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  • Open Access

    ARTICLE

    The Estimation of the Higher Heating Value of Biochar by Data-Driven Modeling

    Jiefeng Chen1, Lisha Ding1, Pengyu Wang1, Weijin Zhang2, Jie Li3, Badr A. Mohamed4, Jie Chen1, Songqi Leng1, Tonggui Liu1, Lijian Leng2,*, Wenguang Zhou1,*

    Journal of Renewable Materials, Vol.10, No.6, pp. 1555-1574, 2022, DOI:10.32604/jrm.2022.018625

    Abstract Biomass is a carbon-neutral renewable energy resource. Biochar produced from biomass pyrolysis exhibits preferable characteristics and potential for fossil fuel substitution. For time- and cost-saving, it is vital to establish predictive models to predict biochar properties. However, limited studies focused on the accurate prediction of HHV of biochar by using proximate and ultimate analysis results of various biochar. Therefore, the multi-linear regression (MLR) and the machine learning (ML) models were developed to predict the measured HHV of biochar from the experiment data of this study. In detail, 52 types of biochars were produced by pyrolysis… More >

  • Open Access

    ARTICLE

    Clinical Data-Driven Finite Element Analysis of the Kinetics of Chewing Cycles in Order to Optimize Occlusal Reconstructions

    Simon Martinez1, Jürgen Lenz1, Hans Schindler1,2, Willi Wendler1, Stefan Rues3, Karl Schweizerhof1,*, Sophia Terebesi2, Nikolaos Nikitas Giannakopoulos2, Marc Schmitter2

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.3, pp. 1259-1281, 2021, DOI:10.32604/cmes.2021.017422

    Abstract The occlusal design plays a decisive role in the fabrication of dental restorations. Dentists and dental technicians depend on mechanical simulations of mandibular movement that are as accurate as possible, in particular, to produce interference-free yet chewing-efficient dental restorations. For this, kinetic data must be available, i.e., movements and deformations under the influence of forces and stresses. In the present study, so-called functional data were collected from healthy volunteers to provide consistent information for proper kinetics. For the latter purpose, biting and chewing forces, electrical muscle activity and jaw movements were registered synchronously, and individual More >

  • Open Access

    ARTICLE

    Optimization of Heat Treatment Scheduling for Hot Press Forging Using Data-Driven Models

    Seyoung Kim1, Jeonghoon Choi1, Kwang Ryel Ryu2,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 207-220, 2022, DOI:10.32604/iasc.2022.021752

    Abstract Scheduling heat treatment jobs in a hot press forging factory involves forming batches of multiple workpieces for the given furnaces, determining the start time of heating each batch, and sorting out the order of cooling the heated workpieces. Among these, forming batches is particularly difficult because of the various constraints that must be satisfied. This paper proposes an optimization method based on an evolutionary algorithm to search for a heat treatment schedule of maximum productivity with minimum energy cost, satisfying various constraints imposed on the batches. Our method encodes a candidate solution as a permutation More >

  • Open Access

    ARTICLE

    Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

    Michal Prauzek*, Tereza Paterova, Jaromir Konecny, Radek Martinek

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2601-2618, 2022, DOI:10.32604/cmc.2022.019705

    Abstract Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which… More >

  • Open Access

    ARTICLE

    Data-Driven Determinant-Based Greedy Under/Oversampling Vector Sensor Placement

    Yuji Saito*, Keigo Yamada, Naoki Kanda, Kumi Nakai, Takayuki Nagata, Taku Nonomura, Keisuke Asai

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 1-30, 2021, DOI:10.32604/cmes.2021.016603

    Abstract A vector-measurement-sensor-selection problem in the undersampled and oversampled cases is considered by extending the previous novel approaches: a greedy method based on D-optimality and a noise-robust greedy method in this paper. Extensions of the vector-measurement-sensor selection of the greedy algorithms are proposed and applied to randomly generated systems and practical datasets of flowfields around the airfoil and global climates to reconstruct the full state given by the vector-sensor measurement. More >

  • Open Access

    ARTICLE

    An Improved Data-Driven Topology Optimization Method Using Feature Pyramid Networks with Physical Constraints

    Jiaxiang Luo1,2, Yu Li2, Weien Zhou2, Zhiqiang Gong2, Zeyu Zhang1, Wen Yao2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 823-848, 2021, DOI:10.32604/cmes.2021.016737

    Abstract Deep learning for topology optimization has been extensively studied to reduce the cost of calculation in recent years. However, the loss function of the above method is mainly based on pixel-wise errors from the image perspective, which cannot embed the physical knowledge of topology optimization. Therefore, this paper presents an improved deep learning model to alleviate the above difficulty effectively. The feature pyramid network (FPN), a kind of deep learning model, is trained to learn the inherent physical law of topology optimization itself, of which the loss function is composed of pixel-wise errors and physical More >

  • Open Access

    ARTICLE

    A Cyber Kill Chain Approach for Detecting Advanced Persistent Threats

    Yussuf Ahmed1,*, A.Taufiq Asyhari1, Md Arafatur Rahman2

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2497-2513, 2021, DOI:10.32604/cmc.2021.014223

    Abstract The number of cybersecurity incidents is on the rise despite significant investment in security measures. The existing conventional security approaches have demonstrated limited success against some of the more complex cyber-attacks. This is primarily due to the sophistication of the attacks and the availability of powerful tools. Interconnected devices such as the Internet of Things (IoT) are also increasing attack exposures due to the increase in vulnerabilities. Over the last few years, we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G networks. Edge technology brings… More >

  • Open Access

    ARTICLE

    A Self-Learning Data-Driven Development of Failure Criteria of Unknown Anisotropic Ductile Materials with Deep Learning Neural Network

    Kyungsuk Jang1, Gun Jin Yun2,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1091-1120, 2021, DOI:10.32604/cmc.2020.012911

    Abstract This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests. Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation. The proposed method can overcome such practical challenges. The methodology is formalized by combining four ideas: 1) The deep learning neural network (DLNN)-based material constitutive model, 2) Self-learning inverse finite element (SELIFE) simulation, 3) Algorithmic identification of failure points from the self-learned stress-strain curves and 4) Derivation of the failure criteria through symbolic regression More >

  • Open Access

    ARTICLE

    Smart Healthcare Using Data-Driven Prediction of Immunization Defaulters in Expanded Program on Immunization (EPI)

    Sadaf Qazi1, Muhammad Usman1, Azhar Mahmood1, Aaqif Afzaal Abbasi2, Muhammad Attique3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 589-602, 2021, DOI:10.32604/cmc.2020.012507

    Abstract Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases, child mortality and morbidity. Expanded Program on Immunization (EPI) is a nation-wide program in Pakistan to implement immunization activities, however the coverage is quite low despite the accessibility of free vaccination. This study proposes a defaulter prediction model for accurate identification of defaulters. Our proposed framework classifies defaulters at five different stages: defaulter, partially high, partially medium, partially low, and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization schedule. Different machine learning algorithms are… More >

  • Open Access

    ARTICLE

    Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

    Xiangao Jiang1, Megan Coffee2, 3, *, Anasse Bari4, *, Junzhang Wang4, Xinyue Jiang5, Jianping Huang1, Jichan Shi1, Jianyi Dai1, Jing Cai1, Tianxiao Zhang6, Zhengxing Wu1, Guiqing He1, Yitong Huang7

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 537-551, 2020, DOI:10.32604/cmc.2020.010691

    Abstract The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel… More >

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