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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Anna Maria Mihel Špela Pečnik Grega Vrbančič Jonatan Lerga Nino Krvavica Department of Computer Engineering Faculty of Engineering University of Rijeka Rijeka Croatia Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Hydrotechnics and Geotechnics Faculty of Civil Engineering University of Rijeka Rijeka Croatia

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data.

关键词： Measurement Weather forecasting Predictive models Wavelet analysis Data models Rivers Numerical models Forecasting Spectrogram Water resources

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A Novel Approach to Menstrual Cycle Prediction Using GAN-Generated Data and Transformer Model for Tabular Data

A Novel Approach to Menstrual Cycle Prediction Using GAN-Gen...

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2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Ahamed, Md Younus Syed, Md Asif Bin Tani, Mafuza Nasrin Tasnim, Tanpia Habib, Al Zadid Sultan Bin West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States Army Medical College Rangpur Rangpur5400 Bangladesh Green University of Bangladesh Narayanganj Dhaka1461 Bangladesh

ISBN: (纸本)9798331535476

Accurate menstrual cycle prediction is crucial for women's health and fertility management. While prior studies have utilized machine learning models to predict cycle length and classify regularity, they often struggled with the low Observationto-Variable Ratio (OVR) inherent in menstrual cycle data. This study tackles this issue by employing Generative Adversarial Networks (GANs) to generate synthetic data and Transformerbased models for both classification a nd r egression t asks. We evaluated multiple machine learning models for menstrual cycle phase classification a nd l ength p rediction. F or t he classification of regular versus irregular cycles, TabNet significantly outperformed baseline models, including Logistic Regression, Random Forest, and LSTM, achieving 99.67% accuracy, an AUC of 0.96, an F1 score of 0.99, 92.37% specificity, a nd 99.66% sensitivity, with CTGAN-generated synthetic data enhancing its performance. In the regression task of predicting cycle length, TabNet demonstrated strong predictive power, with an MSE of 1.29, RMSE of 1.13, and an R2 score of 0.87. Although Random Forest performed slightly better with an MSE of 0.958 and an R2 score of 0.90, TabNet remained competitive. These results highlight the potential of GAN-generated data and advanced models like TabNet in overcoming low OVR challenges and improving menstrual cycle prediction. Future research may incorporate personalized health data to further enhance model accuracy and generalizability. © 2024 IEEE.

关键词： Prediction models

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ORDER-OPTIMAL INSTANCE-DEPENDENT BOUNDS FOR OFFLINE REINFORCEMENT LEARNING WITH PREFERENCE FEEDBACK

arXiv

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arXiv 2024年

作者： Chen, Zhirui Tan, Vincent Y.F. The Department of Industrial Systems Engineering and Management National University of Singapore Singapore The Department of Mathematics The Department of Electrical and Computer Engineering National University of Singapore Singapore

We consider offline reinforcement learning (RL) with preference feedback in which the implicit reward is a linear function of an unknown parameter. Given an offline dataset, our objective consists in ascertaining the optimal action for each state, with the ultimate goal of minimizing the simple regret. We propose an algorithm, RL with Locally Optimal Weights or RL-LOW, which yields a simple regret of exp(−Ω(n/H)) where n is the number of data samples and H denotes an instance-dependent hardness quantity that depends explicitly on the suboptimality gap of each action. Furthermore, we derive a first-of-its-kind instance-dependent lower bound in offline RL with preference feedback. Interestingly, we observe that the lower and upper bounds on the simple regret match order-wise in the exponent, demonstrating order-wise optimality of RL-LOW. In view of privacy considerations in practical applications, we also extend RL-LOW to the setting of (Ε, δ)differential privacy and show, somewhat surprisingly, that the hardness parameter H is unchanged in the asymptotic regime as n tends to infinity;this underscores the inherent efficiency of RL-LOW in terms of preserving the privacy of the observed rewards. Given our focus on establishing instance-dependent bounds, our work stands in stark contrast to previous works that focus on establishing worst-case regrets for offline RL with preference feedback. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第1期2卷 40-51页

作者： Ferrando, Robert Pagnier, Laurent Mieth, Robert Liang, Zhirui Dvorkin, Yury Bienstock, Daniel Chertkov, Michael University of Arizona Program in Applied Mathematics and Department of Mathematics TucsonAZ85721 United States Department of Industrial and Systems Engineering at Rutgers University PiscatawayNJ08854 United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218-2625 United States Elec. and Comp. Eng. and Ralph o'Connor Sustainable Energy Institute Department of Civil and Systems Engineering Johns Hopkins University BaltimoreMD21218-2625 United States Columbia University IEOR New YorkNY10027 United States

This article addresses the challenge of efficiently recovering exact solutions to the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market-clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels. © 2023 IEEE.

