Article
Electrochemistry
M. Kunaver, Z. Rojec, V. Subotic, S. Pereverzyev, M. Zic
Summary: This paper proposes a new strategy for extracting the distribution function of relaxation times (DRT) using the Levenberg-Marquardt algorithm (LMA). By numerically approximating the Jacobian matrix and smoothing the DRT data using a finite difference matrix, the extracted DRT profiles correspond well to their analytical counterparts. The findings demonstrate that modifying LMA allows for the data-driven solution of the Fredholm integral equation and obtaining applicable DRT data for general EIS study.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2022)
Article
Robotics
Yuting Qiao, Junyi Cao, Guohui Huang, Huan Liu, Yaguo Lei, Qinghua Liu
Summary: Industrial robots have become critical for manufacturing automation due to their large workspaces and flexibility. However, their low stiffness and high compliance can cause vibration, making it important to accurately determine the robot's modal properties to improve operation accuracy. A new improved subspace identification method is proposed to update the state parameters of industrial robots and enhance the identification accuracy of modal parameters. Experimental measurements of a six-degrees-of-freedom industrial robot were conducted, and the results showed that the improved subspace modal method outperforms traditional methods, providing an exact characterization of modal frequencies throughout the robot's workspace. The proposed method effectively improves the identification accuracy of modal parameters and investigates the influence of robot pose changes on modal parameters through experimental measurements.
JOURNAL OF FIELD ROBOTICS
(2023)
Article
Mathematics, Applied
Meilan Zeng, Guanghui Zhou
Summary: This paper improves the convergence results of an efficient Levenberg-Marquardt method by proving global and superlinear convergence under Holderian continuity and local error bound conditions. Numerical experiments confirm the convergence of the algorithm for singular problems satisfying these conditions.
JOURNAL OF APPLIED MATHEMATICS AND COMPUTING
(2022)
Article
Automation & Control Systems
Yan Ji, Zhen Kang, Ximei Liu
Summary: This article discusses the parameter identification problem of multiple-input single-output Hammerstein nonlinear systems. By applying data filtering technique and hierarchical identification principle, a multi-stage Levenberg-Marquardt algorithm is proposed to identify each subsystem interactively. A numerical simulation example is provided to demonstrate the effectiveness of the proposed algorithms.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Engineering, Electrical & Electronic
Jiawei Qi, Dianjun Zhang, Zhaoyu Ren, Aijun Cui, Fei Yin, Jian Qin, Jie Zhan, Jinshan Zhu
Summary: In this study, the log-ratio model (LRM) was applied to shallow water bathymetry of multispectral images. By determining initial value ranges and analyzing the sensitivity of model parameters from partial differential perspective, the credibility and efficiency of bathymetric retrieval were improved.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Materials Science, Multidisciplinary
Zhenglei Yu, Zezhou Xu, Ruiyao Liu, Renlong Xin, Lunxiang Li, Lixin Chen, Pengwei Sha, Wanqing Li, Yining Zhu, Yunting Guo, Jiale Zhao, Zhihui Zhang, Luquan Ren
Summary: This study established the corresponding relationship between SLM-NiTi alloy transition temperatures and energy density, accurately predicting process parameters and alloy transition temperatures. The prediction model was validated using DSC, XRD, and SEM, resulting in successful production of B190NiTi and B2NiTi. Additionally, a composite gradient NiTi was created, effectively improving the strength and toughness of the alloy.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
(2021)
Article
Electrochemistry
M. Zic, L. Vlasic, V Subotic, S. Pereverzyev, I Fajfar, M. Kunaver
Summary: This study presents the application of an analytical approximation method and Levenberg-Marquardt algorithm (LMA) to extract applicable DRT, and the tests conducted show that LMA is capable of successfully extracting DRT from impedance data.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2022)
Article
Polymer Science
Mingzhu Qiu, Peng Cao, Liang Cao, Zhifei Tan, Chuantao Hou, Long Wang, Jianru Wang
Summary: This study uses genetic algorithm (GA) and Levenberg-Marquardt (L-M) algorithm to optimize parameter acquisition for 2S2P1D and Havriliak-Negami (H-N) viscoelastic models. It investigates the effects of different combinations of optimization algorithms on parameter acquisition accuracy in these two constitutive equations. The study also proposes an improved semi-analytical method for fitting the H-N model.
