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Distance-Based intelligent Particle Swarm Optimization for Optimal Design of Permanent Magnet Synchronous Machine

IEEE TRANSACTIONS ON MAGNETICS 2017年第6期53卷

作者： Lee, Jin Hwan Kim, Jong-Wook Song, Jun-Young Kim, Dae-Woo Kim, Yong-Jae Jung, Sang-Yong Sungkyunkwan Univ Sch Elect & Elect Engn Suwon 16419 South Korea Dong A Univ Dept Elect Engn Busan 49315 South Korea Chosun Univ Dept Elect Engn Gwangju 61452 South Korea

In this paper, we propose a novel particle swarm optimization (PSO), which is based on the Euclidian distance of particles. In the conventional PSO, the convergence speed is decreased, since several particles that are far from swarm does not converge but wander around search area. The proposed algorithm, which is named after distance based intelligent PSO, assigns a new position and velocity to the furthest particle. Therefore, all particles gather around global optimum, and it is able to search for global optimum faster than that of the conventional PSO. To validate effectiveness of the proposed algorithm, we compare it to the conventional PSO using three numerical test functions. In addition, interior permanent magnet synchronous motor (IPMSM), which has characteristic of magnetic saturation in magnetic steel sheet, is chosen as target model of optimal design. Total harmonic distortion of back-electromotive force is optimized by optimization of rotor topology. Through optimal design of IPMSM, effectiveness of the proposed algorithm for electric machine design is verified.

关键词： intelligent algorithm interior permanent magnet synchronous machine (IPMSM) optimal design particle swarm optimization (PSO)

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Roughness Prediction and Performance Analysis of Data-Driven Superalloy Belt Grinding

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FRONTIERS IN MATERIALS 2022年 9卷

作者： Zhang, Youdong Xiao, Guijian Gao, Hui Zhu, Bao Huang, Yun Li, Wei Chongqing Univ Coll Mech & Vehicle Engn Chongqing Peoples R China

Abrasive belt grinding has unique advantages in avoiding machining defects and improving surface integrity while grinding hard materials such as superalloys. However, the random distribution of abrasive particles on the abrasive belt surface is uncontrollable, and chatter and machining errors accompany the machining process, leading to unclear mapping relationship between process parameters and surface roughness, which brings great challenges to the prediction of surface roughness of superalloy. Traditional empirical equations are highly dependent on empirical knowledge and the development of scientific theories and can only solve problems with relatively simple and clear mechanisms, but cannot effectively solve complex and mutually coupled problems. The method based on data-driven patterns has a better idea for mining the implicit mapping relationship and eliminating the uncertainty of complex problems. This study presents a data-driven roughness prediction method for GH4169 superalloy. First, a superalloy grinding platform is built. According to the grinding empirical equation, the mapping relationship between process parameters and surface roughness is analyzed, and a prediction model is established based on the error back propagation (BP) algorithm. Second, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to optimize the weights and thresholds of the neural network, and the global optimal solution is obtained. Finally, the prediction performance of different algorithms is compared. The results show that the non-uniform absolute errors of the BP algorithm, GA-BP algorithm, and PSO-BP algorithm are 0.12, 0.085, and 0.078, respectively. The results show that the roughness prediction algorithm based on PSO-BP is more suitable for GH4169 superalloy.

关键词： belt grinding GH4169 superalloy roughness prediction data driven intelligent algorithm

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Improving reliability of JXTA-Overlay P2P platform: A comparison study for two fuzzy-based systems

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JOURNAL OF HIGH SPEED NETWORKS 2015年第1期21卷 27-42页

作者： Liu, Yi Sakamoto, Shinji Matsuo, Keita Ikeda, Makoto Barolli, Leonard Xhafa, Fatos Fukuoka Inst Technol Grad Sch Engn Fukuoka Japan Fukuoka Prefectural Fukuoka Tech High Sch Fukuoka Japan Fukuoka Inst Technol Dept Informat & Commun Engn Fukuoka Japan Tech Univ Catalonia Dept Languages & Informat Syst Barcelona Spain

