• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.612-614
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• 2022

This paper propose a clustering algorithm for mobile nodes that possible more efficient clustering using context-aware attribute information in adaptive learning. In typically, the data will be provided to classify interrelationships within cluster properties. If a new properties are treated as contaminated information in comparative clustering, it can be treated as contaminated properties in comparison clustering. In this paper, To solve this problems in this paper, we have new present a context-awareness learning based model that can analyzes the clustering attributed parameters from the node properties using accumulated information properties.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.97-113
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• 2023

Swift and appropriate responses in emergency situations like objects falling on the road can bring convenience to road users and effectively reduces secondary traffic accidents. In Korea, current intelligent transportation system (ITS)-based detection systems for emergency road situations mainly rely on loop detectors and CCTV cameras, which only capture road data within detection range of the equipment. Therefore, a new detection method is needed to identify emergency situations in spatially shaded areas that existing ITS detection systems cannot reach. In this study, we propose a ResNet-based algorithm that detects and classifies emergency situations from vehicle camera footage. We collected front-view driving videos recorded on Korean highways, labeling each video by defining the type of emergency, and training the proposed algorithm with the data.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.201-208
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• 2014

In this paper, we propose the classifier structure design algorithm using DTW. Proposed algorithm uses DTW result to design the binary tree architecture based on the SVM which classify the multi-class data. Design the binary tree architecture for Support Vector Machine(SVM-BTA) using the threshold criterion calculated by the sum columns in square matrix which components are the reference data from each class. For comparison the performance of the proposed algorithm, compare the results of classifiers which binary tree structure are designed based on database and k-means algorithm. The data used for classification is 333 signals from 18 classes of underwater transient noise. The proposed classifier has been improved classification performance compared with classifier designed by database system, and probability of detection for non-biological transient signal has improved compare with classifiers using k-means algorithm. The proposed SVM-BTA classified 68.77% of biological sound(BO), 92.86% chain(CHAN) the mechanical sound, and 100% of the 6 kinds of the other classes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.97-102
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• 2019

There are many difficulties to define because there is no systematic classification and analysis method using accurate criteria or numerical values for creative people. In order to solve this problem, this study attempts to analyze how to distinguish creative people and what kind of personality they have when distinguishing creative people. In this study, I first survey the Big 5 personality trait, classify and analyze the data set using the data mining tool WEKA, and then analyze the data set related to the creativity The goal is to analyze the features using various machine learning techniques. I use seven feature selection algorithms, select feature groups classified by feature selection algorithms, apply them to machine learning algorithms to find out the accuracy, and derive the results.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.870-874
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• 2009

Genetic algorithms can be effectively used when generating abstract images in an automatic way. However, managing huge number of automatically generated images has been problematic without sufficient managing mechanisms. This paper presents effective classification scheme for the abstract Affine images using form, emotion and color facets, and implements image databases.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.15-26
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• 2003

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an alternative approach which uses the least mean square (LMS) method and expectation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm In conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Proceedings of the ESK Conference
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• 1995.10a
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• pp.37-37
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• 1995

CNS의 인간공학적 실험 평가 항목중 중요 요소의 하나인 운전원의 인지적 작업부하(정보입력, 의사결정, 및 정서적 부하)를 평가하기 위해 인지적부하를 부과할 수 있는 사건 시나리오 구성방법, 피실험자에 대한 훈련 방법, 실험과 디브리핑을 통한 운전원 행위 및 사건 경위 기록 체계를 개발하였다. 운전원 행위자료 데이타베이스에는 특정 요소행위 시점, 관측행위 분류, 사용된 표시장치, 의사결정내용, 사용된 조종장치, 조종행위분류, 운전원간 의사소통 내용, 정서상태, 표시장치 및 조종장치의 사용편의성 여부 및 불편이유, 주요사건 발생시점에 관한 정보가 기록되었다. 운전중 CNS 운전원에 제시되는 자극의 변화에 수반되는 운전원의 생리적 변화를 측정하여 이로부터 운전원이 받는 정신부하를 측정하는 체계를 개발하였다. 이를 위해 ECG, EEG, 호흡패턴,피부온도, 수평EOG, 수직EOG, 심박률, 및 호흡률의 생리변수에 대한 측정이 이루 어졌으며 각각의 신호를 초당 100개씩 샘플링하여 A/D 변환하고 정신부하 추정 알고리즘을 거쳐 정신부하 여부를 판정하는 점수를 구하였다. 다차원의 정신부하 판정 점수는 OR gate에 의해 통합되어 정신부하 발생시점과 함께 정신부하데이터베이스에 기록되었다.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.39-47
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• 2017

The Internet have increased the number of digital web documents related to the history and traditions of Korean Culture. However, users who search for creators or materials related to traditional cultures are not able to get the information they want and the results are not enough. Document classification is required to access this effective information. In the past, document classification has been difficult to manually and manually classify documents, but it has recently been difficult to spend a lot of time and money. Therefore, this paper develops an automatic text classification model of traditional cultural contents based on the data of the Korean information culture field composed of systematic classifications of traditional cultural contents. This study applied TF-IDF model, Bag-of-Words model, and TF-IDF/Bag-of-Words combined model to extract word frequencies for 'Korea Traditional Culture' data. And we developed the automatic text classification model of traditional cultural contents using Support Vector Machine classification algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1085-1089
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• 2015

Text-mining technique in the past had difficulty in realizing the analysis algorithm due to text complexity and degree of freedom that variables in the text have. Although the algorithm demanded lots of effort to get meaningful result, mechanical text analysis took more time than human text analysis. However, along with the development of hardware and analysis algorithm, big data technology has appeared. Thanks to big data technology, all the previously mentioned problems have been solved while analysis through text-mining is recognized to be valuable as well. However, applying text-mining to Korean text is still at the initial stage due to the linguistic domain characteristics that the Korean language has. If not only the data searching but also the analysis through text-mining is possible, saving the cost of human and material resources required for text analysis will lead efficient resource utilization in numerous public work fields. Thus, in this paper, we compare and evaluate the public document classification by handwork to public document classification where word frequency(TF-IDF) in a text-mining-based text and Cosine similarity between each document have been utilized in big data environment.