Earticle

본 연구에서는 광산 지반침하 발생 위험도를 상대적 순위 관점에서 평가할 수 있는 GIS 기반의 소프트웨어를 개발하였다. 한국광해관리공단에 구축된 광산GIS 자료로부터 추출 가능한 지반침하 영향인자 15개를대상으로 통계 기반의 빈도비 모델, 반정량적 기법인 퍼지 소속 함수, 가중치를 산정하는 계층분석기법을 결합하여 적용할 수 있도록 설계하였다. 소프트웨어 구현을 위해 Visual Basic.NET과 ArcObjects 프로그래밍을 수행하였으며, ArcMap 프로그램 확장 툴바 형태로 개발하였다. 4단계 과정(인자 선택, 가중치 산정, 빈도비 또는 퍼지분석, 지도 작성 옵션)을 통해 분석된 결과는 ArcGIS 프로그램에서 가시화(2, 3차원)할 수 있도록 하였다. 개발된소프트웨어를 강원도 폐탄광 지역에 적용한 결과 갱도, 지형, 지질, 수계 조건을 함께 고려하여 분석을 수행할수 있었으며, 기 지반침하지와의 공간적 비교 분석을 통한 검증 결과 신뢰할 수 있는 평가 결과를 얻을 수 있었다. 본 연구에서 개발한 소프트웨어가 폐광산 지역의 지반침하 예측 및 관리를 지원할 수 있는 도구로서 유용하게활용될 수 있을 것으로 기대한다.

This paper presents a GIS-based software for evaluating subsidence susceptibility in abandoned mine areas. The software is designed to combine frequency ratio model, fuzzy membership functions, analytic hierarchy process to prioritize areas vulnerable to subsidence incorporating 15 contributing factors which can be extracted from Mine GIS of Mine Reclamation Corporation. ArcObject programming using Visual Basic.NET was performed to implement the software as an extension toolbar in ArcMap program. It enables to provide 2D or 3D visualization of final resultant data obtained from four step processes (factor selection, weighting, correlation analysis, mapping option). Application of the software to the abandoned coal mine areas in Gangwon-do revealed that the software can analyze ground stability considering drift, topography, geology and hydrological conditions. Consequently, it gave a reasonable subsidence susceptibility map by spatially comparing with subsidence inventory map. The developed software can be effectively utilized to support subsidence prediction and management of abandoned mine areas

본 연구에서는 마이크로소프트 키넥트 센서와 굽힘 감지 데이터 글러브를 이용하여 광업 분야 소프트웨어를제어할 수 있는 3차원 사용자 인터페이스를 개발하였다. 개발된 인터페이스는 키넥트 센서로 추적되는 사용자의 손위치에 따라 윈도우즈 환경에서 마우스 커서를 제어한다. 또한 굽힘 감지 데이터 글러브로 측정한 사용자 손가락의구부러짐 정도에 따라 윈도우즈 환경에서 마우스 클릭 이벤트를 처리한다. 인터페이스의 개발에는 오픈소스 하드웨어인 아두이노 Lilypad 보드와 National Instrument의 LabVIEW 소프트웨어가 사용되었다. 3차원 사용자 인터페이스를 광업 분야에서 사용되고 있는 몇 가지 3차원 소프트웨어에 적용해본 결과, 마우스나 키보드와 같은 전통적인 인터페이스보다 더 직관적으로 3차원 소프트웨어를 제어할 수 있음을 확인할 수 있었다.

In this study, we developed a 3D user interface (UI) using the Microsoft’s Kinect sensor and bend-sensing data glove to control software in the mining industry. The developed UI controls the mouse cursor in Windows according to the user’s hand position tracked by the Kinect sensor. In addition, it controls the mouse click events in Windows by measuring the user’s finger flexion using the bend-sensing data glove. An open-source hardware, Arduino Lilypad Board, and National Instrument’s LabVIEW software were used to develop the UI. Applications of the 3D UI to several 3D software utilized in the mining industry revealed that it can control the 3D software in more intuitive way than traditional UIs such as mouse and keyboard.

