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Improving Power Response of Single-Controllable VSG-Based Active Distribution Network

CSEE Journal of Power and Energy Systems 2025年第1期11卷 102-114页

作者： Shah Fahad Arman Goudarzi Pierluigi Siano Ji Xiang Department of Electrical and Computer Engineering Missouri University of Science and TechnologyUSA University of Victoria Canada Department of Management&Innovation Systems University of SalernoItaly Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg2006South Africa National Laboratory of Industrial Control Technology College of Electrical EngineeringZhejiang UniversityHangzhou310058China

Controlling an active distribution network(ADN)from a single PCC has been advantageous for improving the performance of coordinated Intermittent RESs(IRESs).Recent studies have proposed a constant PQ regulation approach at the PCC of ADNs using coordination of non-MPPT based ***,due to the intermittent nature of DGs coupled with PCC through uni-directional broadcast communication,the PCC becomes vulnerable to transient *** address this challenge,this study first presents a detailed mathematical model of an ADN from the perspective of PCC regulation to realize rigidness of PCC against ***,an H_(∞)controller is formulated and employed to achieve optimal performance against disturbances,consequently,ensuring the least oscillations during transients at ***,an eigenvalue analysis is presented to analyze convergence speed limitations of the newly derived system ***,simulation results show the proposed method offers superior performance as compared to the state-of-the-art methods.

关键词： Cooperative control of DGs hierarchical control system high penetration of intermittent RESs H_(∞)control optimal control in power systems single-controllable active distribution networks

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Improved Coronary Artery Disease (CAD) Prediction using Machine Learning Method with Ensemble Technique 4

Improved Coronary Artery Disease (CAD) Prediction using Mach...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Mehmood, Waqas Khaliq, Abdul Qamar, Zoha Qudsia, Mammona Raza, Syed Saqib Capital University of Science & Technology Computer Science Department Islamabad Pakistan Capital University of Science & Technology Electrical Engineering Department Islamabad Pakistan Institute of Space Technology Electrical Engineering Department Islamabad Pakistan National University of Science & Technology Computer Science Department Islamabad Pakistan

ISBN: (纸本)9798350391862

Predicting Coronary Artery Disease (CAD) presents a critical and intricate challenge within medical science. Late-stage detection of CAD can gravely affect cardiac and vascular health, often leading to obstructions in the veins and perilous conditions. The application of Machine Learning (ML) in assessing CAD risks is pivotal for the early identification and mitigation of these severe consequences. Our study introduces a voting classifier that integrates eight distinct classification algorithms, employing an ensemble method to refine the accuracy of CAD diagnosis. This approach incorporates supervised learning algorithms such as Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees, Bagging, Boosting, with multi model approach to construct a more generalized and robust predictive framework. A comparative evaluation with existing methodologies was performed to ascertain effectiveness of our proposed model. The experimental findings reveals that our proposed technique markedly enhances diagnostic precision, achieving an accuracy rate of 99% by leveraging the benefits of multi model aggregation in ensemble learning. The high accuracy of our ensemble and more generalized model not only improves diagnostic precision but also holds potential for better patient outcomes by facilitating earlier intervention and treatment. © 2024 IEEE.

关键词： Risk assessment

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Unsupervised IC Security with Machine Learning for Trojan Detection 66

Unsupervised IC Security with Machine Learning for Trojan De...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Ghimire, Ashutosh Amsaad, Fathi Hoque, Tamzidul Hopkinson, Kenneth Rahman, Md Tauhidur Wright State University Department of Computer Science and Engineering United States University of Kansas Department of Electrical Engineering and Computer Science United States Air Force Institute of Technology Department of Computer Science United States Florida International University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350302103

The detection of Hardware Trojans is crucial for ensuring trust in the semiconductor IC supply chain. However, existing detection methods that rely on side-channel analysis often require golden chips for verification. This paper presents a new approach to Hardware Trojan detection that utilizes unsupervised machine learning with side-channel analysis to eliminate the need for golden data. Trojans of varying sizes were implemented on an FPGA to evaluate the method to perform unsupervised clustering and detect anomalies. The proposed model achieved high accuracy, 93%, and improved the detection of small and short-triggered Trojans compared to competing approaches. The unsupervised learning techniques demonstrated a better false positive rate and similar accuracy to supervised approaches such as the KNN classifier, SVM, and Gaussian classifier which require golden data for training. This research contributes a new approach to Hardware Trojan detection that can improve the trustworthiness of semiconductor IC supply chains. © 2023 IEEE.

