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conventional and Metaheuristic Optimization algorithms for Solving Short Term Hydrothermal Scheduling Problem: A Review

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IEEE ACCESS 2021年 9卷 25993-26025页

作者： Fakhar, Muhammad Salman Liaquat, Sheroze Kashif, Syed Abdul Rahman Rasool, Akhtar Khizer, Muhammad Iqbal, Muhammad Ahmad Baig, Muhammad Anas Padmanaban, Sanjeevikumar Univ Engn & Technol Lahore Dept Elect Engn Lahore 54890 Pakistan Natl Univ Comp & Emerging Sci Lahore Dept Elect Engn Lahore 54000 Pakistan Sharif Coll Engn & Technol Lahore Dept Elect Engn Lahore 54000 Pakistan Aalborg Univ Dept Energy Technol DK-6700 Esbjerg Denmark

Short term hydrothermal scheduling (STHTS) is a non-linear, multi-modal and very complex constrained optimization problem which has been solved using several conventional and modern metaheuristic optimization algorithms. A number of research articles have been published addressing STHTS using different techniques. This article presents a comprehensive review of research published for solving the STHTS problem in the last four decades.

关键词： Optimization Reservoirs Heuristic algorithms Approximation algorithms Power generation Mathematical model Scheduling Short term hydrothermal scheduling conventional algorithms meta-heuristic algorithms no free lunch theorem

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A Comparison of conventional and Meta-Model Based Global Optimization Methods

A Comparison of Conventional and Meta-Model Based Global Opt...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

作者： Saad, Abdulbaset Lohrasbipeydeh, Hannan Dong, Zuomin Tzanetakis, George Gulliver, T. Aaron Univ Victoria Dept Mech Engn Victoria BC V8W 2Y2 Canada Univ Victoria Dept Elect & Comp Engn Victoria BC Canada Univ Victoria Dept Comp Sci Victoria BC Canada

ISBN: (纸本)9781467377881

Motivated by the growing number of applications in engineering, physics, science and other fields, interest in the development of global optimization algorithms is increasing. In this paper, two categories of global optimization methods are considered, namely conventional and meta-model based algorithms. conventional algorithms require values of the objective function to obtain a solution, while meta-model based algorithms can be used with incomplete information or when there is a limit on the available time or cost. Complex functions pose a challenge to gradient-free algorithms as they may need a significant number of function evaluations, thus meta-model based techniques may be preferred. In the paper, these algorithms are compared using a set of benchmark problems which include convex and non-convex problems, as well as smooth and non-smooth problems.

关键词： global optimization conventional algorithms meta-model based optimization

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Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm

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IET IMAGE PROCESSING 2020年第11期14卷 2365-2375页

作者： Deeba, Farah Kun, She Ali Dharejo, Fayaz Zhou, Yuanchun Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China

It is very interesting to reconstruct high-resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super-resolution method for CT medical images in the sparse representation domain with dictionary learning. The sparse coupled K-singular value decomposition (KSVD) algorithm is employed for dictionary learning purposes. Images are divided into two sets of low resolution (LR) and high resolution (HR), to improve the quality of low-resolution images, the authors prepare dictionaries over LR and HR image patches using the KSVD algorithm. The main idea behind the proposed method is that sparse coupled dictionaries learn about each patch and establish the relationship between sparse coefficients of LR and HR image patches to recover the HR image patch for LR image. The proposed method is compared to conventional algorithms in terms of mean peak signal-to-noise ratio and structural similarity index measurements by using three different data set images, including CT chest, CT dental and CT brain images. The authors also analysed the proposed improved method for different dictionary sizes and patch size to obtain a similar high-resolution image. These parameters play an essential role in the reconstruction of the HR images.

