Ezhilmathi Nagarathinam¹, Buvana Devaraju², Karthiyayini Jayamoorthy³, Padmavathi Radhakrishnan⁴, Santhana Lakshmi ChandraMohan⁵, Vijayakumar Perumal⁶, Karthikeyan Balakrishnan^7,*

Energy Engineering, Vol.121, No.8, pp. 2129-2142, 2024, DOI:10.32604/ee.2024.052280

Abstract Maximum Power Point Tracking (MPPT) is crucial for maximizing the energy output of photovoltaic (PV) systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental conditions. This work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer (DGWO). It dynamically adjusts the parameters of the MPPT controller, specifically the duty cycle of the SEPIC converter, to efficiently track the Maximum Power Point (MPP). The proposed system aims to enhance the energy harvesting capability of solar PV More >

Zeyi Wang¹, Mingxi Jiao¹, Daliang Wang¹, Minxu Liu¹, Minglei Jiang², He Wang³, Shiqiang Li^3,*

Energy Engineering, Vol.121, No.8, pp. 2275-2291, 2024, DOI:10.32604/ee.2024.028223

Abstract This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method. Three different electrical quantities are selected as observations in the compressed sensing algorithm. The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels. Subsequently, by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances,… More >

Punam Gulande^*, R. N. Awale

Computer Systems Science and Engineering, Vol.48, No.4, pp. 937-952, 2024, DOI:10.32604/csse.2024.046123

Abstract The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm More >

Zerui Xiao, Wei Liu, Zhiwei Ye^*, Jiatang Yuan, Shishi Liu

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1009-1029, 2024, DOI:10.32604/csse.2023.040475

Abstract Nowadays, construction site safety accidents are frequent, and wearing safety helmets is essential to prevent head injuries caused by object collisions and falls. However, existing helmet detection algorithms have several drawbacks, including a complex structure with many parameters, high calculation volume, and poor detection of small helmets, making deployment on embedded or mobile devices difficult. To address these challenges, this paper proposes a YOLOv5-based multi-head detection safety helmet detection algorithm that is faster and more robust for detecting helmets on construction sites. By replacing the traditional DarkNet backbone network of YOLOv5s with a new backbone… More >

Umer Nauman¹, Yuhong Zhang², Zhihui Li³, Tong Zhen^1,3,*

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 477-510, 2024, DOI:10.32604/iasc.2024.050726

Abstract Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications. Nevertheless, existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets, such as preservation and server infrastructure, in a limited number of large-scale worldwide data facilities. Optimizing the deployment of virtual machines (VMs) is crucial in this scenario to ensure system dependability, performance, and minimal latency. A significant barrier in the present scenario is the load distribution, particularly when striving for improved energy consumption in a hypothetical grid computing framework. This design… More >

Darakhshan Syed^*, Ghulam Muhammad, Safdar Rizvi

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 437-476, 2024, DOI:10.32604/iasc.2024.050681

Abstract Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. More >

Jiawei Qin^1,2, Hua Zhang^1,*, Hanbing Yan², Tian Zhu², Song Hu¹, Dingyu Yan²

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 527-544, 2024, DOI:10.32604/iasc.2024.047509

Abstract By the analysis of vulnerabilities of Android native system services, we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server. The existing research cannot find the above two types of vulnerabilities and the test cases of them face the problem of low coverage. In this paper, we propose an extraction method of test cases based on the native system services of the client and design a case construction method that supports multi-parameter mutation based on genetic algorithm and priority strategy. Based on the above method, More >

Sana Jawarneh^*

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 511-525, 2024, DOI:10.32604/iasc.2024.047126

Abstract High-dimensional datasets present significant challenges for classification tasks. Dimensionality reduction, a crucial aspect of data preprocessing, has gained substantial attention due to its ability to improve classification performance. However, identifying the optimal features within high-dimensional datasets remains a computationally demanding task, necessitating the use of efficient algorithms. This paper introduces the Arithmetic Optimization Algorithm (AOA), a novel approach for finding the optimal feature subset. AOA is specifically modified to address feature selection problems based on a transfer function. Additionally, two enhancements are incorporated into the AOA algorithm to overcome limitations such as limited precision, slow More >

Tengda Li, Gang Wang, Qiang Fu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2559-2586, 2024, DOI:10.32604/cmes.2024.052039

Abstract Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation (DTA) and high-dimensional decision space with single agent, this paper combines the deep reinforcement learning (DRL) theory and an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG-D2) algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA. The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, and considers the introduction of a double noise mechanism to increase the action exploration… More >

Lirong Yin¹, Lei Wang¹, Siyu Lu^2,*, Ruiyang Wang², Haitao Ren², Ahmed AlSanad³, Salman A. AlQahtani³, Zhengtong Yin⁴, Xiaolu Li⁵, Wenfeng Zheng^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2315-2347, 2024, DOI:10.32604/cmes.2024.050853

Abstract At present, super-resolution algorithms are employed to tackle the challenge of low image resolution, but it is difficult to extract differentiated feature details based on various inputs, resulting in poor generalization ability. Given this situation, this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block (AFB) for feature extraction. This module mainly comprises dynamic convolution, attention mechanism, and pixel-based gating mechanism. Combined with dynamic convolution with scale information, the network can extract more differentiated feature information. The introduction of a channel More >