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Search Results (17)
  • Open Access

    ARTICLE

    Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things: A Hybrid Approach

    Amal Al-Rasheed1, Rahim Khan2,3,*, Tahani Alsaed4, Mahwish Kundi2,5, Mohamad Hanif Md. Saad6, Mahidur R. Sarker7,8

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1305-1323, 2024, DOI:10.32604/cmc.2024.049215

    Abstract Due to the overwhelming characteristics of the Internet of Things (IoT) and its adoption in approximately every aspect of our lives, the concept of individual devices’ privacy has gained prominent attention from both customers, i.e., people, and industries as wearable devices collect sensitive information about patients (both admitted and outdoor) in smart healthcare infrastructures. In addition to privacy, outliers or noise are among the crucial issues, which are directly correlated with IoT infrastructures, as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing, i.e.,… More >

  • Open Access

    ARTICLE

    A Multi-Constraint Path Optimization Scheme Based on Information Fusion in Software Defined Network

    Jinlin Xu1,2, Wansu Pan1,*, Longle Cheng1,2, Haibo Tan1,2, Munan Yuan1,*, Xiaofeng Li1,2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1399-1418, 2024, DOI:10.32604/cmc.2024.049622

    Abstract The existing multipath routing in Software Defined Network (SDN) is relatively blind and inefficient, and there is a lack of cooperation between the terminal and network sides, making it difficult to achieve dynamic adaptation of service requirements and network resources. To address these issues, we propose a multi-constraint path optimization scheme based on information fusion in SDN. The proposed scheme collects network topology and network state information on the network side and computes disjoint paths between end hosts. It uses the Fuzzy Analytic Hierarchy Process (FAHP) to calculate the weight coefficients of multiple constrained parameters… More >

  • Open Access

    ARTICLE

    Research on Driver’s Fatigue Detection Based on Information Fusion

    Meiyan Zhang1, Boqi Zhao1, Jipu Li2, Qisong Wang1,*, Dan Liu1, Jinwei Sun1, Jingxiao Liao1,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1039-1061, 2024, DOI:10.32604/cmc.2024.048643

    Abstract Driving fatigue is a physiological phenomenon that often occurs during driving. After the driver enters a fatigued state, the attention is lax, the response is slow, and the ability to deal with emergencies is significantly reduced, which can easily cause traffic accidents. Therefore, studying driver fatigue detection methods is significant in ensuring safe driving. However, the fatigue state of actual drivers is easily interfered with by the external environment (glasses and light), which leads to many problems, such as weak reliability of fatigue driving detection. Moreover, fatigue is a slow process, first manifested in physiological… More >

  • Open Access

    ARTICLE

    Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion

    Kuosheng Jiang1, Chengrui Han1, Yasheng Chang2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3329-3352, 2024, DOI:10.32604/cmes.2024.046729

    Abstract Angular contact ball bearings have been widely used in machine tool spindles, and the bearing preload plays an important role in the performance of the spindle. In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties, a roller bearing preload test method based on the improved D-S evidence theory multi-sensor fusion method was proposed. First, a novel controllable preload system is proposed and evaluated. Subsequently, multiple sensors are employed to collect data on the bearing parameters during preload application. Finally, a multisensor fusion algorithm is used More >

  • Open Access

    ARTICLE

    A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

    Abdul Haseeb1, Muhammad Attique Khan2,*, Majed Alhaisoni3, Ghadah Aldehim4, Leila Jamel4, Usman Tariq5, Taerang Kim6, Jae-Hyuk Cha6

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3895-3920, 2023, DOI:10.32604/cmc.2023.045244

    Abstract Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the least-important features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks.… More >

  • Open Access

    ARTICLE

    Swin-PAFF: A SAR Ship Detection Network with Contextual Cross-Information Fusion

    Yujun Zhang*, Dezhi Han, Peng Chen

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2657-2675, 2023, DOI:10.32604/cmc.2023.042311

    Abstract Synthetic Aperture Radar (SAR) image target detection has widespread applications in both military and civil domains. However, SAR images pose challenges due to strong scattering, indistinct edge contours, multi-scale representation, sparsity, and severe background interference, which make the existing target detection methods in low accuracy. To address this issue, this paper proposes a multi-scale fusion framework (Swin-PAFF) for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure (FPN). Firstly, to tackle the issue of inadequate perceptual image context information… More >

  • Open Access

    ARTICLE

    A Monitoring Method for Transmission Tower Foots Displacement Based on Wind-Induced Vibration Response

    Zhicheng Liu1, Long Zhao1,*, Guanru Wen1, Peng Yuan2, Qiu Jin1

    Structural Durability & Health Monitoring, Vol.17, No.6, pp. 541-555, 2023, DOI:10.32604/sdhm.2023.029760

    Abstract The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element… More > Graphic Abstract

    A Monitoring Method for Transmission Tower Foots Displacement Based on Wind-Induced Vibration Response

  • Open Access

    ARTICLE

    State Accurate Representation and Performance Prediction Algorithm Optimization for Industrial Equipment Based on Digital Twin

    Ying Bai1,*, Xiaoti Ren2, Hong Li1

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2999-3018, 2023, DOI:10.32604/iasc.2023.040124

    Abstract The combination of the Industrial Internet of Things (IIoT) and digital twin (DT) technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance. However, conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information. The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model. In this paper, a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state. Firstly, a global real-time feedback and the dynamic adjustment mechanism… More >

  • Open Access

    ARTICLE

    An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

    Mingyong Li1, Lirong Tang1, Longfei Ma1, Honggang Zhao1, Jinyu Hu1, Yan Wei1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2349-2371, 2023, DOI:10.32604/cmes.2023.022604

    Abstract The learning status of learners directly affects the quality of learning. Compared with offline teachers, it is difficult for online teachers to capture the learning status of students in the whole class, and it is even more difficult to continue to pay attention to students while teaching. Therefore, this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion. Specifically, a facial expression recognition model and an eye state recognition model are constructed to detect students’ emotions and fatigue, respectively. By integrating the detected data with the More >

  • Open Access

    ARTICLE

    Multisensor Information Fusion for Condition Based Environment Monitoring

    A. Reyana1,*, P. Vijayalakshmi2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1013-1025, 2023, DOI:10.32604/iasc.2023.032538

    Abstract Destructive wildfires are becoming an annual event, similar to climate change, resulting in catastrophes that wreak havoc on both humans and the environment. The result, however, is disastrous, causing irreversible damage to the ecosystem. The location of the incident and the hotspot can sometimes have an impact on early fire detection systems. With the advancement of intelligent sensor-based control technologies, the multi-sensor data fusion technique integrates data from multiple sensor nodes. The primary objective to avoid wildfire is to identify the exact location of wildfire occurrence, allowing fire units to respond as soon as possible. More >

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