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Data-driven modeling of power system dynamics:Challenges,state of the art,and future work

iEnergy 2023年第3期2卷 200-221页

作者： Heqing Huang Yuzhang Lin Yifan Zhou Yue Zhao Peng Zhang Lingling Fan Department of Electrical and Computer Engineering University of Massachusetts LowellLowellMA 01852USA Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794USA Interdisciplinary Science Department Brookhaven National LaboratoryUptonNY 11973USA Department of Electrical and Computer Engineering University of South FloridaTampaFL 33620USA

With the continual deployment of power-electronics-interfaced renewable energy resources,increasing privacy concerns due to deregulation of electricity markets,and the diversification of demand-side activities,traditional knowledge-based power system dynamic modeling methods are faced with unprecedented ***-driven modeling has been increasingly studied in recent years because of its lesser need for prior knowledge,higher capability of handling large-scale systems,and better adaptability to variations of system operating *** paper discusses about the motivations and the generalized process of datadriven modeling,and provides a comprehensive overview of various state-of-the-art techniques and *** also comparatively presents the advantages and disadvantages of these methods and provides insight into outstanding challenges and possible research directions for the future.

关键词： Data-driven modeling machine learning model construction parameter identification power system dynamics system identification

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DanceOnStage: positioning training of dancing stage performance in virtual reality 6

DanceOnStage: positioning training of dancing stage performa...

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6th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2024

作者： Wu, Ssu-Hsuan Wang, Kai-Ting Chiang, Chia-Chen Chang, Hsin-Yu Yeh, Cheng-Yun Hu, Min-Chun National Tsing Hua University International Intercollegiate Ph.D.Program Hsinchu Taiwan National Tsing Hua University Master of Computer Science Program Hsinchu Taiwan National Tsing Hua University Interdisciplinary Program of Electrical Engineering and Computer Science Hsinchu Taiwan National Tsing Hua University Interdisciplinary Program of Technology and Art Hsinchu Taiwan National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9798350372021

This research aims to create a virtual reality-based practice system that simulates real-stage performance environments, assisting amateur dancers in overcoming stage fright and practicing group choreography that is difficult to achieve during individual practice. In this study, users can engage in performance simulations within the created system using the HTC VIVE virtual reality device. Within the system, we have developed a virtual stage space system that only requires a 2m x 3.5m physical area to simulate a large virtual stage experience. Users can also simulate choreographic positioning with virtual dance partners within this system, providing amateur dancers with group dance rehearsal practice opportunities that are not feasible during solo practice. Additionally, by creating three different audience modes, we aim to help users simulate various audience scenarios they may encounter during a performance. The system includes a recording feature that captures users' simulated performances, allowing them to review their actions and receive feedback after practice. © 2024 IEEE.

关键词： Virtual reality

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Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection 38

Textual Training for the Hassle-Free Removal of Unwanted Vis...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lee, Saehyung Mok, Jisoo Park, Sangha Shin, Yongho Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm Korea YH Seoul Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT. © 2024 Neural information processing systems foundation. All rights reserved.

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Adversarial Environment Design via Regret-Guided Diffusion Models 38

Adversarial Environment Design via Regret-Guided Diffusion M...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chung, Hojun Lee, Junseo Kim, Minsoo Kim, Dohyeong Oh, Songhwai Interdisciplinary Program in Artificial Intelligence ASRI Seoul National University Korea Republic of Department of Electrical and Computer Engineering ASRI Seoul National University Korea Republic of

Training agents that are robust to environmental changes remains a significant challenge in deep reinforcement learning (RL). Unsupervised environment design (UED) has recently emerged to address this issue by generating a set of training environments tailored to the agent's capabilities. While prior works demonstrate that UED has the potential to learn a robust policy, their performance is constrained by the capabilities of the environment generation. To this end, we propose a novel UED algorithm, adversarial environment design via regret-guided diffusion models (ADD). The proposed method guides the diffusion-based environment generator with the regret of the agent to produce environments that the agent finds challenging but conducive to further improvement. By exploiting the representation power of diffusion models, ADD can directly generate adversarial environments while maintaining the diversity of training environments, enabling the agent to effectively learn a robust policy. Our experimental results demonstrate that the proposed method successfully generates an instructive curriculum of environments, outperforming UED baselines in zero-shot generalization across novel, out-of-distribution environments. Project page: https://***/projects/ADD. © 2024 Neural information processing systems foundation. All rights reserved.

