Article
Energy & Fuels
Ye Zhang, Wenyu Zhang, Zhenhai Guo, Shuwen Zhang
Summary: The hybrid multistep wind speed prediction model EWP-CS-RELM outperforms seven other prediction models with the smallest statistical errors, by using ensemble empirical mode decomposition and wavelet packet transform for adaptive processing.
Article
Computer Science, Artificial Intelligence
Umar Farooq, Muhammad Wasif Shabir, Muhammad Awais Javed, Muhammad Imran
Summary: This paper presents two energy prediction techniques for fog nodes, based on Recursive Least Square and Artificial Neural Network, to enable intelligent energy-aware task offloading. Simulation results show that the ANN-based technique has up to 20% less root mean square error compared to the RLS-based technique.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Zhongqiang Wu, Xueqin Lu
Summary: A microgrid fault diagnosis method based on whale algorithm optimizing extreme learning machine (ELM) is proposed. The method analyzes the three-phase fault voltage using wavelet packet decomposition, and uses a whale algorithm to optimize the extreme learning machine to establish a diagnostic model for identifying and diagnosing microgrid faults. The proposed method achieves faster learning speed, stronger generalization ability and higher recognition accuracy compared to other neural network models.
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2023)
Article
Mathematics, Interdisciplinary Applications
Jujie Wang, Quan Cui, Maolin He
Summary: In this study, a novel predicting model is proposed to predict carbon price by combining the advantages of the improved variational mode decomposition algorithm, multiscale entropy algorithm, and the extreme learning machine model improved by the intelligent optimization algorithm. The performance indicators of the proposed model are significantly lower than others, indicating its effectiveness in time series prediction.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Computer Science, Hardware & Architecture
Xiaojun Bai, Zhenxi Ma, Wei Chen, Shenhang Wang, Yanfang Fu
Summary: A new method for diagnosing faults in laser gyroscopes using Kernel Extreme Learning Machine (KELM) is proposed in this study. The method efficiently extracts signal features using Wavelet Packet Decomposition (WPD) and optimizes parameters using the Improved Dung Beetle Optimizer (IDBO) algorithm, resulting in improved diagnostic accuracy.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Engineering, Manufacturing
Dong-Dong Li, Wei-Min Zhang, Yuan-Shi Li, Feng Xue, Juergen Fleischer
Summary: This paper investigates two indicators for chatter detection in milling process and successfully integrates them into a support vector machine model. A mapping model between image and chatter indicators is established, and experimental results confirm the feasibility and effectiveness of the proposed solution.
ADVANCES IN MANUFACTURING
(2021)
Article
Computer Science, Information Systems
Yang Liu, Bo Jiang, Jun Feng, Jingzhao Hu, Haibo Zhang
Summary: The article discusses the challenges and solutions in computer-aided diagnosis of epilepsy based on EEG analysis using machine learning. It introduces a feature dimension reduction algorithm called epilepsy locality preserving projections (E-LPP) which preserves low-dimensional manifold for accurate analysis of nonlinear, non-stationary and high-dimensional signals. The proposed method outperforms traditional dimensionality reduction algorithms, manifold learning algorithms, and deep learning methods in epilepsy detection on various metrics.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Engineering, Civil
Yongtao Wang, Jian Liu, Rong Li, Xinyu Suo, EnHui Lu
Summary: This study proposes an innovative approach for medium and long-term precipitation prediction using wavelet decomposition, optimization algorithms, and neural networks. The model demonstrates high prediction accuracy and low error rates, allowing for effective guidance in flood control, drought relief, and water resource allocation.
WATER RESOURCES MANAGEMENT
(2022)
Review
Biochemical Research Methods
Alhassan Alkuhlani, Walaa Gad, Mohamed Roushdy, Abdel-Badeeh M. Salem
Summary: This paper discusses the importance of computational intelligence techniques for glycosylation site prediction and their applications. Various studies have analyzed the performance of intelligent techniques in different aspects, highlighting the challenges and difficulties faced by software developers and knowledge engineers in this field.
