) (8.3 V/dec), and on-off current ratio (∼104) with minimized hysteresis. The structural analysis of the Li-doped CuxO films using X-ray diffraction (XRD) does not exhibit any significant lattice distortion and an increase in the grain size implies the reduction of trap sites. Consequently, the successful fabrication of Li-doped CuxO TFTs on polyimide substrates, using ZrOxO as a gate dielectric layer, demonstrates its compatibility with flexible electronics. This approach enhances the electrical performance of p-type CuxO TFTs and presents a scalable and efficient pathway for the development of advanced TFT technology in flexible electronics. © 2022 IEEE.

关键词： Sol-gel process

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Hybrid two-level protection system for preserving pre-trained DNN models ownership

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Neural Computing and Applications 2024年第34期36卷 21415-21449页

作者： Fkirin, Alaa Moursi, Ahmed Samy Attiya, Gamal El-Sayed, Ayman Shouman, Marwa A. Department of Electrical Engineering Faculty of Engineering Fayoum University Fayoum Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia Governorate Menouf32952 Egypt

Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands substantial time, vast datasets and high computational costs. However, these valuable models face significant risks. Attackers can steal and sell pre-trained DNN models for profit. Unauthorised sharing of these models poses a serious threat. Once sold, they can be easily copied and redistributed. Therefore, a well-built pre-trained DNN model is a valuable asset that requires protection. This paper introduces a robust hybrid two-level protection system for safeguarding the ownership of pre-trained DNN models. The first-level employs zero-bit watermarking. The second-level incorporates an adversarial attack as a watermark by using a perturbation technique to embed the watermark. The robustness of the proposed system is evaluated against seven types of attacks. These are Fast Gradient Method Attack, Auto Projected Gradient Descent Attack, Auto Conjugate Gradient Attack, Basic Iterative Method Attack, Momentum Iterative Method Attack, Square Attack and Auto Attack. The proposed two-level protection system withstands all seven attack types. It maintains accuracy and surpasses current state-of-the-art methods. © The Author(s) 2024.

关键词： Deep neural networks

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EEG Prediction with Deep Recurrent Learning

EEG Prediction with Deep Recurrent Learning

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Zou, Jiadao Zhang, Qingxue Purdue Univeristy School of Engineering and Technology Electrical and Computer Engineering IN United States Purdue Univeristy School of Engineering and Technology Electrical and Computer Engineering Biomedical Engineering IN United States

ISBN: (纸本)9798350324136

Electroencephalogram (EEG) reveals the brain dynamics that are of a great value in clinical applications, e.g., epilepsy diagnosis, cognitive status, and other disorders. Intracranial Electroencephalogram (iEEG) is encoding rich dynamics, with its invasive electrodes that go deep into the brain. Despite the high precision, the iEEG surgery may limit its use in daily lives due to the risk, setup time and cost. In this study, we propose a deep learning framework that can reconstruct iEEG from scalp EEG, thereby demonstrating the potential of convenient deep brain monitoring in daily lives. We leverage a long short-Term memory (LSTM) model for ultra-deep iEEG prediction from the non-invasive scalp EEG. Our simulation has demonstrated that, our deep learning model, with the nonlinear dynamics mining capability, facilitates the deep source signal estimation performance, compared with the linear model. Furthermore, we find that the estimation performance may be improved with selection of more EEG measurement sites. This study will greatly advance brain dynamics decoding and broadly benefit diverse brain-computer-interface and smart health applications. © 2024 IEEE.

关键词： Brain computer Interface Electroencephalogram Recurrent Learning Smart Health

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QoS-Aware Application Assignment and Resource Utilisation Maximization Using AHP in Edge Computing

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IEEE Internet of Things Journal 2025年第11期12卷 17717-17728页

作者： Koganti, Yasasvitha Sridhar, Vidhyuth Yadav, Ram Narayan Pratap, Ajay Iitram Department of Electrical and Computer Science Engineering Ahmedabad380026 India Indiana University Department of Computer Science and Engineering Bloomington United States Banaras Hindu University Indian Institute of Technology Department of Computer Science and Engineering Varanasi India

Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can be done in the proximity of IoT users. Thus, the required constraints related to tasks such as latency requirements, and the Quality Of Service (QoS) can be guaranteed. However, the question that how to determine the task offloading strategy under various constraints of resources, distance, and cost is still an open issue. In this paper, we study the task offloading problem from a matching perspective and propose an Edge-User Assignment Algorithm (EUAA) that aims to maximize the resource utilization of edge servers and maximize the number of assigned IoT users. The main concern, in any matching algorithm, is how to generate the preference order of either side. To generate preference orders for edge servers, we use the concept of the Analytical Hierarchy Process (AHP). We have considered the following criteria: distance from users to the server, latency, available resources, and pricing. This generates the priority of the users for matching to edge servers. From IoT users' perspective, we use cost, and QoS parameters to improve their satisfaction. We have used state-of-the-art schemes to compare the performance of the proposed algorithm under different simulation scenarios. We compare the performance using the number of assigned users, servers' profit, number of satisfied users, edge server resources used, and execution time. The simulation results confirm that significant profit and satisfied user can be achieved by the proposed algorithm. © 2014 IEEE.

关键词： Computation offloading

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Cardiac Ischemia Detection Using Self-Organizing Map Feature Selection Method 31

Cardiac Ischemia Detection Using Self-Organizing Map Feature...

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31st National and 9th International Iranian Conference on biomedical engineering, ICBME 2024

作者： Karampour, Ramin Aghili Bakhtiar, Amir Arshia Hajizamani, Kiarash Fallahi, Alireza Zadeh, Hamid Soltanian Hamedan University of Technology Biomedical Engineering Department Hamedan Iran School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529710

Cardiac ischemia, a prevalent cause of heart failure, remains the leading cause of death in Iran. Early diagnosis of this condition is crucial, and electrocardiogram (ECG) signal processing techniques offer valuable insights. By analyzing ECG signals from ischemic patients and comparing them to those of normal individuals, we can extract relevant features for classification. Our study extracted some features from ECG signals, including statistical, time-based, frequency, and time-frequency features. We employed the self-organizing maps method (SOM) to identify the most informative features. Subsequently, we evaluated the classification accuracy using several machine learning algorithms, including support vector machines (SVM), Naive Bayes, Linear Discriminant Analysis (LDA), and Logistic Regression. The results demonstrated the superiority of the SOM method in selecting the best features compared to PCA, with accuracies of 9 1.2%, 84.3%, 72.5%, and 88.9% for SVM, LDA, NB, and LR methods, respectively. Our findings contribute to the early detection and management of cardiac ischemia, potentially improving patient outcomes. © 2024 IEEE.

关键词： Conformal mapping

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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