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  • Open Access

    ARTICLE

    Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

    Feisha Hu1, Qi Wang1,*, Haijian Shao1,2, Shang Gao1, Hualong Yu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2405-2424, 2023, DOI:10.32604/cmes.2023.026732 - 09 March 2023

    Abstract Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global More > Graphic Abstract

    Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

  • Open Access

    ARTICLE

    Numerical Simulation of Low Cycle Fatigue Behavior of Ti2AlNb Alloy Subcomponents

    Yanju Wang1, Zhenyu Zhu2, Aixue Sha1, Wenfeng Hao3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2655-2676, 2023, DOI:10.32604/cmes.2023.025749 - 09 March 2023

    Abstract Many titanium alloy subcomponents are subjected to fatigue loading in aerospace engineering, resulting in fatigue failure. The fatigue behavior of Ti2AlNb alloy subcomponents was investigated based on the Seeger fatigue life theory and the improved Lemaitre damage evolution theory. Firstly, the finite element models of the standard openhole specimen and Y-section subcomponents have been established by ABAQUS. The damage model parameters were determined by fatigue tests, and the reliability of fatigue life simulation results of the Ti2AlNb alloy standard open-hole specimen was verified. Meanwhile, the fatigue life of Ti2AlNb alloy Y-section subcomponents was predicted. Under the same More >

  • Open Access

    ARTICLE

    Interaction of Foam and Microemulsion Components in Low-Tension-Gas Flooding

    Jing Zhao, Jun Yang*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.7, pp. 1951-1961, 2023, DOI:10.32604/fdmp.2023.026115 - 08 March 2023

    Abstract Low-Tension-Foam (LTF) flooding is an emerging enhanced oil recovery technique for low-permeability carbonate reservoirs. Foam capacity is closely related to the salinity environment (or, equivalently, the phase behavior of the oil/water/surfactant system). Therefore, the interactions between microemulsion and foam components are of primary importance in the LTF process. In this study, the phase behavior of an oil/water/surfactant system under equilibrium is analyzed, firstly by assuming perfect mixing. Meanwhile, the formation kinetics of microemulsion are monitored through a novel low-field NMR technique, which is able to provide quantitative assessment on the microemulsion evolution characteristics. Then, foam More >

  • Open Access

    ARTICLE

    Short Term Traffic Flow Prediction Using Hybrid Deep Learning

    Mohandu Anjaneyulu, Mohan Kubendiran*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1641-1656, 2023, DOI:10.32604/cmc.2023.035056 - 06 February 2023

    Abstract Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In… More >

  • Open Access

    ARTICLE

    Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors

    Hammad Rustam1, Muhammad Muneeb1, Suliman A. Alsuhibany2, Yazeed Yasin Ghadi3, Tamara Al Shloul4, Ahmad Jalal1, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2331-2346, 2023, DOI:10.32604/cmc.2023.028712 - 06 February 2023

    Abstract Hand gesture recognition (HGR) is used in a numerous applications, including medical health-care, industrial purpose and sports detection. We have developed a real-time hand gesture recognition system using inertial sensors for the smart home application. Developing such a model facilitates the medical health field (elders or disabled ones). Home automation has also been proven to be a tremendous benefit for the elderly and disabled. Residents are admitted to smart homes for comfort, luxury, improved quality of life, and protection against intrusion and burglars. This paper proposes a novel system that uses principal component analysis, linear More >

  • Open Access

    ARTICLE

    Classification of Nonlinear Confusion Component Using Hybrid Multi-Criteria Decision Making

    Nabilah Abughazalah1, Iqra Ishaque2, Majid Khan2,*, Ammar S. Alanazi3, Iqtadar Hussain4,5

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1451-1463, 2023, DOI:10.32604/iasc.2023.031855 - 05 January 2023

