Blar i NTNU Open på forfatter "Cheng, Xu"
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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 ... -
An efficient neural-network based approach to automatic ship docking
Shuai, Yonghui; Li, Guoyuan; Cheng, Xu; Skulstad, Robert; Xu, jinshan; Liu, Honghai; Zhang, Houxiang (Journal article; Peer reviewed, 2019)Automatic ship docking is one of the applications of autonomous ships. How to realize autonomous low-speed maneuver under environmental disturbances for docking is the fundamental problem at present. This paper presents ... -
Assessment of the impacts of different policy instruments on achieving the deep decarbonization targets of island energy systems in Norway – The case of Hinnøya
Zhou, Wenji; Hagos, Dejene Assefa; Stikbakke, Sverre; Huang, Lizhen; Cheng, Xu; Onstein, Erling (Peer reviewed; Journal article, 2022)Norway enjoys an electricity-dominant clean energy system with a high share of hydropower. The power and heating sectors are characterized by high penetration of renewables. But the transportation and offshore industries ... -
Automatic Fault Detection for Marine Diesel Engine Degradation in Autonomous Ferry Crossing Operation
Ellefsen, Andre; Cheng, Xu; Holmeset, Finn Tore; Ushakov, Sergey; Æsøy, Vilmar; Zhang, Houxiang (IEEE International Conference on Mechatronics and Automation;, Chapter, 2019)The maritime industry generally anticipates having semi-autonomous ferries in commercial use on the west coast of Norway by the end of this decade. In order to schedule maintenance operations of critical components in a ... -
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, ... -
A Class-Imbalanced Heterogeneous Federated Learning Model for Detecting Icing on Wind Turbine Blades
Cheng, Xu; Shi, Fan; Liu, Yongping; Zhou, Jiehan; Liu, Xiufeng; Huang, Lizhen (Peer reviewed; Journal article, 2022)Wind farms are typically located at high latitudes, resulting in a high risk of blade icing. Data-driven approaches offer promising solutions for blade icing detection, but they rely on a considerable amount of data. Data ... -
Data Analysis and Modeling of Ship Motion Data for Offshore Operations
Cheng, Xu (Doctoral theses at NTNU;2020:180, Doctoral thesis, 2020)Ship intelligence aims to make the marine and offshore industries more efficient, innovative, and adaptable to future operations. In fact, ship intelligence has been listed as an important part of the digital agenda, one ... -
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 ... -
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 ... -
Machine learning based digital twin framework for aquaculture net cage system
Skauen, Håvard (Master thesis, 2022)I denne master oppgaven er det presentert et rammeverk for digital tvilling for fiskeoppdrettsanlegg ved å bruke numerisk simulering, maskin læring og sensor data. De numeriske simuleringene er utført med programvaren FhSim ... -
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 ... -
Multi-Bit & Sequential Decentralized Detection of a Noncooperative Moving Target through a Generalized Rao Test
Cheng, Xu; Ciuonzo, Domenico; Salvo Rossi, Pierluigi; Wang, Xiaodong; Wang, Wei (Peer reviewed; Journal article, 2021)We consider decentralized detection (DD) of an uncooperative moving target via wireless sensor networks (WSNs), measured in zero-mean unimodal noise. To address energy and bandwidth limitations, the sensors use multi-level ... -
Multi-bit Decentralized Detection through Fusing Smart and Dumb Sensors based on Rao Test
Cheng, Xu; Ciuonzo, Domenico; Salvo Rossi, Pierluigi (Journal article; Peer reviewed, 2019)We consider Decentralized Detection (DD) of an unknown signal corrupted by zero-mean unimodal noise via Wireless Sensor Networks (WSNs). We assume the presence of both smart and dumb sensors: the former transmit unquantized ... -
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 ... -
Multiscale Wavelet-Driven Graph Convolutional Network for Blade Icing Detection of Wind Turbines
Lai, Zhichen; Cheng, Xu; Liu, Xiufeng; Huang, Lizhen; Liu, Yongping (Peer reviewed; Journal article, 2022)Blade icing detection is critical to maintaining the health of wind turbines, especially in cold climates. Rapid and accurate icing detection allows proper control of wind turbines, including shutting down and clearing the ... -
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 ...