Article
Computer Science, Artificial Intelligence
Liang Zhang, Kefan Wang, Luyuan Xu, Wenjia Sheng, Qi Kang
Summary: This study proposes a new MGP-based algorithm specifically designed for addressing imbalanced classification problems. The algorithm optimizes false positive rate, false negative rate, and tree size through an efficient evolutionary strategy. It also considers the performance of each classifier in majority and minority classes to make a weighted ensemble decision. Experimental results demonstrate that the proposed method outperforms existing approaches in imbalanced classification metrics.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Chemistry, Analytical
Zhaolong Gao, Rongyu Tang, Qiang Huang, Jiping He
Summary: The study proposed a controller for finger joint angle estimation using sEMG, with training data gathered from a commercial EMG sensor, the Myo armband. Results showed that the proposed model had good performance across all test subjects, demonstrating accuracy and generalization abilities in daily life movements.
Article
Computer Science, Information Systems
Honggui Han, Cong Chen, Haoyuan Sun, Shengli Du, Junfei Qiao
Summary: Multi-objective model predictive control (MMPC) is an effective method for solving the problem of nonlinear systems with multiple conflicting control objectives. This paper proposes an MMPC method with the gradient eigenvector algorithm (MMPC-GEA) to comprehensively deal with multiple conflicting control objectives and reduce computational cost. The proposed method combines a fuzzy neural network identifier and a receding optimization algorithm, and employs the gradient eigenvector algorithm to obtain the optimal solution for control objectives in nonlinear systems.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Theory & Methods
Jingwei Hao, Senlin Luo, Limin Pan
Summary: Computer-aided product design utilizes artificial intelligent systems to automatically design various industrial products, incorporating deep neural networks to optimally fuse multiple decisions. The use of multi-objective decision-making and deep CNNs enables the evaluation and optimization of fused decisions. The proposed method involves a quality-guided deep neural network and weighting scheme to achieve multi-objective decision-making, potentially benefiting applications like 3D reconstruction and system optimization.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Thermodynamics
Tianyi Zhang, Lei Chen, Jin Wang
Summary: The application of machine learning, specifically neural networks (NNs) and genetic algorithm (GA), is studied for multi-objective optimization of heat exchangers. Taking the tube fin heat exchanger (TFHE) as the research object, the study optimizes the inlet air velocity and tube ellipticity. Computational Fluid Dynamics (CFD) simulation is used to obtain optimal heat transfer performance and pressure drop performance for different Reynolds and tube ellipticity values. The simulation data is then used to train Back-Propagation neural networks and establish prediction models for heat transfer coefficient and pressure drop. The non-dominated multi-objective genetic algorithm (NSGA-II) is employed to optimize the NNs' prediction results. The optimization results show significant improvements in pressure drop and heat transfer coefficient for specific Reynolds and ellipticity values.
Article
Engineering, Chemical
Sam Durairaj, Joanofarc Xavier, Sanjib Kumar Patnaik, Rames C. Panda
Summary: An important step in nonlinear system identification in industrial units is the use of recurrent and convolution-type deep learning methods.Methods such as pH neutralization schemes used in chemical/pharmaceutical/wastewater process units face challenges due to the inherent nonlinear dynamics during the neutralization process. This research proposes a deep Temporal Convolution Network (TCN) with a larger receptive field to learn the dynamics of the pH neutralization process.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Engineering, Manufacturing
Tomer Geva, Maytal Saar-Tsechansky
Summary: The study presents a data-driven approach to effectively rank expert workers based on decision quality without the need for peer-expert evaluation. By addressing the challenge of unknown correct decisions and incomplete information, the research introduces a new business data science problem and develops a machine-learning-based method to tackle it. Empirical results demonstrate robust performance, positioning the method as a benchmark for future research on the Ranking Expert decision makers' unobserved decision Quality (REQ) problem.
PRODUCTION AND OPERATIONS MANAGEMENT
(2021)
Article
Engineering, Multidisciplinary
Fuad Noman, Gamal Alkawsi, Ammar Ahmed Alkahtani, Ali Q. Al-Shetwi, Sieh Kiong Tiong, Nasser Alalwan, Janaka Ekanayake, Ahmed Ibrahim Alzahrani
Summary: This paper presents a multistep short-term wind speed prediction method using multivariate exogenous input variables, with the Nonlinear Auto-Regressive Exogenous (NARX) model outperforming all other methods in achieving accurate predictions. The study also evaluates different transfer learning methods and neural networks for wind speed prediction, providing insights for further improvement in feature selection and model parameters.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Optics
Luis C. B. Silva, Marcelo E. V. Segatto
Summary: This paper presents a nonlinear autoregressive with external input neural network (NARXNET) capable of predicting the nonlinear dynamics of supercontinuum generation in optical fibers. The NARXNET structure allows low prediction error, fast training, satisfactory generalization ability, and low computational resources for the training and testing stages.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA B-OPTICAL PHYSICS
(2023)
Article
Computer Science, Theory & Methods
Rui Hou, Guowen Ren, Wei Gao, Lijun Liu
Summary: The existing energy network security defense system uses various technologies to build a fortress-like static defense system, but is prone to weaknesses when facing unknown attacks. To address multi-objective decision-making problems, multiple criteria can be utilized to evaluate and optimize alternative solutions.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Environmental Sciences
Fabio Di Nunno, Giovanni de Marinis, Rudy Gargano, Francesco Granata
Summary: This study developed tide prediction models for the Venice Lagoon based on NARX neural networks. The models were trained, tested, and validated, showing good predictive capability in the entire lagoon.
