Article
Engineering, Multidisciplinary
Zhijiang Lou, Zedong Li, Youqing Wang, Shan Lu
Summary: This paper introduces an improved neural component analysis (INCA) method, which addresses the issue of NCA's inability to handle non-Gaussian features by proposing a new cost function based on kurtosis. It also improves the extraction of key information from process data by selecting principal components (PCs) in the original data space. Experimental results show that INCA outperforms other methods in fault detection.
Article
Chemistry, Analytical
Carollina de Melo Molinari Ortiz Antunes, Frederico Luis Felipe Soares, Noemi Nagata
Summary: Chemical analyses based on digital images are widely studied due to their non-invasive nature and simplicity. However, controlling instrumental and structural parameters for image acquisition is crucial for analysis repeatability and reproducibility. The high cost of accessing robust instruments is also a practical limitation. To overcome these limitations, a low-cost prototype using Raspberry Pi and multivariate tools was developed.
MICROCHEMICAL JOURNAL
(2023)
Article
Fisheries
Luis C. B. Silva, Bruna Lopes, Maria J. Pontes, Isidro Blanquet, Marcelo E. Segatto, Carlos Marques
Summary: Aquaculture requires monitoring multiple parameters simultaneously to maintain fish health and optimize production. The use of PCA allows for the transformation of correlated parameters into principal components, increasing data interpretability while minimizing information loss.
Article
Engineering, Chemical
Chun-Chin Hsu, Po-Chou Shih, Fang-Chih Tien
Summary: A novel weight strategy for multiblock PCA was proposed in this study, which considers the dependence and skewness of data to additionally take distribution information into account. The proposed weight matrix based on non-parametric ranks leads to shorter computation time. Experimental results show that the proposed method outperforms regular PCA, dynamic PCA, multiblock PCA, and WCMBPCA in fault detection rate for Tennessee Eastman (TE) process monitoring. Furthermore, the weight matrix calculation time is significantly shorter for the proposed method compared to WCMBPCA.
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
(2021)
Article
Ecology
Michael L. Collyer, Dean C. Adams
Summary: Phylogenetically aligned component analysis (PACA) is a new ordination approach that aligns phenotypic data with phylogenetic signal, allowing visualization of trends in phylogenetic signal in multivariate data spaces. By maximizing variation in directions that describe phylogenetic signal, PACA can distinguish between weak and strong phylogenetic signals, providing a more precise description of the phylogenetic signal in studies focused on phylogenetic signal. Comparing PACA and Phy-PCA results can help determine the relative importance of phylogenetic and other signals in the data.
METHODS IN ECOLOGY AND EVOLUTION
(2021)
Article
Chemistry, Analytical
Yusheng Lang, Lilin Zhou, Yutaka Imamura
Summary: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is an important analysis technique for gathering information from surfaces. This study proposed a new approach that treats ToF-SIMS spectra as images and uses convolutional neural network (CNN) for analysis, avoiding the challenges of descriptor generation.
ANALYTICAL CHEMISTRY
(2022)
Article
Energy & Fuels
Vinicius Tertulino Parede, Alexandre Rasi Aoki, Mateus Duarte Teixeira, Thelma S. Piazza Fernandes, Nathan Elias Maruch Barreto, Flavio Lori Grando, Vanderlei Aparecido da Silva, Fabio Alessandro Guerra, Milton Pires Ramos, Clayton Hilgemberg da Costa, Bruna Machado Mulinari, Germano Lambert-Torres, Ricardo Rodrigues de Almeida, Rafael Rodrigues, Victor Frederico Muller Jr, Andre Katayama dos Santos
Summary: Synchronized phasor measurement systems, known as Phasor Measurement Units (PMUs), are crucial for the operation of large electrical power systems (EPS) globally. These systems record and communicate dynamic EPS data in a synchronized manner using GPS and have a high sampling rate, generating extensive data sets that can be used for event detection. This study proposes a data management system architecture for a real PMU system located in Parana, Brazil, which utilizes principal component analysis and Pearson correlation to effectively detect and store electrical events occurring in the Brazilian national interconnected system.
Article
Automation & Control Systems
Hanqi Li, Mingxing Jia, Zhizhong Mao
Summary: An improved method called dynamic reconstruction principal component analysis (DRPCA) is proposed for process monitoring and fault detection. DRPCA utilizes the overall dynamic information of the training data set and improves the monitoring performance of dynamic industrial processes. The performance of DRPCA is evaluated on a cold rolling mill system, and the results show its superiority over other methods in terms of computation speed, timely alerts, detection rates, and false alarm rates.
JOURNAL OF PROCESS CONTROL
(2023)
Article
Automation & Control Systems
Atefeh Daemi, Bhushan Gopaluni, Biao Huang
Summary: In this article, we propose a novel transfer learning approach, called domain adversarial probabilistic principal component analysis (DAPPCA), to monitor processes with data from multiple distributions. DAPPCA automatically learns feature representations that are relevant across different operational modes and improves fault detection accuracy by transferring knowledge from previously known modes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Review
Instruments & Instrumentation
J. Renwick Beattie, Francis W. L. Esmonde-White
Summary: Spectroscopy rapidly captures a large amount of data, which is processed using principal component analysis to simplify complex spectral datasets into comprehensible information. Despite its wide use, the linear algebra behind principal component analysis is often not well understood by applied scientists and spectroscopists. The process traces the journey of spectra and relies solely on the information within the spectra to provide meaningful interpretation and analysis.
