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57th Annual Conference on Information Sciences and Systems, CISS 2023

作者： Jia, Mian Pu, Kang Zhao, Yue Stony Brook University Department of Applied Mathematics and Statistics Stony BrookNY11794 United States Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

ISBN: (纸本)9781665451819

As the penetration of behind-the-meter (BTM) rooftop solar energies continues to increase in power distribution systems, it is of paramount importance for load serving entities and system operators to forecast net loads in the system. In this paper, novel algorithms for training high-performance predictors for day-ahead net load forecasting are developed. Importantly, the overall method only utilizes metered net load data and does not require any monitoring data of solar generation. Methodologically, the net load data trace is disaggregated into estimated BTM solar and load traces, based on which separate predictors are then trained for solar generation and load forecasting exploiting their distinct natures, respectively. For solar generation forecasting, time data, weather forecast, and potentially solar irradiance forecast are used as input features of the predictor. For load forecasting, time data, weather forecast, and judiciously chosen load data in the recent past are used as input features of the predictor. The two predictors' outputs are combined to produce the final net load forecast. The developed method is comprehensively evaluated based on two real-world smart meter data sets from Ithaca, NY and Clifton park, NY, respectively. High accuracy of day-ahead net load forecast is demonstrated. © 2023 IEEE.

关键词： Smart meters

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On efficient and scalable computation of the nonparametric maximum likelihood estimator in mixture models

The Journal of Machine Learning Research

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The Journal of Machine Learning research 2024年第1期25卷 439-484页

作者： Yangjing Zhang Ying Cui Bodhisattva Sen Kim-Chuan Toh Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Department of Industrial Engineering and Operations Research University of California Berkeley Department of Statistics Columbia University Department of Mathematics Institute of Operations Research and Analytics National University of Singapore

In this paper, we focus on the computation of the nonparametric maximum likelihood estimator (NPMLE) in multivariate mixture models. Our approach discretizes this infinite dimensional convex optimization problem by setting fixed support points for the NPMLE and optimizing over the mixing proportions. We propose an efficient and scalable semismooth Newton based augmented Lagrangian method (ALM). Our algorithm outperforms the state-of-the-art methods (Kim et al., 2020; Koenker and Gu, 2017), capable of handling n≈106 data points with m ≈ 104 support points. A key advantage of our approach is its strategic utilization of the solution's sparsity, leading to structured sparsity in Hessian computations. As a result, our algorithm demonstrates better scaling in terms of m when compared to the mixsqp method (Kim et al., 2020). The computed NPMLE can be directly applied to denoising the observations in the framework of empirical Bayes. We propose new denoising estimands in this context along with their consistent estimates. Extensive numerical experiments are conducted to illustrate the efficiency of our ALM. In particular, we employ our method to analyze two astronomy data sets: (i) Gaia-TGAS Catalog (Anderson et al., 2018) containing approximately 1.4 × 106 data points in two dimensions, and (ii) a data set from the APOGEE survey (Majewski et al., 2017) with approximately 2.7 × 104 data points.

关键词： augmented Lagrangian method denoising empirical Bayes Gaussian location mixture model heteroscedastic errors semismooth Newton method sparse secondorder information

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A computational supervised neural network procedure for the fractional SIQ mathematical model

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European Physical Journal: Special Topics 2023年第5期232卷 535-546页

作者： Mukdasai, Kanit Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Singkibud, Peerapongpat Sadat, R. Ali, Mohamed R. Department of Mathematics Faculty of Science Khon Kaen University Khon Kaen 40002 Thailand Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Applied Mathematics and Statistics Faculty of Science and Liberal Arts Rajamangala University of Technology Isan Nakhon Ratchasima 30000 Thailand Department of Mathematics Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University in Egypt New Cairo 11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Benha Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. © 2022, The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature.

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Estimation of Time-Varying Graph Topologies from Graph Signals 48

Estimation of Time-Varying Graph Topologies from Graph Signa...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Yuhao Cui, Chen Ajirak, Marzieh Djurić, Petar M. Stony Brook University Department of Applied Mathematics and Statistics Stony BrookNY11794 United States Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

ISBN: (纸本)9781728163277

In science and engineering, we often deal with signals that are acquired from time-varying systems represented by dynamic graphs. We observe these signals, and the interest is in finding the time-varying topology of the graphs. We propose two Bayesian methods for estimating these topologies without assuming any specific functional relationships among the signals on the graphs. The two methods exploit Gaussian processes, where the first method uses the length scale of the kernel and relies on variational inference for optimization, and the second method is based on derivatives of the functions and Monte Carlo sampling. Both methods estimate the time-varying topologies of the graphs sequentially. We provide numerical tests that show the performance of the methods in two settings. © 2023 IEEE.

关键词： estimation of topology Monte Carlo sampling sequential estimation time-varying graphs variational inference

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A Dynamic Approach for Handling UAV in Crowded Environment Using Deep Q-Networks 4

A Dynamic Approach for Handling UAV in Crowded Environment U...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Upadhyay, Yash Gahlawat, Muskan Nivetha, S. Rana, Saurabh Natarajan, B. Gupta, Pratik Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India Department of Mathematics Chandigarh University India Department of Computer Science and Engineering RVS College of Engineering & Technology Coimbatore641402 India Amrita Vishwa Vidyapeetham Chennai601103 India Department of Applied Mathematics Gautam Buddha University Noida India

ISBN: (纸本)9798350305258

The demand for wireless connections is rising, and the advent of mobile internet has sparked a hunt for fresh, creative ways to expand the capacity and reach of cellular networks. One strategy to provide ground mobile users with long-term communication coverage is to employ Unmanned Aerial vehicles (UAV) as mobile base stations. The proposed method incorporates a Deep Reinforcement Learning (DRL) method to address the challenges that persist with traditional approaches. We introduced Deep Q-Learning with an attention mechanism-based multi-agent model to develop an energy-efficient control system for UAVs in cellular networks. Due to the proposed strategy's dynamic nature, it efficiently handles the crowded environment by resolving scalability and mobility issues. The researchers evaluated the proposed model using the benchmark dataset, producing promising results. Further, the researchers tested the proposed model using the sum rate metric to analyze the capability of the proposed algorithm to provide a reasonable throughput rate. © 2023 IEEE.

