Study of data analysis model based on big data technology

2016 ◽  
Author(s):  
Jinhua Chen ◽  
Qin Jiang ◽  
Yuxin Wang ◽  
Jing Tang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Analysis Model ◽  
Model Based ◽  
Big Data Technology

scholarly journals A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

2019 ◽  
Vol 11 (13) ◽  
pp. 3499 ◽  
Author(s):  
Se-Hoon Jung ◽  
Jun-Ho Huh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Transmission Line ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Principal Component ◽  
Big Data Analysis ◽  
Standard Normal Distribution ◽  
Analysis Model ◽  
Q Learning

This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

scholarly journals Big Data Analysis Model for MRO Business Using Artificial Intelligence System Concept

2018 ◽  
Vol 7 (3.33) ◽  
pp. 134
Author(s):  
Inhwan JUNG ◽  
He SUN ◽  
Jangmook KANG ◽  
Choong Hyong Lee ◽  
Sangwon LEE
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analysis ◽  
Business Models ◽  
Analysis Model ◽  
Artificial Intelligence System ◽  
Shipbuilding Industry ◽  
Intelligence System ◽  
System Concept ◽  
Ship Maintenance

The rapidly changing environment of the shipbuilding industry has put Korea’s shipbuilding industry in a crisis. The purpose of this study was to develop a business model to maintain, maintain and operate Big Data-based MRO(Maintenance, Repair, and Operation) consumables, which is expected to be the new growth engine for the domestic shipbuilding industry. Although Korean shipbuilders have world-class technologies for ship dogma, the market for ship maintenance and repair is still in its infancy. For Korean shipbuilders, MRO business can be a growth engine that will provide food for the next 30 years, but to do so, we need to make sure that everything that happens in the entire process, from ship design to maintenance and maintenance. Therefore, by systematically establishing Big Data related to components and developing MRO business models based on data analysis capabilities using Artificial Intelligence system concept, we can develop new growth engines for related industries in Ship Industry.  

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