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Zhiguo Liang, Lijun Zhang and Xizhe Wang
Since failure of steam turbines occurs frequently and can causes huge losses for thermal plants, it is important to identify a fault in advance. A novel clustering fault diagnosis method for steam turbines based on t-distribution stochastic neighborhood ...
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Zidong Wu, Xiaoli Wang and Baochen Jiang
The algorithm described in this paper can be applied to the real-time fault diagnosis of wind turbine. By using this algorithm, the fault type of wind turbine can be determined according to the real-time monitoring parameters of SCADA system.
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Ricardo Aguasca-Colomo, Dagoberto Castellanos-Nieves and Máximo Méndez
We present a comparative study between predictive monthly rainfall models for islands of complex orography using machine learning techniques. The models have been developed for the island of Tenerife (Canary Islands). Weather forecasting is influenced bo...
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Vahid Safavi, Arash Mohammadi Vaniar, Najmeh Bazmohammadi, Juan C. Vasquez and Josep M. Guerrero
Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the batte...
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Usman Sammani Sani, Owais Ahmed Malik and Daphne Teck Ching Lai
Wireless network parameters such as transmitting power, antenna height, and cell radius are determined based on predicted path loss. The prediction is carried out using empirical or deterministic models. Deterministic models provide accurate predictions ...
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Kefei Zhu, Xu Yang, Yanbo Zhang, Mengkun Liang and Jun Wu
With the rising popularity of the Advanced Driver Assistance System (ADAS), there is an increasing demand for more human-like car-following performance. In this paper, we consider the role of heterogeneity in car-following behavior within car-following m...
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Vlado Francic, Nermin Hasanspahic, Mario Mandu?ic and Marko Strabic
It is of the utmost importance to accurately estimate different ships? weights during their design stages. Additionally, lightship displacement (LD) data are not always easily accessible to shipping stakeholders, while other ships? dimensions are within ...
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Kittipob Saetia and Jiraphat Yokrattanasak
Machine learning for stock market prediction has recently been popular for identifying stock selection strategies and providing market insights. In this study, we adopted machine learning algorithms to analyze technical indicators, and Google Trends sear...
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Fabian Dobmeier, Rui Li, Florian Ettemeyer, Melvin Mariadass, Philipp Lechner, Wolfram Volk and Daniel Günther
Complex casting parts rely on sand cores that are both high-strength and can be easily decored after casting. Previous works have shown the need to understand the influences on the decoring behavior of inorganically bound sand cores. This work uses black...
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Yongdong Wang, Haonan Zhai, Xianghong Cao and Xin Geng
The number of motor vehicles on the road is constantly increasing, leading to a rise in the number of traffic accidents. Accurately identifying the factors contributing to these accidents is a crucial topic in the field of traffic accident research. Most...
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Iris Viana dos Santos Santana, Álvaro Sobrinho, Leandro Dias da Silva and Angelo Perkusich
This study compares the performance of machine learning models for selecting COVID-19 and influenza tests during coexisting outbreaks in Brazil, avoiding the waste of resources in healthcare units. We used COVID-19 and influenza datasets from Brazil to t...
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Ding Liu, Wuyue Rong, Jin Zhang and Ying-En (Ethan) Ge
In this paper, the nonlinear effects of the built environment on bus?metro-transfer ridership are explored, based on Shanghai metro data, with an extreme gradient-boosting decision-trees (XGBoost) model. It was found that the bus-network density had the ...
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Hui Zhu, Wen Yang, Shihong Li and Aiping Pang
Fault detection in heating, ventilation and air-conditioning (HVAC) systems can effectively prevent equipment damage and system energy loss, and enhance the stability and reliability of system operation. However, existing fault detection strategies have ...
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Maya Hilda Lestari Louk and Bayu Adhi Tama
Gradient boosting ensembles have been used in the cyber-security area for many years; nonetheless, their efficacy and accuracy for intrusion detection systems (IDSs) remain questionable, particularly when dealing with problems involving imbalanced data. ...
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Cheng Zhang, Xiong Zou and Chuan Lin
In order to prevent safety risks, control marine accidents and improve the overall safety of marine navigation, this study established a marine accident prediction model. The influences of management characteristics, environmental characteristics, person...
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Song Xin, Ziyi Wang, Huifeng Su, Liuhong Shang, Kun Meng, Xiang Wang, Zhiyong Zhou, Zhongxiao Zhao and Pengfei Zhang
In order to study the safety state of the structure of a cross-sea cable-stayed bridge during its operation period, this paper proposes a combined long-term traffic prediction model based on the XGBoost (eXtreme Gradient Boosting) model and LSTM (Long Sh...
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Yixuan Li, Charalampos Stasinakis and Wee Meng Yeo
Supply Chain Finance (SCF) has gradually taken on digital characteristics with the rapid development of electronic information technology. Business audit information has become more abundant and complex, which has increased the efficiency and increased t...
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Neda Rostamzadeh, Sheikh S. Abdullah, Kamran Sedig, Amit X. Garg and Eric McArthur
Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a di...
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Yunfei Yang, Haiwen Tu, Lei Song, Lin Chen, De Xie and Jianglong Sun
Resistance is one of the important performance indicators of ships. In this paper, a prediction method based on the Radial Basis Function neural network (RBFNN) is proposed to predict the resistance of a 13500 transmission extension unit (13500TEU) conta...
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Jian He, Yong Hao and Xiaoqiong Wang
The reasonable decision of ship detention plays a vital role in flag state control (FSC). Machine learning algorithms can be applied as aid tools for identifying ship detention. In this study, we propose a novel interpretable ship detention decision-maki...
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