In P2P systems, each peer has to obtain information of other peers and propagate the information to other peers through neighboring peers. Thus, it is important for each peer to have some number of neighbor peers. Moreover, it is more significant to discuss if each peer has reliable neighbor peers. In reality, each peer might be faulty or might send obsolete, even incorrect information to the other peers. We have implemented a P2P platform called JXTA-Overlay, which defines a set of protocols that standardize how different devices may communicate and collaborate among them. It abstracts a new layer on the top of JXTA through a set of primitive operations and services that are commonly used in JXTA-based applications and provides a set of primitives that can be used by other applications, which will be built on top of the overlay, with complete independence. JXTA-Overlay provides a set of basic functionalities, primitives, intended to be as complete as possible to satisfy the needs of most JXTA-based applications. In this paper, we present two fuzzy-based systems (called FRS1 and FRS2) to improve the reliability of JXTA-Overlay P2P platform. We make a comparison study between the fuzzy-based reliability systems. Comparing the complexity of FRS1 and FRS2, the FRS2 is more complex than FRS1. However, it considers also the security;therefore, it can be used in real application for secure systems.

关键词： P2P systems JXTA-Overlay fuzzy logic network reliability intelligent algorithm

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A nonlinear approach for predicting pore pressure using genetic algorithm in one of the Iranian petroleum carbonate reservoirs

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ARABIAN JOURNAL OF GEOSCIENCES 2020年第14期13卷 1-13页

作者： Hossein, Mohammadi Ali, Riahi Mohammad Univ Amirkabir Tehran Iran Univ Iran Inst Geophys Tehran Iran

The fluid pressure within a formation pores is called the pore pressure in petroleum engineering. The estimation of pore pressure is a challenging task during a reservoir's life cycle. An impermeable rock, such as shale, confides the fluids which lead to anomalously high pressures. Besides, during the exploitation life of a reservoir, the pressure reduces in the reservoir. The estimation of these high-risk pore pressures is an essential task in planning for infill drilling and field development. Herein, we propose a nonlinear model for pore pressure estimation, using a genetic algorithm. We compare our method with two of the classical linear methods for pore pressure estimation, the modified Eaton method and the Bowers method, using the Modular Formation Dynamics Tester (MDT) and well logs data related to an Iranian oil-bearing carbonate reservoirs. The results of the nonlinear estimation models showed higher accuracy and less uncertainty than the other models. The studied oil field is in the development phase;therefore, a reliable estimation of pore pressure decreases the future drillings risks and can find application in hydraulic fracturing, completion, and cement works operations.

关键词： Pore pressure Genetic algorithm Particle swarm algorithm Well logs intelligent algorithm

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Determining launching construction parameters of superlong superwide bridges: A multiobjective optimization method using machine learning techniques

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STRUCTURES 2022年 41卷 15-28页

作者： Sun, Yuan Tai, Xiao-xiao Liu, Kai Zhu, Ai-zhu Zhu, Hong-ping Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Hubei Peoples R China China Railway 11th Bur Grp Co LTD Survey Design Inst Wuhan 430074 Hubei Peoples R China

The launching construction parameters of bridges, such as the spacing of temporary piers and the length of launching noses, significantly affect the cost, shape and safety of the construction. Determining such parameters generally requires multistage manual comparison studies upon finite element (FE) analysis, which might become extremely tedious when superlong superwide structures with local stress concerns are involved. This paper proposes a multiobjective optimization method for determining the launching construction parameters of bridges while considering local stress constraints. The multiobjective particle swarm optimization (MOPSO) is employed as an optimization tool, whereas the intelligent algorithm (GSA-SVR) is used to establish a surrogate model. This model creates a direct mapping relationship between the design variables and the most adverse local stresses of deck segments in construction. Then, the launching construction of a superlong superwide steel box girder bridge is introduced as a case study. The effectiveness and merits of the method are validated by the optimization results and parametric studies. The study shows how the construction parameters can affect the trade-off between the cost and the shape results..

