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Comparative study for different types of mppt algorithms using direct control method 1

2nd International Conference on Electrical and Electronics engineering, ICEEE 2021

作者： Shams, Immad Mohammed, Karam Khairullah Mekhilef, Saad Tey, Kok Soon Power Electronics and Renewable Energy Research Laboratory Department of Electrical Engineering Faculty of Engineering Kuala Lumpur50603 Malaysia Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (数字)9789811607493

ISBN: (纸本)9789811607486

Maximum power point tracking (MPPT) controllers achieve higher importance due to the high expansion rate of photovoltaic systems. The probability of partial shading conditions (PSCs) is also increased which drastically reduced the maximum power extraction capability from PV systems. In this paper, different types of MPPT algorithms using direct control method have been examined under the same test conditions. The current research trend on MPPT controllers has also been highlighted. This paper gives a brief description to engineers and practitioners working in the area of MPPT controllers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Controllers

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Hierarchical Graph Modeling for Multi-Scale Optimization of Power Systems

arXiv

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arXiv 2023年

作者： Cole, David L. Gangammanavar, Harsha Zavala, Victor M. Department of Chemical and Biological Engineering University of Wisconsin-Madison MadisonWI United States Operations Research and Engineering Management Southern Methodist University DallasTX United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States

Hierarchical optimization architectures are used in power systems to manage disturbances and phenomena that arise at multiple spatial and temporal scales. We present a graph modeling abstraction for representing such architectures and an implementation in the Julia package ***. We apply this framework to a tri-level hierarchical framework arising in wholesale market operations that involves day-ahead unit commitment, short-term unit commitment, and economic dispatch. We show that graph abstractions facilitate the construction, visualization, and solution of these complex problems. © 2023, CC BY.

关键词： Graph theory

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Optimization techniques for modeling with piecewise-linear functions

arXiv

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arXiv 2025年

作者： Dobrovoczki, Péter Kis, Tamás Engineering and Management Intelligence Research Laboratory HUN-REN Institute for Computer Science and Control Kende utca 13-17 Budapest1111 Hungary Department of Operations Research Institute of Mathematics Eötös Loránd University Pázmány Péter sétány 1/C Budapest1117 Hungary

In this paper we aim to construct piecewise-linear (PWL) approximations for functions of multiple variables and to build compact mixed-integer linear programming (MILP) formulations to represent the resulting PWL function. On the one hand, we describe a simple heuristic to iteratively construct a triangulation with a small number of triangles, while decreasing the error of the piecewise-linear approximation. On the other hand, we extend known techniques for modeling PWLs in MILPs more efficiently than state-of-the-art methods permit. The crux of our method is that the MILP model is a result of solving some hard combinatorial optimization problems, for which we present heuristic algorithms. The effectiveness of our techniques is demonstrated by a series of computational experiments including a short-term hydropower scheduling problem. Copyright © 2025, The Authors. All rights reserved.

关键词： Integer linear programming

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Detection of stress for visually impaired people using EEG signals based on time-frequency domain analysis 19

Detection of stress for visually impaired people using EEG s...

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19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Sultana, Samia Rahman, Md Anisur Zavid Parvez, Mohammad BRAC University Software Engineering and HCI Research Group Department of Computer Science and Engineering Bangladesh Charles Sturt University School of Computing and Mathematics Australia

ISBN: (纸本)9780738124261

Stress refers to body's physical, emotional and psychological reaction to any environmental change needing adjustment with major impact on human psychology. Stress is specially difficult to manage for visually impaired people (VIP) as they can become easily stressed in unknown situations. Electroencephalogram (EEG) signals can be used to detect stress as it basically represents the ongoing electrical signal changes in human brain. Literature shows that the stress detection techniques are mostly based on either time or frequency domain analysis. However, using either time or frequency domain analysis may not be sufficient to provide appropriate outcome for stress detection. Hence, in this paper a method is proposed using empirical mode decomposition (EMD) and short-term Fourier transform (STFT) are used to extract features considering spatio-temporal information from EEG signals. In the EMD, the signal is first decomposed into intrinsic mode functions (IMFs) representing a finite number of signals while maintaining the time domain and STFT is used to convert time domain to time-frequency domain. Support vector machine (SVM) is applied to classify the stress of VIP in unfamiliar indoor environments. The performance of the proposed method is compared with a state-of-the-art technique for stress detection. The experimental results demonstrate the superiority of the proposed technique over the existing technique. © 2020 IEEE.

