Article
Chemistry, Multidisciplinary
Arne Van den Kerchove, Arno Libert, Benjamin Wittevrongel, Marc M. Van Hulle
Summary: This study introduces two regularized estimators to improve the performance of brain-computer interfaces (BCIs) and enhance the classification accuracy of EEG signals. Through validation and comparison with limited training data, the results show that these estimators perform well and structured regularization has advantages in terms of training time and memory usage.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Biomedical
Mengfan Li, Haoxin Zuo, Huihui Zhou, Guizhi Xu, Enming Qi
Summary: This study investigates the effect of different actions on motor imagery (MI). The results show that different actions cause variations in MI features and accuracy. Additionally, the amplitude of ERP differs among the actions, suggesting that ERP can serve as an index for MI performance.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Automation & Control Systems
Bernd Kolar, Markus Schoeberl, Johannes Diwold
Summary: We prove that every flat nonlinear discrete-time system can be decomposed by coordinate transformations into a smaller-dimensional subsystem and an endogenous dynamic feedback. No comparable result is available for flat continuous-time systems. The advantage of such a decomposition is that the complete system is flat if and only if the subsystem is flat. The algorithm for decomposition requires constructing state-and input transformations, obtained by straightening out certain vector fields or distributions, making the computation mainly involve the calculation of flows and the solution of algebraic equations.
Article
Mathematics
Tuan Anh Bui, Jun-Sik Kim, Junyoung Park
Summary: Structural design involves geometrically nonlinear analysis to optimize weight and energy efficiency. Nonlinear reduced-order models (NLROMs) have been developed to reduce computational costs, and their accuracy relies on the derivative of the tangential stiffness. This study evaluates different numerical methods for calculating the derivative and proposes the use of the central difference method due to its advantages.
Article
Mathematics, Applied
Feng Guo, Weizhong Dai
Summary: This paper proposes a new absorbing layer approach for simulating soliton propagation on unbounded domain. A two-level finite difference scheme is used to solve the cubic nonlinear Schrodinger equation, and the stability and convergence of the scheme are analyzed. Numerical examples demonstrate the effectiveness of the method.
APPLIED NUMERICAL MATHEMATICS
(2023)
Article
Neurosciences
Yun-Joo Choi, Oh-Sang Kwon, Sung-Phil Kim
Summary: This study aimed to investigate the differences between auditory and visual stimuli in non-invasive brain-computer interfaces (BCIs) and explore the optimal auditory stimulus type. The results showed that natural sounds resulted in higher performance and larger differences between target and non-target stimuli, with each subject having different preferences for auditory stimuli. Consistent with previous studies, visual stimuli had higher performance and more dynamic differences in stimuli amplitudes compared to auditory stimuli.
COGNITIVE NEURODYNAMICS
(2023)
Article
Computer Science, Artificial Intelligence
Zhaodong Liu, Ancai Zhang, Jianlong Qiu, Zhenxing Li
Summary: This paper investigates the distributed event-triggered control problems of second-order nonlinear multi-agent systems with directed graph. It develops an event-triggered consensus algorithm and a tracking algorithm for leader-follower MASs, and demonstrates the effectiveness of the proposed consensus algorithm through numerical simulation.
Article
Automation & Control Systems
Fatemeh Shahlaei, Niraj Bagh, M. S. Zambare, M. Ramasubba Reddy
Summary: This study proposes a technique for localizing ERD/ERS patterns in motor imagery-based BCI and accurately classifying left and right hand movements. The effectiveness of the technique is validated using a competition dataset, demonstrating its superiority over state-of-the-art approaches.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Neurosciences
Kosei Nakayashiki, Hajime Tojiki, Yoshikatsu Hayashi, Shiro Yano, Toshiyuki Kondo
Summary: This study aimed to identify the dominant factor for inducing event-related desynchronization (ERD). The results showed that ERD was significantly attenuated in the absence of force feedback, while it was maintained in the presence of force feedback. Additionally, the extent of ERD was found to reflect neural activity involved in the motor planning process for changing virtual equilibrium point.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Chemistry, Analytical
Manorot Borirakarawin, Yunyong Punsawad
Summary: In this study, an auditory stimulus pattern was developed for improving control and communication in quadriplegia with visual impairment, utilizing EEG channels to observe ERP responses and classification efficiency. The proposed auditory stimulus pattern showed higher accuracy, and multi-loudspeaker patterns provided increased accuracy.
