Zhi Li¹, Yufei Zhao², Yueming Ji², Hanwen Gu², Zaibin Jiao^2,*

Energy Engineering, Vol.121, No.12, pp. 3899-3927, 2024, DOI:10.32604/ee.2024.055899 - 22 November 2024

Abstract By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement, information communication, and other fields, the digital DC distribution network can efficiently and reliably access Distributed Generator (DG) and Energy Storage Systems (ESS), exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play (PnP) operations. However, during device plug-in and -out processes, improper system parameters may lead to small-signal stability issues. Therefore, before executing PnP operations, conducting stability analysis and adjusting parameters swiftly is crucial. This study introduces a four-stage strategy for parameter optimization to enhance… More >

Yunfei Ban, Damin Zhang^*, Qing He, Qianwen Shen

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 567-601, 2024, DOI:10.32604/cmc.2024.055007 - 15 October 2024

Abstract The surge in connected devices and massive data aggregation has expanded the scale of the Internet of Things (IoT) networks. The proliferation of unknown attacks and related risks, such as zero-day attacks and Distributed Denial of Service (DDoS) attacks triggered by botnets, have resulted in information leakage and property damage. Therefore, developing an efficient and realistic intrusion detection system (IDS) is critical for ensuring IoT network security. In recent years, traditional machine learning techniques have struggled to learn the complex associations between multidimensional features in network traffic, and the excellent performance of deep learning techniques,… More >

Mohammad Shoab^*, Loiy Alsbatin^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 625-642, 2024, DOI:10.32604/cmc.2024.053721 - 15 October 2024

Abstract In recent years, machine learning (ML) and deep learning (DL) have significantly advanced intrusion detection systems, effectively addressing potential malicious attacks across networks. This paper introduces a robust method for detecting and categorizing attacks within the Internet of Things (IoT) environment, leveraging the NSL-KDD dataset. To achieve high accuracy, the authors used the feature extraction technique in combination with an auto-encoder, integrated with a gated recurrent unit (GRU). Therefore, the accurate features are selected by using the cuckoo search algorithm integrated particle swarm optimization (PSO), and PSO has been employed for training the features. The More >

Rachid Djoudjou^1,*, Abdeljlil Chihaoui Hedhibi³, Kamel Touileb¹, Abousoufiane Ouis¹, Sahbi Boubaker², Hani Said Abdo^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1809-1825, 2024, DOI:10.32604/cmes.2024.053621 - 27 September 2024

Abstract There are several advantages to the MIG (Metal Inert Gas) process, which explains its increased use in various welding sectors, such as automotive, marine, and construction. A variant of the MIG process, where the same equipment is employed except for the deposition of a thin layer of flux before the welding operation, is the AMIG (Activated Metal Inert Gas) technique. This study focuses on investigating the impact of physical properties of individual metallic oxide fluxes for 304L stainless steel welding joint morphology and to what extent it can help determine a relationship among weld depth… More >

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 >

Shengdong Cheng¹, Juncheng Gao^1,*, Hongning Qi^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 871-892, 2024, DOI:10.32604/cmes.2024.052830 - 20 August 2024

Abstract Driven piles are used in many geological environments as a practical and convenient structural component. Hence, the determination of the drivability of piles is actually of great importance in complex geotechnical applications. Conventional methods of predicting pile drivability often rely on simplified physical models or empirical formulas, which may lack accuracy or applicability in complex geological conditions. Therefore, this study presents a practical machine learning approach, namely a Random Forest (RF) optimized by Bayesian Optimization (BO) and Particle Swarm Optimization (PSO), which not only enhances prediction accuracy but also better adapts to varying geological environments… More > Graphic Abstract

Prasanna Kumar Kannughatta Ranganna¹, Siddesh Gaddadevara Matt², Chin-Ling Chen^3,4,*, Ananda Babu Jayachandra⁵, Yong-Yuan Deng^4,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2557-2578, 2024, DOI:10.32604/cmc.2024.051634 - 15 August 2024

Abstract In recent decades, fog computing has played a vital role in executing parallel computational tasks, specifically, scientific workflow tasks. In cloud data centers, fog computing takes more time to run workflow applications. Therefore, it is essential to develop effective models for Virtual Machine (VM) allocation and task scheduling in fog computing environments. Effective task scheduling, VM migration, and allocation, altogether optimize the use of computational resources across different fog nodes. This process ensures that the tasks are executed with minimal energy consumption, which reduces the chances of resource bottlenecks. In this manuscript, the proposed framework… More >

Imtiaz Ahmad¹, Tariq Hussain², Babar Shah³, Altaf Hussain⁴, Iqtidar Ali¹, Farman Ali^5,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3585-3629, 2024, DOI:10.32604/cmc.2024.050596 - 20 June 2024

Abstract Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost. One of them is a sensor network with embedded sensors working as the primary nodes, termed Wireless Sensor Networks (WSNs), in which numerous sensors are connected to at least one Base Station (BS). These sensors gather information from the environment and transmit it to a BS or gathering location. WSNs have several challenges, including throughput, energy usage, and network lifetime concerns. Different strategies have been applied to get over these… More >

Yasser Hachaichi¹, Ayman E. Khedr¹, Amira M. Idrees^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4081-4105, 2024, DOI:10.32604/cmc.2024.048929 - 20 June 2024

Abstract The diversity of data sources resulted in seeking effective manipulation and dissemination. The challenge that arises from the increasing dimensionality has a negative effect on the computation performance, efficiency, and stability of computing. One of the most successful optimization algorithms is Particle Swarm Optimization (PSO) which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task. This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which… 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 >