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A Complete Numerical Investigation on Implicit and Explicit PV Single-Diode-Models Using I- and V-Approaches

IEEE JOURNAL OF PHOTOVOLTAICS 2021年第3期11卷 827-837页

作者： Mathew, Linta Eliya Panchal, Ashish K. Sardar Vallabhbhai Natl Inst Technol Dept Elect Engn Surat 395007 India

The single-diode-model (SDM) is widely used for the identification of a photovoltaic (PV) module output curve. However, the implicit nature of the model imposes a few difficulties such as need for an iterative solution method, high computation load and large computation time. In the last years, several mathematical approximations have been adopted in order to convert the implicit SDM into explicit one. In turn, this simplicity has invited other complications: inaccuracy in the curve identification, wrong maximum power point computation, additional parameters over the (five) SDM parameters etc. This article considers the complete and comprehensive comparisons of all possible ways (numerical methods) to compute the output curve. This article provides clear guidelines to the researchers in PV modeling and helps to select an appropriate numerical method. The SDM computation is carried out by the I-approach and V-approach using all possible methods. All methods are tested on cells and modules in uniform irradiance conditions, four commonly used case studies in PV literature. Also, the investigation is extended for two more case studies that include a PV string and a PV array operating under partial shading conditions. In summary, this article supplies a useful PV modeling manual that provides freedom to select a suitable method based on the I- or V-approach equations, the convergence criteria and the computation execution burdens. Overall, the work proposes the series expansion of Lambert-W in both approaches is the most suitable method for PV curves computation.

关键词： computation time current-voltage (I-V) curve numerical methods photovoltaic (PV) single-diode-model (SDM)

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Energy-efficient blockchain implementation for Cognitive Wireless Communication Networks (CWCNs)

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ENERGY REPORTS 2021年 7卷 8277-8286页

作者： Chithaluru, Premkumar Al-Turjman, Fadi Stephan, Thompson Kumar, Manoj Mostarda, Leonardo KL Univ Sch Comp Dept CSE Vaddeswaram 522502 Andhra Pradesh India Near East Univ Artificial Intelligence Engn Dept Res Ctr & IoT Mersin 10 Turkey M S Ramaiah Univ Appl Sci Fac Engn & Technol Dept Comp Sci & Engn Bengaluru Karnataka India Univ Petr Energy Studies UPES Sch Comp Sci Dehra Dun 248007 Uttarakhand India Univ Camerino Div Comp Sci Camerino Italy

Considering the computation resources available with sensor devices and the value and validity of Cognitive Wireless Communication Network (CWCN), traditional blockchain is not feasible for CWCN. Further, considering the security and privacy for CWCN that can directly impact human life (as in the case of ambient assisted living applications), blockchain provides a good solution for such applications, however, with some simplicity in the computation of Proof of Work (PoW). Therefore, the fourth objective solution comes up with a simplified energy-efficient blockchain implementation for CWCN that consumes less energy in computation time. The energy-hungry blockchain has been implemented on resource-constrained CWCN for ambient assisted living applications specialized for elderly care. The process includes a collection of physical environmental parameters on a single board computer-based CWCN. The implementation includes possible simplification in the most energy-consuming process, i.e., the mining process, which makes it energy efficient in computation time as energy consumption is a computation time factor. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： CWCN Energy efficiency Blockchain Proof of work computation time Resource-constrained

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An analysis of hot-started ADMM for linear MPC

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IET CONTROL THEORY AND APPLICATIONS 2021年第15期15卷 1999-2016页

作者： Toyoda, Mitsuru Tanaka, Mirai Tokyo Metropolitan Univ Dept Mech Syst Engn 6-6 Asahigaoka Hino Tokyo 1910065 Japan Inst Stat Math Dept Stat Inference & Math Tokyo Japan RIKEN Ctr Adv Intelligence Project Continuous Optimizat Team Tokyo Japan

A convergence analysis of the alternating direction method of multipliers (ADMM) for linear model predictive control (MPC) problems with regularization terms is addressed here. Compared with conventional results, this paper focuses on the dynamical structure of the ADMM and derives the linear convergence of the state variables of the ADMM *** on-line optimization problems associated with MPC problems, because the computation time is a significant issue, a hot-start technique in which an initial point of the algorithm is set as the convergence point for the previous optimization problem is widely employed to improve the convergence property. Here, by utilizing the proposed convergence analysis framework, the effectiveness of the hot-start is explored, and the upper bounds of the number of iterations to guarantee the required accuracy of an obtained iterative solution are deduced. The proposed hot-start framework derives a criterion for choosing the penalty parameter of the ADMM in MPC problems and facilitates effective iteration, which is validated in a numerical example.

