Daixuan Zhou¹, Yujin Liu¹, Xu Wang², Fuxing Wang¹, Yan Jia^2,*

Energy Engineering, Vol.121, No.12, pp. 3573-3616, 2024, DOI:10.32604/ee.2024.055853 - 22 November 2024

Abstract With the increasing proportion of renewable energy in China’s energy structure, among which photovoltaic power generation is also developing rapidly. As the photovoltaic (PV) power output is highly unstable and subject to a variety of factors, it brings great challenges to the stable operation and dispatch of the power grid. Therefore, accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy. Currently, the short-term prediction of PV power has received extensive attention and research, but the accuracy and precision of the prediction have to be further improved. More > Graphic Abstract

Zhaoqiang Wang¹, Fuyin Ni^2,*

Energy Engineering, Vol.121, No.12, pp. 3779-3799, 2024, DOI:10.32604/ee.2024.055535 - 22 November 2024

Abstract Under the partial shading conditions (PSC) of Photovoltaic (PV) modules in a PV hybrid system, the power output curve exhibits multiple peaks. This often causes traditional maximum power point tracking (MPPT) methods to fall into local optima and fail to find the global optimum. To address this issue, a composite MPPT algorithm is proposed. It combines the improved kepler optimization algorithm (IKOA) with the optimized variable-step perturb and observe (OIP&O). The update probabilities, planetary velocity and position step coefficients of IKOA are nonlinearly and adaptively optimized. This adaptation meets the varying needs of the initial… More > Graphic Abstract

Aijia Ding, Tingzhang Liu^*

Energy Engineering, Vol.121, No.12, pp. 3735-3759, 2024, DOI:10.32604/ee.2024.054687 - 22 November 2024

Abstract The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources. This paper introduces novel dual-backup controllers utilizing advanced fractional order proportional integral derivative (FOPID) controllers to enhance frequency and tie-line power stability in microgrids amid increasing renewable energy integration. To improve load frequency control, the proposed controllers are applied to a two-area interconnected microgrid system incorporating diverse energy sources, such as wind turbines, photovoltaic cells, diesel generators, and various storage technologies. A novel meta-heuristic algorithm is adopted to select the optimal parameters of the proposed controllers. The efficacy… More >

Bo Yang¹, Rui Zhang¹, Jie Zhang², Xianlong Cheng², Jiale Li³, Yimin Zhou¹, Yuanweiji Hu¹, Bin He¹, Gongshuai Zhang⁴, Xiuping Du⁴, Si Ji⁵, Yiyan Sang⁶, Zhengxun Guo^7,8,*

Energy Engineering, Vol.121, No.12, pp. 3487-3547, 2024, DOI:10.32604/ee.2024.054662 - 22 November 2024

Abstract In recent years, the large-scale grid connection of various distributed power sources has made the planning and operation of distribution grids increasingly complex. Consequently, a large number of active distribution network reconfiguration techniques have emerged to reduce system losses, improve system safety, and enhance power quality via switching switches to change the system topology while ensuring the radial structure of the network. While scholars have previously reviewed these methods, they all have obvious shortcomings, such as a lack of systematic integration of methods, vague classification, lack of constructive suggestions for future study, etc. Therefore, this… More >

Sagiraju Srinadhraju^*, Samaresh Mishra, Suresh Chandra Satapathy

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1641-1668, 2024, DOI:10.32604/csse.2024.055612 - 22 November 2024

Abstract This paper introduces the integration of the Social Group Optimization (SGO) algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model (COCOMO). COCOMO’s fixed coefficients often limit its adaptability, as they don’t account for variations across organizations. By fine-tuning these parameters with SGO, we aim to improve estimation accuracy. We train and validate our SGO-enhanced model using historical project data, evaluating its performance with metrics like the mean magnitude of relative error (MMRE) and Manhattan distance (MD). Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost More >

Jian Su¹, Haijian Shao^1,2,*, Xing Deng¹, Yingtao Jiang²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2219-2242, 2024, DOI:10.32604/cmc.2024.057604 - 18 November 2024

Abstract The rapidly advancing Convolutional Neural Networks (CNNs) have brought about a paradigm shift in various computer vision tasks, while also garnering increasing interest and application in sensor-based Human Activity Recognition (HAR) efforts. However, the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained systems. This paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain, which reduces the model’s depth and accelerates activity inference. Unlike traditional pruning methods that focus on the spatial domain and the importance of filters, this… More >

Yuefan Liu, Ducheng Zhang, Chen Guo^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3281-3299, 2024, DOI:10.32604/cmc.2024.057294 - 18 November 2024

Abstract Craniocerebral injuries represent the primary cause of fatalities among riders involved in two-wheeler accidents; nevertheless, the prevalence of helmet usage among these riders remains alarmingly low. Consequently, the accurate identification of riders who are wearing safety helmets is of paramount importance. Current detection algorithms exhibit several limitations, including inadequate accuracy, substantial model size, and suboptimal performance in complex environments with small targets. To address these challenges, we propose a novel lightweight detection algorithm, termed GL-YOLOv5, which is an enhancement of the You Only Look Once version 5 (YOLOv5) framework. This model incorporates a Global DualPooling… More >

Arar Al Tawil^1,*, Laiali Almazaydeh², Doaa Qawasmeh³, Baraah Qawasmeh⁴, Mohammad Alshinwan^1,5, Khaled Elleithy⁶

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3395-3412, 2024, DOI:10.32604/cmc.2024.057279 - 18 November 2024

Abstract Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information, a practice known as phishing. This study utilizes three distinct methodologies, Term Frequency-Inverse Document Frequency, Word2Vec, and Bidirectional Encoder Representations from Transformers, to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks. The study uses feature extraction methods to assess the performance of Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron algorithms. The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). Word2Vec’s More >

Keke Zhou, Guoqiang Zheng^*, Huihui Zhai, Xiangshuai Lv, Weizhen Zhang

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2571-2585, 2024, DOI:10.32604/cmc.2024.056863 - 18 November 2024

Abstract Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies. Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents, thereby providing a guarantee for the safety of drivers. However, detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds, varying target scales, and different resolutions. Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios, this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5. The algorithm integrates Attention-based Intra-scale Feature… More >

Yuanyuan Ma¹, Ang Chen¹, Cunzhi Hou¹, Ruixia Jin², Jinghui Zhang¹, Ruixiang Li^3,4,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3301-3323, 2024, DOI:10.32604/cmc.2024.056854 - 18 November 2024

Abstract Currently, telecom fraud is expanding from the traditional telephone network to the Internet, and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights. However, existing telecom fraud identification methods based on blacklists, reputation, content and behavioral characteristics have good identification performance in the telephone network, but it is difficult to apply to the Internet where IP (Internet Protocol) addresses change dynamically. To address this issue, we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering (DC-FIPD). First, we… More >