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International Forum on Computer Science-Technology and Applications

作者： Liu Taian Wang Yunjia Liu Wentong CUMT Coll Environm & Spatial Informat Xuzhou 221116 Peoples R China SDUST Dept Informat & Engn Tai An 271019 Shandong Peoples R China Nanyang Technol Univ Singapore 639798 Singapore

ISBN: (纸本)9780769539300

LS-SVM(least squares support vector machine) has been widely used in engineering practice. However, the solving of LS-SVM still remains difficult under the condition of large sample. Based on algorithm of combinatorial optimization, this paper put forward the combinatorial optimization least squares support vector machine algorithm. On several different data aggregation of dimensions, the numerical value experiment and comparison are carried out on traditional LS-SVM algorithm, COLS-SVM algorithm and its improvement algorithm. The numerical value test has shown that COLS-SVM algorithm and its improvement algorithm are effective and have certain advantages on time and regression accuracy, compared with traditional LS-SVM algorithm.

关键词： Least squares support vector machine Sparse method combinatorial optimization algorithm Linear equations least squares support vector machine

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Ultrasonic Computerized Tomography Imaging Method With combinatorial optimization algorithm for Concrete Pile Foundation

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IEEE ACCESS 2019年 7卷 132395-132405页

作者： Wang, Guan Chen, Yao Li, Qiufeng Lu, Chao Chen, Guo Huang, Lixia Nanchang Hongkong Univ Key Lab Jiangxi Prov Image Proc & Pattern Recogni Nanchang 330063 Jiangxi Peoples R China Nanchang Hongkong Univ Key Lab Nondestruct Testing Minist Educ Nanchang 330063 Jiangxi Peoples R China

Concrete pile foundations are the main load-carrying and aseismic structures for many large-scale building structures. Their structural quality will have a great impact on the safety of building structures. However, the imaging results still cannot easily meet practical demands in current ultrasonic Computerized Tomography (CT) imaging tests of concrete structures. In view of the current difficulties, a combinatorial optimization tomographic imaging method is proposed. First, a quadric broadening objective function with a clear physical meaning is established according to the characteristics of ultrasonic propagation in concrete. Then, a new CT imaging method of concrete pile foundation is formed by combining fast adaptive optimization search ability of Genetic algorithm (GA) with the global search control ability of Simulated Annealing algorithm (SAA). The numerical simulation experiments have shown that the usage of the correct priori information and the excellent characteristic of the Simulated Annealing Genetic algorithm (SAGA) in searching for the global minimum value of the function have produced accurate and effective results with stable numerical values. Finally, the imaging method is verified by experiment, where results show that SAGA requires fewer iterations, has faster computation speed and gives more accurate imaging results compared with the single GA.

关键词： Concrete pile foundation ultrasonic CT inversion imaging algorithm combinatorial optimization algorithm SAGA

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Prediction of RNA secondary structure based on stem region replacement using the RSRNA algorithm

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2021年第1期24卷 101-114页

作者： Xu, Chengzhen Gao, Longjian Li, Jin Shen, Longfeng Liang, Hong Luan, Kuan Wu, Xiaomin Huaibei Normal Univ Sch Comp Sci & Technol Huaibei Peoples R China Huaibei Normal Univ Coll Life Sci Huaibei Peoples R China Harbin Engn Univ Coll Automat Harbin Peoples R China

RNA functions, including the regulation of various cellular activities, seem to be closely related to its structure. However, accurately predicting RNA secondary structures can be difficult. Structural prediction can be achieved by selecting stem areas that are suitable and compatible from stem pools. Here, we propose a method for predicting the secondary structure of non-coding RNA based on stem region substitution, which we named RSRNA. This method is compatible with nested RNA secondary structures, while reducing any randomness. Our algorithm had higher performance and prediction accuracy than other algorithms, which deems it more effective for future RNA structure studies.

关键词： RNA secondary structure rsRNA stem pool combinatorial optimization algorithm

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A novel approach to CNC machining center processing parameters optimization considering energy-saving and low-cost

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JOURNAL OF MANUFACTURING SYSTEMS 2021年 59卷 535-548页

作者： Xiao, Yongmao Jiang, Zhigang Gu, Quan Yan, Wei Wang, Ruping Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 55800 Peoples R China Hubei Univ Automot Technol Key Lab Automot Power Train & Elect Shiyan 442002 Peoples R China Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan 430081 Hubei Peoples R China Univ Glasgow MRC Univ Glasgow Glasgow G12 8QB Lanark Scotland China West Normal Univ Sch Management Nanchong 637002 Peoples R China

