Article
Nanoscience & Nanotechnology
Mengdie Hou, Mengjun Xu, Jiangtao Xu, Jiafeng Lu, Yi An, Liangjin Huang, Xianglong Zeng, Fufei Pang, Jun Li, Lilin Yi
Summary: We demonstrate the first simulation and experimental realization of decomposing cylindrical vector (CV) and orbital angular momentum (OAM) modes by reconstructing multi-view images of the projected intensity distribution. The use of deep learning-based stochastic parallel gradient descent (SPGD) algorithm allows for the retrieval of modal coefficients and optical field distributions with high efficiency and accuracy. The generated donut modes are experimentally decomposed into CV and OAM modes, with a purity of 99.5% for CV modes. Rapid switching of vortex modes is achieved by electrically driving the polarization controller. Our findings provide a convenient way to characterize and deepen the understanding of CV or OAM modes, with potential applications in information coding and quantum computation.
Article
Engineering, Electrical & Electronic
Zhanshe Yang, Yunhao Wang, Chenzai Kong
Summary: This article introduces a novel model incorporating EEMD, gray wolf optimization, and SVR to predict the RUL of LIBs, enhancing prediction accuracy by decoupling global and local degradation phenomena in battery capacity time series.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Mathematics, Applied
Felix O. Mettle, Lydia Pomaa Boateng, Enoch N. B. Quaye, Emmanuel Kojo Aidoo, Issah Seidu
Summary: Most researchers assume time-homogeneity in analyzing Markov chains due to limited literature on time-inhomogeneous Markov chains. This paper proposes a methodology to analyze the long-run behaviors of time-inhomogeneous Markov chains and applies it to historical data on exchange rates. The results show that under certain conditions, the behaviors of time-inhomogeneous chains are similar to those of time-homogeneous chains, and analyzing Markov chains under the assumption of time-homogeneity is recommended.
JOURNAL OF APPLIED MATHEMATICS
(2022)
Article
Economics
Mukhriz Izraf Azman Aziz, Zaghum Umar, Mariya Gubareva, Tatiana Sokolova, Xuan Vinh Vo
Summary: Demand shocks appreciate forex rates for both net oil-producing and net oil-consuming economies, while supply-driven moves in oil prices have a marginal influence on forex rates for most countries. Risk shocks have depreciating effects on the ASEAN-5 exchange rates, indicating that the open-oriented nature of these economies makes them susceptible to constant fluctuations in the global oil market.
Article
Chemistry, Analytical
Xihui Bian, Deyun Wu, Kui Zhang, Peng Liu, Huibing Shi, Xiaoyao Tan, Zhigang Wang
Summary: This study proposes a weighted multiscale support vector regression method based on variational mode decomposition for food and herb analysis. The method decomposes the spectra into discrete mode components, builds sub-models using support vector regression, and obtains the final prediction by averaging the predictions of the sub-models. Experimental results show that the method has potential in model accuracy.
Article
Computer Science, Information Systems
Shifei Ding, Zichen Zhang, Lili Guo, Yuting Sun
Summary: This study proposes a hybrid model called EEMD-GRU-TWSVRCSSA to address the issues in support vector regression (SVR), such as long training time and difficulty in fitting complex data. The model utilizes twin support vector regression (TWSVR) and cloud salp swarm algorithm (CSSA) to achieve better prediction accuracy and competitiveness.
INFORMATION SCIENCES
(2022)
Article
Business
Michaela Chocholata
Summary: This paper examines the weekly stock market data of the Hungarian, Czech, and Polish stock indices from January 7, 2001 to April 18, 2021. The results show high volatility persistence in individual markets, with significant differences between regimes. The presence of the leverage effect is confirmed by the GJR-GARCH and MS-GJR-GARCH models. The MS-GARCH-type models capture various volatility switches during the analyzed period, mainly attributed to the global financial crisis, European debt crisis, and Covid-19 pandemic.
