• 한국위성정보통신학회논문지
• /
• 제10권3호
• /
• pp.11-15
• /
• 2015

본 연구에서는 수재해 정보 플랫폼 내 자료처리 시스템 설계를 위해 자료처리 과정의 복잡도를 분석하고 이에 따른 설계 방안을 제시하였다. 일반적으로 자료를 수집하고 분석하는 시스템은 자료와 알고리즘의 자료처리 과정이 고정된 고정 자료-알고리즘 자료처리 방식을 사용한다. 하지만 시스템의 복잡도가 증가하면 자료처리 시스템에서 관리해야 하는 자료처리 과정의 수가 급증하는 문제가 발생한다. 이를 해결하기 위해 자료와 알고리즘 사이에 인터페이스가 존재하는 동적 자료-알고리즘 자료처리 방식을 적용할 수 있다. 각 방식의 장단점을 분석한 뒤, 수재해 정보 플랫폼에 최적화된 자료처리 시스템의 설계안을 제시할 수 있었다.

• Journal of the Korean Data and Information Science Society
• /
• 제21권1호
• /
• pp.185-194
• /
• 2010

In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.

• 자동차안전학회지
• /
• 제14권2호
• /
• pp.51-56
• /
• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• 설비공학논문집
• /
• 제15권3호
• /
• pp.159-165
• /
• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. To detect partial faults of the air-conditioning system, a neural network algorithm may be used. In this study, the neural network algorithm using normalized input data by the standard deviation was applied. And the [7$\times$10$\times$10$\times$1] neural network structure was selected. Test results showed that the neural network algorithm using normalized input data was very effective to detect the condenser fouling and the evaporator fan fault of an air-conditioning system.

• 자동차안전학회지
• /
• 제5권1호
• /
• pp.5-10
• /
• 2013

This paper describes a Forward Collision Warning (FCW) system based on the radar driven fusion with camera. The objective of FCW system is to provide an appropriate alert with satisfying the evaluation scenarios of US-NCAP and a driver acceptance. For this purpose, this paper proposed a data fusion algorithm and a collision warning algorithm. The data fusion algorithm generates information of fusion target depending on the confidence of camera sensor. The collision warning algorithm calculates indexes and determines an appropriate alert-timing by using analysis results of manual driving data. The FCW system with the proposed data fusion and collision warning algorithm was investigated via scenarios of US-NCAP and a real-road driving. It is shown that the proposed FCW system can improve the accuracy of an alarm-timing and reduce the false alarm in real roads.

• KEPCO Journal on Electric Power and Energy
• /
• 제7권1호
• /
• pp.73-77
• /
• 2021

As the big data analysis technologies has been developed worldwide, the importance of asset management for electric power facilities based data analysis is increasing. It is essential to secure quality of data that will determine the performance of the RISK evaluation algorithm for asset management. To improve reliability of asset management, asset data must be preprocessed. In particular, the process of cleaning dirty data is required, and it is also urgent to develop an algorithm to reduce time and improve accuracy for data treatment. In this paper, the result of the development of an automatic cleaning algorithm specialized in overhead transmission asset data is presented. A data cleaning algorithm was developed to enable data clean by analyzing quality and overall pattern of raw data.

• 한국항행학회논문지
• /
• 제23권5호
• /
• pp.339-344
• /
• 2019

정보통신 기술 발전에 따른 데이터 보안 위협의 상승에 대비하기 위하여 유도탄 점검 장비에 저장된 자료의 안전성을 보장할 수 있는 기술은 중요하다. 이를 위하여 자료가 누출 되더라도 복원할 수 없게 데이터 저장 시 암호화를 수행하여야 하고, 해당 데이터를 복호화한 후에도 무결성이 보장되어야 한다. 본 논문에서는 데이터 저장 시 대칭키 암호시스템인 AES 알고리즘을 유도탄 점검장비에 적용하고, 각 AES의 각 비트 별 데이터 양에 따른 암호화 복호화 시간을 측정하였다. 또한 기존 점검 시스템에 AES Rijndael 알고리즘을 구현하여 암호화 수행으로 인한 영향을 분석하였고 제안한 암호화 알고리즘을 기존 시스템에 적용하는 것이 적합한지 확인 하였다. 용량별 / 알고리즘 비트수별로 분석한 결과 제안한 알고리즘 적용이 시스템 운용에 영향 없음을 확인하였고, 최적의 알고리즘을 도출할 수 있었다. 추가로 복호화 결과를 초기 데이터와 비교하였고, 해당 알고리즘이 데이터 무결성을 보장할 수 있음을 확인할 수 있었다.

• International Journal of Contents
• /
• 제17권3호
• /
• pp.74-83
• /
• 2021

A location-aware algorithm is proposed in this study to optimize the system performance of distributed systems for processing big data with low data reliability and application performance. Compared with previous algorithms, the location-aware data block placement algorithm uses data block placement and node data recovery strategies to improve data application performance and reliability. Simulation and actual cluster tests showed that the location-aware placement algorithm proposed in this study could greatly improve data reliability and shorten the application processing time of I/O interfaces in real-time.

• KEPCO Journal on Electric Power and Energy
• /
• 제7권1호
• /
• pp.79-84
• /
• 2021

The fundamental element to be kept for big data analysis, artificial intelligence technologies and asset management system is a data quality, which could directly affect the entire system reliability. For this reason, the momentum of data cleaning works is recently increased and data cleaning methods have been investigating around the world. In the field of electric power, however, asset data cleaning methods have not been fully determined therefore, automatic cleaning algorithm of asset data for transmission cables has been studied in this paper. Cleaning algorithm is composed of missing data treatment and outlier data one. Rule-based and expert opinion based cleaning methods are converged and utilized for these dirty data.

• 대한임베디드공학회논문지
• /
• 제8권6호
• /
• pp.339-345
• /
• 2013

This paper addresses the system lifetime maximization algorithm in multi-hop sensor network system. A multi-hop sensor network consists of many battery-driven sensor nodes that collaborate with each other to gather, process, and communicate information using wireless communications. As sensor-driven applications become increasingly integrated into our lives, we propose a energy-aware scheme where each sensor node transmits informative data with adaptive data rate to minimize system energy consumption. We show the optimal data rate to maximize the system lifetime in terms of remaining system energy. Furthermore, the proposed algorithm experimentally shows longer system lifetime in comparison with greedy algorithm.