Research on the hydrographic survey cycle for updating navigational charts

2021 ◽  
pp. 1-13
Author(s):  
Jing Duan ◽  
Xiaoxia Wan ◽  
Jianan Luo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Calculation Model ◽  
Impact Factors ◽  
Factors Affecting ◽  
Material Resources ◽  
Hydrographic Survey ◽  
Survey Planning ◽  
The Impact ◽  
The Relationship

Abstract Due to the vast ocean area and limited human and material resources, hydrographic survey must be carried out in a selective and well-planned way. Therefore, scientific planning of hydrographic surveys to ensure the effectiveness of navigational charts has become an urgent issue to be addressed by the hydrographic office of each coastal state. In this study, a reasonable calculation model of hydrographic survey cycle is established, which can be used to make the plan of navigational chart updating. The paper takes 493 navigational charts of Chinese coastal ports and fairways as the research object, analyses the fundamental factors affecting the hydrographic survey cycle and gives them weights, proposes to use the BP neural network to construct the relationship between the cycle and the impact factors, and finally establishes a calculation model of the hydrographic survey cycle. It has been verified that the calculation cycle of the model is effective, and it can provide reference for hydrographic survey planning and chart updating, as well as suggestions for navigation safety.

Prediction of Mine Inrush Water Based on BP Neural Network Method

2014 ◽  
Vol 989-994 ◽  
pp. 1814-1820 ◽  
Author(s):  
Ai Jun Shao ◽  
Qing Xin Meng ◽  
Shi Wen Wang ◽  
Ying Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Mine Water ◽  
Influential Factors ◽  
Water Inflow ◽  
Impact Factors ◽  
Artificial Neural ◽  
Inrush Water ◽  
The Impact

Based on predictions of the mine inflow of water and the complexity of influential factors, a method of BP neural network is put forward for mine inrush water prediction in this paper. We chose proper impact factors and establish non-linear artificial neural network prediction model after analyzed the impact factors of mine water inflow in Shandong Heiwang iron, and also made one prediction with normal mine water inflow during the iron mining operation. It turned out that the result can match with the actual prediction data, which make it possible to predict the mine water inflow with the prediction of Artificial Neural Network.

scholarly journals Macro Modeling of V-Shaped Electro-Thermal MEMS Actuator with Human Error Factor

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 622
Author(s):  
Dongpeng Zhang ◽  
Anjiang Cai ◽  
Yulong Zhao ◽  
Tengjiang Hu
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Bp Neural Network ◽  
Human Error ◽  
Calculation Model ◽  
Fuzzy Mathematics ◽  
Ansys Simulation ◽  
Macro Modeling ◽  
Error Factor ◽  
Simulation Results

The V-shaped electro-thermal MEMS actuator model, with the human error factor taken into account, is presented in this paper through the cascading ANSYS simulation model and the Fuzzy mathematics calculation model. The Fuzzy mathematics calculation model introduces the human error factor into the MEMS actuator model by using the BP neural network, which effectively reduces the error between ANSYS simulation results and experimental results to less than 1%. Meanwhile, the V-shaped electro-thermal MEMS actuator model, with the human error factor included, will become more accurate as the database of the V-shaped electro-thermal actuator model grows.

scholarly journals A Method of Ore Blending Based on the Quality of Beneficiation and Its Application in a Concentrator

2021 ◽  
Vol 11 (11) ◽  
pp. 5092
Author(s):  
Bingyu Liu ◽  
Dingsen Zhang ◽  
Xianwen Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Industrial Test ◽  
Modeling Method ◽  
Daily Work ◽  
Production Site ◽  
Neural Network Algorithm ◽  
The Relationship ◽  
Ore Dressing

Ore blending is an essential part of daily work in the concentrator. Qualified ore dressing products can make the ore dressing more smoothly. The existing ore blending modeling usually only considers the quality of ore blending products and ignores the effect of ore blending on ore dressing. This research proposes an ore blending modeling method based on the quality of the beneficiation concentrate. The relationship between the properties of ore blending products and the total concentrate recovery is fitted by the ABC-BP neural network algorithm, taken as the optimization goal to guarantee the quality of ore dressing products at the source. The ore blending system was developed and operated stably on the production site. The industrial test and actual production results have proved the effectiveness and reliability of this method.

scholarly journals Factors affecting the organizational performance of manufacturing firms

2017 ◽  
Vol 9 ◽  
pp. 184797901771262 ◽  
Author(s):  
Ahmad Adnan Al-Tit
Keyword(s):  
Organizational Performance ◽  
Measurement Instrument ◽  
Manufacturing Firms ◽  
Factors Affecting ◽  
Mediating Role ◽  
Values And Beliefs ◽  
The Individual ◽  
The Impact ◽  
The Relationship

Numerous studies have been conducted to explore the individual effects of organizational culture (OC) and supply chain management (SCM) practices on organizational performance (OP) in different settings. The aim of this study is to investigate the impact of OC and SCM on OP. The sample of the study consisted of 93 manufacturing firms in Jordan. Data were collected from employees and managers from different divisions using a reliable and valid measurement instrument. The findings confirm that both OC and SCM practices significantly predict OP. The current study is significant in reliably testing the relationship between SCM practices and OP; however, it is necessary to consider cultural assumptions, values and beliefs as the impact of OC on OP is greater than the impact of SCM practices. Based on the results, future studies should consider the moderating and mediating role of OC on the relationship between SCM practices and OP.

