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  • Open Access



    Benjamin Lepersa,∗, Thomas Seitza, Guido Linka, John Jelonneka,b, Mark Zinkc

    Frontiers in Heat and Mass Transfer, Vol.6, pp. 1-6, 2015, DOI:10.5098/hmt.6.20

    Abstract A compact 10 kW microwave applicator operating at 2.45 GHz for fast volumetric heating and thermal cracking of lignite briquettes has been successfully designed and tested. In this paper, the applicator design and construction are presented together with a sequentially coupled electromagnetic, thermal-fluid and mechanical Comsol model. In a first step, this model allows us to calculate the power density inside the lignite material and the temperature distribution in the applicator for different water flow rates. In a second step, the total stress due to the thermal dilatation, the internal pressure inside the ceramic and the contact pressure from the… More >

  • Open Access


    Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

    Seungwoo Kang1, Seyha Ros1, Inseok Song1, Prohim Tam1, Sa Math2, Seokhoon Kim1,3,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020

    Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks. To establish… More >

  • Open Access


    A Memory-Guided Anomaly Detection Model with Contrastive Learning for Multivariate Time Series

    Wei Zhang1, Ping He2,*, Ting Li2, Fan Yang1, Ying Liu3

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1893-1910, 2023, DOI:10.32604/cmc.2023.044253

    Abstract Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification. These limitations can result in the misjudgment of models, leading to a degradation in overall detection performance. This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block (CLME) to overcome the above limitations. The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations. The memory block can record normal patterns of these representations through the utilization of… More >

  • Open Access


    Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

    Kalpesh Jadav1, Vishal Sorathiya1,*, Walid El-Shafai2, Torki Altameem3, Moustafa H. Aly4, Vipul Vekariya5, Kawsar Ahmed6, Francis M. Bui6

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2009-2030, 2023, DOI:10.32604/cmc.2023.043168

    Abstract Shadow extraction and elimination is essential for intelligent transportation systems (ITS) in vehicle tracking application. The shadow is the source of error for vehicle detection, which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting, vehicle detection, vehicle tracking, and classification. Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets, but the process of extracting shadows from moving vehicles in low light of real scenes is difficult. The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods… More >

  • Open Access


    Visualization for Explanation of Deep Learning-Based Fault Diagnosis Model Using Class Activation Map

    Youming Guo, Qinmu Wu*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1489-1514, 2023, DOI:10.32604/cmc.2023.042313

    Abstract Permanent magnet synchronous motor (PMSM) is widely used in various production processes because of its high efficiency, fast reaction time, and high power density. With the continuous promotion of new energy vehicles, timely detection of PMSM faults can significantly reduce the accident rate of new energy vehicles, further enhance consumers’ trust in their safety, and thus promote their popularity. Existing fault diagnosis methods based on deep learning can only distinguish different PMSM faults and cannot interpret and analyze them. Convolutional neural networks (CNN) show remarkable accuracy in image data analysis. However, due to the “black box” problem in deep learning… More >

  • Open Access


    DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

    Xiaoqi Xin*, Dezhi Han, Mingming Cui

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2407-2425, 2023, DOI:10.32604/cmc.2023.042308

    Abstract Person Search is a task involving pedestrian detection and person re-identification, aiming to retrieve person images matching a given objective attribute from a large-scale image library. The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively. The current popular Person Search models, whether end-to-end or two-step, are based on anchor boxes. However, due to the limitations of the anchor itself, the model inevitably has some disadvantages, such as unbalance of positive and negative samples and redundant calculation, which will affect the performance of models. To… More >

  • Open Access


    PoIR: A Node Selection Mechanism in Reputation-Based Blockchain Consensus Using Bidirectional LSTM Regression Model

    Jauzak Hussaini Windiatmaja, Delphi Hanggoro, Muhammad Salman, Riri Fitri Sari*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2309-2339, 2023, DOI:10.32604/cmc.2023.041152

    Abstract This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation (PoIR) as an alternative to traditional Proof of Work (PoW). PoIR addresses the limitations of existing reputation-based consensus mechanisms by proposing a more decentralized and fair node selection process. The proposed PoIR consensus combines Bidirectional Long Short-Term Memory (BiLSTM) with the Network Entity Reputation Database (NERD) to generate reputation scores for network entities and select authoritative nodes. NERD records network entity profiles based on various sources, i.e., Warden, Blacklists, DShield, AlienVault Open Threat Exchange (OTX), and MISP (Malware Information Sharing Platform). It summarizes these profile records into… More >

  • Open Access


    A Nonstandard Computational Investigation of SEIR Model with Fuzzy Transmission, Recovery and Death Rates

    Ahmed H. Msmali1, Fazal Dayan2,*, Muhammad Rafiq3, Nauman Ahmed4, Abdullah Ali H. Ahmadini1, Hassan A. Hamali5

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2251-2269, 2023, DOI:10.32604/cmc.2023.040266

    Abstract In this article, a Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model is considered. The equilibrium analysis and reproduction number are studied. The conventional models have made assumptions of homogeneity in disease transmission that contradict the actual reality. However, it is crucial to consider the heterogeneity of the transmission rate when modeling disease dynamics. Describing the heterogeneity of disease transmission mathematically can be achieved by incorporating fuzzy theory. A numerical scheme nonstandard, finite difference (NSFD) approach is developed for the studied model and the results of numerical simulations are presented. Simulations of the constructed scheme are presented. The positivity, convergence and consistency of the… More >

  • Open Access


    Modelling and Performance Analysis of Visible Light Communication System in Industrial Implementations

    Mohammed S. M. Gismalla1,2, Asrul I. Azmi1,2, Mohd R. Salim1,2, Farabi Iqbal1,2, Mohammad F. L. Abdullah3, Mosab Hamdan4,5, Muzaffar Hamzah4,*, Abu Sahmah M. Supa’at1,2

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2189-2204, 2023, DOI:10.32604/cmc.2023.035250

    Abstract Visible light communication (VLC) has a paramount role in industrial implementations, especially for better energy efficiency, high speed-data rates, and low susceptibility to interference. However, since studies on VLC for industrial implementations are in scarcity, areas concerning illumination optimisation and communication performances demand further investigation. As such, this paper presents a new modelling of light fixture distribution for a warehouse model to provide acceptable illumination and communication performances. The proposed model was evaluated based on various semi-angles at half power (SAAHP) and different height levels for several parameters, including received power, signal to noise ratio (SNR), and bit error rate… More >

  • Open Access


    Notes on Convergence and Modeling for the Extended Kalman Filter

    Dah-Jing Jwo*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2137-2155, 2023, DOI:10.32604/cmc.2023.034308

    Abstract The goal of this work is to provide an understanding of estimation technology for both linear and nonlinear dynamical systems. A critical analysis of both the Kalman filter (KF) and the extended Kalman filter (EKF) will be provided, along with examples to illustrate some important issues related to filtering convergence due to system modeling. A conceptual explanation of the topic with illustrative examples provided in the paper can help the readers capture the essential principles and avoid making mistakes while implementing the algorithms. Adding fictitious process noise to the system model assumed by the filter designers for convergence assurance is… More >

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