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  • Open Access

    PROCEEDINGS

    In-Situ Process Monitoring and Quality Evaluation for Fused Deposition Modeling with Foaming Materials

    Zhaowei Zhou1, Kaicheng Ruan1, Donghua Zhao1, Yi Xiong1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011376

    Abstract Fused deposition modeling (FDM) with foaming materials offers the capability to generate internal porous structures through in-situ foaming, imparting favorable characteristics such as weight reduction, shock absorption, thermal insulation, and sound insulation to printed objects. However, the process planning for this technology presents challenges due to the difficulty in accurately controlling the foaming rate, stemming from a complex underlying mechanism that remains poorly understood. This study introduces a multi-sensor platform for FDM with foaming materials, facilitating in-situ process monitoring of temperature field information during material modeling and quality evaluation of printed objects, i.e., abnormal foaming… More >

  • Open Access

    REVIEW

    Analysing Recent Breakthroughs in Fault Diagnosis through Sensor: A Comprehensive Overview

    Sumika Chauhan, Govind Vashishtha*, Radoslaw Zimroz

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1983-2020, 2024, DOI:10.32604/cmes.2024.055633 - 31 October 2024

    Abstract Sensors, vital elements in data acquisition systems, play a crucial role in various industries. However, their exposure to harsh operating conditions makes them vulnerable to faults that can compromise system performance. Early fault detection is therefore critical for minimizing downtime and ensuring system reliability. This paper delves into the contemporary landscape of fault diagnosis techniques for sensors, offering valuable insights for researchers and academicians. The papers begin by exploring the different types and causes of sensor faults, followed by a discussion of the various fault diagnosis methods employed in industrial sectors. The advantages and limitations More >

  • Open Access

    ARTICLE

    Multi-UAV Collaborative Mission Planning Method for Self-Organized Sensor Data Acquisition

    Shijie Yang1, Jiateng Yuan1, Zhipeng Zhang1, Zhibo Chen1,2, Hanchao Zhang4, Xiaohui Cui1,2,3,*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1529-1563, 2024, DOI:10.32604/cmc.2024.055402 - 15 October 2024

    Abstract In recent years, sensor technology has been widely used in the defense and control of sensitive areas in cities, or in various scenarios such as early warning of forest fires, monitoring of forest pests and diseases, and protection of endangered animals. Deploying sensors to collect data and then utilizing unmanned aerial vehicle (UAV) to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method. The current strategies for efficient data collection in above scenarios are still imperfect, and the low quality of the collected data and the excessive… More >

  • Open Access

    ARTICLE

    Optimization Model Proposal for Traffic Differentiation in Wireless Sensor Networks

    Adisa Hasković Džubur*, Samir Čaušević, Belma Memić, Muhamed Begović, Elma Avdagić-Golub, Alem Čolaković

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1059-1084, 2024, DOI:10.32604/cmc.2024.055386 - 15 October 2024

    Abstract Wireless sensor networks (WSNs) are characterized by heterogeneous traffic types (audio, video, data) and diverse application traffic requirements. This paper introduces three traffic classes following the defined model of heterogeneous traffic differentiation in WSNs. The requirements for each class regarding sensitivity to QoS (Quality of Service) parameters, such as loss, delay, and jitter, are described. These classes encompass real-time and delay-tolerant traffic. Given that QoS evaluation is a multi-criteria decision-making problem, we employed the AHP (Analytical Hierarchy Process) method for multi-criteria optimization. As a result of this approach, we derived weight values for different traffic… More >

  • Open Access

    ARTICLE

    A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

    Chandraumakantham Om Kumar1,*, Sudhakaran Gajendran2, Suguna Marappan1, Mohammed Zakariah3, Abdulaziz S. Almazyad4

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 471-501, 2024, DOI:10.32604/cmc.2024.054966 - 15 October 2024

    Abstract The security of the wireless sensor network-Internet of Things (WSN-IoT) network is more challenging due to its randomness and self-organized nature. Intrusion detection is one of the key methodologies utilized to ensure the security of the network. Conventional intrusion detection mechanisms have issues such as higher misclassification rates, increased model complexity, insignificant feature extraction, increased training time, increased run time complexity, computation overhead, failure to identify new attacks, increased energy consumption, and a variety of other factors that limit the performance of the intrusion system model. In this research a security framework for WSN-IoT, through… More >