关键词： initial point linear convergence regularization terms hot-start framework convergence property ADMM dynamics convex programming convergence analysis framework alternating direction method dynamical structure hot-start technique on-line optimization problems convergence convergence point predictive control MPC problems linear model predictive control problems iterative methods state variables conventional results computation time hot-started ADMM

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Comparison of different models for energy management strategy design of a parallel hybrid electric vehicle: Impact of the rotating masses

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IET ELECTRICAL SYSTEMS IN TRANSPORTATION 2021年第1期11卷 36-46页

作者： Pam, Abdoulaye Bouscayrol, Alain Fiani, Philippe Faval, Fabien Univ Lille Arts & Metiers Inst Technol Cent Lille ULR 2697 L2EP F-59000 Lille France Sherpa Engn Paris France French Network HEVs MEGEVH Network Lille France

Simulation is a key step during the development of energy management strategies (EMSs) for hybrid electric vehicles (HEVs). Moreover, optimal algorithms such as dynamic programming (DP) are used off-line to define a benchmark for real-time EMSs. However, several assumptions are made on the model to determine the appropriate energy management of an HEV. The rotating masses of the mechanical transmission, which are related to the vehicle dynamics, are often neglected for EMS development. In this study, different models for the EMS design of a parallel HEV are compared. An EMS based on the dynamical model that considers the rotating masses is used as a benchmark. A static model considering only the static mass of the vehicle is also studied. Intermediate models, such as an average gear model and an average mass model, are also considered. Simulations are performed using different driving cycles. The comparisons are based on the fuel consumption and also the computation time resulting from EMSs derived from the different models. Globally, the static model leads to an average error up to 20% on the energy consumption in comparison with the dynamical model. The average mass model leads to an error less than 1%. For real-time simulations, the average mass model allows to reduce the computation time by a factor of 10 in comparison with the dynamical model.

关键词： real-time EMSs EMS development static model static mass vehicle dynamics dynamical model intermediate models energy consumption appropriate energy management dynamic programming energy management strategy design hybrid electric vehicles parallel hybrid electric vehicle average gear model rotating masses energy management systems energy management strategies EMS design computation time gears parallel HEV average mass model

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Learning an artificial neural network to discover bit-quad-based formulas to compute basic object properties

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PATTERN RECOGNITION 2023年第1期142卷

作者： Arce, Fernando Gomez-Flores, Wilfrido Escalona, Uriel Sossa, Humberto Ctr Invest Opt AC Loma Bosque 115 Lomas Campestre Leon 37150 Guanajuato Mexico Ctr Invest & Estudios Avanzados IPN Unidad Tamaulipas Parque TECNOTAM Ciudad Victoria 87130 Tamaulipas Mexico Inst Politecn Nacl CIC Av Juan De Dios Batiz S-N Ciudad De Mexico 07738 Mexico Tecnol Monterrey Campus GuadalajaraAv Gral Ramon Corona 2514 Zapopan 45138 Jalisco Mexico

Shape analysis requires estimating object properties in many applications, including optical character recognition, tumor classification, skin cancer recognition, leaf and plant identification, and cell analy-sis. In particular, bit-quad-based linear expressions proposed by Gray and Duda for calculating the area and perimeter of binary images are widely used in the literature. Nevertheless, these formulas require computing 14 or 15 bit-quad patterns out of 16 possible, becoming critical in applications with limited computing resources. Hence, this paper introduces a method based on a single-layer artificial neural net-work (ANN) to discover new expressions to calculate the area and perimeter with fewer bit-quads than the original formulas without losing measuring accuracy. Besides, an iterative elimination process re-moves irrelevant bit-quads whose corresponding weights approach zero. After that, an inductive analysis from observing the learned weights provides interpretable formulas for the area and perimeter. Further-more, the proposed approach is also applied to find bit-quad-based formulas for directly computing the contact perimeter property, whose original formula requires precomputing Gray's area and perimeter of the object. In addition, aiming to show our method's versatility in other applications, we address a real-world problem to discover a bit-quad-based formula to distinguish between two classes of loss of bone density caused by hyperthyroidism and aging in rats. The experimental results show that the proposed approach reduces by approximately half the bit-quads needed to calculate the area and perimeter of Gray and Duda. Likewise, the number of bit-quads to compute the contact perimeter is reduced from nine to six. Besides, the estimated value on test sets by all the found formulas is the same as their original coun-terparts. On the other hand, the classification of loss of bone density type using the found bit-quad-based formula reaches an accuracy o

