Zhuo Chen^1,*, Ningning Wang², Wenbo Jin³, Dui Li¹

Energy Engineering, Vol.121, No.4, pp. 1007-1026, 2024, DOI:10.32604/ee.2023.045270

Abstract A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines. To ensure the safe operation of crude oil pipelines, an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines. Aiming at the shortcomings of the ENN prediction model, which easily falls into the local minimum value and weak generalization ability in the implementation process, an optimized ENN prediction model based on the IRSA is proposed. The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition… More > Graphic Abstract

Abidhan Bardhan^1,*, Raushan Kumar Singh², Mohammed Alatiyyah³, Sulaiman Abdullah Alateyah^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1521-1555, 2024, DOI:10.32604/cmes.2023.044467

Abstract This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results featuring five influencing factors was… More >

Jiajie He^1,2, Fuzheng Liu³, Xiangyi Geng³, Xifeng Liang¹, Faye Zhang^3,*, Mingshun Jiang³

Structural Durability & Health Monitoring, Vol.18, No.1, pp. 37-54, 2024, DOI:10.32604/sdhm.2023.029428

Abstract Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods, making it challenging to ensure the fault diagnosis accuracy and reliability. A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator (ESGMD-CC) and artificial fish swarm algorithm (AFSA) optimized extreme learning machine (ELM) is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis. Firstly, SGMD decomposes the raw vibration signal into multiple Symplectic geometry components (SGCs). Secondly, the iterations are reset by the… More >

Xiaoqin Wu, Songrong Qian^*, Ming Yang

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3349-3366, 2023, DOI:10.32604/cmc.2023.043671

Abstract Safety helmets can reduce head injuries from object impacts and lower the probability of safety accidents, as well as being of great significance to construction safety. However, for a variety of reasons, construction workers nowadays may not strictly enforce the rules of wearing safety helmets. In order to strengthen the safety of construction site, the traditional practice is to manage it through methods such as regular inspections by safety officers, but the cost is high and the effect is poor. With the popularization and application of construction site video monitoring, manual video monitoring has been realized for management, but the… More >

Wei Li^1,2, Benjian Zou¹, Yuxiang Luo², Ning Yang², Faye Zhang^1,*, Mingshun Jiang¹, Lei Jia¹

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 485-500, 2023, DOI:10.32604/sdhm.2023.025989

Abstract As a critical structure of aerospace equipment, aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system. In this study, a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of spacecraft. Firstly, together with numerical simulation, the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed, to establish the damage data. Subsequently, the amplitude-frequency characteristics of impact damage signals are extracted and put into an extreme learning machine… More >

Muhammad Younis¹, Nudrat Aisha Akram^1,*, Arafat Abdel Hamed Abdel Latef^2,*, Muhammad Ashraf³

Phyton-International Journal of Experimental Botany, Vol.92, No.10, pp. 2761-2784, 2023, DOI:10.32604/phyton.2023.028801

Abstract To counteract the effects of drought stress, scientists have adopted several approaches including the use of different chemicals both inorganic and organic, which is contemplated as a highly efficient and cost-effective shot-gun approach. Ascorbic acid (AsA) is a potential organic substance, which widely occurs in plants, and is considered to be an effective antioxidant to counteract reactive oxygen species (ROS). Thus, a pot experiment was performed to assess the relative mitigating impacts of synthetic AsA and naturally occurring AsA in the form of lemon juice (LJ) and orange juice (OJ) on two cultivars of okra (Abelmoschus esculentus L.) namely Sabz… More >

P. Sabitha^*, G. Meeragandhi

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1983-2000, 2023, DOI:10.32604/iasc.2023.034402

Abstract Developing an automatic and credible diagnostic system to analyze the type, stage, and level of the liver cancer from Hematoxylin and Eosin (H&E) images is a very challenging and time-consuming endeavor, even for experienced pathologists, due to the non-uniform illumination and artifacts. Albeit several Machine Learning (ML) and Deep Learning (DL) approaches are employed to increase the performance of automatic liver cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations. In this work, we present a new Ensemble Classifier (hereafter called ECNet) to classify the H&E stained… More >

Muath Jarrah¹, Hussam Al Hamadi^4,*, Ahmed Abu-Khadrah², Taher M. Ghazal^1,3

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 19-33, 2023, DOI:10.32604/cmc.2023.032775

Abstract The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. There is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising rapidly. The IoMT is a novel reality transforming our daily lives. It can renovate modern healthcare by delivering a more personalized, protective, and collaborative approach to care. However, the current healthcare system for… More >

Cherifa Nakkach^*, Amira Zrelli, Tahar Ezzedine

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 545-560, 2023, DOI:10.32604/iasc.2023.036385

Abstract Due to the development of diversified and flexible building energy resources, the balancing energy supply and demand especially in smart buildings caused an increasing problem. Energy forecasting is necessary to address building energy issues and comfort challenges that drive urbanization and consequent increases in energy consumption. Recently, their management has a great significance as resources become scarcer and their emissions increase. In this article, we propose an intelligent energy forecasting method based on hybrid deep learning, in which the data collected by the smart home through meters is put into the pre-evaluation step. Next, the refined data is the input… More >

Hongyu Lin, Feng Jiang^*, Yu Jiang, Huiyin Luo, Jian Yao, Jiaxin Liu

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5321-5336, 2023, DOI:10.32604/cmc.2023.036893

Abstract Detecting non-motor drivers’ helmets has significant implications for traffic control. Currently, most helmet detection methods are susceptible to the complex background and need more accuracy and better robustness of small object detection, which are unsuitable for practical application scenarios. Therefore, this paper proposes a new helmet-wearing detection algorithm based on the You Only Look Once version 5 (YOLOv5). First, the Dilated convolution In Coordinate Attention (DICA) layer is added to the backbone network. DICA combines the coordinated attention mechanism with atrous convolution to replace the original convolution layer, which can increase the perceptual field of the network to get more… More >