Abstract
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.
Highlights
In recent years, the application of big data analytics models in various industrial/service/ technical sectors, including financial/banking [1,2], health care [3,4], Internet of Things (IoT) [5,6], communication [7,8], smart cities [9,10], and transportation [11] has resulted in many significant innovations supporting market growth in their respective systems
This study proposes an analysis and prediction model for transmission line tower outlier data to assess problems with transmission line tower big data based on an altered K-means algorithm that supplements the K-means algorithm according to non-labeled sensor data and deep reinforcement learning for self-study
The rest of this paper is organized as follows: Chapter 2 introduces previous studies on the old K-means algorithm for data classification, reinforcement learning, and transmission line tower big data analysis models; Chapter 3 proposes an altered K-means algorithm to assess outliers with transmission line tower big data and describes a reinforcement learning model of A-Deep Q-Learning to increase the efficiency of transmission line tower big data analysis; Chapter 4 describes the policy simulations performed to examine the usability of the proposed A-Deep Q-Learning; Chapter 5 discusses the experiments and performance evaluations carried out for the proposed technique; Chapter 6 presents the content and findings of this study and proposes future research plans
Summary
The application of big data analytics models in various industrial/service/ technical sectors, including financial/banking [1,2], health care [3,4], Internet of Things (IoT) [5,6], communication [7,8], smart cities [9,10], and transportation [11] has resulted in many significant innovations supporting market growth in their respective systems. As a technique of supervised learning to increase analysis efficiency, correlation analysis should include a method of estimating and testing the relational expression after predicting relations based on dispersion in order to analyze non-linear internal data. Another problem with using correlation analysis research in analyzing transmission line tower IoT data is the difficulty of a certain data item to influence two data items in a complex way. This study proposes an analysis and prediction model for transmission line tower outlier data to assess problems with transmission line tower big data based on an altered K-means algorithm that supplements the K-means algorithm according to non-labeled sensor data and deep reinforcement learning for self-study. The rest of this paper is organized as follows: Chapter 2 introduces previous studies on the old K-means algorithm for data classification, reinforcement learning, and transmission line tower big data analysis models; Chapter 3 proposes an altered K-means algorithm to assess outliers with transmission line tower big data and describes a reinforcement learning model of A-Deep Q-Learning to increase the efficiency of transmission line tower big data analysis; Chapter 4 describes the policy simulations performed to examine the usability of the proposed A-Deep Q-Learning; Chapter 5 discusses the experiments and performance evaluations carried out for the proposed technique; Chapter 6 presents the content and findings of this study and proposes future research plans
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