关键词： Costs

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Guaranteeing Service in Connected Microgrids: Storage Planning and Optimal Power Sharing Policy

Guaranteeing Service in Connected Microgrids: Storage Planni...

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American Control Conference (ACC)

作者： Arnab Dey Vivek Khatana Ankur Mani Murti V. Salapaka Department of Electrical and Computer Engineering University of Minnesota Twin Cities USA Department of Department of Industrial and Systems Engineering University of Minnesota Twin Cities USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

The integration of renewable energy sources (RES) into power distribution grids poses challenges to system reliability due to the inherent uncertainty in their power production. To address this issue, battery energy sources (BESs) are being increasingly used as a promising solution to counter the uncertainty associated with RES power production. During the overall system planning stage, the optimal capacity of the BESs has to be decided. In the operational phase, policies on when to charge the BESs and when to use them to support loads must be determined so that the BESs remain within their operating range, avoiding depletion of charge on one hand and remaining within acceptable margins of maximum charge on the other. In this paper, a stochastic control framework is used to determine BESs capacity, for microgrids (MGs), ensuring that during the operational phase, BESs' operating range is respected with pre-specified high probability. We provide an explicit analytical expression of the required BESs energy capacity for a single MG with RES as the main power source. Leveraging insights from the single MG case, the article focuses on the design and planning of BESs for the two-MG scenario. In this setting, MGs are allowed to share power while respecting the capacity constraints imposed by the power lines. We characterize the optimal power transfer policy between the MGs and the optimal BES capacity for the MGs. This provides the BES savings arising from connecting the microgrids,

关键词： Renewable energy sources Uncertainty Design methodology Power distribution Microgrids Production Planning

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Unharvested Palm Fruit Bunch Ripeness Detection with Hybrid Color Correction

SSRN

引用

SSRN 2024年

作者： Cheng, Chang Parthiban, Rajendran Kalavally, Vineetha Wang, Xin Department of Mechanical Engineering School of Engineering Monash University Malaysia Subang Jaya47500 Malaysia Department of Electrical and Computer Systems Engineering School of Engineering Monash University Malaysia Subang Jaya47500 Malaysia Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton3800 Australia

Unharvested palm fruit bunch (PFB) ripeness detection is crucial for efficient palm oil production as labor costs rise and worker shortages grow. Challenges remain under varying illumination, affecting detection performance. Although the deep learning-based color constancy model shows promise, the limitation lies in the lack of data. This paper addresses these issues by proposing a hybrid color correction method that combines the learning-based and the physical-based color constancy technique, then trains with YOLOv8 to enhance unharvested PFB detection performance. Specifically, the captured images and their corresponding simultaneous spectra are used to generate the ground truth images, then map the images to other ambient spectra to train the color constancy model, and finally, train the YOLOv8 with the corrected images for ripeness detection. Results demonstrate a 1.5% improvement in the mean Average Precision (mAP) @0.5 of YOLOv8, increasing from 88.2% to 89.7% after applying the proposed hybrid color correction method. For each ripeness category, the mAP@0.5 for unripe and ripe bunches increased by 1.7% and 1.8%, respectively. Underripe bunches exhibit the most increment in mAP of 2.1%, while the overripe bunch gives the least increase of 0.4%. The qualitative results show that the model has enhanced the ability to distinguish subtle differences in PFB ripeness. Consequently, it advances the effectiveness and reliability of PFB ripeness detection in practical palm oil production scenarios, especially for unripe to ripe bunches wherein color gives the most information. This research demonstrates potential in object detection tasks that incorporate illumination-based color correction methods. © 2024, The Authors. All rights reserved.

关键词： Wages

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Exploring the Impact of Vibrotactile Feedback on Boom Lift Safety Training: A Study Design

Exploring the Impact of Vibrotactile Feedback on Boom Lift S...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Minseok Kang Chae Heon Lim Seul Chan Lee Changgu Kang Kangsoo Kim Department of Electrical and Software Engineering University of Calgary Department of Industrial and Systems Engineering Gyeongsang National University Department of Computer Science Gyeongsang National University

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

In this paper, we present our research plan to investigate how various locations of vibrotactile feedback influence user experience and risk perception within a virtual reality (VR) based safety training simulation. We describe our planned user study where the study participants experience a construction boom lift ride (or operation) in an immersive VR environment. During the virtual lift ride, participants perform stage-maintenance tasks, such as assembling light bulbs and electrical lines, which involve certain safety risks—for example, hitting electrical lines and structures during the lift operation. In the study, the vibrotactile feedback condition is varied in three levels: (1) No-Vibration, (2) Controller-Vibration, and (3) Floor-Vibration. We plan to collect and analyze both subjective and objective data related to general user experience, sense of presence, and risk perception to examine the effectiveness of the safety training. Further details of our study and the boom lift ride simulation are described, and future plans are discussed.

