Article
Mathematical & Computational Biology
Xiaoqiang Dai, Kuicheng Sheng, Fangzhou Shu
Summary: This paper proposes a method for ship power load forecasting using support vector machine and improved particle swarm optimization algorithm. The experimental results show that this method can reduce prediction error, improve prediction accuracy, and is of great significance for the stability and safety of ship power system.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Maliheh Abbaszadeh, Saeed Soltani-Mohammadi, Ali Najah Ahmed
Summary: This article introduces the application of the support vector classifier in geological modeling and proposes an improved method based on particle swarm optimization to select the best model parameters. Through the application in the modeling process of the Iju porphyry copper deposit, the effectiveness and superiority of this method are demonstrated.
COMPUTERS & GEOSCIENCES
(2022)
Article
Management
Johannes Jakubik, Adrian Binding, Stefan Feuerriegel
Summary: Particle swarm optimization (PSO) is an iterative search method that improves optimization by introducing concepts from Bayesian optimization and a stochastic surrogate model of the objective function. It shows desirable properties for both exploratory and exploitative behavior, outperforming baseline implementations and achieving substantial performance improvements on popular benchmark functions compared to state-of-art surrogate-assisted evolutionary algorithms.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Mechanics
An Gong, Yongan Zhang, Youzhuang Sun, Wei Lin, Jing Wang
Summary: In this study, an NMR proxy model based on adaptive ensemble learning was proposed to efficiently and economically predict the rock movable fluid indexes. The model combines 33 base learners through political optimization to improve the prediction accuracy. The results show that this new strategy outperforms the other 33 base learners.
Article
Economics
Bangzhu Zhu, Shunxin Ye, Ping Wang, Julien Chevallier, Yi-Ming Wei
Summary: This paper introduces a new multi-objective least squares support vector machine model to improve asset price forecasting accuracy and trading performance by incorporating mixture kernel function and multi-objective fitness function. Results show that high directional forecasting typically leads to higher trading performance.
JOURNAL OF FORECASTING
(2022)
Article
Environmental Sciences
Okan Mert Katipoglu, Metin Sarigol
Summary: With the increasing frequency of floods due to global warming, flood routing models are crucial in predicting floods, preventing loss of life and property, and protecting agricultural lands. This research compares the performance of hybrid machine learning models in flood routing estimation in Ordu, Turkey. The particle swarm optimization least-squares support vector machine technique is found to be the most successful model, with a data partition ratio of Train:70:Test:30. These findings are essential for flood management and taking necessary precautions.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Geosciences, Multidisciplinary
Jingsheng Yang
Summary: In this study, we aim to improve the prediction accuracy of slope stability by introducing the adaptive CE factor quantum-behaved particle swarm optimization (ACE-QPSO) and least-square support vector machine (LSSVM). The results show that the ACE-QPSO algorithm has better global search capability compared to other algorithms. Through training and testing real slope project data, the ACE-QPSO-LSSVM algorithm demonstrates better model fit, minor prediction error, and faster convergence, indicating its feasibility and efficiency in predicting slope stability.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Green & Sustainable Science & Technology
Mei-Li Shen, Cheng-Feng Lee, Hsiou-Hsiang Liu, Po-Yin Chang, Cheng-Hong Yang
Summary: This study introduces a new forecasting approach, FSPSOSVR, which combines particle swarm optimization, random forest feature selection, and support vector regression for accurately predicting exchange rates. Empirical results show that the FSPSOSVR algorithm consistently outperforms competing models and has practical relevance for foreign exchange carry trades.
Article
Engineering, Civil
Saeed Mozaffari, Saman Javadi, Hamid Kardan Moghaddam, Timothy O. Randhir
Summary: A simulation-optimization hybrid model using the PSO algorithm was developed to forecast groundwater levels in aquifers. The model outperformed other models in terms of RMSE and R 2 , providing a reliable tool for decision support and management of similar aquifers.
WATER RESOURCES MANAGEMENT
(2022)
Article
Thermodynamics
Zhenyu Zhao, Yao Zhang, Yujia Yang, Shuguang Yuan
Summary: Load forecasting analysis is crucial for regional electric power project planning and consumption management. This paper proposes a new forecasting model that combines Grey Model and Least Squares Support Vector Machine to improve the accuracy and usability of long-term load forecasting by extracting load characteristics. Through a case study in Beijing, the results show that the electric consumption intensity exhibits positive spatial correlation and a decreasing trend overall, with increasing internal variations over time. These findings provide valuable insights for electric construction planning and the formulation of regionally energy-saving policies.
Article
Engineering, Civil
Fatemeh Rezaie Adaryani, S. Jamshid Mousavi, Fatemeh Jafari
Summary: This study compares the performances of three machine and deep learning-based rainfall forecasting approaches and improves the accuracy of the models through event classification and adding more predictors.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Multidisciplinary
Guize Liu, Jinqing Ye, Yuan Chen, Xiaolong Yang, Yanbin Gu
Summary: This study investigates the degradation of the ecosystem in the Liaohe estuary coastal zone due to water pollution. A prediction system based on the SVM-PSO algorithm is proposed for analyzing the pollution status and predicting the water pollution index. The results indicate that the SVM-PSO algorithm has good sewage prediction ability and can be applied in water pollution control.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2022)
Article
Thermodynamics
Jingyu Wang, Zirui Wang, Peng Guo, Xingjun Hu, Jia Zhu, Tianming Yu
Summary: This paper establishes an optimization model based on PSO-SVM and improved NSGA-II algorithm to balance the heat dissipation performance and lightweight of a battery. The results show that the model has high accuracy and feasibility.
APPLIED THERMAL ENGINEERING
(2024)
Article
Mathematical & Computational Biology
Dashe Li, Xueying Wang, Jiajun Sun, Huanhai Yang
Summary: A hybrid dissolved oxygen concentration prediction model (AI-HydSu) is proposed, which improves the accuracy and convergence rate of dissolved oxygen concentration prediction through data preprocessing and optimizing learning factors.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Physics, Multidisciplinary
Yingjun Chen, Zhigang Zhu
Summary: This paper presents a novel method that integrates piecewise linear representation (PLR), improved particle swarm optimization (IPSO), and a feature-weighted support vector machine (FW-WSVM) to analyze the nonlinear relationships between trading signals and stock data hidden in historical data. The proposed method achieves higher prediction accuracy and profitability, indicating the effectiveness of IPSO-FW-WSVM in trading signal prediction.
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)