• Journal of Convergence for Information Technology
• /
• v.7 no.1
• /
• pp.55-60
• /
• 2017

In order to provide intelligent services without human intervention in the Internet of Things environment, it is necessary to analyze the big data generated by the IoT device and learn the normal pattern, and to predict the abnormal symptoms such as faulty or malfunction based on the learned normal pattern. The purpose of this study is to implement a machine learning model that can predict product failure by analyzing big data generated in various devices of product process. The machine learning model uses the big data analysis tool R because it needs to analyze based on existing data with a large volume. The data collected in the product process include the information about product faulty, so supervised learning model is used. As a result of the study, I classify the variables and variable conditions affecting the product failure, and proposed a prediction model for the product failure based on the decision tree. In addition, the predictive power of the model was significantly higher in the conformity and performance evaluation analysis of the model using the ROC curve.

• The Korean Journal of Applied Statistics
• /
• v.29 no.6
• /
• pp.1077-1094
• /
• 2016

Apache Spark is a fast and general-purpose cluster computing package. It provides a new abstraction named resilient distributed dataset, which is capable of support for fault tolerance while keeping data in memory. This type of abstraction results in a significant speedup compared to legacy large-scale data framework, MapReduce. In particular, Spark framework is suitable for iterative machine learning applications such as logistic regression and K-means clustering, and interactive data querying. Spark also supports high level libraries for various applications such as machine learning, streaming data processing, database querying and graph data mining thanks to its versatility. In this work, we introduce the concept and programming model of Spark as well as show some implementations of simple statistical computing applications. We also review the machine learning package MLlib, and the R language interface SparkR.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.535-536
• /
• 2021

빅데이터를 활용한 기계학습 모델을 개발하기 위해서는 빅데이터 처리를 위한 플랫폼과 딥러닝 프레임 워크 등 고급 분석을 수행할 수 있는 도구의 활용이 동시에 요구된다. 그러나 빅데이터 플랫폼과 딥러닝 프레임워크를 자유롭게 활용하기 위해서는 상당한 수준의 기술적 지식과 경험이 필요하다. 또한 빅데이터를 이용한 딥러닝 모델을 개발할 경우 분산처리와 병렬처리에 대한 지식과 추가적인 작업이 요구된다. 본 연구에서는 빅데이터를 활용한 기계학습 모형을 자유롭게 개발 및 공유하고 분산 딥러닝을 위한 시스템적 지원을 통해 분야별로 딥러닝 모형을 개발하는 응용 연구자들이 활용할 수 있는 플랫폼을 제시하였다. 본 연구를 통해 다양한 분야의 연구자들이 자신의 데이터를 이용하여 모형을 개발할 경우 분산처리와 병렬처리를 위한 기술적 제약을 극복하고 보다 빠르고 효율적인 방법으로 모형을 개발하고 현업에 활용할 수 있을 것으로 기대한다.

• Journal of the KSME
• /
• v.43 no.6
• /
• pp.58-61
• /
• 2003

이 글에서는 굴착기의 용접구조물에 대한 필드 하자데이터의 분석과, 용접조인트의 피로이론을 적용한 수명예측, 및 가속수명시험 결과들 사이에 상관관계 분석을 통한 신뢰도 높은 구조물 수명예측 및 검증 프로세스에 관해 소개하고자 한다.

• Journal of the Society of Disaster Information
• /
• v.16 no.4
• /
• pp.682-690
• /
• 2020

Purpose: This study builds a database by collecting and refining disaster occurrence data and real-time weather and atmospheric data. In conjunction with the public data provided by the API, we propose a service model for the Big Data-based Urban Safety Index. Method: The plan is to provide a way to collect various information related to disaster occurrence by utilizing public data and SNS, and to identify and cope with disaster situations in areas of interest by real-time dashboards. Result: Compared with the prediction model by extracting the characteristics of the local safety index and weather and air relationship by area, the regional safety index in the area of traffic accidents confirmed that there is a significant correlation with weather and atmospheric data. Conclusion: It proposed a system that generates a prediction model for safety index based on machine learning algorithm and displays safety index by sector on a map in areas of interest to users.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.2
• /
• pp.225-230
• /
• 2020

Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.05a
• /
• pp.275-276
• /
• 2023

산업 현장의 기계 시설물 고장 문제는 큰 인명피해와 경제적 손실을 초래할 수 있기 때문에, 기계 시설물의 상태를 기반하여 고장을 진단하는 것은 대단히 중요하다. 따라서, 본 연구에서는 전류 센서 데이터를 활용하여, 시설물의 고장 여부를 진단할 수 있는 알고리즘을 제안한다. 본 연구에 활용된 전류 센서 데이터는 x, y, z축을 가진 3상 전류 데이터로 구성되어 있으며, 2kHz로 1초간 샘플링 되어 있다. 본 연구에서는 2차원적 특성을 가지는 전류 센서 데이터를 분석하기 위해 CNN(Convolution Neural Network)을 활용한다. 시설물의 고장진단에 가장 적합한 모델을 선정하기 위해 CNN의 대표적인 백본 네트워크를 활용하여, 결과를 비교하였다. 실험 결과, 본 연구에서 구성한 후보 백본 네트워크 중 ResNet의 분류 정확도가 98.5%로 가장 높게 나타났다.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.46 no.6
• /
• pp.44-55
• /
• 2009

Diagnosis of diseases using gene expression data obtained from microarray chip is an active research area recently. It has been done by general machine learning algorithms, because it is difficult to analyze directly. However, recent research results about the analysis based on the interaction between genes is essential for the gene expression analysis, which means the analysis using the traditional machine learning algorithms has limitations. In this paper, we classify the gene expression data using the hyper-network model that considers the higher-order correlations between the features, and then compares the classification accuracies. And also, we present the new hypo-network model that improve the disadvantage of existing model, and compare the processing performances of the existing hypo-network model based on general sequential processor and the improved hypo-network model implemented on parallel processors. In the experimental results, we show that the performance of our model shows improved and competitive classification performance than traditional machine learning methods, as well as, the existing hypo-network model. We show that the performance is maximized when the hypernetwork model is implemented on our parallel processors.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2018.05a
• /
• pp.358-361
• /
• 2018

비정형 데이터 증가로 텍스트 마이닝을 사용해 데이터를 분석하는 연구가 주목받고 있다. 감성분석은 단어와 문맥을 분석하여 텍스트의 감정을 파악하는 기술이다. 본 논문에서는 감성분석 연구 동향, 적용분야, 방법론에 관해 분석하고 기술하려 한다. 감성분석은 2001년 채팅의 감정을 분석하면서 시작되었고, 2008년부터 본격적으로 연구가 진행되었다. 감성분석은 SNS, 상품 후기, 영화평, 뉴스 기사 등 다양한 데이터에 적용되고 있으며, 사회이슈 찬반 분석과 장소 선호도 분석 등 다양한 연구에서 사용되었다. 감성분석 방법은 감성사전을 이용하는 방식과 기계학습을 사용하는 방식으로 나누어지며 분석 방법을 발전시키기 위한 연구가 진행되고 있다.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.5
• /
• pp.1105-1115
• /
• 2015

Big data analysis for disaster have been recently started especially to text data such as social media. Social data usually supports for the final two stages of disaster management, which consists of four stages: prevention, preparation, response and recovery. Otherwise, big data analysis for meteorologic data can contribute to the prevention and preparation. This motivated us to review big data technologies dealing with non-text data rather than text in natural disaster area. To this end, we first explain the main keywords, big data, data mining and machine learning in sec. 2. Then we introduce the state-of-the-art machine learning techniques in meteorology-related field sec. 3. We show how the traditional machine learning techniques have been adapted for climatic data by taking into account the domain specificity. The application of these techniques in natural disaster response are then introduced (sec. 4), and we finally conclude with several future research directions.