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  • Open Access

    ARTICLE

    Impact Damage Identification of Aluminum Alloy Reinforced Plate Based on GWO-ELM Algorithm

    Wei Li1,2, Benjian Zou1, Yuxiang Luo2, Ning Yang2, Faye Zhang1,*, Mingshun Jiang1, Lei Jia1

    Structural Durability & Health Monitoring, Vol.17, No.6, pp. 485-500, 2023, DOI:10.32604/sdhm.2023.025989

    Abstract As a critical structure of aerospace equipment, aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system. In this study, a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of spacecraft. Firstly, together with numerical simulation, the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed, to establish the damage data. Subsequently, the amplitude-frequency characteristics of impact damage signals are extracted and put into an extreme learning machine… More >

  • Open Access

    ARTICLE

    An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

    Shazia Shamas1, Surya Narayan Panda1,*, Ishu Sharma1,*, Kalpna Guleria1, Aman Singh2,3,4, Ahmad Ali AlZubi5, Mallak Ahmad AlZubi6

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1051-1075, 2024, DOI:10.32604/cmes.2023.030712

    Abstract The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarm intelligent techniques. The techniques… More >

  • Open Access

    ARTICLE

    Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption

    Mohanad A. Mohammed*, Hala B. Abdul Wahab

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1731-1748, 2024, DOI:10.32604/cmes.2023.030528

    Abstract Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges. Concurrently, the Internet of Things (IoT) has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services, ultimately enhancing human lives. This paper presents a new approach utilizing lightweight blockchain technology, effectively reducing the computational burden typically associated with conventional blockchain systems. By integrating this lightweight blockchain with IoT systems, substantial reductions in implementation time and computational complexity can be achieved. Moreover, the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm, renowned for… More >

  • Open Access

    ARTICLE

    Algorithm Selection Method Based on Coupling Strength for Partitioned Analysis of Structure-Piezoelectric-Circuit Coupling

    Daisuke Ishihara*, Naoto Takayama

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1237-1258, 2024, DOI:10.32604/cmes.2023.030211

    Abstract In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis of structure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectric coupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulations are used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely (1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weakly coupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially strongly coupled and… More >

  • Open Access

    ARTICLE

    Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

    Yue Zhu, Tao Zhao*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1139-1158, 2024, DOI:10.32604/cmes.2023.030178

    Abstract The optimization of the rule base of a fuzzy logic system (FLS) based on evolutionary algorithm has achieved notable results. However, due to the diversity of the deep structure in the hierarchical fuzzy system (HFS) and the correlation of each sub fuzzy system, the uncertainty of the HFS's deep structure increases. For the HFS, a large number of studies mainly use fixed structures, which cannot be selected automatically. To solve this problem, this paper proposes a novel approach for constructing the incremental HFS. During system design, the deep structure and the rule base of the HFS are encoded separately. Subsequently,… More >

  • Open Access

    ARTICLE

    Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

    Mohamed Zarouan1, Ibrahim M. Mehedi1,2,*, Shaikh Abdul Latif3, Md. Masud Rana4

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1341-1364, 2024, DOI:10.32604/cmes.2023.030037

    Abstract Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamless operation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0. Specifically, various modernized industrial processes have been equipped with quite a few sensors to collect process-based data to find faults arising or prevailing in processes along with monitoring the status of processes. Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Due to the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experience and human knowledge, intellectual… More >

  • Open Access

    ARTICLE

    Adaptive H Filtering Algorithm for Train Positioning Based on Prior Combination Constraints

    Xiuhui Diao1, Pengfei Wang1,2,*, Weidong Li2, Xianwu Chu2, Yunming Wang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1795-1812, 2024, DOI:10.32604/cmes.2023.030008

    Abstract To solve the problem of data fusion for prior information such as track information and train status in train positioning, an adaptive H filtering algorithm with combination constraint is proposed, which fuses prior information with other sensor information in the form of constraints. Firstly, the train precise track constraint method of the train is proposed, and the plane position constraint and train motion state constraints are analysed. A model for combining prior information with constraints is established. Then an adaptive H filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor. Finally, the positioning… More >

  • Open Access

    ARTICLE

    An Adaptive Edge Detection Algorithm for Weed Image Analysis

    Yousef Alhwaiti1,*, Muhammad Hameed Siddiqi1, Irshad Ahmad2

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3011-3031, 2023, DOI:10.32604/csse.2023.042110

    Abstract Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments. Therefore, in this paper, a… More >

  • Open Access

    ARTICLE

    A Novel Incremental Attribute Reduction Algorithm Based on Intuitionistic Fuzzy Partition Distance

    Pham Viet Anh1,3, Nguyen Ngoc Thuy4, Nguyen Long Giang2, Pham Dinh Khanh5, Nguyen The Thuy1,6,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2971-2988, 2023, DOI:10.32604/csse.2023.042068

    Abstract Attribute reduction, also known as feature selection, for decision information systems is one of the most pivotal issues in machine learning and data mining. Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problem of attribute reduction. Unfortunately, the intuitionistic fuzzy sets based methods have not received much interest, while these methods are well-known as a very powerful approach to noisy decision tables, i.e., data tables with the low initial classification accuracy. Therefore, this paper provides a novel incremental attribute reduction method to deal more effectively with noisy decision tables,… More >

  • Open Access

    ARTICLE

    Security Test Case Prioritization through Ant Colony Optimization Algorithm

    Abdulaziz Attaallah1, Khalil al-Sulbi2, Areej Alasiry3, Mehrez Marzougui3, Mohd Waris Khan4,*, Mohd Faizan4, Alka Agrawal5, Dhirendra Pandey5

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3165-3195, 2023, DOI:10.32604/csse.2023.040259

    Abstract Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems. One of the challenges in software security testing is test case prioritization, which aims to reduce redundancy in fault occurrences when executing test suites. By effectively applying test case prioritization, both the time and cost required for developing secure software can be reduced. This paper proposes a test case prioritization technique based on the Ant Colony Optimization (ACO) algorithm, a metaheuristic approach. The performance of the ACO-based technique is evaluated using the Average Percentage of Fault… More >

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