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A Cost-Sensitive Genetic Algorithm for Customer Segmentation in Auto Insurances 14

A Cost-Sensitive Genetic Algorithm for Customer Segmentation...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Khajenoori, Alireza Abadeh, Mohammad Saniee Mohammadzadeh, Mohsen Faculty of Interdisciplinary Science and Technology Tarbiat Modares University Tehran Iran Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9798331511272

In the auto insurance industry, accurate risk assessment is essential for determining fair premiums and managing claims effectively. As customer data grows in volume and complexity, machine learning has become crucial for precise customer segmentation and risk prediction. However, the industry faces significant challenges, particularly the class imbalance in data and the unequal costs associated with misclassification errors, where some errors are far more costly than others. This study compares two approaches to address these issues: traditional sampling techniques commonly used to mitigate class imbalance, and a cost-sensitive learning framework that optimizes the cost matrix using a genetic algorithm to minimize the financial impact of misclassification. The findings demonstrate that the cost-sensitive approach significantly enhances overall model performance by more effectively prioritizing costly errors, leading to more accurate and economically sound decision-making. This research highlights the importance of integrating advanced machine learning techniques in the insurance sector, showing that such approaches can substantially improve the fairness and efficiency of risk prediction models, ultimately benefiting insurers by enabling more precise premium setting and increasing customer satisfaction. © 2024 IEEE.

关键词： Adversarial machine learning

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Doungtap, Surasachai Petchhan, Jirayu Phanichraksaphong, Varinya Wang, Jenq-Haur International Graduate Program of Electrical Engineering and Computer Science Taiwan National Taiwan University of Science & Technology Dept. of Electrical Engineering Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle. © 2023 IEEE.

关键词： Virtual reality

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LOCOS: A cosine based local gene expression pattern finding algorithm on time-series data

LOCOS: A cosine based local gene expression pattern finding ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Suk, Youjeong Jeon, Jaemin Jung, Inuk Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of

ISBN: (纸本)9798350362480

In gene expression analysis, understanding a biological event that is observed at some time instance often requires capturing genes whose expression levels modulate before and after the event. Such genes are expected to be the responders to the event and will exhibit similar expression patterns during the event. However, after the effect of the event fades, their expression levels may loose their correlation. Hence, it is a non-trivial task to identify genes that share highly similar local expression patterns nearby the event and also allowing some level of divergent expression patterns further away from the event's time point. Here, we propose LOCOS (LOcal COSine), a novel time-course clustering algorithm tailored to cluster genes exhibiting similar expression patterns within a specified time interval. LOCOS is an extension of the traditional Non-negative Matrix Factorization (NMF), which incorporates the cosine similarity and Euclidean distances in its update procedure. LOCOS maximizes the cosine similarity within an interval of interest, while allowing a relaxed minimization of Euclidean distance outside it. Using synthetic and non-biological time-course data, we showed that LOCOS was able to correctly detect the known local patterns within a specified interval. Furthermore, we used longitudinal single-cell RNA-seq samples from four patients showing deteriorating and recovering health conditions to identify genes related to each of the phenotype. As a result, LOCOS was able to capture gene clusters with distinct expression patterns that aligned with the intervals embedding the clinical deterioration and recovery events. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Photovoltaic Power Forecasting with an Ensemble Multi-Input Deep Learning Approach 8

Photovoltaic Power Forecasting with an Ensemble Multi-Input ...

