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Electric And Power engineering (EPEi), IEEE International Conference And Exposition On

作者： A S M Jahid Hasan Md Shazid Islam Md Saydur Rahman Md Saiful Islam Sajol Jubair Yusuf Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Electrical and Computer Engineering University of California Riverside Riverside CA USA Department of Computer Science and Engineering Louisiana State University Baton Rouge LA USA

ISBN: (数字)9798350356199

ISBN: (纸本)9798350356205

Buildings consume a significant portion of a nation’s total energy, accounting for about 70 percent of electricity use in the US. Predicting building energy demand accurately is crucial for utilities to ensure efficient operation. Deep learning-based approaches have demonstrated great potential for this task. However, building loads are heavily influenced by local weather conditions. Conventional deep learning models are typically location-specific and demand extensive local data for accurate predictions. A location independent model can offer great advantage, which can be attained through application of domain adaptation. This method involves training a deep learning model initially on a source domain dataset and then use this pre-trained model to perform prediction on target domain dataset, where the probability distributions may differ but are comparable. This study applies domain adaptation techniques to develop a deep learning model that predicts building energy demand independent of location. Our proposed model uses a pre-trained deep learning architecture trained on data from one location to predict building load in another location, requiring only minimal data from the target domain. The findings demonstrate that the domain-adapted model achieves improvements in both accuracy and computational speed compared to non-adapted models.

关键词： Deep learning Training Adaptation models Energy consumption Accuracy Computational modeling Buildings Predictive models Data models Load modeling

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Differentially Private Coded Computing

Differentially Private Coded Computing

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Liu, Hsuan-Po Soleymani, Mahdi Mahdavifar, Hessam University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Halicioglu Data Science Institute University of California San Diego La Jolla CA92093 United States

ISBN: (纸本)9781665475549

Distributed computing has attracted significant recent attention for speeding up large-scale computations by disseminating computational jobs from a central master node across several worker nodes/servers. However, worker nodes are often untrusted and can also collude to gain unauthorized access to sensitive data. Hence, sharing sensitive data with them raises data privacy concerns. Coded computing has emerged as a promising framework for speeding up distributed computing and can be also adapted to address security and privacy concerns utilizing tools from secret sharing and multi-party computing. However, ensuring perfect information-theoretic privacy imposes a strict threshold on the maximum number of colluding workers the protocol can tolerate and, also, necessitates quantizing/mapping data to finite fields. Differential privacy is a widely accepted practical measure to capture the privacy leakage of the shared data. The mainstream approach is then to add perturbations to the data via randomized mechanisms. In this paper, we revisit coded computing, and especially when it is adapted to handle real-valued data, and analyze the privacy guarantees through the lens of differential privacy in terms of the (?,d)-differential privacy metric, for the first time in the literature. All the computations are done over the field of real/complex numbers and data privacy, in terms of differential privacy, is attained by adding noise terms in a certain structured way. In particular, the noise is added through the secret sharing mechanism (which can be, in principle, decoded and cancelled out at the master) as means of ensuring differential privacy. Furthermore, we propose a differentially private distributed matrix multiplication protocol for matrix multiplications that keeps the privacy of data in the worst adversarial case. © 2023 IEEE.

关键词： Sensitive data

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E²GO: Free Your Hands for Smartphone Interaction

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International Journal of Human-computer Interaction 2025年

作者： Yan, Shaoming Ju, Yuanliang Quan, Rong Tu, Huawei Liang, Dong MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Information Technology La Trobe University Melbourne Australia

Current eye-gaze interaction technologies for smartphones are considered inflexible, inaccurate, and power-hungry. These methods typically rely on hand involvement and accomplish partial interactions. In this paper, we propose a novel eye-gaze smartphone interaction method named Event-driven Eye-Gaze Operation ((Formula presented.)), which can realize comprehensive interaction using only eyes and gazes to cover various interaction types. Before the interaction, an anti-jitter gaze estimation method was exploited to stabilize human eye fixation and predict accurate and stable human gaze positions on smartphone screens to further explore refined time-dependent eye-gaze interactions. We also integrated an event-triggering mechanism in (Formula presented.) to significantly decrease its power consumption to deploy on smartphones. We have implemented the prototype of (Formula presented.) on different brands of smartphones and conducted a comprehensive user study to validate its efficacy, demonstrating (Formula presented.) ‘s superior smartphone control capabilities across various scenarios. Demo videos. © 2025 Taylor & Francis Group, LLC.

