Ti-Hung Chen¹, Ming-Feng Yeh^2,*

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 407-420, 2020, DOI:10.32604/iasc.2020.013917

Abstract On the basis of JADE (adaptive differential evolution with optional external archive) and the modified differential evolution with p-best crossover (MDE_pBX), this study attempts to propose a modified mutation strategy termed "DE/(pbest)/1" for the differential evolution (DE) algorithm, where “(pbest)” represents the mean of p top-best vectors. Two modified parameter adaptation mechanisms are also proposed to update the crossover rate and the scale factor, respectively, in an adaptive manner. The DE variant with the proposed mutation strategy and two modified adaptation mechanisms is termed adaptive differential evolution with mean-of-pbest mutation strategy, denoted by ADE_pBM is comparable to or More >

Qiming Zou¹, Ling Wang^{1, *}, Jie Liu¹, Yingtao Jiang²

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 631-639, 2020, DOI:10.32604/iasc.2020.012549

Abstract The real-time requirements imposed on a feedback control system are often hard to be met, as the controller spends a disproportionately large amount of time waiting for a control cycle to reach its final state. When such a final state is established, multiple tasks have to be prioritized and launched altogether simultaneously, and the system is given an extremely short time window to generate its output. This huge gap between the wait and action times, perceived as a load unbalancing problem, hinders a control decision to be made in real time. To address this challenging… More >

He Ni^1,*, Yongqiao Wang¹, Buyun Xu²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 227-236, 2020, DOI:10.31209/2019.100000068

Abstract The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such More >

Yonghong Zhang^1,2, Guangyi Ma^1,2,*, Wei Tian³, Jiangeng Wang⁴, Shiwei Chen^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1149-1165, 2020, DOI:10.32604/cmes.2020.010932 - 21 August 2020

Abstract Snow cover is an important parameter in the fields of computer modeling, engineering technology and energy development. With the extensive growth of novel hardware and software compositions creating smart, cyber physical systems’ (CPS) efficient end-to-end workflows. In order to provide accurate snow detection results for the CPS’s terminal, this paper proposed a snow cover detection algorithm based on the unsupervised Gaussian mixture model (GMM) for the FY-4A satellite data. At present, most snow cover detection algorithms mainly utilize the characteristics of the optical spectrum, which is based on the normalized difference snow index (NDSI) with… More >

S. B. Manoojkumaar^{1, *}, C. Poongodi²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1649-1670, 2020, DOI:10.32604/cmc.2020.010781 - 20 August 2020

Abstract Maximizing network lifetime is measured as the primary issue in Mobile Adhoc Networks (MANETs). In geographically routing based models, packet transmission seems to be more appropriate in dense circumstances. The involvement of the Heuristic model directly is not appropriate to offer an effectual solution as it becomes NP-hard issues; therefore investigators concentrate on using Meta-heuristic approaches. Dragonfly Optimization (DFO) is an effective meta-heuristic approach to resolve these problems by providing optimal solutions. Moreover, Meta-heuristic approaches (DFO) turn to be slower in convergence problems and need proper computational time while expanding network size. Thus, DFO is More >

Li Ma, Meng Zhao^*, Dongchao Ma, Yingxun Fu

Journal of New Media, Vol.2, No.1, pp. 11-20, 2020, DOI:10.32604/jnm.2020.09715 - 14 August 2020

Abstract Low-power wide area network (LPWAN) has developed rapidly in recent years and is widely used in various Internet of Things (IoT) services. In order to reduce cost and power consumption, wide coverage, LPWAN tends to use simple channel access control protocols, such as the Aloha protocol. This protocol is simple with poor extension capability. In high-density environment, Aloha protocol will lead to low channel utilization, prolonged access and high conflict probability. Therefore, in order to solve the above problems, we propose an enhanced channel access control mechanism based on the existing LoRaWAN protocol, that is, More >

Jian Wang^{1, 2}, Ming Fang^{3, *}, Hui Li^{1, 2}

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.1, pp. 79-106, 2020, DOI:10.32604/cmes.2020.09470 - 19 June 2020

Abstract Structural components may enter an initial-elastic state, a plastic-hardening state and a residual-elastic state during strong seismic excitations. In the residual-elastic state, structural components keep in an unloading/reloading stage that is dominated by a tangent stiffness, thus structural components remain residual deformations but behave in an elastic manner. It has a great potential to make model order reduction for such structural components using the tangent-stiffness-based vibration modes as a reduced order basis. In this paper, an adaptive substructure-based model order reduction method is developed to perform nonlinear seismic analysis for structures that have a priori… More >

Jianwei Zhang¹, Ze Qin¹, Shunfeng Wang^{1, *}

Journal of Cyber Security, Vol.2, No.1, pp. 25-36, 2020, DOI:10.32604/jcs.2020.06429

Abstract Digital images have been applied to various areas such as evidence in courts. However, it always suffers from noise by criminals. This type of computer network security has become a hot issue that can’t be ignored. In this paper, we focus on noise removal so as to provide guarantees for computer network security. Firstly, we introduce a well-known denoising method called Expected Patch Log Likelihood (EPLL) with Gaussian Mixture Model as its prior. This method achieves exciting results in noise removal. However, there remain problems to be solved such as preserving the edge and meaningful… More >

Yuheng Sun¹, Ye Mu^{1, 2, 3, 4, *}, Qin Feng⁵, Tianli Hu^{1, 2, 3, 4}, He Gong^{1, 2, 3, 4}, Shijun Li^{1, 2, 3, 4}, Jing Zhou⁶

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1317-1328, 2020, DOI:10.32604/cmc.2020.010510 - 10 June 2020

Abstract In large-scale deer farming image analysis, K-means or maximum betweenclass variance (Otsu) algorithms can be used to distinguish the deer from the background. However, in an actual breeding environment, the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer. Also, when the target and background grey values are similar, the multiple background targets cannot be completely separated. To better identify the posture and behaviour of deer in a deer shed, we used digital image processing to separate the deer from the… More >

Ying Chen^{1, 2}, Yibin Tang³, Lin Zhou¹, Yan Zhou^{3, 4}, Jinxiu Zhu³, Li Zhao^{1, *}

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1219-1232, 2020, DOI:10.32604/cmc.2020.010638 - 10 June 2020

Abstract Graph filtering, which is founded on the theory of graph signal processing, is proved as a useful tool for image denoising. Most graph filtering methods focus on learning an ideal lowpass filter to remove noise, where clean images are restored from noisy ones by retaining the image components in low graph frequency bands. However, this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective. To address this issue, we propose an adaptive weighted graph filtering (AWGF) method to replace the design of… More >