关键词： Cellular Communication Convolutional Neural Network Deep Q-Networks Deep Reinforcement Learning Deployment Optimization Energy Consumption Optimum Transport Target Network Unmanned Aerial Vehicle

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A Comparative Analysis of IoT Data Using Machine Learning Algorithms 15th

A Comparative Analysis of IoT Data Using Machine Learning Al...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Munrathnam, Meram Angeline Kavitha, M. Department of Computer Science and Applications School of CSA REVA University Bangalore India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.K. Valley Kadapa District Andhra Pradesh Idupulapaya516330 India Department of Mathematics Voorhees College Vellore632001 India

ISBN: (纸本)9783031889912

IoT (Internet of Things) is revolutionizing the commercial environment by acquiring vast amounts of data from various sources. However, processing and analyzing this data is challenging due to the large volume of IoT devices. AI-powered IoT creates intelligent machines that mimic human intelligence, aiding decision-making with minimal human input. Both individuals and professionals’ benefit from this combination, as IoT is concerned with communicating online, but AI allows machines to learn from data and experience. This study demonstrates the analysis of IoT data using machine learning algorithms and compares the accuracy of various machine learning algorithms used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Non-dominated Sorting Advanced Butterfly Optimization Algorithm for Multi-objective Problems

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Journal of Bionic engineering 2023年第2期20卷 819-843页

作者： Sushmita Sharma Nima Khodadadi Apu Kumar Saha Farhad Soleimanian Gharehchopogh Seyedali Mirjalili Department of Mathematics National Institute of Technology AgartalaTripura799046India Department of Civil and Environment Engineering Florida International University MiamiMiamiFL33199USA Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia***Iran Centre for Artificial Intelligence Research and Optimization Torrens UniversityFortitude ValleyQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea

This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization *** is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective *** to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance *** benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint *** performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance *** is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared *** all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence.

关键词： Multi-objective problems Butterfly optimization algorithm Non-dominated sorting Crowding distance

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Learning-based Real-time Outage Location Identification in Power Distribution Systems with Sparse Sensors 14

Learning-based Real-time Outage Location Identification in P...

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14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Pu, Kang Xu, Ce Zhao, Yue Stony Brook University Department of Applied Mathematics and Statistics Stony BrookNY11794 United States Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

ISBN: (纸本)9781665455541

Real-time identification of outage locations in power distribution systems is an essential task for utilities to mitigate the negative impact of power system outages. In this paper, a learning-based model-assisted method is developed for identifying outage locations in power distribution systems in real time. Notably, the method utilizes primarily only a) sparsely located supervisory control and data acquisition (SCADA) measurements, and potentially b) last gasp signals from a small number of smart meters. The method exploits offline training of outage location predictors based on data simulated with synthetically generated load profiles and outage scenarios. The trained predictors can then be used in an online fashion to accurately identify outage locations in new scenarios in real time. With physical-model-based network partitioning, the offline learning is decoupled into training predictors for much smaller sub-regions so that the learning efficiency is much improved. Importantly, the trained predictors based only on SCADA measurements and entropy loss functions can be integrated with smart meter last gasp signals, without toss of any optimality, regardless of smart meter locations, outage scenarios, and performance evaluation metrics. Evaluation of the method based on real-world power distribution feeder and load data demonstrates high accuracy in outage location identification even using SCADA measurements only. We then demonstrated how having just a handful of smart meters with last gasp capabilities can further improve the outage location accuracy significantly. © 2023 IEEE.

关键词： Smart meters

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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computer Modeling in engineering & Sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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Identifying the Optimal Size of a Hybrid Power System Using a Modified Genetic Algorithm 2

Identifying the Optimal Size of a Hybrid Power System Using ...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Suresh, S. Muthulakshmi, M. Bhuvanesh, A. Sankarasubramanian, R.S. Rajendran, S. Jeyamurugan, J. Sri Eshwar College of Engineering Department Electronics and Communication Engineering Tamil Nadu Coimbatore India PSN Engineering College Department of Information Technology Tamil Nadu Tirunelveli India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu India PSG Institute of Technology and Applied Research Department of Mathematics Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department of Electrical and Electronics Engineering Tamil Nadu India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350327533

The need for energy is at an increasing pace and it cannot be easily fulfilled by the normal energy systems which affect the environment in various ways. To overcome this problem, a hybrid power system (HPS) is used nowadays which is a combination of energy systems based on renewable and non-renewable. This study presents a wind hybrid energy system using a modified genetic algorithm mainly aimed at minimizing the implementation cost, and emission levels, and maximizing the high power supply reliability. The optimization results are improved by integrating the sizing methods to prevent load interruptions. The fitness function of the modified GA is set as power supply probability (LPSP) loss and nonrenewable energy factor (NREF). This model is confirmed by a case study and the optimal HPS results are evaluated with the conventional techniques. With the help of the battery depth of discharge, the discharge and state charge of battery are optimized. The outcome of the suggested model proves that it achieves effective LPSP output with appropriate sizing capacities by utilizing both solar and wind power. © 2024 IEEE.

关键词： Energy Optimization Hybrid Power System Logistic Chaos Loss of Power Supply Probability Modified Genetic Algorithm Wind Energy

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