关键词： Empirical mode decomposition

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Risk Assessment of Pregnancy-induced Hypertension Using a Machine Learning Approach 6

Risk Assessment of Pregnancy-induced Hypertension Using a Ma...

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Joint 6th International Conference on Digital Arts, Media and Technology with 4th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunication engineering, ECTI DAMT and NCON 2021

作者： Wanriko, Sirinat Hnoohom, Narit Wongpatikaseree, Konlakom Jitpattanakul, Anuchit Musigavong, Olarik Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakhon Pathom Thailand Mahidol University Faculty of Engineering Department of Computer Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand Prachin Buri Thailand

ISBN: (纸本)9781665415699

This research aimed to develop a predictive model of the risk assessment of pregnancy-induced hypertension using a machine learning approach. Pregnancy-induced hypertension is a complication that has a serious impact on pregnant women and fetuses. It is the world's top three cause of death among pregnant women [1]. Nowadays, the exact cause of pregnancyinduced hypertension is unknown and therefore cannot be prevented. Early detection and received treatment can reduce the severity and danger. A public dataset of Logan (2020) was used in this research [2] the dataset was collected from a casecontrol study on the determinants of 83 pre-eclampsia and five eclampsia cases among 352 pregnant women delivering in county hospitals in Nairobi, Kenya. According to the dataset, 75 percent of the pregnant women were healthy. Only 25 percent of the pregnant women were pre-eclampsia and eclampsia. Thus, this would result in a problem of an imbalanced classification when one of the two classes had more data than the other class. As such, this problem was resolved with the Synthetic Minority Over-sampling Technique (SMOTE). Risk assessment of pregnancy-induced hypertension was performed on seven machine learning algorithms, which were logistic regression (LR), K-nearest neighbor (KNN), decision tree (DT), random forest (RF), multilayer perceptron neural network(MLP), support vector machines (SVM), and naive Bayes (NB). In the experimental results, RF had the highest accuracy at 89.62 percent compared to other machine learning algorithms. © 2021 IEEE.

关键词： Risk assessment

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Understanding Dynamic Associations: Gait Recognition via Cross-view Spatiotemporal Aggregation Network

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IEEE Transactions on Circuits and Systems for Video Technology 2022年 1-1页

作者： Chen, Jianyu Wang, Zhongyuan Zheng, Caixia Zeng, Kangli Zou, Qin Xiong, Zixiang School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China School of Computer Science Wuhan University Wuhan China Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

Gait recognition is a technique to recognize subjects under long-distance conditions. Existing gait recognition methods generally rely on extracting different motion representations of body parts to capture the gait information. However, the differences in appearance silhouettes between subjects are not discriminative enough to identify the distinct gaits. Therefore, exploring the representations of unique motion differences of subjects from the temporal domain is an effective way to boost gait recognition performance. In this paper, we propose a cross-view spatiotemporal aggregation network (CSTAN) to learn the most discriminative motion representations of subjects from multi-scale features. Particularly, CSTAN uses temporal contextual feature aggregation (TCFA) block to interact multi-scale features, increasing the diversity of temporal representations in feature sequences and producing suitable motion representations. Besides, a potential spatial feature assessment (PSFA) block is proposed to address the problem of missing silhouettes due to occlusion. Considering the multi-view context, we propose a cross-view prediction learning (CVPL) block to mitigate the impact of the view itself. By aggregating features in the spatiotemporal and view domains, CSTAN can learn a more appropriate representation of human motion differences. Extensive experiments on two public gait recognition datasets demonstrate the state-of-the-art performance of our proposed CSTAN. It is worth mentioning that the performance of CSTAN achieves a breakthrough in some complex environments, e.g., the subjects wearing different coats or carrying a bag. IEEE

关键词： Data mining

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Sampled-data glucose regulation with reach-and-stay specifications through time-varying contracts

Sampled-data glucose regulation with reach-and-stay specific...