APPLIED SPECTROSCOPY
(2021)
Article
Automation & Control Systems
Yang Tao, Hongbo Shi, Bing Song, Shuai Tan
Summary: In this article, a distributed adaptive principal component regression algorithm is proposed for the online indicator monitoring of large-scale dynamic process. The algorithm constructs distributed data subblocks according to the process operation units and uses an adaptive resampling method to extract process local and global information simultaneously. The effectiveness of the proposed method is demonstrated through a numerical example and the Tennessee Eastman process.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Energy & Fuels
Jian Long, Siyi Jiang, Tianbo Liu, Kai Wang, Renchu He, Liang Zhao
Summary: In this study, a modified hybrid strategy combining adjustable characteristic wavelength selection and data-driven robust optimization (DDRO) was proposed for online NIR prediction modeling in the refining process. Industrial application results showed that the strategy improved the prediction accuracy of property models and achieved a high blending success rate under uncertainties.
Article
Computer Science, Artificial Intelligence
Jinping Liu, Jie Wang, Xianfeng Liu, Tianyu Ma, Zhaohui Tang
Summary: This study proposes an online fault monitoring scheme based on sparse principal component analysis, which utilizes sliding window and recursive calculation to reduce computational complexity and achieve real-time monitoring of time-varying industrial processes.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Engineering, Multidisciplinary
Tao Meng, Huichao Shi, Chi Wang, Bo Wu
Summary: A new method based on Principal Component Analysis (PCA) is proposed to accurately evaluate the flow stability of a flow standard facility by separating the flow fluctuation signal from the measuring instruments and the instrument noise, resulting in better magnitude of flow fluctuation compared to direct measurement by the flowmeter and calculation using measured data from the pressure sensor.
Article
Engineering, Multidisciplinary
Tao Meng, Huichao Shi, Chi Wang, Bo Wu
Summary: A new method based on PCA principle has been proposed to accurately evaluate the flow stability of a flow standard facility and obtain the magnitude of flow fluctuation. By combining the output results of the flowmeter and the pressure sensor into a two-dimensional data matrix and decomposing the principal component, the first principal component is used to calculate the flow fluctuation magnitude. The results demonstrate that the proposed method outperforms direct measurement by the flowmeter and calculation using the data from the pressure sensor.
Article
Computer Science, Information Systems
G. Alan Wang, Xiaomo Liu, Jianling Wang, Min Zhang, Weiguo Fan
INFORMATION SYSTEMS FRONTIERS
(2015)
Article
Computer Science, Information Systems
Mi (Jamie) Zhou, Baozhou Lu, Weiguo (Patrick) Fan, G. Alan Wang
INFORMATION SYSTEMS FRONTIERS
(2018)
Article
Business
Min Zhang, Chengshang Ren, G. Alan Wang, Zhen He
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2018)
Review
Computer Science, Information Systems
Shihao Zhou, Zhilei Qiao, Qianzhou Du, G. Alan Wang, Weiguo Fan, Xiangbin Yan
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Richard Gruss, Alan S. Abrahams, Weiguo Fan, G. Alan Wang
DECISION SUPPORT SYSTEMS
(2018)
Article
Computer Science, Information Systems
Hong Hong, Qiang Ye, Qianzhou Du, G. Alan Wang, Weiguo Fan
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
(2020)
Review
Environmental Studies
Min Zhang, Lu Wang, Yiwei Li, Alan Wang
Summary: This article examines the impact of other customers' responses on subsequent review volume and validates the hypothesis through empirical research. The results demonstrate that other customers' responses can significantly increase the number of reviews, but the effect weakens if subsequent customers do not observe the responses.
TOURISM MANAGEMENT
(2022)
Article
Business, Finance
Min Zhang, Lin Sun, Yuzhuo Li, G. Alan Wang, Zhen He
Summary: Understanding customer needs is crucial for the success of new product development and customer-centric product design. Online reviews, particularly initial reviews, are commonly used to mine customer requirements. However, supplementary reviews after product use are often overlooked. This study proposes a framework that combines initial and supplementary reviews for identifying customer requirements through text mining. Two case studies in the laptop and cell phone industries demonstrate the effectiveness of this method. Proper utilization of supplementary reviews can provide valuable guidance for product design and development strategies.
JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING
(2023)
Article
Business
Min Zhang, Yuzhuo Li, Lin Sun, G. Alan Wang, Jiangang Du
Summary: Comparative reviews, especially positive ones, have a positive impact on product sales. Negative comparative reviews have a lesser effect on sales. Positive comparative reviews have a greater impact than negative ones.
JOURNAL OF INTERACTIVE MARKETING
(2023)
Article
Information Science & Library Science
Xiaomo Liu, G. Alan Wang, Weiguo Fan, Zhongju Zhang
INFORMATION SYSTEMS RESEARCH
(2020)
Article
Management
Min Zhang, Fang Qin, G. Alan Wang, Cheng Luo
SERVICE INDUSTRIES JOURNAL
(2020)
Review
Computer Science, Artificial Intelligence
Hong Hong, Di Xu, G. Alan Wang, Weiguo Fan
DECISION SUPPORT SYSTEMS
(2017)
Article
Business
Hong Hong, Mukun Cao, G. Alan Wang
JOURNAL OF ELECTRONIC COMMERCE RESEARCH
(2017)
Article
Information Science & Library Science
Hong Hong, Di Xu, Dapeng Xu, G. Alan Wang, Weiguo Fan
INFORMATION DISCOVERY AND DELIVERY
(2017)
Article
Business
Min Zhang, Biying Jin, G. Alan Wang, Thong Ngee Goh, Zhen He
JOURNAL OF BUSINESS ETHICS
(2016)
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)