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 >

Yaren Aydın¹, Gebrail Bekdaş^1,*, Sinan Melih Nigdeli¹, Zong Woo Geem^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2471-2499, 2024, DOI:10.32604/cmes.2024.056693 - 31 October 2024

Abstract Dynamic impacts such as wind and earthquakes cause loss of life and economic damage. To ensure safety against these effects, various measures have been taken from past to present and solutions have been developed using different technologies. Tall buildings are more susceptible to vibrations such as wind and earthquakes. Therefore, vibration control has become an important issue in civil engineering. This study optimizes tuned mass damper inerter (TMDI) using far-fault ground motion records. This study derives the optimum parameters of TMDI using the Adaptive Harmony Search algorithm. Structure displacement and total acceleration against earthquake load More >

Tajim Md. Niamat Ullah Akhund^1,2,*, Waleed M. Al-Nuwaiser³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222 - 12 September 2024

Abstract This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While More >

Xiaodan Wu¹, Jie Li^1,*, Yongbin Cai², Sihui Huang¹

Energy Engineering, Vol.121, No.7, pp. 1963-1990, 2024, DOI:10.32604/ee.2024.048477 - 11 June 2024

Abstract Aiming at the problems of large energy consumption and serious pollution of winter heating existing in the rural buildings in Southern Xinjiang, a combined active-passive heating system was proposed, and the simulation software was used to optimize the parameters of the system, according to the parameters obtained from the optimization, a test platform was built and winter heating test was carried out. The simulation results showed that the thickness of the air layer of 75 mm, the total area of the vent holes of 0.24 m, and the thickness of the insulation layer of 120… More >

Huihui Zhai¹, Dongqi Jiao², Haichao Zhou^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 955-975, 2024, DOI:10.32604/cmes.2023.043322 - 30 December 2023

Abstract The wheels have a considerable influence on the aerodynamic properties and can contribute up to 25% of the total drag on modern vehicles. In this study, the effect of the wheel spoke structure on the aerodynamic performance of the isolated wheel is investigated. Subsequently, the 35° Ahmed body with an optimized spoke structure is used to analyze the flow behavior and the mechanism of drag reduction. The Fluent software is employed for this investigation, with an inlet velocity of 40 m/s. The accuracy of the numerical study is validated by comparing it with experimental results obtained… More >

Anıl Sezgin^1,2,*, Aytuğ Boyacı³

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2121-2143, 2023, DOI:10.32604/cmc.2023.040287 - 30 August 2023

Abstract By identifying and responding to any malicious behavior that could endanger the system, the Intrusion Detection System (IDS) is crucial for preserving the security of the Industrial Internet of Things (IIoT) network. The benefit of anomaly-based IDS is that they are able to recognize zero-day attacks due to the fact that they do not rely on a signature database to identify abnormal activity. In order to improve control over datasets and the process, this study proposes using an automated machine learning (AutoML) technique to automate the machine learning processes for IDS. Our ground-breaking architecture, known… More >

Dan Zhang¹, Yu Zhang², Yiwen Liang^1,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2317-2336, 2023, DOI:10.32604/cmc.2023.038026 - 30 August 2023

Abstract The dendritic cell algorithm (DCA) is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system. Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA. The loss function of DCA is ambiguous due to its complexity. To reduce the uncertainty, several researchers simplified the algorithm program; some introduced gradient descent to optimize parameters; some utilized searching methods to find the optimal parameter combination. However, these studies are either time-consuming or need to be revised in the case of non-convex… More >

Zhiwei Ye¹, Ziqian Fang¹, Zhina Song^1,*, Haigang Sui², Chunyan Yan¹, Wen Zhou¹, Mingwei Wang¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2019-2035, 2023, DOI:10.32604/iasc.2023.039949 - 21 June 2023

Abstract Hyperparameters play a vital impact in the performance of most machine learning algorithms. It is a challenge for traditional methods to configure hyperparameters of the capsule network to obtain high-performance manually. Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a combinatorial optimization problem. However, these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted. The inspiration for the hybrid rice optimization (HRO) algorithm is from the breeding technology of three-line hybrid rice in China, which has the advantages of… More >

Hadeel Alsolai¹, Mohammed Rizwanullah^2,*, Mashael Maashi³, Mahmoud Othman⁴, Amani A. Alneil², Amgad Atta Abdelmageed²

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 975-992, 2023, DOI:10.32604/cmc.2023.037936 - 08 June 2023

Abstract Soil classification is one of the emanating topics and major concerns in many countries. As the population has been increasing at a rapid pace, the demand for food also increases dynamically. Common approaches used by agriculturalists are inadequate to satisfy the rising demand, and thus they have hindered soil cultivation. There comes a demand for computer-related soil classification methods to support agriculturalists. This study introduces a Gradient-Based Optimizer and Deep Learning (DL) for Automated Soil Classification (GBODL-ASC) technique. The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches. In the… More >

Sait Can Yucebas^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 49-71, 2023, DOI:10.32604/iasc.2023.036871 - 29 April 2023

Abstract The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyper-parameter optimization to get the best performance from different… More >