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  • Open Access

    ARTICLE

    Reliable iterative techniques for solving the KS equation arising in fluid flow

    Munirah Alotaibi1, Doaa Rizk2, Amal Al−Hanaya1, Ahmed Hagag3

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.1, pp. 1-9, 2024, DOI:10.23967/j.rimni.2024.02.003 - 23 February 2024

    Abstract In this study, we examine the Kuramoto-Sivashinsky equation which is a nonlinear model that describes several physical and chemical events arising in fluid flow. The approximate analytical solution for the fractional KS (FKS) problem is calculated using the Temimi-Ansari method (TAM) and the natural decomposition method (NDM). The projected procedure (NDM) combines the adomian decomposition method with the natural transform. Each technique can deal with nonlinear terms without making any assumptions. The methodologies under consideration provide ωn-curves that display the convergence window of the power series solution that approaches the exact solution. We explore two distinct More >

  • Open Access

    ARTICLE

    Intelligent Diagnosis of Highway Bridge Technical Condition Based on Defect Information

    Yanxue Ma1, Xiaoling Liu1,*, Bing Wang2, Ying Liu1

    Structural Durability & Health Monitoring, Vol.18, No.6, pp. 871-889, 2024, DOI:10.32604/sdhm.2024.052683 - 20 September 2024

    Abstract In the bridge technical condition assessment standards, the evaluation of bridge conditions primarily relies on the defects identified through manual inspections, which are determined using the comprehensive hierarchical analysis method. However, the relationship between the defects and the technical condition of the bridges warrants further exploration. To address this situation, this paper proposes a machine learning-based intelligent diagnosis model for the technical condition of highway bridges. Firstly, collect the inspection records of highway bridges in a certain region of China, then standardize the severity of diverse defects in accordance with relevant specifications. Secondly, in order… More >

  • Open Access

    ARTICLE

    Structural Health Monitoring by Accelerometric Data of a Continuously Monitored Structure with Induced Damages

    Giada Faraco, Andrea Vincenzo De Nunzio, Nicola Ivan Giannoccaro*, Arcangelo Messina

    Structural Durability & Health Monitoring, Vol.18, No.6, pp. 739-762, 2024, DOI:10.32604/sdhm.2024.052663 - 20 September 2024

    Abstract The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring, such as that carried out by a series of accelerometers placed on the structure, is certainly a goal of extreme and current interest. In the present work, the results obtained from the processing of experimental data of a real structure are shown. The analyzed structure is a lattice structure approximately 9 m high, monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels. The data used refer to continuous monitoring that lasted for a total of 1… More >

  • Open Access

    ARTICLE

    SteelGuard-yolo: Steel surface defect detection network based on improved YOLOv5s

    Zheng Zhou1, Min Yuan1

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.2, pp. 1-7, 2024, DOI:10.23967/j.rimni.2024.05.011 - 10 June 2024

    Abstract Steel is playing an increasingly important role in industry, and the detection of defects on its surface is also of great significance. The complex and diverse defects on the steel surface bring great challenges to the detection. In this paper, we propose a SteelGuard-yolo based steel surface defect detection model, whose main role is to improve the existing algorithms for detecting steel surface defects. First, we design the C2f module with weight aggregation and introduce the BiFormer attention mechanism to improve the feature extraction capability of the model. Second, we design a new up-sampling structure More >

  • Open Access

    ARTICLE

    Research on computer network data security storage technology in the era of big data

    Liying Zhang1, Xin Gu1, Qiang Zhao2

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.2, pp. 1-7, 2024, DOI:10.23967/j.rimni.2024.05.001 - 17 May 2024

    Abstract In the burgeoning epoch of big data, the imperative for secure computer network data storage is confronted with formidable challenges, including the perils of data breaches and a paucity of robust security measures. An enhanced storage paradigm, predicated upon a refined Hash algorithm—termed H-AONT—is herein delineated. This methodology augments data storage security through the formulation of an apposite system model, the amalgamation of the merits inherent in conventional encryption algorithms, and the deployment of the H-AONT dual encryption algorithm in data processing. Empirical evidence substantiates that, vis-à-vis alternative approaches, the proposed method significantly bolsters data More >