  • Open Access

    ARTICLE

    Elevating Localization Accuracy in Wireless Sensor Networks: A Refined DV-Hop Approach

    Muhammad Aamer Ejaz1,*, Kamalrulnizam Abu Bakar1, Ismail Fauzi Bin Isnin1, Babangida Isyaku1,2,*, Taiseer Abdalla Elfadil Eisa3, Abdelzahir Abdelmaboud4, Asma Abbas Hassan Elnour3

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1511-1528, 2024, DOI:10.32604/cmc.2024.054938 - 15 October 2024

    Abstract Localization is crucial in wireless sensor networks for various applications, such as tracking objects in outdoor environments where GPS (Global Positioning System) or prior installed infrastructure is unavailable. However, traditional techniques involve many anchor nodes, increasing costs and reducing accuracy. Existing solutions do not address the selection of appropriate anchor nodes and selecting localized nodes as assistant anchor nodes for the localization process, which is a critical element in the localization process. Furthermore, an inaccurate average hop distance significantly affects localization accuracy. We propose an improved DV-Hop algorithm based on anchor sets (AS-IDV-Hop) to improve… More >

  • Open Access

    PROCEEDINGS

    3D Printing of Electrically Conductive and Degradable Hydrogel for Epidermal Strain Sensor

    Binbin Guo1,2, Hui Ying Yang1,*, Jiaming Bai2,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012673

    Abstract Due to excellent electrical conductivity, stretchability, and biocompatibility, electrically conductive hydrogels have been widely used in flexible wearable strain sensors. Generally, conductive fillers need to be integrated with the hydrogel matrix to impart electrical conductivity. According to the method of composite formation between electronic conductive fillers and hydrogel matrix, conductive hydrogels can be classified into embedded conductive and coated conductive hydrogels. Additionally, due to the intrinsic chemical and physical crosslinking networks, traditional hydrogels are not degradable, resulting in severe environmental pollution problems. Herein, we designed electrically conductive and degradable hydrogels for the epidermal strain sensor… More >

  • Open Access

    ARTICLE

    Quantitative Identification of Delamination Damage in Composite Structure Based on Distributed Optical Fiber Sensors and Model Updating

    Hao Xu1, Jing Wang2, Rubin Zhu2, Alfred Strauss3, Maosen Cao4, Zhanjun Wu1,*

    Structural Durability & Health Monitoring, Vol.18, No.6, pp. 785-803, 2024, DOI:10.32604/sdhm.2024.051393 - 20 September 2024

    Abstract Delamination is a prevalent type of damage in composite laminate structures. Its accumulation degrades structural performance and threatens the safety and integrity of aircraft. This study presents a method for the quantitative identification of delamination identification in composite materials, leveraging distributed optical fiber sensors and a model updating approach. Initially, a numerical analysis is performed to establish a parameterized finite element model of the composite plate. Then, this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations. The radial basis function neural network surrogate model is then constructed More >

  • Open Access

    ARTICLE

    Fuzzy Machine Learning-Based Algorithms for Mapping Cumin and Fennel Spices Crop Fields Using Sentinel-2 Satellite Data

    Shilpa Suman1, Abhishek Rawat2,*, Anil Kumar3, S. K. Tiwari4

    Revue Internationale de Géomatique, Vol.33, pp. 363-381, 2024, DOI:10.32604/rig.2024.053981 - 18 September 2024

    Abstract In this study, the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means (PCM) and Noise Clustering (NC) classifiers were examined and mapped the cumin and fennel rabi crop. Two training sample selection approaches that have been investigated in this study are “mean” and “individual sample as mean”. Both training sample techniques were applied to the PCM and NC classifiers to classify the two indices approach. Both approaches have been studied to decrease spectral information in temporal data processing. The Modified Soil Adjusted Vegetation Index 2 (MSAVI-2) and Class-Based Sensor… More >

  • Open Access

    ARTICLE

    Machine Fault Diagnosis Using Audio Sensors Data and Explainable AI Techniques-LIME and SHAP

    Aniqua Nusrat Zereen1, Abir Das2, Jia Uddin3,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886 - 12 September 2024

    Abstract Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning More >

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