关键词： Shape analysis Artificial neural network computation time Bit-quads Area Perimeter Contact perimeter

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A Low-Cost Implementation of Sample Entropy in Wearable Embedded Systems: An Example of Online Analysis for Sleep EEG

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021年 70卷 1-12页

作者： Wang, Yung-Hung Chen, I-Yu Chiueh, Herming Liang, Sheng-Fu Natl Cent Univ Dept Mech Engn Taoyuan 32001 Taiwan Natl Cheng Kung Univ NCKU Dept Comp Sci & Informat Engn Tainan 701 Taiwan Natl Cheng Kung Univ NCKU Inst Med Informat Tainan 701 Taiwan Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 300 Taiwan

Sample entropy (SpEn) is a measure of the underlying regularity or complexity of a system that is achieved by assessing the entropy of a time series recorded from the system. It is a powerful signal processing tool and has received increasing attention in recent years. SpEn has been successfully applied in biomedical measurements and other applications. In particular, many emerging applications require measuring the SpEn of signals in real-time embedded systems. However, the standard implementation of SpEn requires a computational complexity of O(n(2)), where n is the data length, making it difficult to meet real-time constraints, especially for large n. Moreover, power consumption and computation latency must be considered as well. The data length used in previous studies was approximately several hundred, and it remains a challenging task to operate on longer data lengths. In this article, we propose the assisted sliding box (SBOX) algorithm to accelerate the computation of SpEn without any approximation while maintaining a low memory overhead so that the algorithm can be executed in embedded systems for edge computing. We also develop an electroencephalogram (EEG)-based wearable device for comfortable overnight recording. The SBOX algorithm is then implemented in the system to measure the online SpEn of an overnight sleep EEG signal. The results show that, compared with the standard algorithm, the SBOX algorithm speeds up the computation time by a factor of 60, thereby reducing power consumption by 98% when measuring a 30-s epoch of sleep EEG with n = 7500 and a 250-Hz sampling rate.

关键词： computation time edge computing microcontroller (MCU) sample entropy (SpEn) sleep electroencephalogram (EEG) wearable device

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Offset free generalized model predictive control for 3-phase LCL-filter based grid-tied inverters

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年第1期153卷

作者： Zamani, Hasan Abbaszadeh, Karim Karimi, MohammadHadi Gyselinck, Johan KN Toosi Univ Technol Tehran Iran Univ Liber Bruxelles B-1050 Brussels Belgium

This paper presents a Generalized Model Predictive Control (GPC) in the natural reference frame for a 3-phase LCL-filter based grid-tied inverter. The proposed controller can track the sinusoidal reference without steadystate phase and amplitude offsets. This is achieved by parameterizing the control law matrices with considering the future reference trajectory. To cope with the LCL filter resonance, traditional controllers need a damping method. The proposed controller has inherent damping against the LCL filter resonance and extra sensors are avoided. The controller parameters are selected by a sensitivity analysis that guarantees the closed-loop stability. The proposed controller provides high accuracy in controlling active and reactive currents, robustness against grid inductance variations, low real-time computation, and a fixed switching frequency. The simulation and experimental results prove the performance of the controller in tracking the grid-side reference currents.

关键词： Generalized model predictive control Steady state offset Natural reference frame Active damping computation time

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Performance Evaluation for a Post-Quantum Public-Key Cryptosystem

Performance Evaluation for a Post-Quantum Public-Key Cryptos...