关键词： medical image processing image representation diseases image reconstruction singular value decomposition dictionaries image resolution brain image denoising computerised tomography learning (artificial intelligence) sparse coupled dictionaries sparse coefficients image patches HR image patch LR image conventional algorithms different dictionary sizes high-resolution image HR images computed tomography images reconstruction coupled dictionary high-resolution reconstruction-based methods high-resolution images image processing applications image analysis medical imaging high-resolution computed tomography medical images super-resolution method CT medical images sparse representation domain K-singular value decomposition algorithm dictionary learning purposes low-resolution images KSVD algorithm

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Robust measurement validation for radar target tracking using prior information

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IET RADAR SONAR AND NAVIGATION 2019年第10期13卷 1842-1849页

作者： Lee, Gyuejeong Kwon, Jihoon Kwak, Nojun Yang, Eunjung Seoul Natl Univ GSCST Seoul South Korea Agcy Def Dev Seoul South Korea

In this study, the authors propose a robust measurement validation algorithm for radar target tracking in a heavy-clutter environment. This algorithm defines the prior information with the radar measurement information from the estimated target trajectory. An additional validation gate is set up within the conventional validation gate using the statistics of the prior information then only a part of the validation measurement is selected to update the target state. To verify the effectiveness of the proposed algorithm, the authors designed a simulation for target tracking. The simulation results show that the proposed algorithm has a lower root mean squared position error compared to conventional algorithms in a heavy-clutter environment.

关键词： mean square error methods radar tracking target tracking radar target tracking prior information robust measurement validation algorithm heavy-clutter environment radar measurement information estimated target trajectory additional validation gate conventional validation gate validation measurement target state conventional algorithms

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Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System

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IEEE ACCESS 2021年 9卷 48679-48703页

作者： Pervez, Imran Pervez, Arsalan Tariq, Mohd Sarwar, Adil Chakrabortty, Ripon K. Ryan, Michael J. Aligarh Muslim Univ Dept Elect Engn ZHCET Aligarh 202002 Uttar Pradesh India Exterran Water Solut Calgary AB T2E7E1 Canada UNSW Canberra ADFA Sch Engn & IT Capabil Syst Ctr Canberra ACT 2612 Australia

When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. conventional algorithms-such as perturb and observe (P&O) and hill-climbing (HC)-are not suitable to be employed to track the optimal peak due to their convergence to local maxima. To address this issue, various artificial intelligence (AI) based algorithms-such as an artificial neural network (ANN) and fuzzy logic control (FLC)-have been employed to track the maximum power point (MPP). Although these algorithms provide satisfactory results under PS conditions, a very large amount of data is required for their training process, thereby imposing an excessive burden on processor memory. Consequently, this paper proposes a novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability. The algorithm successfully eliminates the issues associated with existing conventional and AI-based algorithms. Moreover, the proposed algorithm outperforms other state-of-the-art stochastic search-based techniques in terms of fewer fluctuations, robustness, simplicity, and faster convergence to the optima. Extensive analysis of results obtained from MATLAB(R) is done to prove the above performance parameters under static insolation conditions (using a three, four and a five-module series-connected PV system), under dynamically varying insolation (using a four-module series connected system), by changing the PV module rating (using a four-module series connected system) and using an IEC standard.

关键词： Maximum power point trackers Fluctuations Robustness Memory management Convergence Optimization Space exploration Adaptive jaya (Ajaya) maximum power point tracking (MPPT) metaheuristic algorithms conventional algorithms photovoltaic (PV)

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Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

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CURRENT MEDICAL IMAGING 2020年第2期16卷 156-163页

作者： Wang, Jingwen Wang, Xu Yang, Dan Wang, Kaiyang China Jiliang Univ Coll Informat Engn Hangzhou 310018 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the ill-posed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

关键词： MIT imaging reconstruction fourier coefficients FRI sparsity conventional algorithms

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Vertex-based inter-frame boundary encoding scheme

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ELECTRONICS LETTERS 1998年第13期34卷 1302-1303页

作者： Kim, KJ Suh, JY Kang, MG Park, KT Yonsei Univ Dept Elect Engn Seodaemun Gu Seoul 120749 South Korea

The boundary is approximated by a polygon which can be encoded with the smallest number of bits for maximum distortion. The temporal redundancy between two successive frames is efficiently removed with the proposed sc... 详细信息

关键词： lower bit rate proposed scheme temporal redundancy maximum distortion Codes vertex-based inter-frame boundary encoding scheme successive frames Optical information, image and video signal processing Interpolation and function approximation (numerical analysis) Information theory conventional algorithms polygon video coding approximation theory encoded image sequences Computer vision and image processing techniques

