Article
Engineering, Electrical & Electronic
Srimath Tirumala Pallerlamudi Srinivas, Kesanakurthy Shanti Swarup
Summary: The paper introduces a new linearization approach for finding the optimal protective relay settings in power systems. By using bilinear relaxations to convexify the CP formulation, the global optimum is achieved iteratively by updating variable bounds to reduce errors from linear approximations.
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS
(2021)
Article
Acoustics
Xindong Si, Hongli Yang
Summary: This article investigates the Constrained Regulation Problem (CRP) for continuous-time stochastic systems, proposing new existence conditions for linear feedback control laws and introducing a computation method for solving such problems. The study establishes conditions for polyhedral invariance and asymptotic stability, presenting linear programming models and algorithms for constrained regulation problems in stochastic systems. The approach connects the stochastic constrained regulation problem with positively invariant set theory and suggests the use of optimization methodology, demonstrating the efficacy through numerical examples.
JOURNAL OF VIBRATION AND CONTROL
(2022)
Article
Engineering, Multidisciplinary
Suzana Pil Ramli, Hazlie Mokhlis, Wei Ru Wong, Munir Azam Muhammad, Nurulafiqah Nadzirah Mansor
Summary: This paper proposes a hybrid optimization algorithm based on Firefly Algorithm and Linear Programming (FA-LP) to achieve optimal coordination and settings of directional overcurrent relays (DOCRs) by linearizing the equation and relaxing the search space. It also considers a mixed type of IEC relay characteristics. The results show a significant reduction in relay operating time compared to other techniques in different test systems.
AIN SHAMS ENGINEERING JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Amir Mohammad Entekhabi-Nooshabadi, Hamed Hashemi-Dezaki, Seyed Abbas Taher
Summary: This paper introduces a novel method to optimize the coordination of microgrids' directional overcurrent relays (DOCRs) under various system topologies, achieving an optimal protection scheme without selectivity constraint. The proposed approach decreases DOCRs' operating time by about 80% compared to existing methods, and it is validated based on protection simulations.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Y. Gonzalez, A. Conde, J. Trevino
Summary: In this paper, a new coordination method for directional overcurrent relays (DOCRs) is proposed to achieve desired relay times. The traditional objective of DOCR coordination, which aims to minimize relay time without limitations, leads to uncertainty in relay times due to the functional dependence between relays caused by the sequence of operation between primary and backup functions. The proposed method resolves this uncertainty by delimiting DOCRs in time intervals, resulting in bounded coordination times. Each relay is committed to ensure that its time curve complies with the time intervals, thus avoiding very long or very short times that may impact the operation or safety of the electrical network. The results in test systems demonstrate the feasibility and effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Sethembiso Nonjabulo Langazane, Akshay Kumar Saha
Summary: This paper presents a comprehensive sensitivity analysis to evaluate the effect of control parameters on the performance of particle swarm optimization and genetic algorithms in solving overcurrent relay coordination problems. The findings show that particle swarm optimization is more sensitive to inertia weight and swarm size, and appropriate parameter settings can improve its performance.
Article
Engineering, Multidisciplinary
Xindong Si, Hongli Yang
Summary: This paper addresses the Constrained Regulation Problem for linear continuous-time fractional-order systems, aiming to find the existence conditions of linear feedback control law and provide a numerical solving method using positively invariant sets. Necessary and sufficient conditions for the polyhedral set to be a positive invariant set are presented, along with an optimization model and algorithm for solving linear state feedback control law based on the positive invariance of polyhedral sets. The proposed approach transforms the fractional-order CRP problem into a linear programming problem that can be easily solved computationally, as illustrated by numerical examples.
INTERNATIONAL JOURNAL OF NONLINEAR SCIENCES AND NUMERICAL SIMULATION
(2021)
Article
Energy & Fuels
Faraj Al-Bhadely, Aslan Inan
Summary: In recent years, the effective coordination of directional overcurrent relays (DOCRs) in smart microgrids has become a significant challenge for power system operators. This study proposes a hybrid GA-SQP algorithm to enhance this coordination in distributed power networks. Through testing on three case studies and comparing with previous methods, the proposed optimization approach demonstrates significant advantages in accuracy, operating time, and continuity of relay pair margin time requirements.
Article
Computer Science, Information Systems
Fulin Wang, Gang Xu, Mo Wang
Summary: In this paper, an improved genetic algorithm based on two-direction crossover and grouped mutation is proposed for solving constrained optimization problems. The algorithm improves search efficiency by utilizing the direction information of parent individual and searching in the better direction of the two directions. It also enhances search efficiency by utilizing mutation operators with different properties for each group in the population. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms on both constrained real-parameter optimization and real-world constrained optimization problems. The algorithm is further applied to optimize a single-stage cylindrical gear reducer.
Article
Automation & Control Systems
Hongzhe Liu, Wei Xing Zheng, Wenwu Yu
Summary: This article studies convex optimization problems with general constraints and proposes a distributed algorithm to solve the problem. The optimality condition of the optimization problem is developed using saddle point theory, and a continuous-time primal-dual algorithm is constructed accordingly.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Computer Science, Information Systems
Md. Asadujjaman, Humyun Fuad Rahman, Ripon K. Chakrabortty, Michael J. Ryan
Summary: The study introduces a hybrid immune genetic algorithm (IGA) to solve NPV-based resource constrained project scheduling problems and improves algorithm efficiency and accuracy through enhanced performance and application of local search methods.
Article
Computer Science, Artificial Intelligence
Jingyu Luo, Mario Vanhoucke, Jose Coelho, Weikang Guo
Summary: Machine learning techniques, especially genetic programming, have been successful in designing priority rules for resource-constrained project scheduling problems. This research proposes a new genetic programming hyper-heuristic method and investigates the impact of training data selection and fitness evaluation. Experimental results show that the proposed algorithm outperforms traditional genetic programming methods and is capable of generating efficient priority rules.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Multidisciplinary Sciences
Mehdi Azari, Kazem Mazlumi, Mansour Ojaghi
Summary: This paper presents a novel methodology to apply PWLC for coordinating DOCRs in meshed power systems of any scale. By utilizing DIgSILENT software to obtain maximum short-circuit fault currents, each relay's PWLC is determined based on fault currents achieved, resulting in a more flexible and desirable approach for overcurrent protection coordination compared to NSCs. The proposed approach shows notable reduction in relay operating time and improved coordination in various power network scenarios.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Sapan Kumar Das
Summary: This article addresses a fully fuzzy triangular linear fractional programming problem with parameters and decision variables characterized by triangular fuzzy numbers. A new concept is proposed to reduce computational complexity without sacrificing effectiveness. Mathematical models are used to evaluate the legitimacy, usefulness, and applicability of the method, showing that the novel strategies are superior to current techniques.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Xindong Si, Hongli Yang, IvAN G. Ivanov
Summary: The constrained regulation problem for fractional-order nonlinear continuous-time systems is investigated, with new existence conditions and a computation method proposed. Conditions for positive invariance of a polyhedron are established, with a linear feedback controller model formulated as a linear programming problem for easy implementation. The proposed method is illustrated with numerical examples.
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE
(2021)
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)