  • Open Access

    ARTICLE

    A empirical research on AI-powered athletic posture detection in sports training

    Shunyong Wang1, Gaoyang Zhang2

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.2, pp. 1-6, 2024, DOI:10.23967/j.rimni.2024.06.002 - 26 June 2024

    Abstract The current investigation delineates the efficacy of AI-facilitated detection of athletic postures within the realm of sports training. Employing a synthesis of literature review and empirical methodologies, data were amassed and scrutinized, affirming the study’s validity. The salient outcomes are manifold: (1) The frame difference algorithm efficaciously discerns inter-frame variances, evidencing pronounced adaptability and robustness, thereby enabling the recognition of weightlifting postures. (2) Confronting the challenge of negligible inter-frame disparities inherent in the frame difference algorithm, the research introduces a novel detection technique predicated on the cumulative inter-frame differences, which precisely pinpoints regions of posture… More >

  • Open Access

    ARTICLE

    Research on early fault detection method for a new distribution system based on automatic arc power

    Ling Wang1, Xin Chen1, Yichen Zhao1, Jinghang Yu1, Haodong Zou1

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.2, pp. 1-10, 2024, DOI:10.23967/j.rimni.2024.04.003 - 26 April 2024

    Abstract The integration of a large amount of renewable energy sources into the new distribution system has significantly altered its fault characteristics, resulting in variable operation modes and limited short-circuit currents. Hence, early arc faults can serve as indicators of impending system short-circuits, and prevent the difficulty of tripping the new distribution system by effectively detecting them in advance. A quantitative analytical model for the early fault current and temperature is developed, and a theoretical analysis reveals the challenges of achieving reliable detection based solely on either current or temperature. A novel early fault detection method More >

  • Open Access

    ARTICLE

    A Study on Outlier Detection and Feature Engineering Strategies in Machine Learning for Heart Disease Prediction

    Varada Rajkumar Kukkala1, Surapaneni Phani Praveen2, Naga Satya Koti Mani Kumar Tirumanadham3, Parvathaneni Naga Srinivasu4,5,*

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1085-1112, 2024, DOI:10.32604/csse.2024.053603 - 13 September 2024

    Abstract This paper investigates the application of machine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely; Z-Score incorporated with Grey Wolf Optimization (GWO) as well as Interquartile Range (IQR) coupled with Ant Colony Optimization (ACO). Using a performance index, it is shown that when compared with the Z-Score and GWO with AdaBoost, the IQR and ACO, with AdaBoost are not very accurate (89.0% vs. 86.0%) and less discriminative (Area Under the Curve (AUC) score of 93.0% vs. 91.0%). The Z-Score and GWO… More >

  • Open Access

    ARTICLE

    A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

    Jyun-Guo Wang*

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1149-1170, 2024, DOI:10.32604/csse.2024.052931 - 13 September 2024

    Abstract In many Eastern and Western countries, falling birth rates have led to the gradual aging of society. Older adults are often left alone at home or live in a long-term care center, which results in them being susceptible to unsafe events (such as falls) that can have disastrous consequences. However, automatically detecting falls from video data is challenging, and automatic fall detection methods usually require large volumes of training data, which can be difficult to acquire. To address this problem, video kinematic data can be used as training data, thereby avoiding the requirement of creating… More >

  • Open Access

    ARTICLE

    Modern Mobile Malware Detection Framework Using Machine Learning and Random Forest Algorithm

    Mohammad Ababneh*, Ayat Al-Droos, Ammar El-Hassan

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1171-1191, 2024, DOI:10.32604/csse.2024.052875 - 13 September 2024

    Abstract With the high level of proliferation of connected mobile devices, the risk of intrusion becomes higher. Artificial Intelligence (AI) and Machine Learning (ML) algorithms started to feature in protection software and showed effective results. These algorithms are nonetheless hindered by the lack of rich datasets and compounded by the appearance of new categories of malware such that the race between attackers’ malware, especially with the assistance of Artificial Intelligence tools and protection solutions makes these systems and frameworks lose effectiveness quickly. In this article, we present a framework for mobile malware detection based on a… More >

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