Article
Chemistry, Analytical
Min-Chan Kim, Jong-Hyun Lee, Dong-Hun Wang, In-Soo Lee
Summary: In this study, an induction motor simulator was constructed to collect vibration datasets for three states. Support vector machine, multilayer neural network, convolutional neural network, gradient boosting machine, and XGBoost machine learning models were used for fault diagnosis. Experimental results demonstrate the suitability of this technique for diagnosing faults in induction motors.
Article
Engineering, Multidisciplinary
Tian Han, Longwen Zhang, Zhongjun Yin, Andy C. C. Tan
Summary: This paper combines CNN and SVM for bearing fault diagnosis, improving the model's generalization ability and accuracy. Experimental results show the system has advantages of less time-consuming, high accuracy, and strong generalization ability.
Review
Green & Sustainable Science & Technology
B. Li, C. Delpha, D. Diallo, A. Migan-Dubois
Summary: This review systematically studies the application of Artificial Neural Network (ANN) and hybridized ANN models for PV fault detection and diagnosis, extracting and analyzing the targeted PV faults, detectable faults, data types and amounts, model configurations, and FDD performance for each application. The main trends, challenges, and prospects for the application of ANN for PV FDD are identified and presented.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Engineering, Civil
Gang Zheng, Wenbin Zhang, Wengang Zhang, Haizuo Zhou, Pengbo Yang
Summary: This paper introduces SVM and ANN models for predicting liquefaction-induced uplift displacement of tunnels, evaluates their performance using statistical parameters, and compares their applications. The sensitivity of input variables is quantified using relative importance analysis, and the precision of the models is demonstrated with centrifuge test results from previous studies.
Article
Optics
Hongwei Li, Hailiang Chen, Yuxin Li, Qiang Chen, Xiaoya Fan, Shuguang Li, Mingjian Ma
Summary: In this paper, support vector machines (SVMs) based on radial basis functions were used to predict the optical properties of photonic crystal fiber (PCF). Well-trained SVMs can accurately and quickly predict the effective refractive index, chromatic dispersion, and confinement loss of PCF. Compared to artificial neural networks (ANNs), SVMs are more accurate and show stable prediction results.
Article
Biochemistry & Molecular Biology
Antonio Agliata, Deborah Giordano, Francesco Bardozzo, Salvatore Bottiglieri, Angelo Facchiano, Roberto Tagliaferri
Summary: Diabetes is a chronic metabolic disease with high blood sugar levels, and type 2 diabetes is the most common type. Early diagnosis and treatment can prevent or delay complications. Previous studies have used machine learning techniques to predict diabetes, and artificial neural networks have shown promising results as a valuable tool for diabetes management and prevention. The study used machine learning methods to uncover associations between health status and the development of type 2 diabetes, aiming to accurately predict its onset or determine the individual's risk level.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Review
Computer Science, Information Systems
Arijit Chakraborty, Sajal Mitra, Debashis De, Anindya Jyoti Pal, Ferial Ghaemi, Ali Ahmadian, Massimiliano Ferrara
Summary: Protein-Protein Interaction (PPI) is a crucial network in biology that requires fast, accurate, and critical analysis, with Support Vector Machine (SVM) being an effective tool for solving complex classification problems.
Article
Engineering, Marine
Abdullah H. Alshahri, Moussa S. Elbisy
Summary: This study investigates artificial neural network-based approaches for estimating wave-overtopping discharge at coastal structures without a berm, using the newly developed EurOtop database. The GRNN model yields highly accurate results compared to other models.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Chemistry, Analytical
Fengyun Xie, Gan Wang, Jiandong Shang, Hui Liu, Qian Xiao, Sanmao Xie
Summary: Traditional methods of gearbox fault diagnosis are based on manual experience. Our study proposes a gearbox fault diagnosis method based on multidomain information fusion, which achieved high fault recognition accuracy.
Article
Engineering, Multidisciplinary
Xunjie Zhang, Min Zhang, Zaiyu Xiang, Jiliang Mo
Summary: In this paper, an algorithm for brake fault recognition was proposed, combining dynamic feature extraction with convolutional neural network and support vector machine. The algorithm showed excellent classification performance on small samples, achieving 100% accuracy in fault diagnosis.