CURRENT BIOINFORMATICS
(2021)
Article
Engineering, Marine
Sara El Mekkaoui, Loubna Benabbou, Stephane Caron, Abdelaziz Berrado
Summary: Improving maritime operations planning and scheduling is crucial for enhancing the performance and competitiveness of the sector. Accurate ship speed estimation is important for efficient maritime traffic management. This study proposes a data-driven solution using deep learning sequence methods and historical ship trip data, which outperforms the baseline ship speed rates. The findings suggest that deep learning models combined with maritime data can effectively estimate ship speed and improve operational efficiency, navigation safety, and ship emissions estimation and monitoring.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Dheeraj Kumar, Sandeep Kumar Sood, Keshav Singh Rawat
Summary: Among the premier technologies in the healthcare domain, the Internet of Things and Artificial Intelligence (AI) have greatly emerged amidst the COVID-19 humanitarian catastrophe. This paper presents a non-invasive AI-empowered model for predicting viral C-19 infection in the home environment. The model includes four layers: automated data acquisition, data analysis and classification, COVID-19 severity prediction, and communication layer. The evaluation showed that the proposed AI framework outperformed state-of-the-art strategies in terms of temporal approximation, reliability, stability, and predictive performance analysis.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Automation & Control Systems
Houssem Habbouche, Tarak Benkedjouh, Noureddine Zerhouni
Summary: This paper presents a new data-driven approach for bearing prognostics, which can successfully detect the degradation process of bearings and predict their remaining useful life.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Engineering, Multidisciplinary
Tong Liu, YuCheng Jin, Shuo Wang, QinWen Zheng, Guoan Yang
Summary: This paper proposes a denoising method of acoustic emission (AE) signals based on the combination of autoencoder and wavelet packet decomposition (AE-WPD) to address the problem of weak AE signals being submerged in strong background noise in the actual operating conditions of the engine. The proposed method decomposes the engine background noise signals and noise-containing fault AE signals using wavelet packet, enhances the local analysis capability of the autoencoder, and analyzes the differences between the background noise signals and the noise-containing fault signals. The experimental results show that the proposed AE-WPD method outperforms other denoising methods at different signal-to-noise ratios (SNR).
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Environmental Sciences
Ningning Li, Xiaohong Chen, Jing Qiu, Wenhui Li, Bikui Zhao
Summary: This study analyzed the spatio-temporal characteristics of extreme precipitation and investigated its intra-annual distribution using the precipitation concentration degree and precipitation concentration period. It also employed a back-propagation artificial neural network for simulation, verification, and prediction of extreme precipitation. The findings revealed differences in precipitation levels between the upper and lower reaches of the Dongjiang River Basin, with higher concentration and delayed period in the lower reach. Additionally, extreme precipitation increased from northeast to southwest, with more extreme and periodic characteristics in the lower reach. The study predicted a decrease in total precipitation and an increase in extreme precipitation in 2023, with a forecasting qualification rate ranging from 27% to 72%.
Article
Engineering, Electrical & Electronic
Muhammad Mateen Qureshi, Muhammad Kaleem
Summary: This paper presents a novel methodology that combines signal processing and machine learning techniques for patient-specific seizure prediction. The proposed method segments and decomposes the EEG data, extracts features, and uses a support vector machine classifier for classification. The method achieves high accuracy and computational efficiency.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Computer Science, Interdisciplinary Applications
Wen-chuan Wang, Lei Xu, Kwok-wing Chau, Yong Zhao, Dong-mei Xu
Summary: Yin-Yang-pair Optimization (YYPO) is a philosophy-inspired meta-heuristic algorithm that generates candidate solutions by balancing exploitation and exploration, but suffers from low solution quality in exploration. To enhance performance, a new algorithm named orthogonal opposition-based-learning Yin-Yang-pair Optimization (OOYO) is proposed, which utilizes orthogonal experiment design and opposition-based learning to optimize candidate solutions.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Artificial Intelligence
Wen-chuan Wang, Lei Xu, Kwok-wing Chau, Chang-jun Liu, Qiang Ma, Dong-mei Xu
Summary: This paper proposes a lightweight and efficient variant of differential evolution algorithm, Ce-LDE, for solving constrained single-objective optimization problems. The algorithm achieves high competitiveness and practicality through the introduction of a combined constraint handling method and redefinition of control parameters, as demonstrated by experimental results and comparative studies.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Environmental Sciences
Wenchuan Wang, Yanwei Zhao, Yong Tu, Rui Dong, Qiang Ma, Changjun Liu
Summary: This study examines the influence of different methods on the parameter regionalization of distributed hydrological model parameters in hilly areas of Hunan Province. Six parameter regionalization schemes are proposed, and 426 flood events are used for model parameter calibration, with 136 events for validation. The results show that the random forest model is the most stable solution and significantly outperforms other methods. Using the random forest model for parameter regionalization can improve the accuracy of flood simulation in ungauged areas, which is of great significance for flash flood forecasting and early warning.
