Duc-Nghia Tran¹, Viet-Manh Do^1,2, Manh-Tuyen Vi^3,*, Duc-Tan Tran^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2525-2553, 2025, DOI:10.32604/cmc.2025.062590 - 16 April 2025

Abstract This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors. Data collection and behavioral analysis are achieved using machine learning (ML) algorithms through accelerometer sensors. However, behavioral analysis poses challenges due to the complexity of cow activities. The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints. Shorter windows may lack sufficient information, reducing algorithm performance. Additionally, the sensor’s position on the cows may shift during practical use, altering the collected accelerometer… More >

Zhe Gong¹, Ge Yan², Jie Ma¹, Chang-Lin Yan², Fu-Kang Shen¹, Hu Li³, Hua-Ping Wang^1,*

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 473-498, 2025, DOI:10.32604/sdhm.2024.057916 - 03 April 2025

Abstract Vacuum pressure vessels are one of the critical components in the aerospace field, and understanding the mechanical behavior feature is particularly important for safe operation. Therefore, it is meaningful to obtain the stress and strain distributions in the key positions of the vacuum tank, which can contribute to the safe performance assessment, operation efficiency, and fault analysis. Hence, this paper provides the distribution characteristics and variation rules of stress and tank strain of vacuum under different internal and external pressures through the elastic theoretical analysis and iteration method. The quasi-distributed fiber Bragg grating (FBG) sensors… More >

Tabassum Kanwal¹, Saif Ur Rehman^1,*, Azhar Imran², Haitham A. Mahmoud³

Computer Systems Science and Engineering, Vol.49, pp. 185-212, 2025, DOI:10.32604/csse.2024.056535 - 10 January 2025

Abstract This study presents an energy-efficient Internet of Things (IoT)-based wireless sensor network (WSN) framework for autonomous data validation in remote environmental monitoring. We address two critical challenges in WSNs: ensuring data reliability and optimizing energy consumption. Our novel approach integrates an artificial neural network (ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture. The proposed ANN model is designed to simultaneously detect multiple fault types, including spike faults, stuck-at faults, outliers, and out-of-range faults. We collected sensor data at 5-minute intervals over three months, using temperature and humidity sensors. The ANN was trained on 70%… More >

Jinlong Wang^1,2,*, Zhenyu Liu¹, Xingtao Yang¹, Min Li¹, Zhihan Lyu³

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1-39, 2025, DOI:10.32604/cmc.2024.058926 - 03 January 2025

Abstract With the rapid development of artificial intelligence, the Internet of Things (IoT) can deploy various machine learning algorithms for network and application management. In the IoT environment, many sensors and devices generate massive data, but data security and privacy protection have become a serious challenge. Federated learning (FL) can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing. This review aims to deeply explore the combination of FL and the IoT, and analyze the application of federated learning in the IoT from More >

Rahma Gantassi, Zaki Masood, Yonghoon Choi^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 327-343, 2025, DOI:10.32604/cmc.2024.058143 - 03 January 2025

Abstract Wireless sensor network (WSN) technologies have advanced significantly in recent years. Within WSNs, machine learning algorithms are crucial in selecting cluster heads (CHs) based on various quality of service (QoS) metrics. This paper proposes a new clustering routing protocol employing the Traveling Salesman Problem (TSP) to locate the optimal path traversed by the Mobile Data Collector (MDC), in terms of energy and QoS efficiency. To be more specific, to minimize energy consumption in the CH election stage, we have developed the M-T protocol using the K-Means and the grid clustering algorithms. In addition, to improve More >

Kithmini Godewatte Arachchige¹, Mohsin Murtaza², Chi-Tsun Cheng², Bader M. Albahlal^3,*, Cheng-Chi Lee^4,5,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3679-3705, 2024, DOI:10.32604/cmc.2024.059378 - 19 December 2024

Abstract Information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats, with Internet of Things (IoT) security gaining particular attention due to its role in data communication across various industries. However, IoT devices, typically low-powered, are susceptible to cyber threats. Conversely, blockchain has emerged as a robust solution to secure these devices due to its decentralised nature. Nevertheless, the fusion of blockchain and IoT technologies is challenging due to performance bottlenecks, network scalability limitations, and blockchain-specific security vulnerabilities. Blockchain, on the other hand, is a recently emerged information… More >

Shaowu Tang¹, Sicong Liu^1,*, Jian S Dai^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011746

Abstract Origami, a traditional and elegant folding technique, provides a solution for the deformation of three-dimensional structures. Inspired by this, origami-based soft actuators and robots exhibit the advantages of portability, high efficiency, and programmability when performing functions such as locomotion, manipulation, and interaction. However, these deformable origami structures bring challenges to sensing methods and technologies, due to hyperelastic deformations of the soft materials. In this work, a sensing approach is proposed to enable origami robots with proprioceptive and interactive sensing capabilities. The 3D-printed Miura-ori chambers of the robot are embedded with infrared optical sensors (a light-emitting… More >

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 >

Hao Xu¹, Jing Wang², Rubin Zhu², Alfred Strauss³, Maosen Cao⁴, Zhanjun Wu^1,*

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 >

Aniqua Nusrat Zereen¹, Abir Das², Jia Uddin^3,*

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 >