Borui Wang, Dongyue Gao^*, Haiyang Gu, Mengke Ding, Zhanjun Wu

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 455-473, 2025, DOI:10.32604/cmc.2025.060617 - 26 March 2025

Abstract Fatigue damage is a primary contributor to the failure of composite structures, underscoring the critical importance of monitoring its progression to ensure structural safety. This paper introduces an innovative approach to fatigue damage monitoring in composite structures, leveraging a hybrid methodology that integrates the Whale Optimization Algorithm (WOA)-Backpropagation (BP) neural network with an ultrasonic guided wave feature selection algorithm. Initially, a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves, thereby establishing a signal space that correlates with the structural condition. Subsequently, the Relief-F algorithm is applied for signal feature extraction,… More >

Suhwan Yun^1,*, Wonhee Park¹, Duckhee Lee¹, Teasoon Kwon¹, Heeup Lee¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011343

Abstract This study examines the effectiveness of Pressure Relief Ducts (PRDs) in mitigating pressure fluctuations within single-track parallel tunnels of undersea railways during high-speed train operations. Computational fluid dynamics analysis was conducted under five conditions: without PRDs and with PRDs installed at intervals of 250m, 500m, 750m, and 1,000m. The analysis evaluated internal train travel within the tunnel and calculated both internal and external pressure fluctuations of the train. Safety standards were applied to assess cabin pressure fluctuations. Results revealed a significant reduction of approximately 30% in external cabin pressure fluctuations with PRD application, meeting safety More >

Sen-Hui Wang^1,2,*, Xi Kang¹, Cheng Wang¹, Tian-Bing Ma¹, Xiang He², Ke Yang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1405-1427, 2024, DOI:10.32604/cmes.2024.049281 - 20 May 2024

Abstract Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently,… More >

Mohamed Abouhawwash^1,2, S. Sridevi³, Suma Christal Mary Sundararajan⁴, Rohit Pachlor⁵, Faten Khalid Karim⁶, Doaa Sami Khafaga^6,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 239-253, 2023, DOI:10.32604/csse.2023.037812 - 26 May 2023

Abstract One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance… More >

Mahima Dahiya^*, Nitin Nitin

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4183-4201, 2023, DOI:10.32604/cmc.2023.032430 - 31 October 2022

Abstract Intrusion detection is critical to guaranteeing the safety of the data in the network. Even though, since Internet commerce has grown at a breakneck pace, network traffic kinds are rising daily, and network behavior characteristics are becoming increasingly complicated, posing significant hurdles to intrusion detection. The challenges in terms of false positives, false negatives, low detection accuracy, high running time, adversarial attacks, uncertain attacks, etc. lead to insecure Intrusion Detection System (IDS). To offset the existing challenge, the work has developed a secure Data Mining Intrusion detection system (DataMIDS) framework using Functional Perturbation (FP) feature… More >

V. Joseph Raymond^1,2,*, R. Jeberson Retna Raj¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2413-2429, 2023, DOI:10.32604/iasc.2023.030527 - 19 July 2022

Abstract In recent days the usage of android smartphones has increased extensively by end-users. There are several applications in different categories banking/finance, social engineering, education, sports and fitness, and many more applications. The android stack is more vulnerable compared to other mobile platforms like IOS, Windows, or Blackberry because of the open-source platform. In the Existing system, malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware. The attackers target the victim with various attacks like adware, backdoor, spyware, ransomware, and zero-day exploits… More >

Qidai Lin¹, Ying Gong^2,*, Yizhi Shi¹, Changsen Feng², Youbing Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 929-944, 2022, DOI:10.32604/cmes.2022.020752 - 27 June 2022

Abstract The integrated energy systems, usually including electric energy, natural gas and thermal energy, play a pivotal role in the energy Internet project, which could improve the accommodation of renewable energy through multi-energy complementary ways. Focusing on the regional integrated energy system composed of electrical microgrid and natural gas network, a fault risk warning method based on the improved RelieF-softmax method is proposed in this paper. The raw data-set was first clustered by the K-maxmin method to improve the preference of the random sampling process in the RelieF algorithm, and thereby achieved a hierarchical and non-repeated… More >

Xiaohua Chen^1,*, Caifu Lan¹, Honghao Zheng², Wang Li¹, Chao Zhao¹, Wenjun Dang³

Energy Engineering, Vol.119, No.4, pp. 1439-1452, 2022, DOI:10.32604/ee.2022.018208 - 23 May 2022

Abstract For oil pipeline in mountain areas, high hydrostatic pressure in the pipeline may cause error-opening of pressure relief valves, and oil is discharged from the pipeline to the pressure relief tanks, bringing spilling-over risk of the pressure relief tanks. Therefore, simulating the error-opening situations of the pressure relief valves and investigating the oil discharge process are necessary for checking the possibility of the spilling-over accident and then proposing measures to improve the pressure relief system. This research focuses on a continuous undulating oil pipeline with large elevation difference and a station along this pipeline, which… More >

Alaa Khalid Alduraibi^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1295-1309, 2022, DOI:10.32604/iasc.2022.026601 - 03 May 2022

Abstract With technological advancements, deep machine learning can assist doctors in identifying the brain mass or tumor using magnetic resonance imaging (MRI). This work extracts the deep features from 18-pre-trained convolutional neural networks (CNNs) to train the classical classifiers to categorize the brain MRI images. As a result, DenseNet-201, EfficientNet-b0, and DarkNet-53 deep features trained support vector machine (SVM) model shows the best accuracy. Furthermore, the ReliefF method is applied to extract the best features. Then, the fitness function is defined to select the number of nearest neighbors of ReliefF algorithm and feature vector size. Finally, More >

Xiaoxia Zhu^1,2,*, Xu Guo², Yishu Teng^1,*, John Gershenson³

International Journal of Mental Health Promotion, Vol.23, No.2, pp. 289-302, 2021, DOI:10.32604/IJMHP.2021.013691 - 30 April 2021

Abstract When the global outbreak of new coronary pneumonia broke out in 2020, online public opinion events triggered by cultural differences among overseas students had come into the public view. To further explore the relationship between the cultural alienation of overseas students and their own happiness, this study takes visualization and analysis of positive, negative sentiment analysis of Weibo netizens’ comment data in the “Xu Kexin Incident” as the starting point, on the basis of introducing cultural alienation, stress relief methods, and cultural intelligence, combining gender and social ability, social relations and other individual attributes, designed… More >