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A review on optimization techniques for medical image analysis

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第1期35卷

作者： Kaur, Palwinder Singh, Rajesh Kumar IKG Punjab Tech Univ Dept Comp Sci Jalandhar Punjab India SUS Grp Coll Dept Comp Applicat Mohali India

Data mining of medical imaging approaches makes it difficult to determine their value in the disease's insight, analysis, and diagnosis. Image classification presents a significant difficulty in image analysis and plays a vital part in computer-aided diagnosis. This task concerned the use of optimization techniques for the utilization of image processing, pattern recognition, and classification techniques, as well as the validation of image classification results in medical expert reports. The primary intention of this study is to analyze the performance of optimization techniques explored in the area of medical image analysis. For this motive, the optimization techniques employed in existing literature from 2012 to 2021 are reviewed in this study. The contribution of optimization-based medical image classification and segmentation utilized image modalities, data sets, and tradeoffs for each technique are also discussed in this review study. Finally, this review study provides the gap analysis of optimization techniques used in medical image analysis with the possible future research direction.

关键词： brain disease diagnosis breast disease diagnosis heart disease diagnosis lung disease diagnosis medical image analysis optimization algorithm retinal disease diagnosis segmentation techniques

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Cylindricity Error Measurement and Evaluation Based on Step Acceleration algorithm in Crankshaft Measuring Machine

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MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA 2022年第4期37卷 823-832页

作者： Wang, Y-X Wang, H-X Tian, H-H Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China

In the process of developing the vertical crankshaft comprehensive measuring instrument, owing to the problems of complex calculation and low efficiency of the existing crankshaft main journal cylindricity evaluation algorithm, a cylindricity error optimization algorithm based on step acceleration is proposed. As per the requirement of a measuring device of crankshaft, a mathematical model for evaluating cylindricity error is also proposed, and the cylindricity error in crankshaft journal with minimum assessment requirement is calculated by the step acceleration based optimization algorithm. Then, a certain type of engine crankshaft is taken as an object for detecting the main journal cylindricity error using the proposed new algorithm on the platform of a vertical crankshaft comprehensive measuring machine, and the measurement uncertainty analysis is performed. Compared to Talyrond290, the detection error of the measurement results is found to be within 0.5 um. In comparison with the geometric hexagon cylinder optimization algorithm, the results of the proposed methodology are found to be highly consistent and the computation time is reduced by 27.8%. Therefore, the proposed algorithm is practical.

关键词： Cylindricity error Step acceleration optimization algorithm Crankshaft Measurement uncertainty

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Optimal Scheduling of Grid Connected Solar Photovoltaic and Battery Storage System Considering Degradation Cost of Battery

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2022年第4期46卷 1175-1188页

作者： Kumar, Jitendra Kumar, Nagendra SRM IST Delhi NCR Campus Ghaziabad 201204 Uttar Pradesh India GL Bajaj Inst Technol & Management Greater Noida 201306 Uttar Pradesh India

The major objectives of this paper are to optimize the scheduling of solar photovoltaic (SPV) and battery energy storage systems (BESS) with the grid in order to reduce power loss and improve reliability. An unbalanced 8-bus rural distribution network in the village of Jalalabad, in the district of Ghaziabad, Uttar Pradesh, India, is under consideration. The main issue in village-based rural communities is excessive power outages and restricted grid power supplies. A modified artificial bee colony optimization technique has been used to identify optimum sizing, location, and timing in order to minimize the system's total cost and losses in order to overcome the aforementioned challenge. The management resource and demand response strategy are used to manage the load demand profile. The Coulomb Counting method is used to improve the estimation accuracy of the battery. The various results demonstrate the efficacy of the suggested method for determining appropriate PV, BESS, and grid size, location, and timing. In this work, only summer season is considered for SPV generation. In addition, the degradation cost of the battery and the excess power production have also been analyzed in this paper. It is evident that with the increase in the non-essential load shifting fraction beta(NELS) from 0 to 25%, the fraction of excess power production decreases from 9.15 to 6.21%. The results demonstrate that combining solar PV with a rural network reduces carbon dioxide (CO2) emissions while also providing power 24 h a day, seven days a week.

关键词： Solar photovoltaic Battery degradation cost optimization algorithm Demand response Power loss

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ROT8: A Matlab App for Low Reynolds number Airfoil and Rotor Aerodynamic Design

ROT8: A Matlab App for Low Reynolds number Airfoil and Rotor...

