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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Pisco-Jordan, Juan Ramos-Pozo, Luis Townsend-Hinostroza, Danae Tapia-Rosero, Ana ESPOL Polytechnic University Department of Electrical and Computer Engineering Guayaquil Ecuador

ISBN: (纸本)9798350374575

Since arithmetic operations that involve uncertain or imprecise linguistic expressions are often mentally solved, multiple approaches have been developed to emulate them with fuzzy numbers. Each approach may provide different results, and some may depend on a specific context. It is common to compare and analyze these approaches to determine which one is the best in different contexts. These comparisons can be laborious and prone to errors. In this paper a context-free calculator for fuzzy arithmetic is proposed. To facilitate the analysis of comparisons, this calculator is capable of solving an arithmetic problem using multiple approaches while providing reports. The first report shows the solution of each approach while the second report shows their respective graphical representation. The calculator has nine different approaches for addition, subtraction and multiplication with the flexibility to incorporate new approaches for further study. © 2024 IEEE.

关键词： Open source software

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Coupon-based Demand Response for Consumers Facing Flat-rate Retail Pricing

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 1887-1900页

作者： Hongxun Hui Yi Ding Kaining Luan Tao Chen Yonghua Song Saifur Rahman State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao 999078China. College of Electrical Engineering Zhejiang UniversityHangzhou 310027China State Grid Jiangsu Electric Power Co. LTD.Nanjing 210000China School of Electrical Engineering Southeast UniversityNanjing 210096China Advanced Research Institute and Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State UniversityVA 22203USA

Even though smart meters have been widely used in power systems around the world,many consumers are still finding it hard to participate in demand response(DR)due to flat-rate retail pricing *** address this issue,this paper proposes a coupon-based demand response(CDR)scheme to achieve equivalent dynamic retail prices to inspire consumers’inherent ***,a security-constrained unit commitment optimization model is developed in the day-ahead market to obtain coupon rewards,which are then broadcast to consumers to motivate them to reschedule their power consumption *** evaluate the adjustment value of consumers’power consumption,a collective utility function is proposed to formulate the relationship between power quantity and coupon *** this basis,the security-constrained economic dispatch model is developed in the intra-day market to reschedule generating units’output power according to real-time load demands and fluctuating renewable *** the operation interval,a settlement method is developed to quantify consumers’electricity fees and coupon benefits on a monthly *** proposed CDR scheme avoids real-time iterative bidding process and effectively decreases the difficulty of massive,small consumers participating in *** proposed CDR is implemented in a realistic DR project in China to verify consumers’energy cost and renewables’curtailment can both be decreased.

关键词： Coupons demand response flat-rate retail pricing renewable energies small consumers

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A Privacy-preserving Energy Management System Based on Homomorphic Cryptosystem for IoT-enabled Active Distribution Network

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 167-178页

作者： Qian Hu Siqi Bu Wencong Su Vladimir Terzija the Department of Physics Hong Kong Baptist UniversityHong KongChina the Department of Electrical and Electronic Engineering Shenzhen Research InstituteThe Hong Kong Polytechnic UniversityHong KongChina the Department of Electrical and Computer Engineering University of Michigan-DearbornDearbornMI 48128USA the School of Engineering Newcastle UniversityNewcastleUK

Active distribution network(ADN),as a typically cyber-physical system,develops with the evolution of Internet of Things(IoTs),which makes the network vulnerable to cybersecurity *** this paper,the eavesdropping attacks that lead to privacy breaches are addressed for the IoT-enabled ADN.A privacy-preserving energy management system(EMS)is proposed and empowered by secure data exchange protocols based on the homomorphic *** the information transmission among distributed generators and load customers in the EMS,private information including power usage and electricity bidding price can be effectively protected against eavesdropping *** correctness of the final solutions,e.g.,optimal market clearing price and unified power utilization ratio,can be deterministically *** simulation results demonstrate the effectiveness and the computational efficiency of the proposed homomorphically encrypted EMS.

关键词： Eavesdropping attack energy management system homomorphic cryptosystem Internet of Things(IOTs) active distribution network(ADN) privacy-preserving

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Contrastive Self-Supervised Speaker Embedding With Sequential Disentanglement

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 2704-2715页

作者： Tu, Youzhi Mak, Man-Wai Chien, Jen-Tzung Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong SAR999077 Hong Kong National Yang Ming Chiao Tung University Institute of Electrical and Computer Engineering Hsinchu30010 Taiwan

Contrastive self-supervised learning has been widely used in speaker embedding to address the labeling challenge. Contrastive speaker embedding assumes that the contrast between the positive and negative pairs of speech segments is attributed to speaker identity only. However, this assumption is incorrect because speech signals contain not only speaker identity but also linguistic content. In this paper, we propose a contrastive learning framework with sequential disentanglement to remove linguistic content by incorporating a disentangled sequential variational autoencoder (DSVAE) into the conventional contrastive learning framework. The DSVAE aims to disentangle speaker factors from content factors in an embedding space so that the speaker factors become the main contributor to the contrastive loss. Because content factors have been removed from contrastive learning, the resulting speaker embeddings will be content-invariant. The learned embeddings are also robust to language mismatch. It is shown that the proposed method consistently outperforms the conventional contrastive speaker embedding on the VoxCeleb1 and CN-Celeb datasets. This finding suggests that applying sequential disentanglement is beneficial to learning speaker-discriminative embeddings. © 2014 IEEE.