关键词： BIRCH Clustering FPGA IC Security side-channel Trojan unsupervised

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Handling Imbalanced Data Using a Cascade Model for Image-Based Human Action Recognition

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Journal of Computing science and engineering 2023年第4期17卷 207-215页

作者： Wahyono Suprapto Rezky, Adam Rokhman, Nur Jo, Kang-Hyun Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Electrical Engineering University of Ulsan Ulsan Korea Republic of

Human action recognition plays a crucial role in intelligent monitoring systems, which are based on analyzing the possibility of anomalous events related to human behavior, such as theft, fights, and other incidents. However, by definition, anomalous events occur somewhat infrequently, thus leading to small and unbalanced data compared to data on other events. Such a data imbalance causes the human action recognition model to fail to produce optimal accuracy. To overcome the problem of imbalanced data, the typical methods used are oversampling and undersampling. However, these two methods are not considered to be very effective, because they cause the loss of a significant amount of information or deviations from reality. Therefore, the current paper proposes a cascade modeling strategy to address data imbalance problems, particularly in the case of human action recognition. The proposed strategy consists of several steps: preprocessing, feature extraction, modeling, and evaluation. The BAR dataset experiment found that the cascade model outperformed the other examined methods with an accuracy of 56.38%. However, there is still potential for further improvement through continued research. Copyright © 2023. The Korean Institute of Information Scientists and Engineers

关键词： Support vector machines

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DSTL:A dual-step transfer learning-based prediction model for next-generation intelligent cellular networks

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Intelligent and Converged Networks 2025年第1期6卷 82-101页

作者： Waqar A.Aziz Iacovos I.Ioannou Marios Lestas Vasos Vassiliou Department of Computer Science University of CyprusNicosia 2109Cyprusand also with CYENS Centre of ExcellenceNicosia 1076Cyprus Department of Electrical Engineering Frederick UniversityNicosia 1036Cyprus

Traffic modeling and prediction are indispensable to future extensive data-driven automated intelligent cellular *** contributes to proactive and autonomic network control operations within cellular *** methodologies typically rely on established prediction models designed for univariate and multivariate time series ***,these approaches often demand a substantial volume of training data and extensive computational resources for prediction model *** this study,we introduce a dual-step transfer learning(DSTL)-based prediction model specifically designed for the prediction of multivariate spatio-temporal cellular *** technique involves the categorization of gNodeBs(gNBs)into distinct clusters based on their traffic pattern *** of training the prediction model individually on each gNB,a base model is trained on the aggregated dataset of all the gNBs within a base cluster using a combination of recurrent neural network(RNN)and bidirectional long-short term memory(RNN-BLSTM)*** the first-step transfer learning(TL),the base model is provided to the gNBs within the base cluster and to the other clusters,where it undergoes the process of fine-tuning the intra-cluster aggregated *** the model is trained on the aggregated dataset within each cluster,it is provided to the gNBs within the respective cluster in the second-step *** model received by each gNB through the proposed DSTL technique either necessitates minimal fine-tuning or,in some cases,requires no further *** conduct extensive experiments on a real-world Telecom Italia cellular traffic *** results demonstrate that the proposed DSTL-based prediction model achieves a mean absolute percentage error of 2.97%,9.85%,and 9.73%in predicting spatio-temporal Internet,calling,and messaging traffic,respectively,while utilizing less computational resources and requiring less training time than traditional model training and

关键词： dual-step transfer learning(DSTL) multivariate spatio-temporal cellular traffic prediction bidirectional long-short term memory(RNN-BLSTM)

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Design and Development of a Peltier Cooling System for Photovoltaic Thermal Management Using Simulation Tools 4

