• KIPS Transactions on Computer and Communication Systems
• /
• v.7 no.12
• /
• pp.301-312
• /
• 2018

In industrial wireless sensor networks, the continuous object detection such as fire or toxic gas detection is one of major applications. A continuous object occurs at a specific point and then diffuses over a wide area. Therefore, many studies have focused on accurately detecting a continuous object and delivering data to a static sink with an energy-efficient way. Recently, some applications such as fire suppression require mobile sinks to provide real-time response. However, the sink mobility support in continuous object detection brings challenging issues. The existing approaches supporting sink mobility are designed for individual object detection, so they establish one-to-one communication between a source and a mobile sink for location update. But these approaches are not appropriate for a continuous object detection since a mobile sink should establish one-to-many communication with all sources. The one-to-many communication increases energy consumption and thus shortens the network lifetime. In this paper, we propose the origin-centric communication scheme to support sink mobility in a continuous object detection. Simulation results verify that the proposed scheme surpasses all the other work in terms of energy consumption.

• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.187-204
• /
• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Proceedings of the Acoustical Society of Korea Conference
• /
• spring
• /
• pp.417-420
• /
• 2002

수중 음향 탐지 시스템에서 빔 형성 기법 및 개별 센서에 대한 정합회로의 설계는 실제 시스템 설계 시 시스템의 성능을 결정하는 중요한 요소이다. 본 논문에서는 이 두 가지 기법을 통합하고 있으면서 일반 사용자들도 쉽게 최적 빔 설계를 통한 가중치를 구하고 또 개별 소자에 대한 센서 임피던스 정합회로를 설계할 수 있도록 해주는 통합 S/W를 구현하였다. 본 프로그램을 이용하여 최적의 가중치를 구하고 그 가중치를 가지는 개별 센서의 정합회로를 일괄적으로 설계할 수 있다. 앞으로도 실제 사용자로부터 의견을 수렴하여 계속 성능을 보완할 예정이며 교육용이나 실제 산업용으로 사용이 가능할 것으로 생각된다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.11a
• /
• pp.845-848
• /
• 2014

블로그는 인터넷에서 개인의 정보나 의견을 표출하고 커뮤니티를 형성하는데 사용되는 중요한 수단이나, 광고 유치, 페이지 순위 올리기, 쓰레기 데이터 생성 등 다양한 목적을 가진 스팸블로그가 생성되어 악용되기도 한다. 본 연구에서는 이러한 문제를 해결하기 위해 웹 문서에서 나타나는 특징들을 이용한 스팸 탐지 기법을 제안한다. 먼저 블로그 본문의 길이, 태그의 비율, 태그 수, 이미지 수, 랭크의 수 등 하나의 웹 문서에서 추출할 수 있는 특징을 기반으로 각 문서에 대한 특징 벡터를 생성하고 기계학습을 통해 모델을 생성하여 스팸 블로그를 판별한다. 제안 방법의 성능 평가를 위해 블로그 포스트 데이터를 사용하여 제안방법과 기존의 스팸 분류 연구를 비교 실험을 진행하였다. Bayesian 필터링 기법을 사용하는 기존연구와 비교 실험 결과, 제안방법이 더 좋은 정확도를 가지면서 특징 추출 속도 및 메모리 사용 효율성을 보였다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.2
• /
• pp.493-504
• /
• 2017

Recently, tremendous amounts of unstructured text data that is distributed through news, blogs, and social media has gained much attention from many researchers and practitioners as this data contains abundant information about various consumers' opinions. However, as the usefulness of text data is increasing, more and more attempts to gain profits by distorting text data maliciously or nonmaliciously are also increasing. This increase in spam text data not only burdens users who want to obtain useful information with a large amount of inappropriate information, but also damages the reliability of information and information providers. Therefore, efforts must be made to improve the reliability of information and the quality of analysis results by detecting and removing spam data in advance. For this purpose, many studies to detect spam have been actively conducted in areas such as opinion spam detection, spam e-mail detection, and web spam detection. In this study, we introduce core concepts and current research trends of spam detection and propose a methodology to detect the spam tag of a blog as one of the challenging attempts to improve the reliability of blog information.

• Journal of Welding and Joining
• /
• v.13 no.2
• /
• pp.12-24
• /
• 1995

Recently, more researches have been actively performed for the assessment of material degradation and residual-life of elevated temperature plant components, as some of domestic fossil power plants become older than 30 years. In this paper, results of on_site residual life assessment are reported for main steam pipes of Youngwol power station #2 which have operated since 1965. For critical weld locations such as butt welds branch welds, Y_sections and a T-section, replication technique and hardness measurement technique were employed for life_assessment. When cracks were detected by conventional NDT tests, crack growth life was calculated using a computer code. On the other hand, for matrix of pipes, residual life was quantitatively estimated by an analytic method and material degradation was estimated qualitatively using diameter measurement data and grain-boundary etching method. Also, direction in further improvement of on-site life assessment techniques are proposed.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.19 no.4
• /
• pp.300-313
• /
• 1999

