Scientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects. Although the current rainfall monitoring system in China has a dense station network and high-precision rainfall data, the ti...
This work introduces a novel computational approach based on Principal Component Analysis (PCA) for dimensionality reduction of the solution space in optimisation problems with known linear interdependencies among solution variables. By creating synt...
The Independent Component Analysis (ICA) algorithm based on Principal Component Analysis (PCA) is described in this paper to achieve the raw textile defect detection. In the first step, the observed matrix X is constructed from a large number of defe...
Remote sensing data has known an explosive growth in the past decade. This has led to the need for efficient dimensionality reduction techniques, mathematical procedures that transform the high-dimensional data into a meaningful, reduced representati...
With the continuous development of human society, earthquakes are becoming more and more dangerous to the production and life of human society. Researchers continue to try to predict earthquakes, but the results are still not significant. With the de...
In this paper, we propose a boosted 3-D PCA algorithm based on an efficient analysis method. The proposed method involves three steps that improve image detection. In the first step, the proposed method designs a new analysis method to solve the perf...
To improve the signal-to-noise ratio (SNR) of vibration signals in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, a principal component analysis variable step-size normalized least mean square (PCA-VSS-NLMS) denoising method...
Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the m...
Surface electromyogram (sEMG) signals are easy to record and offer valuable motion information, such as symmetric and periodic motion in human gait. Due to these characteristics, sEMG is widely used in human-computer interaction, clinical diagnosis a...
Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these...
With the increasing availability of high-dimensional hyperspectral data from modern remote sensing platforms, accurate and efficient classification methods are urgently needed to overcome challenges such as spectral redundancy, spatial variability, a...
The switching action of the main circuit breaker of the train will produce switching overvoltage. In order to suppress the switching overvoltage, the phase selection control of the circuit breaker is required. However, the mechanical structure of the...
Thermal barrier coatings (TBCs) are usually used in high temperature and harsh environment, resulting in thinning or even spalling off. Hence, it is vital to detect the thickness of the TBCs. In this study, a hybrid machine learning model combined wi...
Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis...
To address the limited cross-domain generalization of single-algorithm models for shield attitude prediction, this study proposes a heterogeneous algorithm-fusion framework based on Stacking. The framework integrates multiple machine learning algorit...
Hyperspectral imaging adoption is hindered by high investment costs, and multicollinearity among contiguous bands requires dimensionality-reduction (DR) algorithms, raising concerns about cost-effectiveness for farmers. In this study, we propose a di...
Thermal barrier coatings (TBCs) are usually subjected to the combined action of compressive stress, tensile stress, and bending shear stress, resulting in the interfacial delamination of TBCs, and finally causing the ceramic top coat to peel off. Hen...
A multi-gas sensing system was developed based on the detection principle of the non-dispersive infrared (NDIR) method, which used a broad-spectra light source, a tunable Fabry–Pérot (FP) filter detector, and a flexible low-loss infrared...
Accurate shear load capacity predictions are crucial to achieving the load-bearing requirements of concrete deep beams in a variety of construction structures. Conventional BP neural networks have the drawbacks of being prone to local optimums and ha...
With the rapid development of communication and information technology, it is difficult for traditional signal detection and recognition methods to accurately acquire and identify the intelligence under complex environments. In order to solve this pr...
Spatial and spectral information are essential sources of information in remote sensing applications, and the fusion of panchromatic and multispectral images effectively combines the advantages of both. Due to the existence of two main classes of fus...
This paper examines the critical decision process of reducing the dimensionality of a dataset before applying a clustering algorithm. It is always a challenge to choose between extracting or selecting features. It is not obvious to evaluate the impor...
With the continuous expansion of industrial production scale, most of the chemical process variables are nonlinear, multi-modal and dynamic. For some traditional multivariate statistical monitoring and fault diagnosis algorithms, such as principal co...
In order to conduct an intelligent early warning assessment of stope rock burst disasters in mining areas, and effectively prevent and control them, the principal component analysis (PCA) method was embraced to perform dimensionality reduction and fe...
Numerous studies have reported the effective use of artificial intelligence approaches, particularly artificial neural networks (ANNs)-based models, to tackle tunnelling issues. However, having a high number of model inputs increases the running time...