Article
Environmental Sciences
Sijia Hao, Wenchuan Wang, Qiang Ma, Changzhi Li, Lei Wen, Jiyang Tian, Changjun Liu
Summary: With limited data, disaster simulation review based on digital information technology is an important guideline for analyzing disaster mechanisms, planning post-disaster reconstruction, and improving defense capability. By using limited measured data, a hydrological-hydrodynamic model was established to study the 7.20 flash flood in the Wangzongdian river basin. The results showed that extreme rainstorm caused flooding in mountainous areas and the collapse of subgrade water damming, contributing to the serious disaster.
Article
Engineering, Environmental
Mingwei Ma, Zhaohang Wang, Huijuan Cui, Wenchuan Wang, Liuyuwei Jiang
Summary: This study constructs a multi-scalar framework for attribution analysis by integrating hydrological modeling into the Budyko-based decomposition method and applies it to the source region of the Yellow River as a case study. The results indicate that climate change is the dominant factor controlling streamflow variation for the annual and wet season, while human activities play a major role in streamflow variation for the dry season. The applicability of the Budyko-based decomposition method within the new multi-scalar framework is verified through hydrologic simulation.
Article
Computer Science, Interdisciplinary Applications
Dong-mei Xu, Xiang Wang, Wen-chuan Wang, Kwok-wing Chau, Hong-fei Zang
Summary: In this study, a coupled forecasting model combining ICEEMDAN, WD, and SVM optimized by SOA is proposed to predict monthly runoff. The model decomposes the original runoff series using ICEEMDAN and WD to obtain IMF and Res components, which are then input into the SOA-SVM model for prediction. The ICEEMDAN-WD-SOA-SVM model achieves the smallest RMSE and MAPE and the largest NSEC and R compared to other benchmarking models, demonstrating its superior prediction accuracy.
JOURNAL OF HYDROINFORMATICS
(2023)
Article
Engineering, Civil
Wen-chuan Wang, Qi Cheng, Kwok-wing Chau, Hao Hu, Hong-fei Zang, Dong-mei Xu
Summary: Reliable runoff prediction is essential for reservoir scheduling, water resources management, and efficient water utilization. To improve the accuracy of monthly runoff prediction, a hybrid model (TVF-EMD-SSA-ELM) combining TVF-based EMD, SSA, and ELM is proposed. The model successfully decomposes the runoff series, optimizes the ELM model with SSA, and generates accurate predictions. Evaluation results show that the TVF-EMD-SSA-ELM model outperforms other models in terms of prediction accuracy. This model provides a new method for monthly runoff prediction and can be applied in similar study areas.
JOURNAL OF HYDROLOGY
(2023)
Review
Engineering, Civil
Wen-chuan Wang, Wei-can Tian, Dong-mei Xu, Kwok-wing Chau, Qiang Ma, Chang-jun Liu
Summary: River flood routing is a crucial aspect of water resources management, with the Muskingum model being the dominant method. This paper reviews the development and parameter estimation research status of the Muskingum model. The combination of mathematical techniques and evolutionary algorithms has shown promising results in recent years. The paper also provides an overview of accuracy evaluation criteria and research case data sets commonly used in the literature, and discusses challenges and future trends in Muskingum model research.
