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  • Open Access

    ARTICLE

    Optimum Calculation of Coal Pillars in Inclined Weathered Oxidation Zone

    Yingbo Zhang1, Shi Chen1,2,*

    Energy Engineering, Vol.118, No.3, pp. 707-714, 2021, DOI:10.32604/EE.2021.013888

    Abstract In the mining process of coal mine, waterproof coal pillars should be set between the weathered oxidation zone and the first mining face. In order to determine the reasonable upper limit of the first mining face of Hongyi Coal Mine, the waterproof coal pillar needs to be wide enough to resist the lateral hydrostatic pressure of the oxidation zone, and to ensure that the top plate aquifer does not run through the water guide crack zone, while also liberating as much stagnant coal as possible. In this paper, the first coal mine face’s waterproof coal pillar was calculated using conventional… More >

  • Open Access

    REVIEW

    Industrial and Small-Scale Biomass Dryers: An Overview

    P. Murugan1, S. Dhanushkodi2,*, K. Sudhakar3,4,*, Vincent H. Wilson5

    Energy Engineering, Vol.118, No.3, pp. 435-446, 2021, DOI:10.32604/EE.2021.013491

    Abstract The quality of the drying process depends mainly on the efficient use of thermal energy. Sustainable systems based on solar energy takes a leading role in the drying of agro-products because of low operating cost. However, they are limited in use during off–sun periods. Biomass dryer is one of the simplest ways of drying because of its potential to dry products regardless of time and climate conditions. The other benefit is that crop residues could be used as fuel in these systems. However, the major limitation of the dryer is unequal drying because of poor airflow distribution in the drying… More >

  • Open Access

    ARTICLE

    Cloud Based Monitoring and Diagnosis of Gas Turbine Generator Based on Unsupervised Learning

    Xian Ma1, Tingyan Lv2,*, Yingqiang Jin2, Rongmin Chen2, Dengxian Dong2, Yingtao Jia2

    Energy Engineering, Vol.118, No.3, pp. 691-705, 2021, DOI:10.32604/EE.2021.012701

    Abstract The large number of gas turbines in large power companies is difficult to manage. A large amount of the data from the generating units is not mined and utilized for fault analysis. This study focuses on F-class (9F.05) gas turbine generators and uses unsupervised learning and cloud computing technologies to analyse the faults for the gas turbines. Remote monitoring of the operational status are conducted. The study proposes a cloud computing service architecture for large gas turbine objects, which uses unsupervised learning models to monitor the operational state of the gas turbine. Faults such as chamber seal failure, load abnormality… More >

  • Open Access

    ARTICLE

    Blockchain-Based Flexible Double-Chain Architecture and Performance Optimization for Better Sustainability in Agriculture

    Luona Song1, Xiaojuan Wang1,*, Peng Wei1, Zikui Lu1, Xiaojun Wang2, Nicolas Merveille3

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1429-1446, 2021, DOI:10.32604/cmc.2021.016954

    Abstract Blockchain is an emerging decentralized distributed technology that can cross the boundaries and guarantee safe and trustworthy value transfers between participants. Combining the blockchain technology with the Internet of Things (IoT) technology to enhance the transparency and sustainability of agricultural supply chains, has attracted researchers from both academia and industry. This paper reviews the latest applications of the blockchain and IoT technologies in the sustainable agricultural supply chain management and explores the design and implementation of a blockchain-based sustainable solution. By placing the sustainable agricultural supply chain management at its core, a blockchain-based framework is designed. Considering the heterogeneity of… More >

  • Open Access

    ARTICLE

    Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

    Dah-Jing Jwo*, Jui-Tao Lee

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 857-876, 2021, DOI:10.32604/cmc.2021.016894

    Abstract This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under… More >

  • Open Access

    ARTICLE

    A Link Analysis Algorithm for Identification of Key Hidden Services

    Abdullah Alharbi1, Mohd Faizan2, Wael Alosaimi1, Hashem Alyami3, Mohd Nadeem2, Suhel Ahmad Khan4, Alka Agrawal2, Raees Ahmad Khan2,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 877-886, 2021, DOI:10.32604/cmc.2021.016887

    Abstract The Tor dark web network has been reported to provide a breeding ground for criminals and fraudsters who are exploiting the vulnerabilities in the network to carry out illicit and unethical activities. The network has unfortunately become a means to perpetuate crimes like illegal drugs and firearm trafficking, violence and terrorist activities among others. The government and law enforcement agencies are working relentlessly to control the misuse of Tor network. This is a study in the similar league, with an attempt to suggest a link-based ranking technique to rank and identify the influential hidden services in the Tor dark web.… More >

  • Open Access

    ARTICLE

    Deep Reinforcement Learning for Multi-Phase Microstructure Design

    Jiongzhi Yang, Srivatsa Harish, Candy Li, Hengduo Zhao, Brittney Antous, Pinar Acar*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1285-1302, 2021, DOI:10.32604/cmc.2021.016829

    Abstract This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These material property enhancements are promising in improving the mechanical, thermal, and dynamic performance in multiple engineering systems, ranging from energy harvesting applications to spacecraft components. The study investigates a novel and efficient computational framework that integrates deep reinforcement learning algorithms into finite element-based material simulations to quantitatively model and design 3-D printed periodic microstructures. These algorithms… More >

  • Open Access

    ARTICLE

    Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

    Shabib Aftab1,2, Saad Alanazi3, Munir Ahmad1, Muhammad Adnan Khan4,*, Areej Fatima5, Nouh Sabri Elmitwally3,6

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1341-1357, 2021, DOI:10.32604/cmc.2021.016814

    Abstract Diabetes mellitus, generally known as diabetes, is one of the most common diseases worldwide. It is a metabolic disease characterized by insulin deficiency, or glucose (blood sugar) levels that exceed 200 mg/dL (11.1 ml/L) for prolonged periods, and may lead to death if left uncontrolled by medication or insulin injections. Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above “normal,” defined as 140 mg/dL. Diabetes is triggered by malfunction of the pancreas, which releases insulin, a natural hormone responsible for controlling glucose levels in blood cells. Diagnosis and comprehensive analysis of this… More >

  • Open Access

    ARTICLE

    DeepFake Videos Detection Based on Texture Features

    Bozhi Xu1, Jiarui Liu1, Jifan Liang1, Wei Lu1,*, Yue Zhang2

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1375-1388, 2021, DOI:10.32604/cmc.2021.016760

    Abstract In recent years, with the rapid development of deep learning technologies, some neural network models have been applied to generate fake media. DeepFakes, a deep learning based forgery technology, can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes. The spread of face manipulation videos is very easy to bring fake information. Therefore, it is important to develop effective detection methods to verify the authenticity of the videos. Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in… More >

  • Open Access

    ARTICLE

    Toward Optimal Cost-Energy Management Green Framework for Sustainable Future Wireless Networks

    Mohammed H. Alsharif1, Abu Jahid2, Mahmoud A. Albreem3, Peerapong Uthansakul4,*, Jamel Nebhen5, Khalid Yahya6

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1321-1339, 2021, DOI:10.32604/cmc.2021.016738

    Abstract The design of green cellular networking according to the traffic arrivals has the capability to reduce the overall energy consumption to a cluster in a cost-effective way. The cell zooming approach has appealed much attention that adaptively offloads the BS load demands adjusting the transmit power based on the traffic intensity and green energy availability. Besides, the researchers are focused on implementing renewable energy resources, which are considered the most attractive practices in designing energy-efficient wireless networks over the long term in a cost-efficient way in the existing infrastructure. The utilization of available solar can be adapted to acquire cost-effective… More >

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