Design and Development of a Peltier Cooling System for Photo...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Oeishee, Moslema Hoque Muyeed, Munyem Ahammad Mehedi, Md. Rejoan Nidhi, Ramisa Sharar Brac University Electrical and Electronic Engineering Department Dhaka Bangladesh Brac University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798331530938

Mitigating the adverse effect of high temperature on photovoltaic (PV) module's efficiency in hot environment by using a thermoelectric cooling (TEC) method with PV through a detailed analysis, is the pivot objective of this paper. This research evaluates the Peltier module's effect on the power output and temperature of the PV modules in different ambient situations for the active cooling mode of the Peltier module. We utilized PVsyst and MATLAB (for initial circuit designs), as the simulation tools to analyze the performance ratio (PR) and thermal loss factor of the PV system for both with and without cooling conditions. Moreover, under an ambient temperature of 25.2°C, our developed TEC system enhances the PV module's performance ratio ranging from 83.42% to 85.8%, while The thermal loss factor is decreased ranging from -10.36% to -6.7%. These findings indicate that controlled cooling is capable of considerably improving PV performance, resulting in significant energy output rise. In addition to creating new paths to achieve more effective and sustainable energy production, this study demonstrates the way TEC systems might enhance solar energy systems, specifically in hot environments. © 2025 IEEE.

关键词： MATLAB

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Comparative analysis of machine learning and ensemble approaches for hepatitis B prediction using data mining with synthetic minority oversampling technique

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Health and Technology 2024年第1期14卷 109-118页

作者： Alizargar, Azadeh Chang, Yang-Lang Tan, Tan-Hsu Liu, Tsung-Yu Department of Electrical Engineering College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei 10608 Taiwan Department of Multimedia and Game Science Lunghwa University of Science and Technology Taoyuan 333326 Taiwan

Purpose: Hepatitis B, caused by the Hepatitis B virus (HBV), can harm the liver without noticeable symptoms. Early detection is crucial to prevent transmission and enhance recovery. The main goal is to predict Hepatitis B through cost-effective lab test data, by utilizing machine learning. The primary focus is on evaluating the effectiveness of various algorithms in predicting the disease and their potential to enhance early diagnosis capabilities. Methods: Six distinct algorithms (Support Vector Machine, K-nearest Neighbors, Logistic Regression, decision tree, extreme gradient boosting, random forest) were employed alongside an ensemble model. Analysis involved two rounds: considering all features and key attributes. The Synthetic Minority Oversampling Technique (SMOTE) was employed for data imbalance. Various metrics, including the confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC) curve, area under the curve (AUC), and mean absolute error (MAE), were utilized to assess the efficacy of each predictive technique. The National Health and Nutrition Examination Survey (NHANES) dataset was employed. Results: The experimental results demonstrate that the ensemble model attained the highest accuracy (97%) and AUC (0.997) in comparison to existing models. The analysis revealed that specific crucial features possess substantial predictive significance within this model. Conclusion: The study underscores the potential of the ensemble model as a valuable tool for medical practitioners, leveraging cost-effective and readily obtainable laboratory test data to predict Hepatitis B with remarkable accuracy. By facilitating early diagnosis and intervention, this research presents a promising avenue to enhance patient outcomes in the context of Hepatitis B. © 2023, The Author(s) under exclusive licence to International Union for Physical and engineering sciences in Medicine (IUPESM).

关键词： Early detection Ensemble model Index terms— Hepatitis B Liver damage Machine learning SMOTE

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EarSteth: Cardiac Auscultation Audio Reconstruction Using Earbuds 46

EarSteth: Cardiac Auscultation Audio Reconstruction Using Ea...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Cao, Alvin Christofferson, Ken Ruth, Parker Rabbani, Naveed Shi, Yuanchun Mariakakis, Alex Wang, Yuntao Patel, Shwetak University of Washington Department of Electrical and Computer Engineering United States University of Toronto Department of Computer Science Canada Stanford University Computer Science Department United States Harvard Medical School Department of Pediatrics United States Tsinghua University Department of Computer Science and Technology China