Article
Computer Science, Information Systems
Esha Tripathi, Upendra Kumar, Surya Prakash Tripathi
Summary: This paper investigates image forgery identification methods, compares different feature extraction methods and classification techniques, and finds that Support Vector Machine with Intensity-Level Multi-Fractal Dimension as the feature extraction method achieves significant efficiency in image forgery identification.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Amit Mathur, Pradeep Kumar, S. P. Harsha
Summary: This article presents a data-driven fault diagnosis method for rolling element bearing under different operating conditions. Filter-type feature selection algorithms are incorporated to rank the time-domain statistical features extracted from vibration data of various bearing defect conditions. The results show that the neighborhood component analysis algorithm achieves the highest accuracies for bearing fault detection with both the support vector machine and artificial neural network. The effective feature ranking before fault classification can lead to efficient and reliable bearing fault diagnosis.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING
(2023)
Article
Spectroscopy
Kiarash Keyvan, Mahmoud Reza Sohrabi, Fereshteh Motiee
Summary: This study proposed a spectrophotometry method for simultaneous analysis of a binary mixture of hepatitis C antivirals containing sofosbuvir and daclatasvir. The method combines feed-forward artificial neural network and least square support vector machine algorithms, showing great potential in predicting component concentrations in pharmaceutical formulations. The proposed method was compared with high-performance liquid chromatography, with no significant difference observed.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Article
Computer Science, Information Systems
Sen Yang, Boran Xu, Hanlin Peng
Summary: An intelligent fault diagnosis scheme for series-connected battery packs based on wavelet characteristics of battery voltage correlations is proposed in this work, which can effectively detect and locate different faults with high success rates of feature extraction and accurate classification. In thermal fault diagnosis, mRVM outperforms ANN, but the overall diagnostic performance of ANN is better.
Article
Computer Science, Information Systems
Shih-Lin Lin
Summary: This study applied the medium Gaussian support vector machine method to machine learning, improving the reliability and accuracy of motor bearing fault estimation, detection, and identification. Additionally, the classification results of motor datasets using different machine learning algorithms were summarized and analyzed.
Article
Mechanics
Pawan Kumar, S. P. Harsha
Summary: In this study, the vibration response of porous functionally graded piezoelectric plates with electro-thermal loading was investigated using finite element formulations. The results showed that the material's unevenness and porosity distribution have an impact on the frequency, and the frequency decreases with an increase in the a/h ratio.
MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES
(2023)
Article
Materials Science, Multidisciplinary
Pawan Kumar, Suraj P. Harsha
Summary: This paper investigates the static and vibration response of the sigmoid functionally graded piezoelectric tapered plate under thermo-electric load with different porosity. The results show that the material gradient index affects the deflection, stress, and frequency of the plate, while positive and negative electric loading have significant effects on the buckling configuration.
JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES
(2022)
Article
Mechanics
Bikramjit Singh, R. S. Mulik, S. P. Harsha
Summary: In this study, static and vibration analyses of functionally graded gears (FGGs) were conducted using a numerical method. The results were compared with bi-material gears and steel gears to evaluate the performance differences.
MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES
(2023)
Article
Engineering, Civil
V. Kumar, S. J. Singh, V. H. Saran, S. P. Harsha
Summary: This paper investigates the vibration response of a porous Functionally Graded Material (FGM) plate with variable thickness. Mathematical modeling is used to describe the plate resting on different types of elastic foundations. The effects of variable foundation and porosity distribution on the plate's behavior are analyzed and compared to that of a homogeneous plate.
INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS
(2023)
Article
Materials Science, Multidisciplinary
Bikramjit Singh, R. S. Mulik, S. P. Harsha
Summary: This study conducted a dynamic response analysis of functionally graded gears (FGGs) using a 6-degree of freedom dynamic model. Results showed that FGGs exhibited lower mesh stiffness and weight compared to steel gears within the considered range of gradient index (GI).
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS
(2023)
Article
Engineering, Mechanical
Rajesh Govindan, V. Huzur Saran, Suraj Prakash Harsha
Summary: This study investigates the transmissibility responses of 14 male subjects exposed to vertical sinusoidal vibration at different frequencies and magnitudes. The results show that the resonance frequency decreases with increasing vibration magnitude for the sternum, abdomen, thigh, and leg. Higher vibration magnitudes result in greater segmental transmissibility at frequencies lower than the resonance frequency. The abdomen exhibits the highest vibration transmissibility, followed by the thigh, sternum, and head.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2023)
Article
Engineering, Mechanical
Vikas Tiwari, Satish C. Sharma, S. P. Harsha
Summary: This paper investigates the performance of suspension systems of high-speed passenger vehicles in a deflated state. A dynamic model with 13 degrees of freedom is developed and the influence of vibration on ride comfort and quality is assessed using Sperling's ride index method. The study focuses on the modelling of the air spring with a laminated rubber isolator. It is concluded that the vehicle should move at a slower speed when the air spring is deflated to maintain passenger comfort.
VEHICLE SYSTEM DYNAMICS
(2023)
Article
Engineering, Mechanical
Maan Singh Rathore, S. P. Harsha
Summary: The proposed framework of VAEGAN-RDCNN is used for bearing fault diagnosis based on nonlinear vibration responses. Imbalanced data augmentation is solved using VAEGAN, and residual deep convolutional neural network (RDCNN) is used to characterize the 2D patterns. Experimental results demonstrate that the proposed method achieves superior results in terms of generated sample quality and performance evaluation compared to conventional methods.
JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES
(2023)
Article
Engineering, Civil
Vikas Tiwari, Satish C. Sharma, S. P. Harsha
Summary: In a railroad wagon, air springs are used to restrict abnormal vibrations caused by track irregularities and improve traveler comfort. However, when air springs deflate, excessive vibrations occur and the ride becomes less comfortable. To alleviate this, a laminated rubber spring can act as a temporary emergency spring to reduce dynamic load. This study examines the effect of deflated air springs on passengers' ride comfort and proposes accurate prediction models. It also finds that the presence of a laminated rubber spring increases the dynamic stiffness of the air spring at higher frequencies and reducing the diameter of the surge pipe decreases its stiffness at lower frequencies. In order to maintain passenger comfort, vehicle speed should be reduced when the air spring is deflated.
INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS
(2023)
Article
Materials Science, Multidisciplinary
Pawan Kumar, Suraj P. Harsha
Summary: This paper analyzes the vibration and buckling responses of the smart porous core sandwich plate (SPCSP) under thermoelectric and thermomechanical loading. The plate has both conventional and unconventional boundary conditions. The thermo-mechanical properties of the plate vary through the thickness direction, and the plate considers geometrical nonlinearity and assumes a quadratic electric potential function. The formulation is derived using the Hamilton principle and the first-order shear deformation theory (FSDT) displacement field, and the governing equation is solved computationally using a modified Newton-Raphson scheme.
JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES
(2023)
Article
Engineering, Mechanical
Maan Singh Rathore, S. P. Harsha
Summary: This paper proposes a data augmentation model SAE-WGAN to address the data imbalance issue in bearing fault diagnosis. The model utilizes Wasserstein distance and informative noise vectors to improve the quality of generated samples for stable training. Metrics such as normalized cross-correlation and Kullback-Leibler divergence are employed for quantitative evaluation. Experimental validation and comparisons demonstrate the effectiveness of the proposed model, achieving improvements compared to state-of-the-art methods. The utilization of one-dimensional convolutional neural network further enhances fault classification performance under limited faulty data, as indicated by receiver operating characteristic curve and area under curve values.
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2023)
Article
Mechanics
V. Kumar, S. J. Singh, V. H. Saran, S. P. Harsha
Summary: This manuscript focuses on the analysis of free and forced vibration considering porosity and orthotropic foundation effect. It presents an exact solution for a variable thickness functionally graded material (FGM) plate resting on an orthotropic foundation using the first-order shear deformation plate theory (FSDT) model. Mathematical modeling takes into account three types of microstructural defects and the effect of the orthotropic Pasternak foundation. The differential equation is derived using a variational approach and solved through the Galerkin method, with results compared and validated against existing literature.
MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES
(2023)
Article
Automation & Control Systems
Amit Mathur, Pradeep Kumar, S. P. Harsha
Summary: This article presents a data-driven fault diagnosis method for rolling element bearing under different operating conditions. Filter-type feature selection algorithms are incorporated to rank the time-domain statistical features extracted from vibration data of various bearing defect conditions. The results show that the neighborhood component analysis algorithm achieves the highest accuracies for bearing fault detection with both the support vector machine and artificial neural network. The effective feature ranking before fault classification can lead to efficient and reliable bearing fault diagnosis.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING
(2023)
Article
Engineering, Mechanical
Anand Prakash, Pawan Kumar, V. H. Saran, S. P. Harsha
Summary: In this study, thermoelastic static and vibration analysis of a thin functionally graded sigmoidal porous plate using higher-order NURBS-based Isogeometric analysis has been conducted. The material properties of the plate vary according to a modified power and sigmoid law. The mathematical model is formulated based on the Kirchhoff-Love theory, virtual work principle, and high-order continuity of NURBS basis functions. The analysis investigates the effects of porosity index, material gradient index, boundary conditions, thermal loading, and geometry on deflection, vibration frequency, and mode shapes.
INTERNATIONAL JOURNAL OF MECHANICS AND MATERIALS IN DESIGN
(2023)
Article
Engineering, Civil
V. Kumar, S. J. Singh, V. H. Saran, S. P. Harsha
Summary: This work investigates the buckling response of a porous plate made of functionally graded materials. It considers a tapered FGM plate under uniaxial and biaxial loading with various boundary conditions. The effects of Pasternak foundation and different porosity patterns on the buckling response are studied. The study utilizes the first order-shear deformation theory and the Galerkin's-Vlasov method to derive the effective equations of motion for buckling analysis.
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