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40th IEEE International Performance, Computing, and Communications Conference (IPCCC)

作者： Prantl, Thomas Prantl, Dominik Beierlieb, Lukas Ifflaender, Lukas Dmitrienko, Alexandra Kounev, Samuel Krupitzer, Christian Univ Wurzburg Wurzburg Germany Univ Hohenheim Stuttgart Germany

ISBN: (纸本)9781665443319

Quantum Computing threatens security of today's systems. Confidence in today's security technologies largely relies on Public Key Cryptography (PKC), which depends on computational difficulty of mathematical problems that cannot be solved efficiently using any technology available today. This will, however, change once a sufficiently capable quantum computer will become available. Similarly, security of symmetric crypto algorithms will also be substantially weakened. Current progress in research proves that Quantum Computing is no longer science fiction. Hence, research and development of postquantum cryptographic algorithms that can withstand attacks in Quantum Computing era are of paramount importance. This paper complements existing research in this domain with a performance analysis of a post-quantum cryptosystem capable of encrypting and decrypting messages either bit-wise or string-wise. Specifically, we describe a workflow for implementing the scheme, design a reproducible hardware performance evaluation testbed for an IoT and an online shopping scenario, define performance metrics, and perform performance evaluation case studies. Our performance analysis shows that bit-wise encryption and decryption and the corresponding key generation fits resource-constrained IoT microcontrollers as well as on average laptops. The encryption and decryption of a bit each take less than 30 ms and the key generation less than 300 ms.

关键词： Post-Quantum Public-Key Cryptosystem Performance Energy Efficiency computation time

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An enhanced self-learning-based clustering scheme for real-time traffic data distribution in wireless networks

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HELIYON 2023年第7期9卷 e17530页

作者： Jain, Arpit Mehrotra, Tushar Sisodia, Ankur Vishnoi, Swati Upadhyay, Sachin Kumar, Ashok Verma, Chaman Illes, Zoltan Koneru Lakshmaiah Educ Fdn KLEF Dept Comp Sci & Engn Guntur 522302 Andhra Pradesh India Sharda Univ Sharda Sch Engn & Technol Dept Comp Sci & Engn Greater Noida India GLA Univ Dept Comp Engn & Applicat Mathura India Sanskriti Univ Dept Comp Sci & Engn Mathura India GLA Univ Dept Comp Sci & Engn Mathura India Eotvos Lorand Univ Fac Informat Dept Media & Educ Informat H-1053 Budapest Hungary

The process of examining the data flow over the internet to identify abnormalities in wireless network performance is known as network traffic analysis. When analyzing network traffic data, traffic classification becomes an important task. The traffic data classification is used to determine whether data in network traffic is in real-time or not. This analysis controls network traffic data in a network and allows for efficient network performance improvement. Real-time and non -real-time data are effectively classified from the given input data set using data mining clustering and classification algorithms. The proposed work focuses on the performance of traffic data classification with high clustering accuracy and low Classification time (CT). This research work is carried out to fill the gap in the existing network traffic classification algorithms. However, the traffic data classification remained unaddressed for performing the network traffic analysis effectively. Then, we proposed an Enhanced Self-Learning-based Clustering Scheme (ESLCS) using an enhanced unsupervised algorithm and adaptive seeding approach to improve the classification accuracy while performing the real-time traffic data distribution in wireless networks. Test-bed results demonstrate that the proposed model enhances the clustering accuracy and True Positive Rate (TPR) effectively as well as reduces the CT time and Communication Overhead (CO) substantially to compare with the peer-existing routing techniques.

关键词： Wireless networks Network traffic computation time Clustering accuracy Overhead

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A parallel rule-based approach to compute rough approximations of dominance based rough set theory

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期115卷

作者： Nosheen, Faryal Qamar, Usman Raza, Muhammad Summair Natl Univ Sci & Technol NUST Coll Elect & Mech Engn Knowledge & Data Sci Res Ctr Comp & Software Engn Dept Islamabad Pakistan Virtual Univ Dept Comp Sci Lahore Pakistan

In many datasets, conditional attributes and decision classes are preference-ordered, however, the classical Rough Set Theory (RST) does not consider the preference order between the values of the attributes. An extension of RST known as a Dominance-based Rough Set Approach (DRSA) provides dominance relation in this regard. In DRSA, data analysis mainly depends on the calculations of lower and upper approximations and these two measures are computationally utilizing many resources i.e., time and memory, due to the consideration of preference order. In this paper, we have proposed a parallel technique for calculating DRSA approximation sets. The proposed method directly computes approximations by following heuristic rules without calculating dominance positive or negative relations. The proposed parallel approach is then compared with the conventional method of calculation of DRSA approximations and a recent another technique of parallel processing using ten UCI publicly available datasets. Results validated the efficiency and effectiveness of the proposed model. An average reduction of 83% was observed in execution time and 86% in memory consumption. The structural complexity of the algorithm also considerably reduced.

关键词： Dominance based Rough Set Approach-DRSA Structural complexity Rough approximations Parallel threads computation time

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