The establishment and development of a set of methods of oil accurate recognition in a different environment are of great significance to the effective management of oil spill pollution. In this work, the concentration-emission matrix (CEM) is formed...
The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa...
The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical ap...
In this study, a multivariate analysis combined with near-infrared (NIR) spectroscopy was employed to classify intact grape berries based on the rootstock x cover crops combination. NIR spectra were collected in diffuse reflection mode using a TANGO...
This research represents a conceptual shift in the process of introducing flexibility into power system frequency stability-related protection. The existing underfrequency load shedding (UFLS) solution, although robust and fast, has often proved to b...
Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and...
Accurate regional wind speed forecasting is critical yet challenging due to inherent spatiotemporal correlations and data non-stationarity. This paper proposes a hybrid framework combining Principal Component Analysis (PCA), Variational Mode Decompos...
Regarding the non-stationary and stochastic nature of wind power, wind power generation forecasting plays an essential role in improving the stability and security of the power system when large-scale wind farms are integrated into the whole power gr...
The human motor system is a complex neuro-musculo sensory system that needs further investigations of neuro-muscular commands and sensory-motor coupling to decode movement execution. Some researchers suggest that the central nervous system (CNS) acti...
An electronic nose (E-nose) has been designed and equipped with software that can detect and classify human armpit body odor. An array of metal oxide sensors was used for detecting volatile organic compounds. The measurement circuit employs a voltage...
The risk evaluation indexes of goaf are multi-source and have complex mutual internal correlations, and there are great differences in the risk identification of goaf from different mines among the various influencing factors. This paper mainly focus...
Self-Organizing Map (SOM) neural networks can project complex, high-dimensional data onto a two-dimensional plane for data visualization, enabling an intuitive understanding of the distribution and symmetric structures of such data, thereby facilitat...
Skin cancer is a prevalent and perilous form of cancer and presents significant diagnostic challenges due to its high costs, dependence on medical experts, and time-consuming procedures. The existing diagnostic process is inefficient and expensive, r...
Industrial automation uses robotics and software to operate equipment and procedures across industries. Many applications integrate IoT, machine learning, and other technologies to provide smart features that improve the user experience. The use of s...
This study builds on the early brain dynamics work of Erol Başar, focusing on the human electroencephalogram (EEG) in relation to the generation of event-related potentials (ERPs) and behaviour. Scalp EEG contains not only oscillations but non-w...
This paper investigates a conceptual, theoretical framework for power system contingency analysis based on agglomerative hierarchical clustering. The security and integrity of modern power system networks have received considerable critical attention...
Principal Component Analysis (PCA) is one of the main methods used for electronic nose pattern recognition. However, poor classification performance is common in classification and recognition when using regular PCA. This paper aims to improve the cl...
The bi-dimensional F1-Transform was applied in image analysis to improve the performances of the F-transform method; however, due to its high computational complexity, the multidimensional F1-transform cannot be used in data analysis problems, especi...
Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding...
Improving the quality of service (QoS) and meeting service level agreements (SLAs) are critical objectives in next-generation networks. This article presents a study on applying supervised learning (SL) algorithms in a 5G/B5G service dataset after be...
Face recognition and identification are very important applications in machine learning. Due to the increasing amount of available data, traditional approaches based on matricization and matrix PCA methods can be difficult to implement. Moreover, the...
Artificial intelligence is reshaping warehouse work through algorithmic task allocation, scanner-based monitoring, KPI feedback, dynamic scheduling, and real-time performance control. Although these systems can improve coordination and operational vi...
This study analyzes the determinants of organizational performance and efficiency in Ecuadorian banana-exporting firms, considering human capital management as a strategic axis of competitiveness. Based on a cross-sectional quantitative design, a str...
Long-staple cotton from Xinjiang is renowned for its exceptional quality. However, it is susceptible to contamination with plastic film during mechanical picking. To address the issue of tricky removal of film in seed cotton, a technique based on hyp...
In this study, principal component analysis and k-means clustering (PCAKM) methods for synthetic aperture radar (SAR) data are analyzed to reduce the sensitivity caused by changes in the parameters and input images of the algorithm, increase the accu...
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