NC machining is currently a machining method widely used in mechanical manufacturing systems. Reasonable selection of process parameters can significantly reduce the processing cost and energy consumption. In order to realize the energy-saving and low-cost of CNC machining, the cutting parameters are optimized from the aspects of energy-saving and low-cost, and a process parameter optimization method of CNC machining center that takes into account both energy-saving and low -cost is proposed. The energy flow characteristics of the machining center processing system are analyzed, considering the actual constraints of machine tool performance and tool life in the machining process, a multi-objective optimization model with milling speed, feed per tooth and spindle speed as optimization variables is established, and a weight coefficient is introduced to facilitate the solution to convert it into a single objective optimization model. In order to ensure the accuracy of the model solution, a combinatorial optimization algorithm based on particle swarm optimization and NSGA-II is proposed to solve the model. Finally, take plane milling as an example to verify the feasibility of this method. The experimental results show that the multi-objective optimization model is feasible and effective, and it can effectively help operators to balance the energy consumption and processing cost at the same time, so as to achieve the goal of energy conservation and low-cost. In addition, the combinatorial optimization algorithm is compared with the NSGA-II, the results show that the combinatorial optimization algorithm has better performance in solving speed and optimization accuracy.

关键词： Machining center Energy saving Processing parameters Multi-objective optimization combinatorial optimization algorithm

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Fuzzy optimization design-based multi-level response surface of bogie frame

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INTERNATIONAL JOURNAL OF STRUCTURAL INTEGRITY 2019年第2期10卷 134-148页

作者： Zhi, Pengpeng Li, Yonghua Chen, Bingzhi Li, Meng Liu, Guannan Dalian Jiaotong Univ Sch Mech Engn Dalian Peoples R China Dalian Jiaotong Univ Sch Locomot & Rolling Stock Engn Dalian Peoples R China

Purpose In a structural optimization design-based single-level response surface, the number of optimal variables is too much, which not only increases the number of experiment times, but also reduces the fitting accuracy of the response surface. In addition, the uncertainty of the optimal variables and their boundary conditions makes the optimal solution difficult to obtain. The purpose of this paper is to propose a method of fuzzy optimization design-based multi-level response surface to deal with the problem. Design/methodology/approach The main optimal variables are determined by Monte Carlo simulation, and are classified into four levels according to their sensitivity. The linear membership function and the optimal level cut set method are applied to deal with the uncertainties of optimal variables and their boundary conditions, as well as the non-fuzzy processing is carried out. Based on this, the response surface function of the first-level design variables is established based on the design of experiments. A combinatorial optimization algorithm is developed to compute the optimal solution of the response surface function and bring the optimal solution into the calculation of the next level response surface, and so on. The objective value of the fourth-level response surface is an optimal solution under the optimal design variables combination. Findings The results show that the proposed method is superior to the traditional method in computational efficiency and accuracy, and improves 50.7 and 5.3 percent, respectively. Originality/value Most of the previous work on optimization was based on single-level response surface and single optimization algorithm, without considering the uncertainty of design variables. There are very few studies which discuss the optimization efficiency and accuracy of multiple design variables. This research illustrates the importance of uncertainty factors and hierarchical surrogate models for multi-variable optimization design.

关键词： Bogie frame combinatorial optimization algorithm Fuzzy optimization Multi-level response surface Optimal level cut set

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Robust optimization Design of Gun Turret Structure for Lightweight 18

Robust Optimization Design of Gun Turret Structure for Light...

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IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Ge, Yao Chen, Longmiao Tan, Jianhui Yue, Caicheng Nanjing Univ Sci & Technol Sch Mech Engn 200 Xiaolingwei Nanjing Jiangsu Peoples R China CHONGQING WANGJIANG Ind Ruitong Youmi 1-11-2Cuntan St Chongqing Peoples R China China North Ind Corp A12 Guanganmen South St Beijing Peoples R China

ISBN: (纸本)9781665441018

Aiming at the structural optimization problem of a gun turret, the robust optimization model of the turret structure is established. This model takes the thickness of structural rib plate as the design variable, the minimization of the structural mass of the turret as the objective, the maximum displacement and the lowest natural frequency as the constraints. It also considers the influence of the structural instabilities and employs the 6 sigma robust optimization design method. The samples are obtained by the experimental design method. The radial bads function neural network is used to construct the surrogate model of the turret structure. The precision of the surrogate model is measured by the determination coefficient R-2 and the Mean of Normalized Absolute Error MNAE. Finally, the robust optimization results are obtained by using The combinatorial optimization algorithm composed of Particle Swarm optimization (PSO) algorithm and Sequential Quadratic Programming-NLPQL. The calculation results show that the 6 sigma robust optimization design method can effectively ensure the robustness of the optimization results. The accuracy of the radial basis function neural network surrogate model can meet the optimization requirements. The mass of structure is reduced by 9.85% after optimization.

