Article
Computer Science, Artificial Intelligence
Taiyong Li, Zijie Qian, Wu Deng, Duzhong Zhang, Huihui Lu, Shuheng Wang
Summary: The paper introduces a novel approach VMD-RSBL that combines VMD and RSBL for forecasting crude oil prices. By decomposing the crude oil price series into components and predicting each component individually using predictors built on random samples and random lags, the final forecasted prices are obtained by aggregating the predictions of all components. Extensive experiments show that VMD-RSBL outperforms many state-of-the-art schemes in terms of several evaluation indicators.
APPLIED SOFT COMPUTING
(2021)
Article
Business
Ranran Li, Yucai Hu, Jiani Heng, Xueli Chen
Summary: This study explores a novel multiscale hybrid paradigm to estimate crude oil prices, which decomposes the price into simple models to improve accuracy in forecasting complex time series.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Economics
Mingchen Li, Zishu Cheng, Wencan Lin, Yunjie Wei, Shouyang Wang
Summary: This study proposes a novel learning paradigm that integrates the trajectory similarity method with machine learning models based on the decomposition-ensemble framework to improve the accuracy of crude oil price forecasting. By decomposing the raw data using variational mode decomposition and dividing the resulting essential modal functions into high and low frequencies using sample entropy, the data is reorganized using the forecasting properties of different models. Experimental results demonstrate that the proposed learning paradigm outperforms other benchmark models, indicating its effectiveness and robustness in crude oil price forecasting.
Article
Automation & Control Systems
Jiaxin Yuan, Jianping Li, Jun Hao
Summary: In this study, a dynamic ensemble forecasting method using clustering approaches is proposed for nonstationary oil prices. Clustering is employed to classify historical observations into clusters, providing a targeted evaluation of individual forecasting models. The proposed model includes a clustering-based weight assignment strategy to balance competitiveness and robustness. Results show that the proposed model outperforms benchmarks and state-of-the-art methods, indicating its competitiveness and robustness. The effectiveness of the proposed model is validated through parameter variation and data missing scenarios, highlighting its potential in improving oil price prediction performance.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Thermodynamics
Tingting Zhang, Zhenpeng Tang, Junchuan Wu, Xiaoxu Du, Kaijie Chen
Summary: This study contributes to the research on crude oil price prediction by proposing a VMD-RES-EEMD-PSO-KELM model, which is validated through empirical analysis to show improved prediction accuracy of crude oil prices. The findings highlight the importance of utilizing hybrid models for forecasting crude oil prices.
Article
Computer Science, Artificial Intelligence
Lean Yu, Mengyao Ma
Summary: A novel memory-trait-driven decomposition-reconstruction-ensemble learning paradigm is proposed to improve prediction performance in oil price forecasting. The methodology involves data decomposition, component reconstruction, individual prediction, and ensemble output based on memory traits. Experimental results show that this methodology outperforms benchmarking models, making it a promising solution for oil price prediction with memory traits.
APPLIED SOFT COMPUTING
(2021)
Article
Green & Sustainable Science & Technology
Jianguo Zhou, Qiqi Wang
Summary: The paper introduces a hybrid carbon price forecasting model based on secondary decomposition and an improved extreme learning machine model. By utilizing complementary ensemble empirical mode decomposition and variational mode decomposition, as well as applying the partial autocorrelation function to obtain model input variables, the proposed model shows effectiveness and stability in predicting carbon prices.
Article
Computer Science, Artificial Intelligence
Lean Yu, Yao Wu, Ling Tang, Hang Yin, Kin Keung Lai
Summary: Ensemble learning has been widely recognized as an excellent solution for crude oil price forecasting, with diversity strategy being one of the key determinants of good performance. This study introduced the efficient RVFL network as base models and examined the impacts of different diversity strategies on the performance of RVFL network ensemble learning, finding that carefully selected diversity strategies can increase the accuracy of ensemble learning models. Additionally, the proposed multistage nonlinear RVFL network ensemble forecasting model outperformed the single RVFL network model consistently.
