Article
Physics, Multidisciplinary
Tiankai Sun, Xingyuan Wang, Kejun Zhang, Daihong Jiang, Da Lin, Xunguang Jv, Bin Ding, Weidong Zhu
Summary: In this paper, a new method of medical image authentication based on wavelet packet and energy entropy is proposed. The method measures the energy of detail information and mines local details to highlight valuable information. Experiments show that the method has good robustness against various attacks.
Article
Engineering, Multidisciplinary
Tong Liu, YuCheng Jin, Shuo Wang, QinWen Zheng, Guoan Yang
Summary: This paper proposes a denoising method of acoustic emission (AE) signals based on the combination of autoencoder and wavelet packet decomposition (AE-WPD) to address the problem of weak AE signals being submerged in strong background noise in the actual operating conditions of the engine. The proposed method decomposes the engine background noise signals and noise-containing fault AE signals using wavelet packet, enhances the local analysis capability of the autoencoder, and analyzes the differences between the background noise signals and the noise-containing fault signals. The experimental results show that the proposed AE-WPD method outperforms other denoising methods at different signal-to-noise ratios (SNR).
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Environmental Sciences
Weichao Liu, Hongyuan Huo, Ping Zhou, Mingyue Li, Yuzhen Wang
Summary: This paper proposes a new method for predicting soil total iron composition. The method screens abnormal samples using a Monte Carlo method based on particle swarm optimization, and adopts feature representation based on Shannon entropy for wavelet packet processing. The selected feature bands based on the correlation coefficient and the CARS algorithm are applied to the soil spectra before and after wavelet packet processing. Finally, a 1D-CNN is used to calculate the Fe content. Experimental results show that the method can effectively handle abnormal samples, improve the correlation between spectra and content, and achieve good results with few samples.
Article
Engineering, Electrical & Electronic
Xidong Zheng, Tao Jin
Summary: This paper proposes a method combining exponential smoothing short-term wind power forecasting with wavelet packet decomposition to improve the power allocation efficiency of HESS. By introducing the concept of standby storage and combining methods such as CEEMDAN, NEE, and NSE, the service life and reliability of HESS are enhanced.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Engineering, Civil
Wen-chuan Wang, Yu-jin Du, Kwok-wing Chau, Dong-mei Xu, Chang-jun Liu, Qiang Ma
Summary: The ESMD-SE-WPD-LSTM model proposed in this study, applied to seven annual series from different areas in China, outperforms other benchmark models in terms of annual runoff prediction, providing higher accuracy and consistency.
WATER RESOURCES MANAGEMENT
(2021)
Article
Engineering, Multidisciplinary
Claudia Barile, Caterina Casavola, Giovanni Pappalettera, Vimalathithan Paramsamy Kannan
Summary: Signal-based acoustic emission data were analyzed in this research to identify damage modes in CFRP composites. Novel methodologies were introduced, and the 'dmey' wavelet was chosen for damage process identification through WPT, which showed consistent results with shifting in spectral density for characterizing damage modes.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2022)
Article
Green & Sustainable Science & Technology
H. Asami, M. Golabi, M. Albaji
Summary: Controlling environmental factors is crucial to prevent environmental degradation, and wastewater treatment plants play a vital role in maintaining the health of industries and the environment. Monitoring and evaluating these systems are essential, and bringing products to standard levels can be seen as promoting cleaner production.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Engineering, Mechanical
Yanpeng Hao, Lida Zhu, Boling Yan, Shaoqing Qin, Dayu Cui, Hao Lu
Summary: This paper proposes an adaptive denoising model based on WPD and RLSVFF, as well as a chatter detection method based on multi-source signals fusion using WPD and power entropy, to address the issues caused by unstable cutting in the machining of thin-walled parts. The study shows that these methods can more reliably detect early chatter and different levels of chatter compared to traditional methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Multidisciplinary
Mengmeng Liu, Ruipeng Gao, Jiao Zhao, Yiran Wang, Wei Shao
Summary: This paper proposes a multi-population state optimization algorithm (MPVHGA) to address the issues of low efficiency, premature convergence, and local optimal solution convergence in traditional genetic algorithms. The fault signal detection results show that MPVHGA has the advantages of fast convergence, high stability, no stagnation, and no limitation in the number of iterations. In 100 independent iterations, the average number of iterations of MPVHGA is about one-fifth of the traditional single genetic algorithm (SGA) and about one-third of the population state optimization algorithm (VHGA). The total convergence number of MPVHGA exceeds SGA and VHGA by 55 and 10, respectively, and its fault diagnosis accuracy can reach 95.04%.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Environmental Sciences