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AIAA Aviation Forum

作者： Ruiz, M. Carreno D'Ambrosio, D. Politecn Torino Dept Mech & Aerosp Engn Cso Duca Abruzzi 24 I-10124 Turin Italy

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

The proposed software aims to solve the increase in demand for rotor design modules generated by the exponential growth of applications involving Unmanned Aerial Systems (UASs). We offer a variety of aerodynamic solvers for rotor flows with different fidelities, including a Blade Element Momentum (BEM) method, a Free Vortex Wake (FVW) method, a Vortex Particle Method (VPM), and a data-driven approach based on experimental data ROT8 allows the calculation of airfoil polars necessary for the aforementioned Reduced Order Models (ROMs) using XFOIL. This general framework can be applied to rotors, propellers, and horizontal-axis wind turbines. The code is a Matlab App that allows using different optimization algorithms for design purposes. The code is intended to be versatile, with straightforward default settings for academic use but allowing the definition of user-defined functions to increase the level of interaction with the code for advanced research purposes.

关键词： Airfoil Databases MATLAB Aerodynamic optimization Horseshoe Vortex Reduced Order Modelling Propeller Performance optimization algorithm Horizontal Axis Wind Turbine Unmanned Aerial Systems Aerodynamic Efficiency

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optimization of engineering parameters of deflagration fracturing in shale reservoirs based on hybrid proxy model

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 231卷

作者： Yang, Xin Guo, Tiankui Yu, Mengqi Chen, Ming Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266520 Peoples R China China Univ Petr Coll Petr Engn Qingdao 266580 Peoples R China

Engineering parameter optimization of deflagration fracturing in shale reservoirs is operated with single factor analysis and orthogonal experimental design analysis. There are problems of the difficulty to search the global optimal solution, time-consuming optimization and low computation efficiency. To resolve these problems, a method of optimizing engineering parameters of deflagration fracturing based on the hybrid proxy model was proposed in this paper. The parameters with significant effects on deflagration fracturing were selected as var-iables, and the models for prediction of reservoir burst degree and stimulated area were established based on Radial Basis Function model, Kriging interpolation model and eXtreme Gradient Boosting regression model respectively. A hybrid proxy model based on the roulette method was established by integrating the priorities of these three models. Then, a multi-objective optimization method of deflagration fracturing based on the hybrid proxy model and was established by integrating the Nondominated Sorting Genetic algorithm II with the en-gineering parameters as variables and the maximum stimulated area and minimum burst degree as objectives. Optimal design of the deflagration fracturing scheme was achieved. The results show that the hybrid proxy model can accurately predict the evaluation index of the fracturing results in vertical and horizontal wells, and the prediction accuracy is maintained at about 0.9. At the same time, compared with numerical model, the proposed method greatly saves the calculation time. The method proposed by this paper provides the solution to the problem of multi-objective optimization of deflagration fracturing engineering parameters, weighs the reservoir failure degree and the stimulation range, and provides guidance for field operation.

关键词： Deflagration fracturing Hybrid proxy model Engineering parameter optimization algorithm Multi-objective optimization

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Holistic optimization of lithium-ion battery negative electrode formulation using a combination of theory of mixtures, Box-Behnken matrix, multi-variant analysis and desirability functions of Derringer-Suich

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CHEMICAL ENGINEERING JOURNAL 2023年 474卷

作者： Urdampilleta, Idoia Bengoechea, Miguel de Meatza, Iratxe Boyano, Iker Blazquez, J. Alberto Lizaso, Lander Mainar, Aroa R. Miguel, Oscar Grande, Hans-Juergen Landa-Medrano, Imanol Kvasha, Andriy Basque Res & Technol Alliance BRTA CIDETEC Donostia San Sebastian 20014 Spain Univ Basque Country EHU UPV Dept Appl Chem Donostia San Sebastian 20018 Spain Univ Pais Vasco UPV EHU Dept Organ & Inorgan Chem Bilbao 48080 Spain Univ Basque Country UPV EHU Chem & Technol Dept Adv Polymers & Mat Phys Donostia San Sebastian 20018 Spain

The battery R & D process should be accelerated towards a faster transition to a greener economy. However, typical battery electrode/electrolyte optimization process is still based on heuristic trial-and-error methodology, which is simple and robust but requires multiple attempts and, as a result, is high resource-time consuming. Therefore, a holistic algorithm for the optimization of a four-component (graphite, carbon black, thickener and binder) waterborne negative electrode formulation for lithium-ion batteries is proposed to improve efficiency, spend fewer resources, and speed-up the whole process. Briefly, the experimental space of possible electrode formulations is defined using a modified Box-Behnken cube matrix L15. Each inactive component's pre-selected range of content is divided into three symmetric levels. Fifteen slurries-electrodes are prepared and comprehensively studied by different experimental methods to obtain six preliminarily selected experimental outputs divided into two groups: feasibility (slurry rheology and electrode peel resistance) and electrochemical perfor-mance in half coin cells (initial coulombic efficiency and discharge capacity under 2D discharge C-rate) and full coin cells (inverse capacity degradation slope under 2C/2D cycling protocol and inverse internal cell resistance). Finally, a multi-variant analysis of the obtained experimental data array coupled with Derringer-Suich desirability functions specially defined for each experimental output are applied to find out an optimal negative electrode formulation with the best combination of the feasibility and electrochemical performance.