Article
Computer Science, Artificial Intelligence
Ke Xu, Dezheng Zhang, Jianjing An, Li Liu, Lingzhi Liu, Dong Wang
Summary: This study proposes an improved genetic algorithm to map the network pruning flow as a multi-objective optimization problem, finding a suitable solution that balances the DNN's model size and workload efficiently. Experiments show up to 34% further reduction in computational workload on the ResNet50 model compared to previous schemes.
Article
Engineering, Aerospace
Taha Yasini, Jafar Roshanian, Amir Taghavipour
Summary: This study proposes a novel control system for satellite motion on the reference orbit (RO) using a comprehensive model of its dynamics and a Non-linear Model Predictive Controller (NMPC). The NMPC calculates sub-optimal control inputs by minimizing a convex cost function at each stage. The weighting parameters of the cost function are optimized using a Genetic Algorithm (GA) to improve the NMPC performance. The results show that the implemented NMPC can resist larger errors and perturbations, and compensate for those errors by returning the satellite to its main orbit.
ADVANCES IN SPACE RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Hitoshi Iima, Yohei Hazama
Summary: This paper addresses an optimization problem with two decision variable vectors. It proposes a less time-consuming and general-purpose genetic algorithm (GA) with a neural network model, which estimates the optimal objective function values of the subproblems. Experimental results show that the proposed method is more effective than other GAs and an exact method.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Chemical
Yuqi Li, Dayong Yang, Chuanmei Wen
Summary: This paper introduces an improved algorithm called WEFOR to address the uncertain relationship between design parameters and coefficients in the NARX-M-for-D model, enabling accurate prediction of system output for nonlinear systems.
Article
Mathematics, Applied
Unai Zalabarria, Eloy Irigoyen, Raquel Martinez, Andrew Lowe
APPLIED MATHEMATICS AND COMPUTATION
(2020)
Article
Computer Science, Interdisciplinary Applications
Unai Zalabarria, Eloy Irigoyen, Andrew Lowe
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2020)
Review
Computer Science, Artificial Intelligence
Mauricio Marcano, Sergio Diaz, Joshue Perez, Eloy Irigoyen
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
(2020)
Article
Engineering, Electrical & Electronic
Hector Fernandez-Rebolleda, Alain Sanchez-Ruiz, Salvador Ceballos, Angel Perez-Basante, Juan Jose Valera-Garcia, Georgios Konstantinou, Josep Pou
Summary: This article introduces a method to minimize or eliminate phase overcurrent during transitions in frequency converters, by analyzing and modeling modulation transitions and identifying the optimal fundamental period angle interval. The proposed method is applicable to any converter topology and modulation technique.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2021)
Article
Computer Science, Artificial Intelligence
Mikel Larrea, Alain Porto, Eloy Irigoyen, Antonio Javier Barragan, Jose Manuel Andujar
Summary: Ensemble Model is a tool that can improve the performance of emerging techniques in solving modeling and classification problems. In this study, an Extreme Learning Machine ensemble is applied to Time Series modeling, achieving satisfactory results through the weighted averaging method and Particle Swarm Optimization algorithm.
Article
Chemistry, Multidisciplinary
Mauricio Marcano, Fabio Tango, Joseba Sarabia, Andrea Castellano, Joshue Perez, Eloy Irigoyen, Sergio Diaz
Summary: This paper presents the design and implementation of an intelligent and adaptive co-pilot system for driver-automation cooperation in automated vehicles. By utilizing a lateral shared controller with adaptive authority levels, the system effectively supports distracted drivers. The results of comparative experiments demonstrate that shared control offers the best balance between performance, safety, and comfort during the driving task.
APPLIED SCIENCES-BASEL
(2021)
Article
Mathematics, Applied
Ignacio Trojaola, Iker Elorza, Eloy Irigoyen, Aron Pujana-Arrese, Carlos Calleja
Summary: This paper presents an iterative learning control (ILC) algorithm for the force control circuit of a hydraulic cushion. By adding an extra ILC feed-forward (FF) signal to counteract valve model uncertainties and using low-pass filters to attenuate unknown valve dynamics, significant improvements are achieved in terms of settling time and overshoot of the pressure signal in the cylinder.
LOGIC JOURNAL OF THE IGPL
(2022)
Article
Computer Science, Information Systems
Ignacio Trojaola, Iker Elorza, Eloy Irigoyen, Aron Pujana-Arrese, Gorka Sorrosal
Summary: This paper proposes a hydraulic press position control method based on the MIMO ILC algorithm, which achieves automated position control and high precision position tracking by inverting known low frequency dynamics. It shows good performance in terms of stability and convergence rate when compared with other existing algorithms.
Article
Energy & Fuels
Gonzalo Abad, Alain Sanchez-Ruiz, Juan Jose Valera-Garcia, Aritz Milikua
Proceedings Paper
Computer Science, Artificial Intelligence
Ignacio Trojaola, Iker Elorza, Eloy Irigoyen, Aron Pujana, Carlos Calleja
14TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2019)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Alain Porto, Eloy Irigoyen, Mikel Larrea
INTERNATIONAL JOINT CONFERENCE SOCO'18-CISIS'18- ICEUTE'18
(2019)
Article
Computer Science, Information Systems
Raquel Martinez, Asier Salazar-Ramirez, Andoni Arruti, Eloy Irigoyen, Jose Ignacio Martin, Javier Muguerza
Article
Computer Science, Information Systems
Unai Zalabarria, Eloy Irigoyen, Raquel Martinez, Mikel Larrea, Asier Salazar-Ramirez
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)
