• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.5
• /
• pp.619-625
• /
• 2007

As low power, low cost wireless communication technology like Bluetooth, Zigbee, RFID has been put to practical use together with the wellbeing trend, the concern about ubiquitous health care has been greatly increased and u-Health is becoming one of the most important application in the sensor network field. Especially, development of the medical services to be able to cope with a state of emergency for solitary senior citizens and the aged in silver town is very meaningful itself and their needs are also expected to continuously increase with a rapid increase in an aging population. In this paper we demonstrate the feasibility of extracting accurate heart rate variability (HRV) measurements from photoelectric plethysmography(PPG) signals gathered by a ring type pulse oximeter sensor attached to the finger. For this, we made 2 types of ring sensor, that is reflective and pervious type, and developed the remote monitoring system which is able to collect HR data from ring sensor, analyze and cope with a state of emergency.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.47-73
• /
• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.2
• /
• pp.213-219
• /
• 2020

In this paper, we propose a classification analysis method for diagnosing and predicting problematic smartphone use in order to provide policy data on problematic smartphone use, which is getting worse year after year. Attempts have been made to identify key variables that affect the study. For this purpose, the classification rates of Decision Tree, Random Forest, and Support Vector Machine among machine learning analysis methods, which are artificial intelligence methods, were compared. The data were from 25,465 people who responded to the '2018 Problematic Smartphone Use Survey' provided by the Korea Information Society Agency and analyzed using the R statistical package (ver. 3.6.2). As a result, the three classification techniques showed similar classification rates, and there was no problem of overfitting the model. The classification rate of the Support Vector Machine was the highest among the three classification methods, followed by Decision Tree and Random Forest. The top three variables affecting the classification rate among smartphone use types were Life Service type, Information Seeking type, and Leisure Activity Seeking type.

• Journal of KIISE:Software and Applications
• /
• v.31 no.2
• /
• pp.137-152
• /
• 2004

Assesment of 30 nutritional minerals and 8 toxic elements in hair are very important not only for determining adequacy, deficiencies and unbalance, but also for assessing their relative relationships in the body. A test has been developed that serves this purpose exceedingly well. This test is known as tissue mineral analysis(TMA). TMA is very popular method in hair mineral analysis for health care professionals in over 46 countries' medical center. However, there are some problems. First, they do not have database which is suitable for korean to do analyze. Second, as the TMA results from TEI-USA is composed of english documents and graphic files prohibited to open, its usability is very low. Third, some of them has low level database which is related to TMA, so hairs are sent to TEI-USA for analyzing and medical services. it bring about an severe outflow of dollars. Finally, TMA results are based on the database of american health and mineral standards, it is possibly mislead korean mineral standards. The purposes of this research is to develope the first Intelligent Medical Expert System(IMES) of TMA, in Korea, which makes clear the problems mentioned earlier IMES can analyze the tissue mineral data with multiple stage decision tree classifier. It is also constructed with multiple fuzzy rule base and hence analyze the complex data from Korean database by fuzzy inference methods. Pilot test of this systems are increased of business efficiency and business satisfaction 86% and 92% respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.353-357
• /
• 2012

요즘 모든 분야에서 실세계의 상황정보 인지를 통해 전자공간과 물리공간을 결합할 수 있는 유비쿼터스 컴퓨팅의 기반 기술을 사용하여 센서와 무선 통신 기술을 결합한 무선 센서 네트워크에 대한 연구가 활발히 이루어지고 있는 추세이다. 또한 선박에서도 유무선 기술을 융합하여 지능형 선박에 적합한 Ship Area Network(SAN) 연구가 진행되고 있으나, 다양한 유무선 네트워크 연동 SAN-브릿지 기술, 이종 센서, 제어기기를 자율적으로 구성관리하거나 상호연동, 원격제어 하는 자율 SAN 구성관리 기술 등의 필요성이 제기되고 있는 실정이다. 선박에서의 모니터링 분야인 구조적 안전과 화물 관리를 위한 모니터링 외에도 선원을 포함한 모든 주변 환경을 안전하게 유지하는 것이다. 이에 본 논문에서는 기후 변화에 대한 감지나 여러 구조물에 대한 온도, 압력 등의 모니터링 시스템을 효율적으로 설계하기 위해 무선 센서 네트워크에서의 에너지 효율을 이용한 라우팅 및 데이터 병합을 위한 기술 동향을 파악하고 자기 구성 클러스터링 방법을 분석하여 선내의 무선 센서 네트워크 구성에 대해 연구하였다.

