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Conference on Holography, Diffractive Optics, and Applications V

作者： Pang, Hui Yin, Shaoyun Deng, Qiling Fu, Yongqi Du, Chunlei Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Sichuan Provinc Peoples R China Univ Elect Sci & Technol China Sch Optoelectron Informat Chengdu 610041 Sichuan Peoples R China Chongqing Inst Green & Intelligent Technol CAS Chongqing 401122 Peoples R China

ISBN: (纸本)9780819493118

In this paper, quite effective method for the design of phase-only and quantized diffractive optical element (DOE) for beam splitting with simulated annealing algorithm (sa) is presented. For this method employs the character that periodic DOE could generate periodic point array, design time and number of sampling point of DOE could be greatly reduced. Besides, the relation of the DOE parameters including the sampling size, number of sampling point and divergence angle are analyzed. The cause and elimination of the high diffraction orders is also investigated. Design result shows that our method is quite effective and can keep the higher diffraction efficiency and lower uniformity error compared to the Gerchberg-saxton algorithm (GS).

关键词： sa algorithm DOE beam splitter

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Optimization of shrinkage in Plastic injection molding process using statistical methods and sa algorithm

Optimization of shrinkage in Plastic injection molding proce...

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2nd International Conference on Mechanical and Aerospace Engineering (ICMAE 2011)

作者： Akbarzadeh, Alireza sadeghi, Mohammad Ferdowsi Univ Mashhad Dept Mech Engn Ctr Appl Res Soft Comp & Intelligent Syst CARSIS Mashhad Iran

ISBN: (纸本)9783037852620

Dimensional changes because of shrinkage is one of the most important problems in production of plastic part using injection molding. In this study, effect of injection molding parameters on the shrinkage in polypropylene (PP) and polystyrene (PS) has been investigated. The relationship between input and output of the process is investigated using regression analysis and ANOVA method. To do this, existing data is used. The selected input parameters are melting temperature, injection pressure, packing pressure and packing time. Effect of these parameters on the shrinkage of above mentioned materials is studied using mathematical modeling. For modeling the process, different types of regression equations including linear polynomial, Quadratic polynomial and logarithmic function, are used to interpolate experiment data. Next, using step backward elimination and 95% confidence level, insignificant parameters are eliminated from model. To check validity of the PP model, correlation coefficient of each model is calculated and the best model is selected. The same procedure is repeated for the PS model. Finally, optimum levels of the input parameters that minimize shrinkage, for both materials are determined. Simulated Annealing (sa) algorithm is applied on the developed mathematical models. The optimization results show that the proposed models and algorithm are effective in solving the mentioned problems.

关键词： Plastic injection molding shrinkage Regression Optimization sa algorithm

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Image Compression Based on the Improved sa algorithm

Image Compression Based on the Improved SA Algorithm

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2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Yiqian Tang Chengdu University,Chengdu,610106,China Chunli Feng Liaoning Institute of Technology,Jinzhou,121001,China Yue Zhao,Lin Shi Chengdu University,Chengdu,610106,China

Aiming at the techniques of the image compression,we put forward a kind of improved local learning adaptive Silva-Almeida(sa) algorithm on the basis of BP neural *** training process of the new algorithm was divided into the acceleration and steady convergence periods, and we adjusted the learning rate and the momentum factor locally at the same *** the experiment of the curve fitting,the improved sa algorithm quickened the training speed,raised precision distinctly,and resisted the oscillation in a large *** the test of the image compression, compared with the resilient propagation(RPROP) method, the new algorithm's training speed and the quality of the reconstruction images were enhanced very greatly,and we acquired a higher ratio of image *** the improved sa algorithm is more effective to the image compression.

关键词： sa algorithm image compression BP networks resilient propagation neural networks

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sa-Based Orbital Design Method for GEO-BisaR Resolution Improvement 32

SA-Based Orbital Design Method for GEO-BiSAR Resolution Impr...