关键词： Gun turret Lightweight Robust optimization Surrogate model combinatorial optimization algorithm

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Environmental lead reduces the resilience of bald eagle populations

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JOURNAL OF WILDLIFE MANAGEMENT 2022年第2期86卷

作者： Hanley, Brenda J. Dhondt, Andre A. Forzan, Maria J. Bunting, Elizabeth M. Pokras, Mark A. Hynes, Kevin P. Dominguez-Villegas, Ernesto Schuler, Krysten L. Cornell Univ Coll Vet Med Wildlife Hlth Lab Publ & Ecosyst Hlth Dept 240 Farrier Rd Ithaca NY 14853 USA Cornell Univ Cornell Lab Ornithol Dept Ecol & Evolutionary Biol 159 Sapsucker Woods Rd Ithaca NY 14853 USA Tufts Univ Wildlife Clin Cummings Sch Vet Med 200 Westboro Rd North Grafton MA 01536 USA Tufts Univ Ctr Conservat Med Cummings Sch Vet Med 200 Westboro Rd North Grafton MA 01536 USA New York State Dept Environm Conservat 108 Game Farm Rd Delmar NY 12054 USA Wildlife Ctr Virginia 1800 South Delphine Ave Waynesboro VA 22980 USA

Bald eagles (Haliaeetus leucocephalus) are considered a recovery success in the United States after rebounding from near extirpation due to widespread use of the insecticide dichlorodiphenyltrichloroethane (DDT) in the twentieth century. Although abundances of bald eagles have increased since DDT was banned, other contaminants have remained in the environment with unknown influence on eagle population trends. Ingestion of spent lead (Pb) ammunition, the source of Pb most available to eagles and other scavengers in the United States, is known to kill individual eagles, but the influence of the contaminant on overall population dynamics remains unclear, resulting in longstanding controversy over the continued legality of the use of Pb in terrestrial hunting ammunition. We hypothesized that mortalities from the ingestion of Pb reduced the long-term growth rate and resiliency of bald eagles in the northeast United States over the last 3 decades. We used Holling's definition of resilience (the ability of a system to absorb changes of state variables, driving variables, and parameters and still persist) to quantify how reduction in survival from Pb-associated mortalities reduced the likelihood of population persistence. We used a population matrix model and necropsy records gathered between 1990 and 2018 from a 7-state area to compare population dynamics under current versus hypothetical Pb-reduced and Pb-free scenarios. Despite a robust increase in eagle abundances in the northeast United States over that period, we estimated that deaths arising from ingestion of Pb was associated with a 4.2% (females) and 6.3% (males) reduction in the asymptotic long-term growth rate (lambda). Comparison between real (current) and counterfactual (Pb-reduced and Pb-free) population dynamics indicated that the deaths from acute Pb poisoning were additive because the mortality events were associated with marked reduction in annual survival performance of hatchlings and reproductive females

关键词： ammunition bald eagle combinatorial optimization algorithm Haliaeetus leucocephalus lead long-term growth rate northeast United States Pb population dynamics population matrix model

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Harmonic detection algorithm based on self-convolution window six-line interpolation and improved all-phase FFT

Harmonic detection algorithm based on self-convolution windo...

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作者： Wenyu Zhang Haizhou Chen Jing Zhang Zidie Chen School of Science Hubei University of Technology

When using fast Fourier transform to analyze harmonics, it is difficult to realize high precision harmonic detection due to the spectrum leakage and fence effect caused by asynchronous *** this paper, a combined optimization algorithm is proposed. By analyzing the characteristics of the self-convoluted window, the self-convolution window six-spectrum-line interpolation algorithm is proposed to estimate the amplitude and frequency parameters of harmonics. Meanwhile, an improved all-phase FFT algorithm is proposed to estimate harmonic phase parameters, considering the fact that the data before zero hour can not be collected. Finally, through simulation experiments, the proposed algorithm is compared with other algorithms in the case of classical harmonic signals and *** results show that the algorithm has higher harmonic detection accuracy and stronger anti-noise capability.

关键词： Harmonic detection Self-convolution window Hexagonal interpolation All-phase fast Fourier transform combinatorial optimization algorithm

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