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8th International Conference on Technology and Energy Management, ICTEM 2023

作者： Dehghan, Fariba Moghaddam, Mohsen Parsa Imani, Maryam Tarbiat Modares University Faculty of Interdisciplinary Science and Technology Tehran Iran Tarbiat Modares University Faculty of Interdisciplinary Science and Technology Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665452854

The inherent intermittency of solar power due to meteorological factors presents a significant challenge in integrating them into grids on large scales. An accurate Photovoltaic (PV) power generation forecasting effectively supports achieving optimal planning and operational stability of power systems. Motivated by satisfactory performance of deep learning methods in the energy sector, an ensemble multi-input deep learning method combining three-dimensional convolution (Conv3D) networks and bidirectional long short-term memory (BiLSTM) networks is proposed in this study. First, one-dimensional (vector) PV power production and atmosphere parameters series are rearranged into a bi-dimensional matrix (2D image) and stacked in the third dimension (3D image) to preserve dependency between the input data. Then, the Conv3D network is utilized to extract non-linear spatial features of the 3D image. Finally, the BiLSTM network is implemented to learn the long-term dependencies of the extracted spatial features. The proposed method is utilized for one-day, three-day, five-day, and seven-day ahead PV power forecasting with one-hour intervals for Tennent operators, Germany dataset. Experimental results show reduced prediction error compared to two-dimensional Convolution (Conv2D), Conv3D, long short-term memory (LSTM), BiLSTM, Conv-LSTM, and ConvLSTM networks. © 2023 IEEE.

关键词： Long short-term memory

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A Communication-free EV Charge Controller for Residential Households

A Communication-free EV Charge Controller for Residential Ho...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Shafiq, Saifullah Nadarajah, Mithulananthan Al-Awami, Ali Taleb School of Electrical Engineering and Computer Science Brisbane Australia Electrical Engineering Department Dhahran Saudi Arabia Interdisciplinary Center for Smart Mobility and Logistics Kfupm Saudi Arabia

ISBN: (纸本)9781665471640

The growing popularity of electric vehicles (EVs) presents a mounting challenge for power system engineers when it comes to integrating them into residential distribution systems. In this paper, a communication-free EV charge controller is proposed that relies on nodal voltage and real-time voltage-to-load sensitivity. The primary objectives of this controller are to minimize voltage violations in the system and to ensure fair charging among EVs, regardless of their charging locations. The performance of the proposed controller is evaluated in a small 12-bus low-voltage (LV) residential system. Simulation results demonstrate the controller's effectiveness in reducing voltage violations and maintaining fairness among all the connected EVs. © 2023 IEEE.

关键词： Communication-free distribution system electric vehicles fairness voltage-to-load sensitivity

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ReMP: Reusable Motion Prior for Multi-domain 3D Human Pose Estimation and Motion Inbetweening

ReMP: Reusable Motion Prior for Multi-domain 3D Human Pose E...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Jang, Hojun Kim, Young Min Seoul National University Dept. of Electrical and Computer Engineering Korea Republic of Interdisciplinary Program in Artificial Intelligence and Inmc Seoul National University Korea Republic of

ISBN: (纸本)9798331510831

We present Reusable Motion prior (ReMP), an effective motion prior that can accurately track the temporal evolution of motion in various downstream tasks. Inspired by the success of foundation models, we argue that a robust spatio-temporal motion prior can encapsulate underlying 3D dynamics applicable to various sensor modalities. We learn the rich motion prior from a sequence of complete parametric models of posed human body shape. Our prior can easily estimate poses in missing frames or noisy measurements despite significant occlusion by employing a temporal attention mechanism. More interestingly, our prior can guide the system with incomplete and challenging input measurements to quickly extract critical information to estimate the sequence of poses, significantly improving the training efficiency for mesh sequence recovery. ReMP consistently outperforms the baseline method on diverse and practical 3D motion data, including depth point clouds, LiDAR scans, and IMU sensor data. Project page is available in https://***/ReMP. © 2025 IEEE.

关键词： Reusability

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