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32nd General Assembly and Scientific Symposium of the International-Union-of-Radio-Science (URSI GASS)

作者： Cheng, Yao Zhang, Shunsheng Zhao, Bingji Hu, Cheng Univ Elect Sci & Technol China Res Inst Elect Sci & Technol Chengdu Sichuan Peoples R China Beijing Inst Spacecraft Syst Engn Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9789082598704

Compared with the monostatic geosynchronous synthetic aperture radar (GEO-saR) system, GEO bistatic saR (GEO-BisaR) shows great advantages in finer spatial resolution and higher signal-to-noise ratio (SNR) with less system complexity. Many scholars have done researches on resolution of GEO-BisaR, their models consist of a GEO transmitter and a low Earth orbit (LEO) receiver. The methods of improving the resolution by using two or more receivers are rare. In this paper, the system consists of a geosynchronous illuminator and two LEO receivers. The spectrum shift of saR images could be introduced by multi-view angles of acquired images in the same coverage. We can widen the bandwidth of saR images by combining the spectrum of two images to improve the resolution. The formation configuration design of two receivers is the crux of the matter. Then in this paper, an orbital design method of two receivers based on simulated-annealing (sa) algorithm is presented.

关键词： GEO-LEO bistatic synthetic aperture radar (BisaR) baseline vector resolution improvement factor spectral shift orbital design sa algorithm

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A Novel Hybrid Deep Learning Model Based on Simulated Annealing and Cuckoo Search algorithms for Automatic Radiomics-Based COVID-19 Diagnosis

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Applied Computational Intelligence and Soft Computing 2025年第1期2025卷

作者： Basma Jumaa saleh Zaid Omar Muhammad Amir As’ari Vikrant Bhateja Lila Iznita Izhar Department of Electronic and Computer Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia utm.my Department of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia Johor Bahru Malaysia utm.my Department of Electronics Engineering Veer Bahadur Singh Purvanchal University Jaunpur Uttar Pradesh India vbspu.ac.in Department of Electrical and Electronic Engineering Universiti Teknologi PETRONAS Seri Iskandar Malaysia utp.edu.my

Since the outbreak of Coronavirus Disease 2019 (COVID-19), the virus has posed a grave threat to human health. Automated segmentation of COVID-19 lung computed tomography (CT) scans is a crucial diagnostic tool that aids physicians in providing accurate and timely diagnoses, as it contains significant radiomics information. Given that the specificity for discriminating between the causes of conventional pulmonary features is lower than its sensitivity, the primary goal of this study is to develop and evaluate a CT-based radiomics model capable of distinguishing between COVID-19 and other lung diseases. To address this, we propose an efficient, modified radiomics feature processing method that integrates an optimal aerial perspective (OAP) parameter-based intensity dark channel prior (IDCP) with a 50-layer residual deep neural network (ResNet50 DNN) for autolesion segmentation (ALS-IOAP-DNN). To further enhance COVID-19 lesion estimation, novel optimization strategies, including a hybrid simulated annealing-cuckoo search (sa-CS) algorithm, are introduced alongside the original sa method. The sa-CS algorithm extends sa by preventing entrapment in local minimum and enhancing global exploration. Five benchmark functions are used to accelerate convergence and address the issue of local optima. As a result, the hybrid approach outperforms 10 recent studies on two publicly available datasets (COVID-CT-Dataset and HUST-19), achieving an average accuracy score of 100% across different epochs, along with perfect accuracy, 100% sensitivity, and 100% specificity. The proposed models significantly outperform the baseline model, with accuracy improvements of 13.6% on Data1 and 2.5% on Data2. While the baseline model achieves 88% accuracy on Data1 and 97.6% on Data2, the proposed ALS-IOAP-DNN4 model attains perfect accuracy (100%) on both datasets, demonstrating the effectiveness of ALS and advanced optimization techniques. Furthermore, the use of OAP with IDCP enhances the precis

关键词： computed tomography COVID-19 pandemic CS algorithm radiomics sa algorithm

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Investigation of energy-efficient heat pump assisted heterogeneous azeotropic distillation for separating of acetonitrile/ethyl acetate/n-hexane mixture

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Chinese Journal of Chemical Engineering 2023年第3期55卷 20-33页