关键词： E-learning

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Mitigating Power System Oscillations in PMSGWind Integrated Grids Using Power Electronics and Inertia Control: A Comprehensive Review 16

Mitigating Power System Oscillations in PMSGWind Integrated ...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Sabo, Aliyu Jibia, Kabir Garba Shahinzadeh, Hossein Hayati, Mohammad Mohsen Abapour, Mehdi Gharehpetian, Gevork B. Department of Electrical and Electronic Engineering Malaysia Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tabriz Iran Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798331533946

The growing demand for reliable and sustainable energy has driven researchers and engineers to explore renewable energy sources to complement traditional power grids. Wind energy conversion systems (WECS), particularly those using permanent magnet synchronous generators (PMSG), are among the most widely adopted renewable energy solutions. Integrating WECS into traditional grids introduces challenges, including power system oscillations, especially during disturbances or faults. Inertia control and power electronics converters are pivotal in mitigating these oscillations and ensuring grid stability. This review investigates the design and optimization of damping controllers, emphasizing their integration with excitation systems to enhance the dynamic performance of PMSG-based wind integrated grids. Additionally, the role of power electronics in control strategies and inertia emulation for stable grid operation is discussed. The findings highlight the critical importance of advanced control measures in addressing the inherent uncertainties of renewable energy systems integrated with conventional grids. © 2025 IEEE.

关键词： Synchronous generators

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Evaluation of Tea Leaf Disease Identification Based on Convolutional Neural Networks VGG16, ResNet50, and DenseNet169 Image Recognitions 12

Evaluation of Tea Leaf Disease Identification Based on Convo...

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12th International electrical engineering Congress, iEECON 2024

作者： Deng, Xianghong Photong, Chonlatee Mahasarakham University Electrical And Computer Engineering Faculty Of Engineering Maha Sarakham Thailand Hunan Mechanical & Electrical Polytechnic Changsha China

ISBN: (纸本)9798350383591

Tea is the most favorite drink in the world and its demand seems to continuously increase;therefore, large amount of tea production while retaining high-quality tea is required for the marketplaces. However, based on many research literacies, the yield and quality of tea are possibly affected by a diversity of diseases, which would need high accuracy in identification in order to determine the most suitable treatment or specific preventive actions. In this study, the tea leaf disease identification based on the three most widely used convolutional Neural Networks VGG16, Resnet50, and DenseNet169 were examined and evaluated. By accommodating the relevant parameters of these three identification models and the learning rates, the performances of the three convolutional neural networks were analyzed and compared. The simulated test results showed that VGG16, Resnet50, and DenseNet169 achieved the highest accuracy rates of 90.6%, 95.2%, and 99.0%, respectively. DenseNet169 provided the fastest convergence speed and the highest accuracy among the models. In addition, the DenseNet169 showed the possibility of the precise diagnosis and identification of other crop leaf diseases as well. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Simulation of Electric Vehicle Energy Consumption: Incorporating Route Planning and Machine Learning-Based Predictions 2

A Comprehensive Simulation of Electric Vehicle Energy Consum...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Baroughi, Reyhaneh Mehdizadeh Attar, Hani Rastegari, Farzaneh Electrical and Computer Engineering Polytechnic University of Turin Turin Italy Zarqa University Faculty of Engineering Jordan College of Business Administration University of Business and Technology Jeddah21448 Saudi Arabia University of Connecticut Computer Science and Engineering Department Storrs United States

ISBN: (纸本)9798350373363

This study presents an evaluation of predictive models for electric vehicle (EV) energy consumption in a simulated environment. Utilizing a synthetic elevation grid and considering variables such as weather conditions and traffic levels, we implemented the A∗ algorithm to simulate EV routes and calculate energy costs. The generated dataset was used to train three regression models: Gradient Boosting, Random Forest, and Linear Regression. Model performance was assessed using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the R-squared (R2) score. Results indicated that the predictive accuracy of the models was limited, with all models exhibiting low R2 scores and high MSE and MAE values. The study highlights the challenges in predicting EV energy consumption and suggests the need for more complex models or additional features that can better capture the dynamics of energy usage in EVs. © 2023 IEEE.