scholarly journals Analyzing the Effect of the Senior Management’s Support on the Relationship between Factors Affecting and Employees’ Performance in the Al-Zawiya University of Libya

2017 ◽  
Vol 13 (3) ◽  
pp. 42
Author(s):  
Nasreddin Ramadhan Dukhan ◽  
Norhisham Mohamad ◽  
Asbi B Ali
Keyword(s):  
Likert Scale ◽  
Factors Affecting ◽  
The Mean ◽  
Multiple Groups ◽  
Strongly Agree ◽  
The Impact ◽  
The Relationship ◽  
Variable Performance

This study aims to test the influence of the senior management’s support as a moderating variable on the relationship between the independent factors (Training, Empowerment, Motivation and Communication) and the dependent variable (Performance of Employees). (SEM-AMOS) is used to test the impact of the moderating variable. Where it is depended on the method of sampling or analysis of what is known as multiple-groups analysis. The paragraphs of the senior management’s support variable are collected and divided into two groups according to the mean of the total paragraphs. In addition, according to the relative weights given to the paragraphs of the questionnaire, using a five- point’s Likert scale: 1= strongly disagree to 5 = strongly agree. The first group consisted of the grades less than the mean and it is considered as the group which is non-supporters of the existence of support. While the second group consisted of the grades higher than the mean and considered as the group which is a supporter of the existence of support. The study found that the model of study in the presence of the support of the senior management’s is appropriate for the second group and inappropriate in light of the lack of support by the senior management’s support for the first group.

The Impact of Foreign Language Learning Style on Strategies Preferences in Art Colleges

2014 ◽  
Vol 971-973 ◽  
pp. 2677-2680
Author(s):  
Di Jiao
Keyword(s):  
Foreign Language ◽  
Learning Styles ◽  
Language Learning ◽  
Learning Strategies ◽  
Learning Style ◽  
Foreign Language Learning ◽  
English Learning ◽  
Factors Affecting ◽  
The Impact ◽  
The Relationship

Factors affecting students’ English learning performances are always debated among language researchers. This research is carried out in art colleges to figure out the students’ preferences in learning styles and learning strategies as well as the relationship between them. Questionnaires have been applied and data have been dealt with by SPSS. This research has shown that students in the art college tend to be visual and individual learners, and thus they prefer to adopt metacognitive, memory and affective strategies.

scholarly journals Examining partial proportional odds model in analyzing severity of high-speed railway accident

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Wang ◽  
Yinghan Wang ◽  
Yichuan Peng ◽  
Jian John Lu
Keyword(s):  
High Speed ◽  
Proportional Odds Model ◽  
Impact Factors ◽  
Proportional Odds ◽  
High Speed Railway ◽  
Severity Level ◽  
Content Type ◽  
Factors Affecting ◽  
Partial Proportional Odds Model ◽  
The Impact

Purpose The operation safety of the high-speed railway has been widely concerned. Due to the joint influence of the environment, equipment, personnel and other factors, accidents are inevitable in the operation process. However, few studies focused on identifying contributing factors affecting the severity of high-speed railway accidents because of the difficulty in obtaining field data. This study aims to investigate the impact factors affecting the severity of the general high-speed railway. Design/methodology/approach A total of 14 potential factors were examined from 475 data. The severity level is categorized into four levels by delay time and the number of subsequent trains that are affected by the accident. The partial proportional odds model was constructed to relax the constraint of the parallel line assumption. Findings The results show that 10 factors are found to significantly affect accident severity. Moreover, the factors including automation train protection (ATP) system fault, platform screen door and train door fault, traction converter fault and railway clearance intrusion by objects have an effect on reducing the severity level. On the contrary, the accidents caused by objects hanging on the catenary, pantograph fault, passenger misconducting or sudden illness, personnel intrusion of railway clearance, driving on heavy rain or snow and train collision against objects tend to be more severe. Originality/value The research results are very useful for mitigating the consequences of high-speed rail accidents.

Thermal–Mechanical Coupling Evaluation of the Panel Performance of a Prefabricated Cabin-Type Substation Based on Machine Learning

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 93
Author(s):  
Xiangsheng Lei ◽  
Jinwu Ouyang ◽  
Yanfeng Wang ◽  
Xinghua Wang ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Numerical Simulation ◽  
Fire Resistance ◽  
Bp Neural Network ◽  
Stress Test ◽  
Normal Operation ◽  
Model Parameters ◽  
Mechanical Coupling ◽  
The Impact

The panel performance of a prefabricated cabin-type substation under the impact of fires plays a vital role in the normal operation of the substation. However, current evaluations of the panel performance of substations under fire still focus on fire resistance tests, which seldom consider the relationship between fire behavior and the mechanical load of the panel under the impact of fires. Aiming at the complex and uncertain relationship between the thermal and mechanical performance of the substation panel under impact of fires, this paper proposes a machine learning method based on a BP neural network. First, the fire resistance test and the stress test of the panel is carried out, then a machine learning model is established based on the BP neural network. According to the collected data, the model parameters are obtained through a series of training and verification processes. Meanwhile, the correlation between the panel performance and fire resistance was obtained. Finally, related parameters are input into the thermal–mechanical coupling evaluation model for the substation panel performance to evaluate the fire resistance performance of the substation panel. To verify the correctness of the established model, numerical simulation of the fire test and stress test of the panel is conducted, and numerical simulation samples are predicted by the trained model. The results show that the prediction curve of neural network is closer to the real results compared with the numerical simulation, and the established model can accurately evaluate the thermal–mechanical coupling performance of the substation panel under fire.

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