Jyun-Guo Wang^*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1149-1170, 2024, DOI:10.32604/csse.2024.052931 - 13 September 2024

Abstract In many Eastern and Western countries, falling birth rates have led to the gradual aging of society. Older adults are often left alone at home or live in a long-term care center, which results in them being susceptible to unsafe events (such as falls) that can have disastrous consequences. However, automatically detecting falls from video data is challenging, and automatic fall detection methods usually require large volumes of training data, which can be difficult to acquire. To address this problem, video kinematic data can be used as training data, thereby avoiding the requirement of creating… More >

Huanan Yu, Hangyu Li, He Wang, Shiqiang Li^*

Energy Engineering, Vol.121, No.6, pp. 1535-1555, 2024, DOI:10.32604/ee.2024.046936 - 21 May 2024

Abstract The escalating deployment of distributed power sources and random loads in DC distribution networks has amplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimal configuration of measurement points, this paper presents an optimal configuration scheme for fault location measurement points in DC distribution networks based on an improved particle swarm optimization algorithm. Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing. The model aims to achieve the minimum number of measurement points while attaining the best compressive sensing reconstruction effect. It incorporates constraints from… More >

Shuai Zhou¹, Dazhi Wang^1,*, Yongliang Ni², Keling Song², Yanming Li²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2187-2207, 2024, DOI:10.32604/cmc.2024.048859 - 15 May 2024

Abstract In the process of identifying parameters for a permanent magnet synchronous motor, the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration, resulting in low parameter accuracy. This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function. This approach addresses the topic of particle swarm optimization in parameter identification from two perspectives. Firstly, the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness… More >

Li Zheng, Gang Xu^*, Wenbin Chen

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 935-957, 2024, DOI:10.32604/cmc.2023.046006 - 30 January 2024

Abstract Drone logistics is a novel method of distribution that will become prevalent. The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption, resulting in cost savings for the company’s transportation operations. Logistics firms must discern the ideal location for establishing a logistics hub, which is challenging due to the simplicity of existing models and the intricate delivery factors. To simulate the drone logistics environment, this study presents a new mathematical model. The model not only retains the aspects of the current models, but also considers the degree of transportation difficulty… More >

Zhonghao Qian¹, Hanyi Ma¹, Jun Rao², Jun Hu¹, Lichengzi Yu^2,*, Caoyi Feng¹, Yunxu Qiu¹, Kemo Ding¹

Energy Engineering, Vol.120, No.9, pp. 2013-2027, 2023, DOI:10.32604/ee.2023.028859 - 03 August 2023

Abstract The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms. To improve the voltage stability and reactive power economy of wind farms, the improved particle swarm optimization is used to optimize the reactive power planning in wind farms. First, the power flow of offshore wind farms is modeled, analyzed and calculated. To improve the global search ability and local optimization ability of particle swarm optimization, the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor. Taking the minimum active power… More >

Qi Zhuang^a,* , Zhuo Chen^b, Dong Liu^c, Yangyang Tian^d

Frontiers in Heat and Mass Transfer, Vol.19, pp. 1-8, 2022, DOI:10.5098/hmt.19.19

Abstract In order to improve the accuracy and efficiency of wax deposition rate prediction of waxy crude oil in pipeline transportation, A GRA-IPSO-ELM model was established to predict wax deposition rate. Using Grey Relational Analysis (GRA) to calculate the correlation degree between various factors and wax deposition rate, determine the input variables of the prediction model, and establish the Extreme Learning Machine (ELM) prediction model, improved particle swarm optimization (IPSO) is used to optimize the parameters of ELM model. Taking the experimental data of wax deposition in Huachi operation area as an example, the prediction performance More >

Haibo Liu^1,*, Yujie Dong², Fuzhong Wang¹

Energy Engineering, Vol.118, No.3, pp. 679-689, 2021, DOI:10.32604/EE.2021.014630 - 22 March 2021

Abstract For the problems of nonlinearity, uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face, the least squares support vector machine (LSSVM) is proposed to establish the prediction model. Firstly, considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization (PSO), an improved PSO (IPSO) algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate. Secondly, the penalty factor and kernel function parameter of LSSVM are searched automatically, and the regression accuracy More >

Ayesha Bin T. Tahir¹, Muhamamd Attique Khan¹, Majed Alhaisoni², Junaid Ali Khan¹, Yunyoung Nam^3,*, Shui-Hua Wang⁴, Kashif Javed⁵

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1099-1116, 2021, DOI:10.32604/cmc.2021.015154 - 22 March 2021

Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two More >

Gongyu Hou¹, Zhedong Xu^1,*, Xin Liu¹, Cong Jin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.2, pp. 317-337, 2019, DOI:10.31614/cmes.2019.04693

Abstract This article proposes an exponential adjustment inertia weight immune particle swarm optimization (EAIW-IPSO) to enhance the accuracy and reliability regarding the selection of shield tunneling parameter values. According to the iteration changes and the range of inertia weight in particle swarm optimization algorithm (PSO), the inertia weight is adjusted by the form of exponential function. Meanwhile, the self-regulation mechanism of the immune system is combined with the PSO. 12 benchmark functions and the realistic cases of shield tunneling parameter value selection are utilized to demonstrate the feasibility and accuracy of the proposed EAIW-IPSO algorithm. Comparison More >