关键词： Mean square error

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Investigation of a New Stator Permanent Magnet with the effects of Flux-Reversal and Flux-Switching 16

Investigation of a New Stator Permanent Magnet with the effe...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Sarshar, Mohammad Reza Kondelaji, Mohammad Amin Jalali Mirsalim, Mojtaba Montazeri, Seyed Hamid Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tehran Iran Department of Electrical Engineering and Computer Science Cambridge United States

ISBN: (纸本)9798331533946

This article studies a new stator-permanent magnet (PM) motor with flux-switching (FS) and flux-reversal (FR) effects synergies. The proposed structure benefits from the splitting stator pole PMs and consequent-pole FRPM, which reduces the PM utilization compared to the conventional ones. Also, the proposed structure is optimized by a random search multiobjective algorithm to attain the structure with the highest output torque and minimum torque ripple. Additionally, the proposed motor is compared to a conventional FSPM to investigate the advantages of the main motor. Using finite element analysis (FEA) studies, the flux density distribution results show that the FR effect lowers the saturation risk compared to FSPM. Also, the back-EMF generation of the proposed motor has improved by 135% compared to its benchmark. Correspondingly, fullload results show that the proposed topology has enhanced the average torque by 75% and deteriorated the ripple by about 55% compared to FSPM. Therefore, the proposed structure offers a profitable for high torque density applications. © 2025 IEEE.

关键词： Permanent magnets

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Exploring the Relationship Between Air Pollution and CNS Disease Mortality in Italy: A Forecasting Study with ARIMA and XGBoost

Exploring the Relationship Between Air Pollution and CNS Dis...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Karami, Mostafa Hamzehei, Sah Rastegari, Farzaneh Akbarzadeh, Omid Polytechnic University of Turin Electronics and Telecommunications Turin Italy University of Connecticut Computer Science and Engineering Department Storrs United States

ISBN: (纸本)9798350327595

There is a correlation between air pollution and Central Nervous System (CNS) disease, according to the research. Air pollution has been linked to an increased risk of developing neurodegenerative diseases like Alzheimer's and Parkinson's. Airborne toxins can penetrate the brain and induce inflammation, resulting in damage and cognitive decline. Air pollution is a significant environmental health issue that can harm people's health in a variety of ways, such as by increasing their risk of heart and respiratory diseases. From 2010 to 2020, we looked at data from 107 Italian communes about air pollution and deaths caused by CNS diseases like Alzheimer's and Parkinson's. Then, we compared the performance of the Autoregressive Integrated Moving Average (ARIMA) model and Extreme Gradient Boosting (XGBoost) with grid search in terms of accuracy and training time for forecasting the number of deaths caused by CNS diseases in the forthcoming year. According to our research, both models could accurately forecast how much PM2.5 and PM10 were in the air and how many people would die from CNS diseases. Also, ARIMA is more accurate than XGBoost, but it takes longer to train. Both ARIMA and XGBoost are good at forecasting CNS death based on air pollution, which is what our result shows. Overall, our findings indicate that air pollution is a significant risk factor for CNS diseases, and accurate forecasting models can aid in mitigating its effects. © 2023 IEEE.

关键词： air pollution CNS disease environmental health Italy machine learning PM10 PM2.5 predictive modeling public health

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Efficient Integrated Magnetics with Winding Cancellation Technique to Reduce Common-Mode EMI Noise for A Single Phase CLLC Converter

Efficient Integrated Magnetics with Winding Cancellation Tec...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Jin, Feng Yuan, Tianlong Nabih, Ahmed Li, Zheqing Li, Qiang Virginia Polytechnic and State University Bradley Department of Electrical and Computer Engineering Blacksburg United States

ISBN: (纸本)9798350376067

This paper proposed a PCB-based integrated magnetics with a winding cancellation technique to reduce common-mode (CM) noise for an 11kW single-phase CLLC (1PCLLC) resonant converter. Shielding design is effective in reducing CM noise, however, its benefits come along with increased cost with more layers of Printed-Circuit-Board (PCB) and increased winding loss due to increased eddy effect in the shielding layers. More shielding layers are required for multiple-turn applications, which makes the idle shielding design unattractive. Implementing windings as active shielding for noise cancellation is a good solution. A symmetrical 1PCLLC converter with symmetrical resonant tanks and symmetrical integrated magnetics is required to achieve the winding cancellation technique. The effectiveness of the proposed concept was demonstrated through a high-efficiency, high-power density 1PCLLC DC/DC converter for an 800V 11kW onboard charger (OBC). The designed converter can achieve a power density of 420 W/in3 and a peak efficiency of 98.5%. Compared with the existing solution, the proposed integrated transformer with winding cancellation techniques shows 17dB CM noise reduction at switching frequency. © 2024 IEEE.

关键词： Noise abatement

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