关键词： Smartphones

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Cloud Resource Allocation with Convex Optimization

arXiv

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arXiv 2025年

作者： Boghani, Shayan Kirimlioglu, Emin Moturi, Amrita Tso, Hao-Ting Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States

We present a convex optimization framework for overcoming the limitations of Kubernetes Cluster Autoscaler by intelligently allocating diverse cloud resources while minimizing costs and fragmentation. Current Kubernetes scaling mechanisms are restricted to homogeneous scaling of existing node types, limiting cost-performance optimization possibilities. Our matrix-based model captures resource demands, costs, and capacity constraints in a unified mathematical framework. A key contribution is our logarithmic approximation to the indicator function, which enables dynamic node type selection while maintaining problem convexity. Our approach balances cost optimization with operational complexity through interior-point methods. Experiments with real-world Kubernetes workloads demonstrate reduced costs and improved resource utilization compared to conventional Cluster Autoscaler strategies that can only scale up or down existing node pools. © 2025, CC BY-SA.

关键词： Convex optimization

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Hands-On Learning: Teaching Parallel and Distributed Computing through Unplugged Activities in Undergraduate CS Courses

Hands-On Learning: Teaching Parallel and Distributed Computi...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Anurag Dasgupta Venkat Margapuri Simon Shamoun Shubbhi Taneja Matthew Toups Computer Science and Engineering Technology Valdosta State University Valdosta GA Department of Computing Sciences Villanova University Villanova PA Department of Computer Science Hofstra University Hempstead NY Department of Computer Science Worcester Polytechnic Institute Worcester Massachusetts Computer Science Department Tulane University New Orleans LA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

The authors present and evaluate an unplugged activity to introduce parallel computing concepts to undergraduate students. Students in five CS classrooms used a deck of playing cards in small groups to consider how parallelization can improve performance and how improvement decreases with increased parallelization. Before and after the activity, students took a short survey about their solution and their ideas about parallelism. The authors carried out this activity in seven courses at five institutions in the 2023-2024 academic year. The results showed that students had an increased appreciation for parallelization and this type of activity.

关键词： Surveys High performance computing Conferences Education Parallel processing Distributed computing

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Toward VR in VR: Assessing Engagement and Social Interaction in a Virtual Conference

Toward VR in VR: Assessing Engagement and Social Interaction...

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作者： Moreira, Catarina Simoes, Francisco P. M. Lee, Mark J. W. Zorzal, Ezequiel R. Lindeman, Robert W. Pereira, Joao Madeiras Johnsen, Kyle Jorge, Joaquim Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Universidade Federal Rural de Pernambuco Departamento de Computação Recife52171-900 Brazil La Trobe University Computer Science and Information Technology BundooraVIC3086 Australia University of Canterbury HIT Lab NZ Christchurch8041 New Zealand Instituto Superior Técnico Universidade de Lisboa INESC-ID Department of Computer Science and Engineering Lisboa1000-029 Portugal University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

The pandemic brought about an unprecedented number of virtual conferences, given the heavy restrictions on travel to in-person meetings. Despite all the advances in technology, people still complain about virtual events. There is Zoom fatigue, confinement malaise, and a longing for personal social interactions. This paper discusses our experience organizing the IEEE Virtual Reality Conference (IEEE VR) as a virtual event. IEEE VR was a success with 1200+ registered paying participants, dozens of workshops and tutorials, and hundreds of technical papers. We used (1) a virtual environment platform, together with (2) discussion tools and (3) videoconferencing/broadcast/online tools to further provide effective social interaction and increase engagement. In this paper, we explore the synergies between virtual environments and other online tools and assess user engagement by analyzing the messages exchanged between participants across different genders and geographical regions. To this end, we apply diverse engagement metrics for online conferences. Our analysis shows that these metrics have the potential to highlight engagement, diversity, and inclusion by combining textual messages, participant geographic and gender information, communities of participants, and visitation patterns in a virtual environment. Drawing on our results and experiences, we propose guidelines for organizing technical virtual events to increase diversity and social interaction. © 2013 IEEE.