Article
Automation & Control Systems
Quande Qin, Zhaorong Huang, Zhihao Zhou, Chen Chen, Rui Liu
Summary: Recent research has shown that introducing online data can significantly improve forecasting ability. In this study, several popular single-model machine learning methods and a stacking multiple-model ensemble learning strategy are used with online data from Google Trends to forecast crude oil prices. The study investigates the effect of Google Trends on crude oil prices and compares multiple-model methods with single-model machine learning methods. The results indicate that introducing Google Trends improves forecasting performance and multiple-model methods outperform single-model machine learning methods in terms of prediction accuracy.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Business, Finance
Kunliang Xu, Weiqing Wang
Summary: A reliable crude oil price forecast is crucial for market pricing. This study incorporates a rolling window into two prevalent EEMD-based modeling paradigms to improve accuracy. The results show that EEMD plays a weak role in improving crude oil price forecasts when only the in-sample set is preprocessed, but the rolling EEMD-denoising model has an advantage for long-term forecasting.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Thermodynamics
Xiaoxu Du, Zhenpeng Tang, Kaijie Chen
Summary: This paper proposes an ensemble trading strategy by combining time-frequency feature extraction and deep reinforcement learning for crude oil futures trading. The strategy decomposes the volume-price series using optimized variational mode decomposition and filters appropriate agents based on the Sharpe ratio. The best features of the three agents are effectively combined and used in the next phase of trading, resulting in excellent return performance and stability in the highly volatile crude oil market.
Article
Environmental Studies
Ranran Li
Summary: This paper proposes a hybrid forecasting model based on a multiscale clustering recognition approach, which splits the original series into multiple sub-series to reduce complexity and determines the optimal mode of input data. The fuzzy cluster method is used to identify the characteristics of sub-series and adaptively divide them into different components with different frequencies. Predictions are made for these components using tuned forecasting engines and the final results are reconfigured with existing time points. The proposed model effectively combines different models and demonstrates promising results for forecasting energy spot price.
Article
Economics
Lili Guo, Xinya Huang, Yanjiao Li, Houjian Li
Summary: This paper introduces artificial intelligence methods to evaluate the optimal forecasting strategy for China's crude oil futures price. Using machine learning and considering historical information, volatility, and non-linear features, the study examines the forecasting effects of various models. Results show that the GRU model outperforms other models in terms of forecast accuracy and performance. Additionally, considering multiple influencing factors improves the forecasting accuracy of the proposed models.
Article
Engineering, Electrical & Electronic
Tingting Zhang, Zhenpeng Tang, Junchuan Wu, Xiaoxu Du, Kaijie Chen
Summary: Research on forecasting electricity prices is crucial for market participants. Due to the nonlinearity and high volatility of electricity prices, forecasting the price series is challenging. This study introduces a two-layer decomposition technique and an optimized hybrid model to improve prediction accuracy and demonstrates the advantages of the proposed model through empirical analysis.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Operations Research & Management Science
Manrui Jiang, Lifen Jia, Zhensong Chen, Wei Chen
Summary: This paper proposes two new stock price prediction models, EMD-ELM-IHS and VMD-ELM-IHS, which combine empirical mode decomposition (EMD) or variational mode decomposition (VMD), extreme learning machine (ELM), and improved harmony search (IHS) algorithm. Results comparing with other methods, including EMD based ELM (EMD-ELM), VMD based ELM (VMD-ELM), autoregressive integrated moving average (ARIMA), ELM, multi-layer perception (MLP), support vector regression (SVR), and long short-term memory (LSTM) models, demonstrate that the proposed models have superior performance in terms of accuracy and stability. Additionally, the size of the sliding window and training set are found to have a significant impact on predictive performance.
ANNALS OF OPERATIONS RESEARCH
(2022)
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)