비파괴검사 기술의 레벨 향상과 안정화를 통한 시간적 재현성이 있는 비파괴검사 결과의 확보를 위해서는 비파괴검사 기술자의 자격 인정 및 인증(nondestructive testing qualification and certification of personnel)제도의 확립이 매우 중요하다. 비파괴검사 결과에 대한 유효성은 비파괴검사을 실시하는 사람의 능력이나 비파괴검사에 대한 책임을 지고 있는 사람의 능력에 크게 의존한다. 1974년부터 원자력 선진국들이 중심이 되어 수행된 PISC 프로그램(program for inspection of steel components) 및 EPRI 등에서는 순회시험 (piping inspection round-robin: PIRR trial) 결과 기존 비파괴검사 방법은 모의 시험편에 있는 상당히 큰 결함도 탐지하지 못하거나 정확한 결함크기를 측정하는데 실패한 경우가 있으며, 검사자의 기량 또는 신체조건에 상당한 차이가 있는 것으로 나타났다[1]. 국제표준화기구(ISO)의 비파괴검사 기술자의 기량인정 및 인증에 대한 국제규격안 DIS(draft international standard) 9712에서는 비파괴검사를 기획 실시 감독 감시 평가를 하기 위한 적절한 이론적 및 실무적 지식을 필요로 하는 기술자의 능력을 평가하고, 문서화하는 방법을 제공하는 절차를 제시하고 있다. 국제표준화기구에 의한 비파괴검사 기술자의 기량인정 및 인증에 대한 국제통합을 추진하는 동기는 제 3자에 의한 체계적인 인증시스템을 가지고 있지 않은 나라와 새로운 NDT 방법에 대해서 인증제도를 적용할 때 세계적인 공통성을 갖도록 유도하기 위함이다. 현재, 우리나라 비파괴검사 기술자의 기술자격 인증제도는 국가기술자격법에 의거하여 한국산업인력공단에서 주관하여 기술사, 기사, 산업기사, 기능사로 구분하여 실시하고 있다. 국제표준화기구의 비파괴검사 기술자의 자격과 인증에 대한 국제 통합화(안)[2]이 거의 마무리 단계에 있고 일본을 비롯한 많은 나라가 국제규격을 기초로 한 새로운 인증제도를 발족시켜 거의 시행 단계에 있다. 반면 국내에서는 한국비파괴검사학회(KSNT)에서 비파괴검사 기술자의 자격인정 및 인증제도의 개선방향이 제시된 바 있고 [3], 표준화위원회에서 나름대로 준비를 하고 있으나 아직 구체적인 실천단계에와 있지 못하다. 본 고에서는 최근 대폭수정 보완된 ISO/DIS 9712 국제규격(안)을 회원들에게 소개하고, 우리나라의 향후 대응방안에 관한 회원 여러분의 의견 수렴에 도움을 주고자 ISO/DIS 9712(1997)를 번역하여 제공한다.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.11
• /
• pp.541-548
• /
• 2016

The use of social media is already essential as a source of information for listening user's various opinions and monitoring. We define social 'risks' that issues effect negative influences for public opinion in social media. This paper aims to discriminate various linguistic features and reveal their effects for building an automatic classification model of social risks. Expecially we adopt a word embedding technique for representation of linguistic clues in risk sentences. As a preliminary experiment to analyze characteristics of individual features, we revise errors in automatic linguistic analysis. At the result, the most important feature is NE (Named Entity) information and the best condition is when combine basic linguistic features. word embedding, and word clusters within core predicates. Experimental results under the real situation in social bigdata - including linguistic analysis errors - show 92.08% and 85.84% in precision respectively for frequent risk categories set and full test set.

• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.4
• /
• pp.77-89
• /
• 2024

Knee osteoarthritis (OA) is a very common musculoskeletal disorder worldwide. The assessment of knee osteoarthritis, which requires a rapid and accurate initial diagnosis, is determined to be different depending on the currently dispersed classification system, and each classification system has different criteria. Also, because the medical staff directly sees and reads the X-ray pictures, it depends on the subjective opinion of the medical staff, and it takes time to establish an accurate diagnosis and a clear treatment plan. Therefore, in this study, we designed the stenosis length measurement algorithm and Osteophyte detection and length measurement algorithm, which are the criteria for determining the knee osteoarthritis grade, separately using CNN, which is a deep learning technique. In addition, we would like to create a grading system that integrates and complements the existing classification system and show results that match the judgments of actual medical staff. Based on publicly available OAI (Osteoarthritis Initiative) data, a total of 9,786 knee osteoarthritis data were used in this study, eventually achieving an Accuracy of 69.8% and an F1 score of 76.65%.

• Journal of Internet Computing and Services
• /
• v.19 no.5
• /
• pp.89-96
• /
• 2018

In online communities, a large number of participants can exchange their opinion using replies without time and space restrictions. While the online space provides quick and free communication, it also easily triggers unnecessary quarrels and conflicts. The network established on the discussion participants is an important cue to analyze the confrontation and predict serious disputes. In this paper, we present a quantitative measure for polarity observed on the discussion network built from reply exchanges in online communities. The proposed method uses the comment exchange information to establish the user interaction network graph, computes its maximum spanning tree, and then performs vertex coloring to assign two colors to each node in order to divide the discussion participants into two subsets. Using the proportion of the comment exchanges across the partitioned user subsets, we compute the polarity measure, and quantify how discussion participants are bipolarized. Using experimental results, we demonstrate the effectiveness of our method for detecting polarization and show participants of a specific discussion subject tend to be divided into two camps when they debate.