作者： Xinhao Li Qing Ye Jinlong Li Lingqiang Yan Xue Jian Licheng Xie Jianyu Zhang Jiangsu Key Laboratory of Advanced Catalytic Materials and Technology School of Petrochemical EngineeringChangzhou UniversityChangzhou 213164China Changzhou University Huaide College Changzhou 213164China College of Chemical and Biological Engineering Zhejiang UniversityHangzhou 310000China

The conventional distillation is hard to accomplish the separation of acetonitrile/ethyl acetate/n-hexane mixture. Herein, a heterogeneous azeotropic distillation(HAD) without adding entrainer is proposed to separate ternary mixture. The proposed scheme is optimized via the simulated annealing algorithm and minimum total annual cost(TAC) is used as objective functions. To minimize energy consumption,heat pump is added on the basis of optimal heterogeneous azeotropic distillation and heat integration technology is used to further improve the energy recovery. The TAC, gas emission, energy consumption and exergy destruction are used to discuss the economy and environmental protection of *** all the processes, the heat pump with higher preheating temperature(HPT) assisted HAD process by combining with heat integration(HAD-HPT-HI) has best performances on economic, environment,energy and exergy. Compared with conventional HAD process, the HAD-HPT-HI achieves the reductions of 52.17%, 68.86%, 65.87% and 65.46% on TAC, total energy consumption, gas emissions and exergy destruction, respectively.

关键词： Heterogeneous azeotropic distillation Energy-saving sa algorithm Heat pump

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Analysis of the material distribution system of wise information technology of 120 under deep learning

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JOURNAL OF SUPERCOMPUTING 2021年第9期77卷 9988-10002页

作者： Tang, sai Zhao, Hongrui Wang, Zhuolin Zhu, Yue Hou, Daiyu Wang, Hongwei Harbin Inst Technol Sch Humanities Social Sci & Law Harbin 150001 Heilongjiang Peoples R China Harbin Finance Univ Dept Accounting Harbin Heilongjiang Peoples R China

In order to explore the feasibility of using deep learning in the Wise Information Technology of 120 (WIT120) material distribution system under deep learning, in the study, first, the Hopfield neural network and simulated annealing (sa) algorithm are used to study the path optimization in the WIT120 material distribution system. Aiming at the poor optimization and robustness of the traditional Hopfield neural network and the low optimization efficiency of sa algorithm, in this study, a memory device is added to the sa algorithm, the distribution starting points of the two algorithms are fixed, and an improved hybrid algorithm is designed by combining Hopfield neural network and sa algorithm. Then, the improved hybrid algorithm of the traditional Hopfield neural network and sa algorithm is used in the actual medical material distribution system, and comparative analysis of the distribution route, cost, and scheduling time before and after using the proposed algorithm is performed. Besides, the difference between the proposed algorithm and other algorithms in path optimization and path planning efficiency is compared and analyzed. The results show that the improved hybrid algorithm proposed in this research reduces the total mileage of distribution by 46.9%, costs by 33.9%, and the time required for scheduling by 99% after applied in the medical material distribution system. It is also better than traditional analog algorithms and neural networks in distribution path optimization. The path optimization results show that the shortest path produced by the improved hybrid algorithm in this study is 27.85, which is superior to the traditional sa algorithm, Hopfield neural network, and genetic algorithm (the shortest paths obtained are 41.21, 44.63 and 36.48, respectively). Compared with other optimization algorithms, the path planning efficiency of the hybrid algorithm is the highest (the amount of distribution tasks completed in unit time is 22). In conclusion, the impr

关键词： WIT120 Material distribution system Deep learning Hopfield neural network sa algorithm

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Optimization of spot pattern in indoor diffuse optical wireless local area networks

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OPTICS EXPRESS 2005年第8期13卷 3000-3014页

作者： Wong, DWK Chen, GCK Yao, JP Nanyang Technol Univ Photon Res Ctr Sch Elect & Elect Engn Singapore 639798 Singapore Univ Ottawa Sch Informat Technol & Engn Ottawa ON K1N 6N5 Canada