• Journal of Intelligence and Information Systems
• /
• v.15 no.3
• /
• pp.151-165
• /
• 2009

Fuzzy neural network is an integrated model of artificial neural network and fuzzy system and it has been successfully applied in control and forecasting area. Recently ANFIS(Adaptive Network-based Fuzzy Inference System) has been noticed widely among various fuzzy neural network models because of its outstanding accuracy of control and forecasting area. We design a new classification model based on ANFIS and evaluate it in terms of classification accuracy. We identified ANFIS-based classification model has higher classification accuracy compared to existing classification model, C5.0 decision tree model by comparing their experimental results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.399-401
• /
• 2018

몇 년 사이에 IoT는 통신 프로토콜과 디바이스 중심의 단순한 개념에서 점점 진화 하여, 디바이스, 인터넷기술, 그리고 사람(사물, 데이터 등)이 보안, 프라이버시, 신뢰도 문제를 해결하는 것까지 포함한 비즈니스 혁신, 재현성, 상호 운용성을 위한 완전한 생태계를 창조하는 것으로 개념이 바뀌어 가고 있다. IERC는 실제와 가상의 사물들이 고유특성과 물리적 가상적 특성을 가지고 있고, 지능형 인터페이스를 사용하며, 끊김없이 정보네트워크를 통합 하는 표준과 상호운용 통신 프로토콜을 기반으로 자기 스스로 재구성이 가능한 동적인 글로벌 네트워크 인프라로 정의함으로써 IoT의 범위를 인프라까지 확대 정의하고 있는 추세이다. 이와 같은 IoT 영역이 확대되면서, 일반적으로 IoT의 4대 기술 분야를 센싱기술, 유 무선 통신 네트워크 기술, 플랫폼 기술 및 서비스 기술로 분류하고 있으며, 이 중에서 다양한 전파서비스를 제공하기 위해서는 무선통신망 구축이 필수이며, 이를 실현하기 위해서는 주파수 자원 확보가 매우 중요하다. 본고에서는 이를 실현화 시킬 수 있는 동북아지역 IoT주파수 동향 및 표준화에 대하여 결과를 제시하고자 한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2019.11a
• /
• pp.39-40
• /
• 2019

우리나라는 국제해사기구(IMO)에 "여객선 탈출지도체계의 기술개발에 관한 정보"라는 안건(IMO SSE4/INF2)을 2017년에 제출한 바 있다. 이 의제에 소개된 지능형 선박 및 인명대피 안내시스템(SEGA)은 한국해양과학기술원의 주관으로 한국해양수산부의 지원을 받아 2016년부터 2020년 3월까지 약 4년간의 프로젝트로 개발 중에 있다. SEGA는 데이터 수집과 분석, 정보 표시의 프로세스를 자동화하여 우리나라 연안에서 항해 중인 선박에 비상상황이 발생할 경우 항해자의 의사결정을 지원하는 시스템이다. SEGA 시스템을 지원하기 위해 구축된 SEGA 서버와 데이터베이스는 해양기상정보, 수심정보, 해상교통정보를 처리 한다. 또한 비상상황 시 2차 사고를 방지하기 위해 선박이 대피 할 수 있는 장소에 대한 정보를 사용자가 확인할 수 있도록 알고리즘이 설계되어 있다. 인명안전을 위해 SEGA는 비상상황 시 선박내부 구조정보와 화재 등 변수사항들을 고려하여 승객들에게 빠른 탈출을 위한 최적대피경로를 제공하며, 원격탐사기술을 이용하여 선박주변의 익수자를 탐지하도록 개발 중에 있다. 보다 상세한 내용은 항해항만학회 VTS 특별세션에서 발표할 예정이다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.2
• /
• pp.278-283
• /
• 2008

As the usefulness of internet is kept changing more productively with web 1.0, web 2.0 usage of XML is also increasing very rapidly. In XML environment the most critical function is the ability of effective retrieval of useful information from XML repository. That makes the W3C XQuery more popular XQuery has very complicated structure as a query language due to the semi_structured nature of XML. FLOWOR, which stand for, let. where, order by, return, is the most commonly used expression in XQuery. In this paper we suggest the methods to handle XQuery FLWOR on relational environments. We also analyze and evaluate our approach to prove its correctness.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.6
• /
• pp.161-169
• /
• 2011

Recent intelligent video surveillance system asks for development of more advanced technology for analysis and recognition of video data. Especially, machine learning algorithm such as Support Vector Machine (SVM) is used in order to recognize objects in video. Because SVM training demands massive amount of computation, parallel processing technique is necessary to reduce the execution time effectively. In this paper, we propose a parallel processing method of SVM training with a multi-core processor. The results of parallel SVM on a 4-core processor show that our proposed method can reduce the execution time of the sequential training by a factor of 2.5.