关键词： Training Ethics Three-dimensional displays Conferences Virtual reality User interfaces User experience

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Multi-Task Learning for Material Property Prediction

Multi-Task Learning for Material Property Prediction

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International Conference on Machine Learning and Applications (ICMLA)

作者： C. M. A. Rahman Nishat B. Alam Amr S. El-Wakeel JuHyeong Ryu P. K. Gyawali Lane Department of Computer Science and Electrical Engineering (LCSEE) West Virginia University Department of Industrial and Management Systems Engineering (IMSE) West Virginia University

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Material property prediction is a critical task within material science and other related fields where the identification of advanced materials with dynamic characteristics is essential for developing innovative technologies, improving product performance, and driving scientific discoveries. The search for suitable candidate materials aimed at specific applications is traditionally conducted through time-consuming experimental methods. Conversely, computational approaches, such as density functional theory (DFT) calculations, necessitate the resolution of complex mathematical equations and significant computational resources. Machine learning (ML) models have significantly transformed this field by automating the tedious process of material searching within extensive search spaces. However, these models are unable to incorporate diverse features of varying dimensions for different materials and properties. Recently, graph convolutional neural networks (GCNNs), have been employed to analyze the complex structures of materials, thereby efficiently predicting their associated properties. In this work, we demonstrate the potential of multi-task learning (MTL) in conjunction with the GCNNs in material property prediction. Our findings suggest that the MTL framework integrated into the GCNN architecture such as Crystal Graph Convolutional Neural Network (CGCNN) and its advanced variant Orbital Graph Convolutional Neural Network (OGCNN) can enhance the generalization and efficacy of property predictions. Notably, we achieved a maximum improvement of 7.95 % in one of our setups, underscoring the potential of these models to handle multiple property predictions with considerable effectiveness.

关键词： Space vehicles Photonic band gap Computational modeling Machine learning computer architecture Predictive models Multitasking Mathematical models Convolutional neural networks Material properties

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Load Balancing Framework for Cross-Region Tasks in Cloud Computing

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computers, Materials & Continua 2022年第1期70卷 1479-1490页

作者： Jaleel Nazir Muhammad Waseem Iqbal Tahir Alyas Muhammad Hamid Muhammad Saleem Saadia Malik Nadia Tabassum Department of Software Engineering The Superior CollegeLahorePakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Statistics and Computer Science University of Veterinary and Animal SciencesLahore54000Pakistan Department of Industrial Engineering Faculty of EngineeringRabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology-RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Virtual University of PakistanLahore54000Pakistan

Load balancing is a technique for identifying overloaded and underloaded nodes and balancing the load between *** maximize various performance parameters in cloud computing,researchers suggested various load balancing *** store and access data and services provided by the different service providers through the network over different regions,cloud computing is one of the latest technology systems for both end-users and service *** volume of data is increasing due to the pandemic and a significant increase in usage of the internet has also been *** of the cloud are looking for services that are intelligent,and,can balance the traffic load by service providers,resulting in seamless and uninterrupted *** types of algorithms and techniques are available that can manage the load balancing in the cloud *** this paper,a newly proposed method for load balancing in cloud computing at the database level is *** database cloud services are frequently employed by companies of all sizes,for application development and business *** balancing for distributed applications can be used to maintain an efficient task scheduling process that also meets the user requirements and improves resource *** balancing is the process of distributing the load on various nodes to ensure that no single node is *** avoid the nodes from being overloaded,the load balancer divides an equal amount of computing time to all *** results of two different scenarios showed the cross-region traffic management and significant growth in revenue of restaurants by using load balancer decisions on application traffic gateways.

关键词： Load balancing performance database region virtualization

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Fuzzy Control of a Hydrodynamic Process

Fuzzy Control of a Hydrodynamic Process

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Igor Krčmar Petar Marić Velibor Đalić Aleksandar Rakić Bojan Derajić Faculty of Electrical Engineering University of Banja Luka Banja Luka BiH The Department of Signals and Systems School of Electrical Engineering University of Belgrade Belgrade Serbia Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany

Hydrodynamic processes, part of many industrial and urban infrastructure systems, usually lack accurate models, hence introducing ambiguity in a controller design procedure. Operators and control engineers provide significant knowledge about successful control of hydrodynamic systems. Fuzzy controller, suitable to exploit and incorporate available knowledge, is designed for control of a benchmark coupled tank system. Successful fuzzy controller must cope with slow dynamics of the process, dead zone of an actuator, and unipolar control signal, when employed in reference tracking tasks. The designed fuzzy controller implements fuzzy inference system with 15 rules, two inputs and one output, as well as three adjustable gain parameters. The proposed controller is successful in reference tracking tasks and disturbance suppression. Experiments, organised as reference tracking tasks and implemented as computer simulations, support the analysis.

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