Hongtao Zhao, Yukun Ma, Jinxiu Fang, Lian Hu, Xuyong Li
Summary: An in-depth understanding of particle size distribution and total suspended solids (TSS) in surface runoff is crucial for managing urban diffuse pollution. Field experiments and model simulation were used to explore the dynamic behavior of TSS and influencing factors, showing that higher TSS concentrations in surface runoff contained coarser particles washed off from road-deposited sediments. Factors such as rainfall characteristics, urban-rural gradients, surface roughness, and climate differences affect the contribution rate of RDS to TSS by altering particle size composition.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Automation & Control Systems
Guoliang Lu, Xin Wen, Guangshuo He, Xiaojian Yi, Peng Yan
Summary: This article introduces a new dynamic modeling approach GMWPCs, which integrates WPD and graph theory to extract correlation information for early warning detection and fault identification. Experimental results validate the effectiveness and suitability of the proposed framework.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Automation & Control Systems
Guoliang Lu, Xin Wen, Guangshuo He, Xiaojian Yi, Peng Yan
Summary: In this article, a new dynamic modeling approach called GMWPCs is proposed for health monitoring of rolling element bearings (REBs) by integrating wavelet packet decomposition (WPD) and graph theory. The GMWPCs can enhance the analysis of WPD and enable early detection and fault identification in REBs, demonstrating effectiveness for real engineering applications.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Energy & Fuels
YuPeng Wu, WenBing Wu
Summary: The wavelet packet decomposition method can divide signals into multiple frequency bands, which helps improve the accuracy of fault diagnosis for mechanical vibration signals. Signals with obvious differences in specific frequency bands are easier to distinguish, leading to a more effective engine fault diagnosis rate.
Article
Construction & Building Technology
Indrashish Saha, R. Vidya Sagar
Summary: This study characterized the acoustic emission generated during tensile fracture in Steel fibre reinforced concrete (SFRC) using Wavelet Packet Decomposition (WPD) and pattern recognition techniques, revealing the impact of steel fibres on AE energy and its significance in understanding damage development in concrete.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Environmental Sciences
Shuai Jiang, Xiu-Ting Zhao, Ning Li
Summary: This paper proposes a new hybrid forecasting model (WPD-VMD-LSTM) based on fuzzy entropy, variational mode decomposition (VMD), wavelet packet decomposition (WPD), and Long Short-Term Memory (LSTM) to accurately predict natural gas consumption and production. The results demonstrate that the proposed model outperforms other comparable models and has practical value. The model can be applied to future energy forecasting in various fields.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Engineering, Multidisciplinary
Ferdi Dogan, Ibrahim Turkoglu
Summary: This study compares the performance of different deep learning models in detecting multiple objects and reveals their strengths. The results show that the best-performing model varies in each class. Vgg16 model performs the best in all classes, while InceptionResnetV2 model performs the worst. This article is an important resource for researchers studying object detection.
JOURNAL OF ENGINEERING RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Kenan Donuk, Ali Ari, Mehmet Fatih Ozdemir, Davut Hanbay
Summary: This article proposes a 3-stage system for emotion detection from facial images, which utilizes methods such as convolutional neural network, binary particle swarm optimization algorithm, and support vector machine to accurately identify facial expressions and improve classification accuracy and speed.
JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI
(2023)
Article
Computer Science, Software Engineering
Huseyin Uzen, Muammer Turkoglu, Muzaffer Aslan, Davut Hanbay
Summary: This paper proposes a novel automatic surface defect detection method based on DSEB-EUNet, which utilizes computer vision and machine learning methods to achieve high-accuracy detection of surface defects. The proposed model outperforms state-of-the-art approaches in experimental works.
Article
Engineering, Multidisciplinary
Nevzat Olgun, Ibrahim Tuerkoglu
Summary: In this study, a method for material identification using an LSTM deep learning model and an independent laser light source is proposed. By training and classifying laser signals, different materials can be accurately defined.
AIN SHAMS ENGINEERING JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Huseyin Firat, Mehmet Emin Asker, Mehmet Ilyas Bayindir, Davut Hanbay
Summary: This study proposes a method that combines the Hybrid 3D/2D Complete Inception module and Hybrid 3D/2D CNN for classification in hyperspectral remote sensing images. By utilizing multi-level feature extraction and PCA preprocessing, the proposed method achieves remarkable classification performance across various datasets.