关键词： Construction Optimization Finite element Multiobjective intelligent algorithm

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Optimal Scheduling of a Mine Water Drainage System Based on Improved Particle Swarm Optimization algorithm: A Case Study of the Guhanshan Coal Mine, China

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MINE WATER AND THE ENVIRONMENT 2022年第2期41卷 475-486页

作者： Wang, Xinyi Li, Fang Kan, Xuedong Guo, Shuitao Li, Wenjun Luo, Wenjun Henan Polytech Univ Inst Resources & Environm Jiaozuo 454000 Henan Peoples R China Collaborat Innovat Ctr Coalbed Methane & Shale Ga Jiaozuo 454100 Henan Peoples R China Collaborat Innovat Ctr Coal Work Safety & Clean H Jiaozuo 454100 Henan Peoples R China Henan Jiaozuo Coal Ind Grp Co Ltd Jiaozuo 454100 Henan Peoples R China

A groundwater drainage system comprising five drainage points and seven submersible pumps was designed to relieve the threat of flooding by 2.9 million m(3) of mine water and a dynamic water supply volume of 718 m(3)/h in the Guhanshan Coal Mine. The key aim of the design was to ensure safe production. A mathematical model of the optimal scheduling of the water drainage system was constructed and solved using an improved particle swarm optimization algorithm to minimize long-term energy consumption. The algorithm had high convergence accuracy, and quickly and efficiently realized the optimal configuration of the pumping volume of the mine drainage system. When the Wucun mine was full of water, the flow of submersible pumps no. 1 (or 2) and nos. 3, 4, 5, and 6 were set to 58, 150, 150, 180, and 180 m(3)/h, respectively, and the energy consumption of the system was minimal. When the water level in the Wucun mine was less than - 40 m, the lowest energy consumption was found when the flow rates of pumps nos. 3, 4, 5, 6, and 7 were, respectively set to 150, 150, 180, 180, and 58 m(3)/h. This overcame the shortcomings of adjusting the submersible pump flow based only on past experience and should be useful for many future mine water drainage projects.

关键词： Mathematical model Multi-constraint optimization intelligent algorithm Water disasters prevention Pumping volume

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NNDI decentralized evolved intelligent stabilization of large-scale systems

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SMART STRUCTURES AND SYSTEMS 2022年第1期30卷 1-15页

作者： Chen, Z. Y. Wang, Ruei-Yuan Jiang, Rong Chen, Timothy Guangdong Univ Petrochem Technol Sch Sci Maoming 525000 Peoples R China CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

This article focuses on stability analysis and fuzzy controller synthesis for large neural network (NN) systems consisting of several interconnected subsystems represented by the NN model. Advanced and fuzzy NN differential inclusion (NNDI) for stability based on the developed algorithm with H infinity can be designed based on the evolved biological design. This representation is constructed using sector linearity for NN models. Sector linearity transforms a non-linear model into a linear model based on proposed operations. New sufficient conditions are realized in the form of LMI (linear matrix inequalities) to ensure the asymptotic stability of the trans-Lyapunov function. This transforms the nonlinear model into a linear model based on multiple rules. At last, a numerical case study with simulations is derived as illustration to prove its feasibility in real nonlinear structures.

关键词： intelligent algorithm large-scale system neural fuzzy control numerical modelling

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A Review of the intelligent Optimization and Decision in Plastic Forming

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MATERIALS 2022年第19期15卷 7019页

作者： Tang, Xuefeng Wang, Zhizhou Deng, Lei Wang, Xinyun Long, Jinchuan Jiang, Xin Jin, Junsong Xia, Juchen Huazhong Univ Sci & Technol Sch Mat Sci & Engn State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Peoples R China