산업용 시스템과 군용 무기체계에서 사용되는 임베디드 시스템들은 높은 신뢰성을 요구하며, 탑재 소프트웨어의 비중이 높아지면서 소프트웨어 품질확보를 위한 테스트의 중요성이 강화되고 있다. 임베디드 소프트웨어에 대한 테스트는 하드웨어와 소프트웨어의 결합력이 매우 높다는 특성 때문에 통합 테스트를 통해 검증되어야 한다. 기존의 통합 테스트에서는 결함이 발생하더라도 디버깅을 위한 정보를 제공해 줄 수 있는 방법이 고려되지 않아 디버깅을 수행하는 데에 많은 시간과 비용이 소모된다는 문제점이 있었다. 본 논문에서는 임베디드 소프트웨어의 통합 테스트단계에서 발생한 결함에 대해 시험 데이터와 소프트웨어 로그를 통합하여 수집하고 연관 규칙을 이용한 마이닝 기반의 분석 방법을 제공함으로써 임베디드 소프트웨어의 디버깅을 보다 원활하게 할 수 있는 방법을 제안한다. 그리고시험을 통해 제안한 방법이 단일 결함에 대해서 디버깅을 위한 정보의 식별이 가능함을 확인하였다.

The embedded systems used in industry systems and military weapon systems require high reliability, and increasing the proportion of the embedded software increases the importance of testing to ensure software quality. Testing for embedded software should be validated through integration testing because of the tight coupling of hardware and software. There was a problem with the existing integration tests that it was not considered a method to provide information for debugging, even if a defect occurred, so it was time consuming and costly to perform debugging. In this paper, we suggest a method to make debugging of embedded software more seamless by integrating test data and software logs for defects occurred in the integrated testing phase of embedded system, and by providing a mining-based analysis method using associative rules. Through the test, it is confirmed that the proposed method can identify information for debugging with respect to single defect.

Software quality assurance is necessary to increase the level of confidence in the developed software and reduce the overall cost for developing software projects. The problem addressed in this research is the prediction of fault prone modules using data mining techniques. Predicting fault prone modules allows the software managers to allocate more testing and resources to such modules. This can also imply a good investment in better design in future systems to avoid building error prone modules. Software quality models that are based upon data mining from previous projects can identify fault-prone modules in the current similar development project, once similarity between projects is established. In this paper, we applied different data mining rule-based classification techniques on several publicly available datasets of the NASA software repository (e.g. PC1, PC2, etc). The goal was to classify the software modules into either fault prone or not fault prone modules. The paper proposed a modification on the RIDOR algorithm on which the results show that the enhanced RIDOR algorithm is better than other classification techniques in terms of the number of extracted rules and accuracy. The implemented algorithm learns defect prediction using mining static code attributes. Those attributes are then used to present a new defect predictor with high accuracy and low error rate.

Software quality is a field of study and practice that describes the desirable attributes of software products. The performance must be perfect without any defects.Software quality metrics are a subset of software metrics that focus on the quality aspects of the product, process, and project.The software defectprediction model helps in early detection of defects and contributes to their efficient removal and producing a quality software system based on several metrics. The main objective of paper is to help developers identify defects based on existing software metrics using data mining techniques and thereby improve the software quality.In this paper, variousclassification techniquesare revisitedwhich are employed for software defect prediction using software metrics in the literature.

As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.

This paper discuss different classification methods toward reliability and quality improvement of software systems by predicting fault-prone module before testing. Classification capability of Data mining techniques and Object-oriented property based knowledge stored in Object-Oriented metrics are used to classify the software module as fault-prone in different error categories or not fault-prone. Three versions of Eclipse, the java-based Open source Integrated Development environment as dataset for training and testing all the classification based data mining techniques are used. First of all, Threshold base feature ranking (i.e. Area under the ROC curve) is used for selecting effective OO-metrics in building prediction model. After that using those subsets of selected attributes, classification models are built with 41 different classifiers for multinomial classification in fault detection systems. Finally, the performance of a classifier is evaluated with respect to the PRC performance metric. Based on the performance results appropriate classifiers (Random Committee, Random Tree, Randomizable Filtered classifier and IBK) which depict a higher performance and accuracy compared to the others are selected. Our results indicate that Random Tree, Random Committee and Randomizable Filtered Classifier have same performance. IBK classifier also has same performance but little bit less and Kstar has less performance compared to previous four selected classifiers.