Article
Computer Science, Artificial Intelligence
Suyel Namasudra, S. Dhamodharavadhani, R. Rathipriya
Summary: This paper proposes a novel neural network model for predicting COVID-19 cases, which can accurately forecast the confirmed, recovered, and death cases. Experimental results show that the model trained with the Levenberg Marquardt algorithm performs the best in predicting COVID-19 data.
NEURAL PROCESSING LETTERS
(2023)
Article
Automation & Control Systems
Ali Dokht Shakibjoo, Mohammad Moradzadeh, Seyed Zeinolabedin Moussavi, Ardashir Mohammadzadeh, Lieven Vandevelde
Summary: This study proposes a new fuzzy approach for load frequency control (LFC) of a multi-area power system. The approach utilizes interval type-2 fuzzy inference systems (IT2FIS) and fractional-order calculus to construct the main control system. The system Jacobian is obtained using a multilayer perceptron neural network (MLP-NN), and uncertainties are modeled using IT2FIS. The Levenberg-Marquardt algorithm (LMA) is used to train fuzzy parameters, and the system stability is analyzed using Matignon's stability method. Comparative simulations with a type-1 fuzzy controller demonstrate the superiority of the proposed controller.
Article
Thermodynamics
Sultan Alpar, Julien Berger, Bolatbek Rysbaiuly, Rafik Belarbi
Summary: This article proposes an inverse analysis method to determine the thermophysical properties of two different soil types using experimental data and numerical algorithm, and carries out efficient simulations.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Engineering, Mechanical
Partha Sengupta, Subrata Chakraborty
Summary: A major challenge in structural health monitoring (SHM) is the limited availability of responses at certain degrees of freedom (DOFs). This study proposes an improved iterative model reduction algorithm that eliminates stiffness terms from the transformation equation, allowing the accurate determination of unknown responses. The proposed algorithm uses an enhanced Levenberg-Marquardt technique and introduces an adaptive damping term to improve the results. It can be easily applied to large finite element models and is demonstrated numerically using beam and multi-storey building models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Interdisciplinary Applications
Sidra Naz, Muhammad Asif Zahoor Raja, Aneela Kausar, Aneela Zameer, Ammara Mehmood, Muhammad Shoaib
Summary: In this study, a novel application of intelligent computing is presented, where a supervised neural network optimized with the Levenberg-Marquardt method is used to study the dynamics of a nonlinear piezoelectric-mechanical system. The dataset for the system is created using a numerical solver and the SNN model is trained, tested, and validated to determine the system's solution for different scenarios. The performance of the SNN is evaluated using mean squared error, error histogram illustrations, and regression analysis.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2022)
Article
Mathematics, Applied
Iftikhar Ahmad, Hira Ilyas, Muhammad Asif Zahoor Raja, Tahir Nawaz Cheema, Hasnain Sajid, Kottakkaran Sooppy Nisar, Muhammad Shoaib, Mohammed S. Alqahtani, C. Ahamed Saleel, Mohamed Abbas
Summary: This article investigates the effects of recurring malaria re-infection on the spread dynamics of the disease using a supervised learning based neural networks model. The study aims to discuss the dynamics of malaria spread and improve prediction and analysis through the use of Levenberg-Marquardt artificial neural networks (LMANNs) and numerical treatment of the malaria model. The results show the reliable performance and efficacy of the LMANNs model.
Article
Computer Science, Artificial Intelligence
Shangjie Li, Xingang Wang
Summary: A novel method using improved artificial neural networks for numerical solution of ordinary differential equations is proposed, which adjusts network parameters through a joint cost function and shows high calculation accuracy and fast convergence speed. The performance of this method is analyzed for different types of nonlinear ODEs.
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