The plastic forming process involves many influencing factors and has some inevitable disturbance factors, rendering the multi-objective collaborative optimization difficult. With the rapid development of big data and artificial intelligence (AI) technology, intelligent process optimization has become one of the critical technologies for plastic forming. This paper elaborated on the research progress on the intelligent optimization of plastic forming and the data-driven process planning and decision-making system in plastic forming process optimization. The development trend in intelligent optimization of the plastic forming process was researched. This review showed that the intelligent optimization algorithm has great potential in controlling forming quality, microstructure, and performance in plastic forming. It is a general trend to develop an intelligent optimization model of the plastic forming process with high integration, versatility, and high performance. Future research will take the data-driven expert system and digital twin system as the carrier, integrate the optimization algorithm and model, and realize the multi-scale, high-precision, high-efficiency, and real-time optimization of the plastic forming process.

关键词： plastic forming intelligent algorithm process optimization data-driven digital twin

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Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting

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ENERGY 2018年 161卷 130-142页

作者： Ghadimi, Noradin Akbarimajd, Adel Shayeghi, Hossein Abedinia, Oveis Islamic Azad Univ Ardabil Branch Dept Engn Ardebil Iran Univ Mohaghegh Ardabili Fac Tech Engn Dept Elect Engn Ardebil Iran Budapest Univ Technol & Econ Dept Elect Engn Budapest Hungary

Short-term load forecasting is of major interest for the restructured environment of the electricity market. Accurate load forecasting is essential for effective power system operation, but electricity load is non-linear with a high level of volatility. Predicting such complex signals requires suitable prediction tools. This paper proposes a hybrid forecast strategy including novel feature selection technique, and a complex forecast engine based on a new intelligent algorithm. The electricity load signal is first filtered by feature selection technique to select appropriate candidates as input for the forecast engine. Then, the proposed two stage forecast engine is implemented based on ridgelet and Elman neural networks. All forecast engine parameters are chosen based on a novel intelligent algorithm to improve its accuracy and capability. Different electricity markets were considered as test cases to compare the proposed method with several current algorithms. Additionally, the proposed forecasting model measures the absolute forecasting errors in this work (among seven types of measurements i.e., absolute forecasting errors, measures based on percentage errors, symmetric errors, measures based on relative errors, scaled errors, relative measures and other error measures). The results validate the effectiveness of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Load forecast Feature selection Two stage forecast engine Ridgelet NN Elman NN intelligent algorithm

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Deterioration identification of stone cultural heritage based on hyperspectral image texture features

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JOURNAL OF CULTURAL HERITAGE 2024年 69卷 57-66页

作者： Li, Xingyue Yang, Haiqing Chen, Chiwei Zhao, Gang Ni, Jianghua Chongqing Univ Sch Civil Engn Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China Natl Joint Engn Res Ctr Geohazards Prevent Reservo Chongqing 400045 Peoples R China Acad Dazu Rock Carvings Chongqing 402360 Peoples R China

Deterioration investigation is an essential foundation for understanding the preservation status of stone cultural heritage, as well as for carrying out emergency and preventive conservation. Traditional photogrammetry method for deterioration investigation in stone cultural heritage heavily relies on personnel experience and has low automation. To accurately evaluate the degree of deterioration and quantify its scale, different algorithms are used to establish the rebound value prediction model and deterioration identification model based on the hyperspectral image. The effects of different wavelength selection methods and different classification models are compared. The results show that the rebound value inversion model constructed by CARS and PLS delivers the most accurate forecasts, with R2 being no less than 0.85. The maximum error of the model when applied in the field does not exceed 20%. Different types of deterioration can be initially identified by the normalized spectral index constructed from the 530 nm and 675 nm wavelengths. In addition, all four classification models based on hyperspectral imaging texture features can identify different types of deterioration. The LGBM model has the highest identification accuracy of 0.98. It also has good performance in field identification. This study provides a new method for deterioration investigation in stone cultural heritage. (c) 2024 Consiglio Nazionale delle Ricerche (CNR). Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Stone cultural heritage Deterioration identification Hyperspectral imaging intelligent algorithm Texture features

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