A probabilistic model of human error assessment for autonomous cargo ships focusing on human–autonomy collaboration

Zhang, Mingyang; Zhang, Di; Yao, Houjie; Zhang, Kai

doi:10.1016/j.ssci.2020.104838

Cited by 126 publications

(54 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Neural network prediction method is a method to conduct economic forecast by using neural network. As most economic problems are non-linear and neural network model has excellent non-linear tting, people have attached more and more importance to predict these complex problems [5]. The scale of neural network structure affects the learning capacity and generalization ability of neural network.…”

Section: Related Workmentioning

confidence: 99%

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

He¹

2020

Preprint

View full text Add to dashboard Cite

Abstract As various factors which affect the development of global market economy become increasingly uncertain, economy and commodity will become more and more fluctuating in economic operation. With its strong non-linear mapping capacity, artificial neural network has already been applied in many fields, time series analysis and trend prediction. Cloud computing can interact fast with service provider at the minimum management cost. This paper proposes an economic forecast and optimized resource allocation model based on cloud computing and BP neural network. Its main goal is to break down a complex prediction task into several sub-tasks, effectively reduce the workload of a single computer and enhance the operating efficiency. Simulation results show that the proposed method does not rely on gradient information, and has strong optimization calculation ability. At the same time, it can analyze and predict economic management, so as to provide strong decision support for decision makers.

show abstract

Section: Related Workmentioning

confidence: 99%

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

He¹

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…These data include video and pictures of the radiator, temperature parameters, etc [20,21]. Using sensors to obtain large amounts of data for condition assessment can help us adjust the cooling system to achieve the best condition [22]. In order to analyze the heat balance of the excavator loader under various load conditions, the heat balance of the whole vehicle is tested, and the performance of the excavator loader system is evaluated.…”

Section: Test Instrument and Data Acquisition Point Arrangementmentioning

confidence: 99%

Heat Dissipation Performance of Loader-Digger Cooling System based on Simulation and Test Data

Chen

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

High temperature of the radiator group is harmful to the power system and hydraulic system. In order to improve the heat dissipation performance of the loader, the flow field characteristics of the cooling chamber are analyzed by simulation and heat balance test. Firstly, the mathematical model of heat flow is established. Secondly, the flow field in the cooling chamber under different speeds is simulated based on CFX. And then the influence of fan position and internal flow field distribution on radiator performance is studied. Through the simulation of four different distances, it is concluded that the optimal distance between cooling fan and radiator is 76mm. Finally, a testing system is built for the temperature acquisition of engine water radiator, torque converter oil radiator, hydraulic oil radiator and air-to-air cooler of the hood structure, and then the simulation results are verified. The test results show that the heat dissipation performance of the whole machine meets the requirements after optimization, and the optimized scheme can make the loader-digger in an efficient and energy-saving operation state.

show abstract

“…Neural network prediction method is a method to conduct economic forecast by using neural network. As most economic problems are nonlinear and neural network model has excellent non-linear fitting, people have attached more and more importance to predict these complex problems [5]. The scale of neural network structure affects the learning capacity and generalization ability of neural network.…”

Section: Related Workmentioning

confidence: 99%

Study of economic management forecast and optimized resource allocation based on cloud computing and neural network

2020

J Wireless Com Network

View full text Add to dashboard Cite

As various factors which affect the development of global market economy become increasingly uncertain, economy and commodity will become more and more fluctuating in economic operation. With its strong nonlinear mapping capacity, artificial neural network has already been applied in many fields, time series analysis, and trend prediction. Cloud computing can interact fast with service provider at the minimum management cost. This paper proposes an economic forecast and optimized resource allocation model based on cloud computing and BP neural network. Its main goal is to break down a complex prediction task into several subtasks, effectively reduce the workload of a single computer and enhance the operating efficiency. Simulation results show that the proposed method does not rely on gradient information and has strong optimization calculation ability. At the same time, it can analyze and predict economic management, so as to provide strong decision support for decision makers.

show abstract

A probabilistic model of human error assessment for autonomous cargo ships focusing on human–autonomy collaboration

Cited by 126 publications

References 51 publications

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

Heat Dissipation Performance of Loader-Digger Cooling System based on Simulation and Test Data

Study of economic management forecast and optimized resource allocation based on cloud computing and neural network

Contact Info

Product

Resources

About