引用

IEEE Conference on Decision and Control

作者： Ilaria Di Loreto Alessandro Borri Maria Domenica Di Benedetto Department of Information Engineering Computer Science and Mathematics Center of Excellence for Research DEWS University of L’Aquila Italy CNR-IASI Biomathematics Laboratory National Research Council of Italy Rome Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The complexity of the glucose-insulin system makes the glucose control problem a hard task to accomplish. In this context, a decentralized approach can be of help, through the exploitation of contracts theory, which allows to formalize the fulfillment of safety/invariance specifications over a system in terms of set of assumptions and guarantees over the composing subsystems. We here take a compositional model-based approach considering, as a first attempt, simplified scalar glucose and insulin subsystems. Assumptions and guarantees sets are piecewise-constant time-varying intervals, computed at sampling times, on the basis of the glucose measurements, so they are not completely known a priori. Updating the intervals may lead to temporary violation of the contracts, according to their classical definition, until the system reaches the new target set. By exploiting the property of monotonicity of the involved subsystems, we define a minimum-time reachability problem, which is solved in closed form to minimize the worst-case contract time violation, and such that the insulin subsystem is steered to a controlled invariant set (reach-and-stay specification). Simulations performed in a non-ideal scenario confirm the potential of the proposed approach.

关键词： Computational modeling Regulation Glucose Complexity theory Insulin Task analysis Contracts

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Quantum Linear System Solvers: A Survey of Algorithms and Applications

arXiv

引用

arXiv 2024年

作者： Morales, Mauro E.S. Pira, Lirandë Schleich, Philipp Koor, Kelvin Costa, Pedro C.S. An, Dong Aspuru-Guzik, Alán Lin, Lin Rebentrost, Patrick Berry, Dominic W. Joint Center for Quantum Information and Computer Science University of Maryland United States Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada ContinoQuantum Sydney Australia Beijing International Center for Mathematical Research Peking University Beijing China Department of Chemistry University of Toronto Canada Canadian Institute for Advanced Research Toronto Canada Acceleration Consortium Toronto Canada Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Canada Department of Materials Science & Engineering University of Toronto Toronto Canada Lebovic Fellow Canadian Institute for Advanced Research Toronto Canada Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Department of Computer Science National University of Singapore Singapore School of Mathematical and Physical Sciences Macquarie University Sydney Australia

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics. © 2024, CC BY.

关键词： Quantum computers

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A Lightweight Deep Learning Network for Sensor-based Human Activity Recognition using IMU sensors of a Low-Power Wearable Device 25

A Lightweight Deep Learning Network for Sensor-based Human A...

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25th International computer science and engineering Conference, ICSEC 2021

作者： Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

Human Activity Recognition (HAR) is an intriguing approach to healthcare monitoring that necessitates the ongoing utilization of wearable sensors to capture everyday activities. The most advanced studies using wearable devices have used a cloud computing paradigm that categorized data at remote systems. This method has drawbacks in terms of energy consumption, latency, and privacy. As a result, we adhere to a low-power computing structure in which wearable device solutions achieve an acceptable level of performance while being energy and memory-efficient. This study proposes a lightweight deep learning model termed a gate recurrent unit (GRU) network for energy-efficient HAR appropriate for low-power wearable devices. The introduced GRU network is evaluated against a benchmark dataset named the w-HAR dataset, including essential deep learning networks. The proposed GRU surpasses baseline deep learning networks in terms of overall accuracy, with a score of 95.160%. © 2021 IEEE.

关键词： Energy efficiency

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Bidirectional Gate Recurrent Unit Neural Network for Recognizing Face Touching Activities using Smartwatch Sensors 25

Bidirectional Gate Recurrent Unit Neural Network for Recogni...

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25th International computer science and engineering Conference, ICSEC 2021

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Faculty of Engineering Image Information and Intelligence Laboratory Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

Globally, the COVID-19 pandemic has caused dev-Astation and continues to do so even a year after its first outbreak. Behavioral modifications could help to mitigate a mechanism for acquiring and spreading illnesses. Using wearable devices such as smartwatches to recognize face contact has the opportunity to decrease face touching and, therefore, the spread of respiratory disease through fomite transmission. The purpose of this paper is to demonstrate how we can utilize accelerometer data from wristwatch sensors to identify face touching actions using deep learning techniques. We proposed the BiGRU deep learning model for the high-performance recognition of hand-To-face actions. The Face Touching dataset is used as a benchmark for evaluating the recognition accuracy of deep learning networks, including our network model. The experimental findings indicate that the BiGRU surpasses other baseline deep learning models regarding accuracy (98.56%) and F1-score (98.56%). © 2021 IEEE.

关键词： Accelerometers

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