Browsing NTNU Open by Author "Chen, Shengyong"
Now showing items 1-16 of 16
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A data-driven sensitivity analysis approach for dynamically positioned vessels
Cheng, Xu; Skulstad, Robert; Li, Guoyuan; Chen, Shengyong; Hildre, Hans Petter; Zhang, Houxiang (Chapter, 2018) -
A Neural Network-Based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion
Cheng, Xu; Li, Guoyuan; Skulstad, Robert; Chen, Shengyong; Hildre, Hans Petter; Zhang, Houxiang (Journal article; Peer reviewed, 2018)Researchers have been investigating data-driven modeling as a key way to achieve ship intelligence for years. This paper presents a novel data analysis approach to data-driven modeling of ship motion. We propose a global ... -
A Step-wise Feature Selection Scheme for a Prognostics and Health Management System in Autonomous Ferry Crossing Operation
Cheng, Xu; Ellefsen, Andre; Li, Guoyuan; Holmeset, Finn Tore; Chen, Shengyong; Zhang, Houxiang (Chapter, 2019)Developing a reliable algorithm to detect faults automatically within critical components in autonomous ferries is essential for safe and cost-beneficial maritime operations. Autonomous ferries are equipped with hundreds ... -
A SVM-based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion
Wang, Chunlin; Cheng, Xu; Chen, Shengyong; Li, Guoyuan; Zhang, Houxiang (Chapter, 2018)This paper presents a novel method that combines support vector machine (SVM) with sensitivity analysis (SA) to analyze sensor data for ship motion modeling. In order to investigate how each model input contributes to the ... -
A Blockchain-Empowered Cluster-based Federated Learning Model for Blade Icing Estimation on IoT-enabled Wind Turbine
Cheng, Xu; Tian, Weiwei; Shi, Fan; Zhao, Meng; Chen, Shengyong; Wang, Hao (Peer reviewed; Journal article, 2022)Wind energy is a fast-growing renewable energy but faces the blade icing. Data-driven methods provide talented solutions for blade icing detection but a considerable amount of data need to be collected to a central server, ... -
Data-driven Uncertainty and Sensitivity Analysis for Ship Motion Modeling in Offshore Operations
Cheng, Xu; Li, Guoyuan; Skulstad, Robert; Major, Pierre Yann; Chen, Shengyong; Hildre, Hans Petter; Zhang, Houxiang (Journal article; Peer reviewed, 2019)To build a compact data-driven ship motion model for offshore operations that require high control safety, it is necessary to select the most influential parameters and to analyze the uncertainty of the input parameters. ... -
An Enhanced Lightweight Convolutional Neural Network for Ship Detection in Maritime Surveillance System
Yin, Yifan; Cheng, Xu; Shi, Fan; Zhao, Meng; Li, Guoyuan; Chen, Shengyong (Peer reviewed; Journal article, 2022)With the extensive application of artificial intelligence, ship detection from optical satellite remote sensing images using deep learning technology can significantly improve detection accuracy. However, the existing ... -
Fused 3-Stage Image Segmentation for Pleural Effusion Cell Clusters
Ma, Sike; Zhao, Meng; Wang, Hao; Shi, Fan; Sun, Xuguo; Chen, Shengyong; Dai, Hong-Ning (Chapter, 2021)The appearance of tumor cell clusters in pleural effusion is usually a vital sign of cancer metastasis. Segmentation, as an indispensable basis, is of crucial importance for diagnosing, chemical treatment, and prognosis ... -
Gated Convolutional Neural Network for Wind Turbine Blade Icing Detection
Tian, Weiwei; Cheng, Xu; Shi, Fan; Li, Guoyuan; Chen, Shengyong; Zhang, Houxiang (Chapter, 2022)Blade icing detection plays an important role in wind turbine protection and maintenance. Employing well trained deep learning model is a promising method for blade ice detection but needs effective neural networks for ... -
Modeling and Analysis of Motion Data from Dynamically Positioned Vessels for Sea State Estimation
Cheng, Xu; Li, Guoyuan; Skulstad, Robert; Chen, Shengyong; Hildre, Hans Petter; Zhang, Houxiang (Chapter, 2019)Developing a reliable model to identify the sea state is significant for the autonomous ship. This paper introduces a novel deep neural network model (SeaStateNet) to estimate the sea state based on the ship motion data ... -
A Multilevel Convolutional Recurrent Neural Network for Blade Icing Detection of Wind Turbine
Tian, Weiwei; Cheng, Xu; Li, Guoyuan; Shi, Fan; Chen, Shengyong; Zhang, Houxiang (Peer reviewed; Journal article, 2021)Blade icing detection becomes increasingly significant as it can avoid revenue loss and power degradation. Conventional methods are usually limited by additional costs, and model-driven methods heavily depend on prior ... -
A Novel Channel and Temporal-wise Attention in Convolutional Networks for Multivariate Time Series Classification
Cheng, Xu; Han, Peihua; Li, Guoyuan; Chen, Shengyong; Zhang, Houxiang (Peer reviewed; Journal article, 2020)Multivariate time series classification (MTSC) is a fundamental and essential research problem in the domain of time series data mining. Recently deep neural networks emerged as an end-to-end solution for MTSC and achieve ... -
A Novel Densely Connected Convolutional Neural Network for Sea State Estimation Using Ship Motion Data
Cheng, Xu; Li, Guoyuan; Ellefsen, Andre; Chen, Shengyong; Hildre, Hans Petter; Zhang, Houxiang (Peer reviewed; Journal article, 2020)Sea state estimation is a fundamental problem in the development of autonomous ships. Traditional methods such as wave buoy, satellites, and wave radars are limited by locations, clouds and costs, respectively. Model-based ... -
Simplifying neural network based model for ship motion prediction: a comparative study of sensitivity analysis
Cheng, Xu; Chen, Shengyong; Diao, Chen; Liu, Mengna; Li, Guoyuan; Zhang, Houxiang (Chapter, 2017)This paper presents a comparative study of sensitivity analysis (SA) and simplification on artificial neural network (ANN) based model used for ship motion prediction. Considering traditional structural complexity of ANN ... -
SpectralSeaNet: spectrogram and convolutional network-based sea state estimation
Cheng, Xu; Li, Guoyuan; Skulstad, Robert; Zhang, Houxiang; Chen, Shengyong (Chapter, 2020)Sea State is significant to the operations on the sea. The traditional model-based approaches need lots of knowledge of vessels, which limit the real-world use. This paper proposes a spectrogram-based deep learning model ... -
Temporal Attention Convolutional Neural Network for Estimation of Icing Probability on Wind Turbine Blades
Cheng, Xu; Shi, Fan; Zhao, Meng; Li, Guoyuan; Zhang, Houxiang; Chen, Shengyong (Peer reviewed; Journal article, 2021)Wind farms are usually located in high-latitude areas, which increases produced energy but creates a high risk of icing. Traditional methods of anti-blade-icing are limited by the extra cost and the potential damages to ...