关键词： Virtual reality

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Forecasting Smart Grid Stability Using Machine Learning Models

Forecasting Smart Grid Stability Using Machine Learning Mode...

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AI, science, engineering, and Technology (AIxSET), Conference on

作者： Raja Kandukuri Yaser M. Banad Department of Computer Science Southern University Baton Rouge LA USA School of Electrical and Computer Engineering University of Oklahoma Norman OK USA

ISBN: (数字)9798350390995

ISBN: (纸本)9798350391008

This paper presents a comprehensive study on designing and evaluating machine learning models for forecasting smart power grid stability. The stability of power grids is crucial for balancing electricity supply and demand, but it is challenged by the increasing use of renewable resources and dynamic pricing. Concepts like Decentralized Smart Grid Control (DSGC) aim to address this issue. We developed and compared machine learning models, including Support Vector Machines (SVM), LightGBM, and deep learning architectures, to identify the most accurate approach for forecasting grid stability. Our results showed that the SVM model achieved the highest accuracy of 99.9%, outperforming other models. These outcomes provide valuable insights and practical recommendations for deploying effective forecasting models in real-world smart grid scenarios, ensuring stability, efficient energy management, and seamless integration of renewable resources.

关键词： Support vector machines Machine learning algorithms Accuracy Machine learning Power system stability Predictive models Prediction algorithms Stability analysis Smart grids Forecasting

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A New Combined PV Output Power Forecasting Model Based on Optimized LSTM Network

A New Combined PV Output Power Forecasting Model Based on Op...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Heydari, Azim Keynia, Farshid Fekih, Afef Tjernberg, Lina Bertling Barchi, Grazia Moser, David Eurac Research Institute for Renewable Energy Bolzano Italy Graduate University of Advanced Technology Institute of Science and High Technology and Environmental Sciences Department of Energy Management and Optimization Kerman Iran University of Louisiana at Lafayette Department of Electrical and Computer Engineering LafayetteLA United States KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350331790

One of the most challenging problems in designing and implementing effective management strategies and demand responses in renewable-rich grids is the uncertainty associated with the power output (PO) of solar photovoltaic (PV) systems. The exact and trustworthy prediction of PV power can provide substantial decision support for planning and operating power systems. This paper develops an intelligent PV output power (PV-OP) forecasting model. The proposed PV-OP forecasting model consists of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) signal decomposition model to decompose the original PV output power signal to different frequencies, LSTM neural network as a main forecaster engine, and multi-objective NSGA-II optimization algorithm as a hyperparameters optimizer. The developed PV power forecasting model was validated using the PV power datasets of an off-grid village located in Chalokwa, Zambia. The obtained results confirmed the performance and accuracy of the proposed PV output power forecasting model. © 2023 IEEE.

关键词： Multiobjective optimization

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Over 2.5-octave OAM Supercontinuum Generation in ZBlaN Ring-core Fiber

Over 2.5-octave OAM Supercontinuum Generation in ZBLAN Ring-...

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2024 Nonlinear Photonics, NP 2024

作者： Ren, Haoyang Geng, Wenpu Yang, Jian Wang, Yuetian Pan, Zhongqi Yue, Yang School of Information and Communications Engineering Xi'an Jiaotong University Xi'an710049 China Institute of Modern Optics Nankai University Tianjin300350 China Department of Optoelectronic Information Science and Engineering School of Physics Xi’an Jiaotong University Xi’an710049 China Department of Electrical & Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States

A low dispersion ring-core ZBlaN fiber is designed and simulated for broadband OAM supercontinuum generation. A 4117-nm supercontinuum is generated spanning from 863 nm to 4980 nm at -40 dB, covering a bandwidth over ... 详细信息

关键词： Supercontinuum generation

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Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Ullah, Kaleem Shakir, Daniyal Abid, Usama Alahmari, Saad Aslam, Sheraz Ullah, Zahid US-Pakistan Centre for Advanced Studies in Energy University of Engineering and Technology Peshawar Peshawar Pakistan Sr. Business Analyst S&P Global Islamabad Islamabad Pakistan Department of Computer Science and Information Technology School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science CTL eurocolege Limassol Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Lombardia Italy

Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Convolutional neural networks

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