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Stock Price Prediction using Deep-Learning Model 4

Stock Price Prediction using Deep-Learning Model

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4th International Conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Pralcash, Tamil A. Sudha Hindustan Inst Technol & Sci Comp Applicat Chennai Tamil Nadu India Hindustan Inst Technol & Sci Dept Comp Applicat Chennai Tamil Nadu India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356;9798350386349

This research study combines offline and real-time data from trading stock markets to present a unique neural network-based approach to enhance stock market forecasts. This research investigates the challenges posed by random weight initialization issues, which often limit the ability of conventional neural network algorithms to accurately represent market dynamics. By introducing the concept of "stock vector," the study highlights how LSTM networks may capture the intricate temporal correlations seen in sequential data, which are akin to language. Additionally, embedding layers are employed to transform words into intricate, meaningful representations, which further enhances the prediction potential of the model. The forecasting accuracy of these models is enhanced by vectoring high-dimensional historical data from several stocks. Based on data from empirical testing, the deep LSTM model with an embedded layer outperforms the others in stock market predictions. This methodological development represents a major step forward in the use of deep learning algorithms, providing increased accuracy and understanding for stock market research projects.

关键词： stock market forecasts real-time data offline data random weight initialization conventional algorithms market features stock vector deep learning long short-term memory

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NOISE ESTIMATION AND REMOVAL IN MR IMAGING: THE VARIANCE-STABILIZATION APPROACH

NOISE ESTIMATION AND REMOVAL IN MR IMAGING: THE VARIANCE-STA...

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8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Foi, Alessandro Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)9781424441280

We develop optimal forward and inverse variance-stabilizing transformations for the Rice distribution, in order to approach the problem of magnetic resonance (MR) image filtering by means of standard denoising algorithms designed for homoskedastic observations. Further, we present a stable and fast iterative procedure for robustly estimating the noise level from a single Rician-distributed image. At each iteration, the procedure exploits variance-stabilization composed with a homoskedastic variance-estimation algorithm. Theoretical and experimental study demonstrates the success of our approach to Rician noise estimation and removal through variance stabilization. In particular, we show that the performance of current state-of-the-art algorithms specifically designed for Rician-distributed data can be matched by combining conventional algorithms designed for additive white Gaussian noise with optimal variance-stabilizing transformations.

关键词： Algorithm design and analysis Gaussian noise MR imaging Maximum likelihood estimation Noise Noise reduction Optimization Rician channels Rician noise estimation biomedical MRI conventional algorithms fast iterative procedure homoskedastic variance-estimation algorithm image denoising inverse variance-stabilizing transformations iterative methods magnetic resonance image filtering medical image processing noise estimation noise level optimal variance-stabilizing transformations single Rician-distributed imaging stable iterative procedure standard denoising algorithms state-of-the-art algorithms white Gaussian noise

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A CNN-based temperature prediction approach for grain storage

A CNN-based temperature prediction approach for grain storag...

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International Journal of Internet Manufacturing and Services

作者： Ge, Liang Chen, Caiyuan Li, Yiyu Mo, Tong Li, Weiping School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China School of Software and Microelectronics Peking University Beijing China

Temperature prediction has a pivotal role in the grain storage phase. Accurate prediction results can optimise the effect of ventilation decisions and reduce the losses of stored grain. Most existing studies have only focused on layer temperature predictions whose predict particle size is very large. In contrast, this paper attempts to use convolutional neural network (CNN) to predict the point temperature of grain piles. The CNN-based approach uses multiple convolution kernels that share weights to capture the characteristics of grain temperature at different locations, which make full use of the temperature information around the target point. Experiments on real business data show that compared to other conventional algorithms, CNN has the best prediction effect on point temperature prediction problems. © 2020 Inderscience Enterprises Ltd.

关键词： Forecasting Convolution Convolutional neural networks Digital storage Particle size Accurate prediction conventional algorithms Grain temperature Multiple convolution Point temperature Real business data Temperature information Temperature prediction

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