JOURNAL OF BUSINESS ECONOMICS AND MANAGEMENT
(2022)
Article
Mathematics
Nagaraj Naik, Biju R. Mohan
Summary: Volatility in stock prices is influenced by various factors such as demand, supply, economic policy, and company earnings. This study utilized MSGARCH and SETAR models to estimate stock price volatility, with the MSGARCH model performing better.
Article
Engineering, Multidisciplinary
Jinde Zheng, Miaoxian Su, Wanming Ying, Jinyu Tong, Ziwei Pan
Summary: The study introduces the improved Uniform Phase Empirical Mode Decomposition (IUPEMD) method, which enhances the accuracy and performance of signal decomposition by adaptively selecting the amplitude of the sinusoidal wave and choosing the optimal result based on orthogonality index.
Article
Environmental Sciences
Hanyu Zhang, Lin Liu, Wei Jiao, Kai Li, Lizhi Wang, Qianjin Liu
Summary: Accurate runoff modeling plays a crucial role in water resource management, but the nonstationarity of runoff time series due to climate variability and vegetation dynamics poses challenges. Analyzing the temporal features of runoff and its influencing factors can enhance modeling accuracy. By using multivariate empirical mode decomposition (MEMD) technique, the study in Yihe watershed of northern China revealed that decomposing the original monthly runoff and its influencing factors into intrinsic mode functions and residue improved the modeling accuracy significantly.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Economics
Roberto Casarin, Mauro Costantini, Anthony Osuntuyi
Summary: This article proposes Bayesian nonparametric inference for panel Markov-switching GARCH models, incorporating series-specific hidden Markov chain processes to drive the GARCH parameters. The article introduces soft parameter pooling through a hierarchical prior distribution and cross-sectional clustering through a Bayesian nonparametric prior distribution to deal with the high-dimensionality of the parameter space. An MCMC posterior approximation algorithm is developed, and its efficiency is studied through simulations. An empirical application to US financial returns data shows that the Bayesian nonparametric panel Markov-switching GARCH model provides good forecasting performances and economic gains in optimal asset allocation.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2023)
Article
Engineering, Electrical & Electronic
Wenchao Miao, Qi Xu, K. H. Lam, Philip W. T. Pong, H. Vincent Poor
Summary: Protection devices are crucial in DC systems, but series arc faults may not be detected by conventional devices, leading to malfunctions and fire hazards. This paper proposes a series arc-fault detection system based on modified EMD technique and SVM algorithm for reliable and efficient operation of DC systems. The effectiveness of arc-fault detection is significantly improved by acquiring accurate arc signatures without predefining various thresholds.
IEEE SENSORS JOURNAL
(2021)
Article
Thermodynamics
Lean Yu, Yueming Ma, Mengyao Ma
Summary: This paper proposes an effective rolling decomposition-ensemble model for quarterly gasoline consumption forecasting in China, involving data decomposition, component prediction, and ensemble output. By utilizing wavelet decomposition and support vector regression, the model addresses data scarcity issue and improves prediction accuracy.
Article
Physics, Multidisciplinary
Ryan Mohr, Maria Fonoberova, Zlatko Drmac, Iva Manojlovic, Igor Mezic
Summary: Hierarchical support vector regression models linearly combine SVR models at different scales, with a phase transition observed in many models where the training error remains relatively constant until a critical scale is reached. A method to predict this critical scale based on data's Fourier transform or Dynamic Mode Decomposition spectrum is introduced, allowing for the determination of the required number of layers prior to training any models.
Article
Engineering, Chemical
Seunghwan Jung, Minseok Kim, Baekcheon Kim, Jinyong Kim, Eunkyeong Kim, Jonggeun Kim, Hyeonuk Lee, Sungshin Kim
Summary: In manufacturing processes using CNC machines, machine tool failures can significantly degrade product quality and process efficiency. Existing fault detection methods using univariate signals have limitations in applying multivariate models. This study proposes a method combining empirical mode decomposition and auto-associative kernel regression to detect faults in machine tools. Experimental results demonstrate the successful detection of actual machine tool faults using this method.
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)