In this paper, a simulated annealing ( sa) algorithm is proposed to be used in the optimization of the spot pattern for the indoor diffuse optical wireless network application. The channel response is analyzed using conventional grid-based patterns and a field of view ( FOV) of 30 degrees is found to give a good performance balance in the uniformity of the received power distribution and multipath dispersion. Using the algorithm to determine the spot pattern for the minimum standard deviation of the received power, an improvement of more than 85% is realized. To optimize the spot pattern at 30 degrees FOV, a merit function is incorporated into the algorithm for two parameters, and the sa algorithm is run to obtain optimized spot patterns for both a 4.5m and 6m extent of the spot pattern. Various weights are used, and a performance improvement of 39% and 78% is observed for the 4.5m and 6m spot pattern sizes respectively which shows that the approach can be used to effectively optimize the spot pattern in the indoor optical wireless application. (C) 2005 Optical Society of America.

关键词： .various FOV annealing obtain determine multipath standard deviation simulated diffuse Power Distribution extent optimize merit sa algorithm Optimization of spot optical wireless grid-based spot patterns

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Inter-domain routing based on simulated annealing algorithm in optical mesh networks

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OPTICS EXPRESS 2004年第14期12卷 3095-3107页

作者： Wang, L Zhang, HY Zheng, XP Tsing Hua Univ Dept Elect Engn Beijing 100084 Peoples R China

For the first time a real-time and distributed routing algorithm based on Simulated Annealing ( sa) algorithm is proposed to promote the intelligence, survivability, and interworking in optical mesh networks. Further, a sa-based Two-steps Optimization Routing algorithm (saTORA) is proposed to obtain the inter-domain routing of global optimization. Compared with the previous approaches, this sa-based routing algorithm can achieve the routing optimization under multiconstraint condition, and can acquire the working and backup paths at one time for network survivability as well. Two crucial factors, the path hop count and the network congestion degree, are considered evaluating the cost of the path. Simulation experiments on different networks indicate that the saTORA can reduce the congestion degree of the networks and enhance the blocking performance of routing calculation for traffic requests when the inter-domain path protection is required. (C) 2004 Optical Society of America.

关键词： .network congestion hop blocking count simulated annealing backup sa algorithm INTER-DOMAIN Mesh networks saTORA sa-based routing algorithm routing optimization routing calculation

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Reliability intelligence analysis of concrete arch bridge based on Kriging model and PSOsa hybrid algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第SUPPL 2期56卷 2667-2685页

作者： Li, Dengguo Ye, Zhouling Lu, Pengzhen Wu, Ying Yang, Liu Wang, Jiahao Zhejiang Univ Technol Hangzhou 310014 Peoples R China Jiaxing Nanhu Univ Jiaxing 3140001 Peoples R China Transportat Bur Longquan 323700 Peoples R China

The traditional probabilistic reliability analysis method has problems such as poor convergence, low calculation accuracy, and long time consumption in calculating the reliability of concrete arch bridges due to factors such as the uncertainty of the structural parameters and the performance function being highly nonlinear. This paper proposes a method for calculating the reliability of concrete arch bridges based on the Kriging model and particle swarm optimization algorithm (PSOsa) of the simulated annealing algorithm. This method takes advantage of the Kriging model in small samples and high-dimensional nonlinear data processing capabilities and establishes a response surface model to approximate the actual limit state function. The optimization of the PSO algorithm is realized through the self-adaptive and variable probability mutation operation of the sa algorithm, which enhances the ability of the PSO algorithm to get rid of the local minimum, effectively avoids falling into the local minimum, and finally makes the calculation result tend to the global optimum. It overcomes the problems of slow convergence speed and premature maturity of traditional PSO algorithms. The correctness and effectiveness of the method proposed in this paper are verified through the example analysis and the actual engineering application of a concrete arch bridge. The research results show that the method proposed in this paper has obvious advantages in sample size, calculation accuracy, and iteration times compared with the existing reliability calculation methods for concrete arch bridges. This paper provides a fast and effective method for the structural reliability calculation of concrete arch bridges.

关键词： Bridge engineering Reliability analysis Kriging surrogate model PSO algorithm sa algorithm

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