WATER RESOURCES MANAGEMENT
(2023)
Article
Computer Science, Interdisciplinary Applications
Dong-mei Xu, Xiao-xue Hu, Wen-chuan Wang, Kwok-wing Chau, Hong-fei Zang
Summary: This research provides a hybrid forecasting model to increase the precision of monthly runoff predictions. It applies complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) to decompose the raw monthly runoff time series. The input-output relationships for all intrinsic mode functions (IMFs) are determined using Harris Hawks Optimization (HHO) algorithm to optimize least squares support vector machine (LSSVM) model. Evaluation indicators demonstrate the effectiveness of the proposed hybrid model in improving prediction accuracy.
EARTH SCIENCE INFORMATICS
(2023)
Article
Environmental Sciences
Wenchuan Wang, Weican Tian, Kwokwing Chau, Hongfei Zang, Mingwei Ma, Zhongkai Feng, Dongmei Xu
Summary: This paper proposes an improved bald eagle search algorithm (CABES) combined with epsilon-constraint method (epsilon-CABES) to tackle the complex reservoir flood control operation problem. Through simulations and comparisons with other algorithms, the superior performance of the CABES algorithm is verified. The results of the tests on single and multi-reservoir systems show that the epsilon-CABES method outperforms other methods in flood control scheduling.
Article
Computer Science, Interdisciplinary Applications
Yi-yang Wang, Wenchuan Wang, Kwok-wing Chau, Dong-mei Xu, Hong-fei Zang, Chang-jun Liu, Qiang Ma
Summary: This article proposes a multi-head attention flood forecasting model (MHAFFM) that combines a multi-head attention mechanism with multiple linear regression for precise and stable multi-hour flood forecasting. Experimental results show that the MHAFFM model significantly improves the prediction performance compared to benchmarking models, while maintaining good stability and interpretability. This research enhances the credibility of deep learning in the field of hydrology and provides a new approach for its application.
JOURNAL OF HYDROINFORMATICS
(2023)
Article
Engineering, Civil
Zhong-kai Feng, Wen-jing Niu, Tai-heng Zhang, Wen-chuan Wang, Tao Yang
Summary: To address the practical requirement, this research proposes a novel artificial intelligence method for deriving reservoir operation policy, which uses fuzzy clustering iteration method and novel twin support vector regression model. The feasibility of the proposed method is evaluated on two real-world huge hydropower reservoirs in China, and the simulations demonstrate better comprehensive benefits than several control methods under uncertain environments. Therefore, the experiments confirm that metaheuristic algorithms and pattern recognition techniques can enhance the performance of standalone artificial intelligence methods in deriving reservoir operation policy.
JOURNAL OF HYDROLOGY
(2023)
Article
Environmental Sciences
Mingwei Ma, Hongfei Zang, Wenchuan Wang, Huijuan Cui, Yanwei Sun, Yujia Cheng
Summary: In this study, a copula-based approach was used to propose the classical severity-duration-frequency (SDF) relationships of streamflow drought in the source area of the Yellow River. Multiple time-varying threshold levels and the integration and elimination of drought events were considered. The findings show that the copula-based SDF relationships can provide more critical information than univariate frequency analysis, as they effectively consider the connection and interaction between drought characteristics.
Article
Engineering, Multidisciplinary
Wenchuan Wang, Weican Tian, Kwok-wing Chau, Yiming Xue, Lei Xu, Hongfei Zang
Summary: The improved Bald Eagle algorithm (CABES) enhances the performance of the Bald Eagle Search algorithm (BES) by integrating Cauchy mutation and adaptive optimization. CABES adjusts the step size in the selection stage to select a better search range, and updates the search position formula with an adaptive weight factor to further improve the local optimization capability of BES. Experimental results demonstrate that CABES exhibits good exploration and exploitation abilities, making it effective and efficient in practical engineering problems.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Wen-Chuan Wang, Yan-Wei Zhao, Chang-Jun Liu, Qiang Ma, Dong-Mei Xu
Summary: This paper uses machine learning methods to improve the accuracy of flood simulation and early warning for ungauged areas, with a case study in Hunan Province. The results show that the regionalization scheme based on the random forest model significantly improves flood simulation accuracy, and the support vector machine-based warning model continues to improve and is expected to reach a high level of accuracy in the coming years.
ADVANCES IN HYDROINFORMATICS: MODELS FOR COMPLEX AND GLOBAL WATER ISSUES-PRACTICES AND EXPECTATIONS
(2022)