Article
Mathematics
Jiaojiao Zhuang, Zhenxing Li, Zongxiang Hou, Chengdong Yang
Summary: This paper investigates the event-triggered consensus problems of nonlinear strict feedback multi-agent systems under directed graph. A state-based event-triggered consensus algorithm is proposed, and a high-gain observer is used when the full state information is unavailable. The effectiveness of both state-based and observer-based event-triggered consensus algorithms is verified through an example.
Article
Engineering, Electrical & Electronic
Mehmet Yagan, Serkan Musellim, Suayb S. Arslan, Tuna Cakar, Nihan Alp, Huseyin Ozkan
Summary: This article presents a new publicly-accessible P300 dataset, which is important for participants with severe motor disabilities due to the ease of stimulation and measurement of the P300 response. The dataset provides challenging settings for training and testing more realistic high-speed P300-based BCI speller systems.
DIGITAL SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Johannes Diwold, Bernd Kolar, Markus Schoeberl
Summary: The study demonstrates that every forward-flat nonlinear discrete-time system with two inputs can be transformed into a structurally flat normal form, allowing for a systematic construction of parameterisation of system variables by the flat output. However, for flat continuous-time systems, no comparable normal form exists.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Metallurgy & Metallurgical Engineering
Zong Meng-fan, Tian Yi, Liang Rong-zhu, Wu Wen-bing, Xu Mei-juan, Mei Guo-xiong
Summary: This study investigates the one-dimensional nonlinear consolidation problem of soil under constant loading by introducing continuous drainage boundary. The numerical solution is derived using the finite difference method and validated against existing analytical and numerical solutions. The research findings show that the consolidation behavior of soil is influenced by interface parameters, stress ratios, and the compression index to permeability index ratio, as well as boundary conditions.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2022)
Article
Mathematics, Applied
Margarita Zvereva, Mikhail Kamenskii, Paul raynaud de Fitte, Ching-Feng Wen
Summary: In this paper, we investigate the deformations process for the Stieltjes strings system located along a geometric star-shaped graph under the influence of an external force. We consider the case where the force can be concentrated at separate points, including a node of the graph. Using variational methods, we establish the necessary and sufficient conditions for the extremum of an energy functional, prove existence and uniqueness theorems for the solution, obtain an explicit formula for the solution, and study the dependence of the solution on the length of the limiter.
JOURNAL OF NONLINEAR AND VARIATIONAL ANALYSIS
(2023)
Article
Engineering, Industrial
Wang Qiaoxiu, Wang Hong, Qi Zuoqiu
Article
Computer Science, Artificial Intelligence
Tianwei Shi, Wenhua Cui, Ling Ren, Chi Zhang
Article
Computer Science, Artificial Intelligence
Tianwei Shi, Hong Wang, Wenhua Cui, Ling Ren
Article
Engineering, Biomedical
L. Ji, H. Wang, T. Q. Zheng, C. C. Hua, N. N. Zhang
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2019)
Article
Engineering, Biomedical
Hong Wang, Chengcheng Hua, Qiaoxiu Wang, Qiang Fu, Tenssay Fetlework
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2019)
Article
Engineering, Biomedical
Hong Wang, Qiaoxiu Wang, Fo Hu
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2019)
Article
Computer Science, Artificial Intelligence
Chengcheng Hua, Hong Wang, Jichi Chen, Tao Zhang, Qiaoxiu Wang, Wenwen Chang
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)