ISBN: (纸本)9798350371499

Cardiac auscultation is often impractical in telehealth settings because it requires that physicians be co-located with patients in order to operate a stethoscope. We address this gap with EarSteth - a system that leverages consumer-grade active noise-cancelling earbuds to reconstruct cardiac auscultation audio signals. The system processes audio captured by the earbuds' inner microphone with a machine learning model that reconstructs audio similar to what would be produced by a digital stethoscope during cardiac auscultation. We evaluate two existing audio super-resolution CNNs and further adapt them for heart sound reconstruction, resulting in a proposed model called EarStethNet. EarSteth models were trained using synchronous audio collected from 15 healthy adult participants with an earbud and a digital stethoscope. We found that EarStethNet was able to estimate interbeat interval with a mean absolute error of 36.6 ± 51.1 ms and was able to reconstruct cardiac auscultation audio with a mean log spectral distance of 1.22 dB. © 2024 IEEE.

关键词： Image analysis

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Stable switching behavior of low-temperature ZrO_(2)RRAM devices realized by combustion synthesis-assisted photopatterning

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Journal of Materials science & Technology 2024年第22期189卷 68-76页

作者： Bongho Jang Junil Kim Jieun Lee Jaewon Jang Hyuk-Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaegu 42988Korea School of Electronic and Electrical Engineering Kyungpook National UniversityDaegu 41566Korea School of Electronics Engineering Kyungpook National UniversityDaegu 41566Korea Convergence Research Advanced Centre for Olfaction DGISTDaegu 42988Korea

We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures(250℃)using these *** synthesis reduces the energy required for oxide conversion,thus accelerating the decomposition of organic ligands in the UV-exposed area,and promoting the formation of metal-oxygen networks,contributing to *** analysis confirmed a reduction in the conversion temperature of combustion precursors,and the prepared combustion ZrO_(2)films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice,along with an amorphous ***,the synergistic effect of combustion synthesis and UV/O_(3)-assisted photochemical activation resulted in patterned ZrO_(2)films forming even more complete metal-oxygen *** devices fabricated with patterned ZrO_(2)films using combustion synthesis exhibited excellent switching characteristics,including a narrow resistance distribution,endurance of 103 cycles,and retention for 105 s at 85℃,despite low-temperature *** synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.

关键词： ZrO_(2) Combustion Sol-gel RRAM Patterning

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Offloading Using Traditional Optimization and Machine Learning in Federated Cloud-Edge-Fog Systems: A Survey

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IEEE Communications Surveys and Tutorials 2023年第2期25卷 1199-1226页

作者： Kar, Binayak Yahya, Widhi Lin, Ying-Dar Ali, Asad National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering and Computer Science Hsinchu300 Taiwan University of Brawijaya Department of Computer Science Malang65145 Indonesia National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan

The huge amount of data generated by the Internet of Things (IoT) devices needs the computational power and storage capacity provided by cloud, edge, and fog computing paradigms. Each of these computing paradigms has its own pros and cons. Cloud computing provides enhanced data storage and computing power but causes high communication latency. Edge and fog computing provide similar services with lower latency but limited capacity, capability, and coverage. A single computing paradigm cannot fulfill all the requirements of IoT devices and a federation between them is needed to extend their capacity, capability, and services. This federation is beneficial to both subscribers and providers and also reveals research issues in traffic offloading between clouds, edges, and fogs. Optimization has traditionally been used to solve the problem of traffic offloading. However, in such a complex federated system, traditional optimization cannot keep up with the strict latency requirements of decision-making, ranging from milliseconds to sub-seconds. Machine learning approaches, especially reinforcement learning, are consequently becoming popular because they could quickly solve offloading problems in dynamic environments with some unknown information. This study provides a novel federal classification between cloud, edge, and fog and presents a comprehensive research roadmap on offloading for different federated scenarios. We survey the relevant literature on the various optimization approaches used to solve this offloading problem and compare their salient features. We then provide a comprehensive survey on offloading in federated systems with machine learning approaches and the lessons learned as a result of these surveys. Finally, we outline several directions for future research and challenges that have to be faced in order to achieve such a federation. © 1998-2012 IEEE.

关键词： Fog computing

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