NEURAL PROCESSING LETTERS
(2023)
Article
Automation & Control Systems
Talha Burak Alakus, Ibrahim Turkoglu
Summary: This study predicted viral-host interactions between SARS-CoV-2 virus and human proteins using computational-based approaches. Protein sequences were converted into numerical expressions and a deep learning model was designed for classification. The entropy-based method showed the best performance, followed by the Z-scale method.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Huseyin Uzen, Muammer Turkoglu, Davut Hanbay
Summary: Controlling surface defects is crucial in manufacturing quality control systems. Automatic defect detection using imaging and deep learning algorithms has shown more successful results compared to manual inspections. This study introduces a multi-dimensional feature extraction-based deep encoder-decoder network (MFE-DEDNet) model, which utilizes depthwise separable convolutions (DSC) layers and multi-input attention gate (MIAG) module to extract powerful and effective features for defect detection in surface datasets containing few images.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Huseyin Firat, Mehmet Emin Asker, Mehmet Ilyas Bayindir, Davut Hanbay
Summary: Hyperspectral remote sensing images (HRSI) are 3D image cubes containing multiple spectral bands. This study proposes a deep learning method, called 3D-RSSCN, for feature extraction and classification of HRSI. The proposed method achieves the best classification accuracy compared to various deep learning-based methods on multiple datasets.
NEURAL COMPUTING & APPLICATIONS
(2023)
Review
Engineering, Electrical & Electronic
Doygun Demiroll, Resul Das, Davut Hanbay
Summary: This study focuses on the security and privacy issues of big data, examining relevant literature and current technologies, and presenting an analysis of security requirements and the elimination of vulnerabilities against cyber attacks.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Computer Science, Information Systems
Gurkan Gurgoze, Ibrahim Turkoglu
Summary: Efficient energy utilization in mobile robots is crucial, including considerations of task qualification, energy usage, and speed relations, as well as how various parameters affecting speed profile should be holistically addressed. Balanced energy distribution and prevention of sharp speed changes are key research focuses.
Article
Engineering, Biomedical
Buket Toptas, Davut Hanbay
Summary: This paper proposes a method for separating arteries from veins in retinal blood vessel images using image preprocessing and deep learning network architecture. The proposed method was evaluated on multiple datasets and showed promising results. This method is suitable for automatic artery/vein separation.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION
(2023)
Article
Engineering, Multidisciplinary
Huseyin Uzen, Muammer Turkoglu, Ali Ari, Davut Hanbay
Summary: This study introduced a novel architecture, Inc-EFIN, based on InceptionV3 for automatic surface defect detection. By integrating features through multiple modules, it achieved superior performance compared to the latest technologies in the literature.
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY
(2023)
Article
Engineering, Multidisciplinary
Huseyin Firat, Davut Hanbay
Summary: Hyperspectral images (HSI) are 3D image cubes with two spatial and one spectral dimensions. Deep learning methods, particularly convolutional neural networks (CNN), have significantly improved HSI classification. This study explores the use of different CNN architectures, such as LeNet5, AlexNet, VGG16, GoogleNet, and ResNet50, for HSI classification. A 3D CNN-based hybrid approach is employed to extract spectral-spatial features simultaneously. Principal component analysis (PCA) is also used for optimal band extraction. Among the tested architectures, VGG16 performs best for Indian pines dataset, ResNet50 for Botswana dataset, and VGG16 for HyRANK-Loukia dataset and Salinas dataset.
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY
(2023)
Article
Environmental Sciences
Huseyin Firat, Mehmet Emin Asker, Davut Hanbay
Summary: The high dimensionality of hyperspectral remote sensing images affects classification performance. In this study, dimension reduction methods such as LDA, PCA, IPCA, ICA, SPCA, RPCA, and SVD were used as a preprocessing step. A hybrid 3D/2D CNN method was also employed to simultaneously extract spectral-spatial features. The results showed that the proposed method outperformed other compared methods in terms of classification accuracy.
REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT
(2022)
Article
Engineering, Multidisciplinary
Firat Huseyin, Davut Hanbay
Summary: Hyperspectral images (HSI) are commonly used in remote sensing, and deep learning with convolutional neural networks (CNN) is an effective method for HSI classification. This study proposes a new 3D CNN model that extracts spectral-spatial features effectively, showing better performance compared to other DL-based methods.
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY
(2022)
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)