    Abstract In today’s digital world, the most inevitable challenge is the protection of digital information. Due to the weak confidentiality preserving techniques, the existing world is facing several digital information breaches. To make our digital data indecipherable to the unauthorized person, a technique for finding a cryptographically strong Substitution box (S-box) have presented. An S-box with sound cryptographic assets such as nonlinearity (NL), strict avalanche criterion (SAC), bit independence criteria (BIC), bit independence criteria of nonlinearity (BIC-NL), Bit independence criteria of Strict avalanche criteria (BIC-SAC), and Input/output XOR is considered as the robust S-box. The Decision-Making… More >

  • Open Access

    ARTICLE

    Chi-Square and PCA Based Feature Selection for Diabetes Detection with Ensemble Classifier

    Vaibhav Rupapara1, Furqan Rustam2, Abid Ishaq2, Ernesto Lee3, Imran Ashraf4,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1931-1949, 2023, DOI:10.32604/iasc.2023.028257 - 05 January 2023

    Abstract Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization. During the last few years, an alarming increase is observed worldwide with a 70% rise in the disease since 2000 and an 80% rise in male deaths. If untreated, it results in complications of many vital organs of the human body which may lead to fatality. Early detection of diabetes is a task of significant importance to start timely treatment. This study introduces a methodology for the classification of diabetic and normal people using… More >

  • Open Access

    ARTICLE

    Novel Scheme for Robust Confusion Component Selection Based on Pythagorean Fuzzy Set

    Nabilah Abughazalah1, Mohsin Iqbal2, Majid Khan3,*, Iqtadar Hussain4,5

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6523-6534, 2023, DOI:10.32604/cmc.2022.031859 - 28 December 2022

    Abstract The substitution box, often known as an S-box, is a nonlinear component that is a part of several block ciphers. Its purpose is to protect cryptographic algorithms from a variety of cryptanalytic assaults. A MultiCriteria Decision Making (MCDM) problem has a complex selection procedure because of having many options and criteria to choose from. Because of this, statistical methods are necessary to assess the performance score of each S-box and decide which option is the best one available based on this score. Using the Pythagorean Fuzzy-based Technique for Order of Preference by Similarity to Ideal… More >

  • Open Access

    ARTICLE

    3D Face Reconstruction from a Single Image Using a Combined PCA-LPP Method

    Jee-Sic Hur1, Hyeong-Geun Lee1, Shinjin Kang2, Yeo Chan Yoon3, Soo Kyun Kim1,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6213-6227, 2023, DOI:10.32604/cmc.2023.035344 - 28 December 2022

    Abstract In this paper, we proposed a combined PCA-LPP algorithm to improve 3D face reconstruction performance. Principal component analysis (PCA) is commonly used to compress images and extract features. One disadvantage of PCA is local feature loss. To address this, various studies have proposed combining a PCA-LPP-based algorithm with a locality preserving projection (LPP). However, the existing PCA-LPP method is unsuitable for 3D face reconstruction because it focuses on data classification and clustering. In the existing PCA-LPP, the adjacency graph, which primarily shows the connection relationships between data, is composed of the e-or k-nearest neighbor techniques. More >

  • Open Access

    ARTICLE

    Functional Nonparametric Predictions in Food Industry Using Near-Infrared Spectroscopy Measurement

    Ibrahim M. Almanjahie1,2,*, Omar Fetitah3, Mohammed Kadi Attouch3, Tawfik Benchikh3,4

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6307-6319, 2023, DOI:10.32604/cmc.2023.033441 - 28 December 2022

    Abstract Functional statistics is a new technique for dealing with data that can be viewed as curves or images. Parallel to this approach, the Near-Infrared Reflectance (NIR) spectroscopy methodology has been used in modern chemistry as a rapid, low-cost, and exact means of assessing an object’s chemical properties. In this research, we investigate the quality of corn and cookie dough by analyzing the spectroscopic technique using certain cutting-edge statistical models. By analyzing spectral data and applying functional models to it, we could predict the chemical components of corn and cookie dough. Kernel Functional Classical Estimation (KFCE),… More >

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