关键词： Lithium-ion batteries Negative electrode formulation optimization algorithm Design of experiments Desirability functions

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Distributed optimization Method for Spacecraft Attitude Control and Vibration Suppression

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第4期46卷 752-760页

作者： Hu, Yabo Bernelli-Zazzera, Franco Geng, Yunhai Harbin Inst Technol Res Ctr Satellite Technol Harbin 150001 Peoples R China Polytech Univ Milan Dept Aerosp Sci & Technol I-20156 Milan Italy

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Adaptive population-based multi-objective optimization in SDN controllers for cost optimization

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PHYSICAL COMMUNICATION 2023年 58卷

作者： Qaffas, Alaa A. Kamal, Shoaib Sayeed, Farrukh Dutta, Papiya Joshi, Shubham Alhassan, Ibrahim Univ Jeddah Coll Business Dept Management Informat Syst Jeddah Saudi Arabia Global Acad Technol Dept Artificial Intelligence & Data Sci Bengaluru 560098 Karnataka India Ace Coll Engn Dept Elect & Elect Engn Trivandrum India Bharat Inst Engn & Technol Dept Elect & Commun Engn Hyderabad 501510 Telangana India SVKMS NMIMS MPSTME Shirpur Campus Dept Comp Engn Savalade India Saudi Elect Univ E Commerce Dept Riyadh Saudi Arabia

In Wireless Sensor Networks, Software Defined Networks (SDN) provide a logically centralized control plane as a potential means of streamlining network management (WSNs). The employment of several SDN controllers to build a physically distributed SDN is a common tactic to boost speed, expand scalability, and offer fault tolerance. However, the deployment of many controllers results in increased synchronization and deployment expenses. Therefore, selecting the optimal location for SDN controllers to improve WSN performance is a research issue. In this paper, the multi-objective optimization problem known as the controller placement problem (CPP) is initially formulated. Cost, time, and reliability are just a few of the restraints that are taken into consideration in this regard. In addition, a new Adaptive Population-Based Cuckoo optimization (APB-CO) for optimal controller placement is implemented. In the end, APB-CO performs experiments to validate the efficacy by analyzing Sync (7.5), Coverage (47), Controller Cost (4.8), and Fitness (0.6983) for the 100th node variation at network 1. The proposed model obtained the controller cost as 34.4, compared to the existing method such as Simulated Annealing (44.3) and Greedy Approach (42.6).(c) 2023 Elsevier B.V. All rights reserved.

关键词： Adaptive population based Cuckoo optimization algorithm Controllers Software defined network Reliability Timing

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Time optimizing of fractal image compression using the Scatter Search algorithm

ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS

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ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS 2022年第47期47卷 365-381页

作者： Al-Bundi, Shaimaa S. Al-Saidi, Nadia M. G. Univ Baghdad Dept Math Coll Educ Pure Sci Ibn Al Ibn Al Haitham Baghdad Iraq Univ Technol Iraq Dept Appl Sci Baghdad Iraq

Image compression is one of the essential requirements for the efficient use of storage space and bandwidth. A new technique based on fractal theory is proposed for encoding the image;it is known as fractal image compression. In the procedure of encoding, the mechanism of search is considered as one of the main problems of this technique. In this work, an attempt to speed up the encoding process with minimal loss of the compressed image quality is adopted based on the Scatter Search algorithm. It is a sibling of Tabu search based on similar origins. The experimental results show a significant reduction in the computation time, where the mean square error measures be-tween blocks are decreased after comparing them to full search methods. Consequently, the decoding process evinced that the reconstructed images were of high quality.

关键词： fractal image coding (FIC) iterated function system (IFS) optimization algorithm metaheuristics scatter search method

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A comparative study of different neural networks in predicting gross domestic product

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JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期31卷 601-610页

作者： Lai, Han Sichuan Top IT Vocat Inst 2000 West Dist Ave Chengdu 611743 Sichuan Peoples R China

Gross domestic product (GDP) can well reflect the development of the economy, and predicting GDP can help better grasp the future economic trends. In this article, three different neural network models, the genetic algorithm - back-propagation neural network model, the particle swarm optimization (PSO) - Elman neural network (Elman NN) model, and the bat algorithm - long short-term memory model, were analyzed based on neural networks. The GDP data of Sichuan province from 1992 to 2020 were collected to compare the performance of the three models in predicting GDP. It was found that the mean absolute percentage error values of the three models were 0.0578, 0.0236, and 0.0654, respectively;the root-mean-square error values were 0.0287, 0.0166, and 0.0465, respectively;and the PSO-Elman NN model had the best performance in GDP prediction. The experimental results demonstrate that neural networks were reliable in predicting GDP and can be used for further applications in practice.

关键词： neural network Sichuan province gross domestic product optimization algorithm forecasting

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