제4차 산업혁명을 이끄는 주요 원동력 중 하나인 인공지능 기술은 이미지와 음성 인식 등 여러 분야에서 사람과 유사하거나 더 뛰어난 능력을 보이며, 사회 전반에 미치게 될 다양한 영향력으로 인하여 높은 주목을 받고 있다. 특히, 인공지능 기술은 의료, 금융, 제조, 서비스, 교육 등 광범위한 분야에서 활용이 가능하기 때문에, 현재의 기술 동향을 파악하고 발전 방향을 분석하기 위한 노력들 또한 활발히 이루어지고 있다. 한편, 이러한 인공지능 기술의 급속한 발전 배경에는 학습, 추론, 인식 등의 복잡한 인공지능 알고리즘을 개발할 수 있는 주요 플랫폼들이 오픈 소스로 공개되면서, 이를 활용한 기술과 서비스들의 개발이 비약적으로 증가하고 있는 것이 주요 요인 중 하나로 확인된다. 또한, 주요 글로벌 기업들이 개발한 자연어 인식, 음성 인식, 이미지 인식 기능 등의 인공지능 소프트웨어들이 오픈 소스 소프트웨어(OSS: Open Sources Software)로 무료로 공개되면서 기술확산에 크게 기여하고 있다. 이에 따라, 본 연구에서는 온라인상에서 다수의 협업을 통하여 개발이 이루어지고 있는 인공지능과 관련된 주요 오픈 소스 소프트웨어 프로젝트들을 분석하여, 인공지능 기술 개발 현황에 대한 보다 실질적인 동향을 파악하고자 한다. 이를 위하여 깃허브(Github) 상에서 2000년부터 2018년 7월까지 생성된 인공지능과 관련된 주요 프로젝트들의 목록을 검색 및 수집하였으며, 수집 된 프로젝트들의 특징과 기술 분야를 의미하는 토픽 정보들을 대상으로 텍스트 마이닝 기법을 적용하여 주요 기술들의 개발 동향을 연도별로 상세하게 확인하였다. 분석 결과, 인공지능과 관련된 오픈 소스 소프트웨어들은 2016년을 기준으로 급격하게 증가하는 추세이며, 토픽들의 관계 분석을 통하여 주요 기술 동향이 '알고리즘', '프로그래밍 언어', '응용분야', '개발 도구'의 범주로 구분하는 것이 가능함을 확인하였다. 이러한 분석 결과를 바탕으로, 향후 다양한 분야에서의 활용을 위해 개발되고 있는 인공지능 관련 기술들을 보다 상세하게 구분하여 확인하는 것이 가능할 것이며, 효과적인 발전 방향 모색과 변화 추이 분석에 활용이 가능할 것이다.

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degr

온라인 수업에 대한 학생들의 니즈 분석은 객관식 설문조사 유형이 주로 수행되어왔다. 그러나 학생들의 정확한 니즈를 분석하기 위해서는 주관식 답변에 의한 비정형 데이터 분석이 요구된다. 빅데이터는 비정형 데이터 분석이 가능하여 다양한 분야에서 활용되고 있다. 본 연구에서는 비대면 온라인 수업방식을 진행되는 교양 소프트웨어 과목에서 학생들이 원하는 과목이나 주제가 무엇인지 조사 및 분석하였다. 실험방법은 학생들에게 주관식 설문조사를 시행하여 얻은 비정형 데이터를 기반으로 빅데이터의 키워드 분석, 연관 분석등을 수행하였다. 이를 통해 학생들이 교양 소프트웨어 과목에서 원하는 키워드가 무엇인지 알 수 있었으며, 이러한 연구 결과는 학생들이 배우고자하는 주제를 파악할 수 있어서 향후 교양 소프트웨어 과목의 기획 및 설계시 중요한 자료가 될 것이다.

Multiple-choice survey types have been mainly performed to analyze students' needs for online classes. However, in order to analyze the exact needs of students, unstructured data analysis by answer for essay question is required. Big data is applied in various fields because it is possible to analyze unstructured data. This study aims to investigate and analyze what students want subjects or topics for software lecture in general education that process on non-face-to-face online teaching methods. As for the experimental method, keyword analysis and association analysis of big data were performed with unstructured data by giving a subjective questionnaire to students. By the result, we are able to know the keyword what the students want for software lecture, so it will be an important data for planning and designing software lecture of liberal arts in the future as students can grasp the topics they want to learn.