• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.334-343
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• 2023

Purpose: In this study, the cavity data found through ground cavity exploration was combined with underground facilities to derive a correlation, and the ground subsidence prediction map was verified based on the AI algorithm. Method: The study was conducted in three stages. The stage of data investigation and big data collection related to risk assessment. Data pre-processing steps for AI analysis. And it is the step of verifying the ground subsidence risk prediction map using the AI algorithm. Result: By analyzing the ground subsidence risk prediction map prepared, it was possible to confirm the distribution of risk grades in three stages of emergency, priority, and general for Busanjin-gu and Saha-gu. In addition, by arranging the predicted ground subsidence risk ratings for each section of the road route, it was confirmed that 3 out of 61 sections in Busanjin-gu and 7 out of 68 sections in Sahagu included roads with emergency ratings. Conclusion: Based on the verified ground subsidence risk prediction map, it is possible to provide citizens with a safe road environment by setting the exploration section according to the risk level and conducting investigation.

• Korean Journal of Applied Biomechanics
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• v.13 no.3
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• pp.277-291
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• 2003

This study was designed to examine the kinematic factors in the phase during the marche fente motion. For this study, the subjects were 5 elite male fencing players. The direct linear transformation (DLT) method was used in calculating 3-D coordinate of the digitized body parts. The cubic spline function was used for smoothing and the kinematic data for displacement, velocity, angle variables were calculated for Kwon3d ver 2.1. And the following conclusions were drawn; 1. It show that the marche phase appeared to longer time than the pante phase In the performance time. For the fast attack, it showed that the subjects should be moving in a short stride width. 2. For a fast and stable movement posture in the marche phase, the vertical change of COG must be maintain the same position as possible, but all subjects appeared to decrease the COG because of a excessive the knee flection. 3. In the COG velocity change, all the subjects showed to the same change in both the marche and the fente phase. However in the attack extremity velocity, it increased velocity in order of upper arm, fore arm, and hand in the marche phase, but it showed different velocity among each subjects at the moment of stabbing. So that in order to do effective stabbing, they have to extend their upper extremity max and do faster the distal segment than the proximal segment. 4. It showed to take a fast and stable movement, because some subjects showed the big anteroposterior angle of the trunk flexed max shoulder angle and elbow angle of their attack arm and the other upper extremity.

• Special Issue of the Society of Naval Architects of Korea
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• 2013.12a
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• pp.35-41
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• 2013

Nowadays, automated modeling system for steel margin based on interactive user interface has been developed and applied to the production design stage. The system could increase design efficiency and minimize human error owing to recent CAD technique. However, there has been no approach to the pre-nesting design stage at all in early modeling process especially where ship model should be handled at more than two design stages using AVEVA Marine. A designer of the design stage needs artificial intelligence system beyond modeling automation when 3D model must be prepared in early modeling process using AVEVA Marine because they have focused on 2D nesting traditionally. In addition, they have a hard time figuring out the model prepared in previous design stage and modifying the model for steel purchase size in early modeling process. In this paper, artificial intelligence modeling system for automated application of steel margin in early modeling process using AVEVA Marine is developed in order to apply to the pre-nesting design stage that can detect effective segments before a calculation to find if a segment locates near block butt boundaries by filtering noise segments among lines, curves and surface intersections based on IT big data analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.204-204
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• 2016

Thailand had set the National Water Management Strategy which covered main six areas in the next 12 years, i.e., by priority: (1) water for household, (2) water for agricultural and industrial production, (3) water for flood and drought management, (4) water for quality issue, (5) water from forest conservation and soil erosion protection, (6) water resources management. However due to the climate change impact, there is a question for all strategies is whether to complete this mission under future climate change. If the impact affects our target, we have to clarify how to mitigate or to adapt with it. Vulnerability assessment was conducted under the framework of ADB's (with the parameters of exposure, sensitivity and adaptive capacity) and the assessments were classified into groups due to their different characteristic and the framework of the National Water Management Strategy, i.e., water supply (rural and urban), water for development (agriculture and others), water disasters (floods (flash, overflow), drought, water quality). The assessments identified the parameters concerned and weight factors used for each groups via expert group discussions and by using GIS mapping technology, the vulnerability maps were produced. The maps were verified with present water situation data (floods, drought, water quality). From the analysis result of this water resources management strategy, we found that 30% of all projects face the big impacts, 40% with low impact, and 30% for no impact. It is clear that water-related agencies have to carefully take care approximately 70% of future projects to meet water resources management strategy. It is recommended that additional issues should be addressed to mitigate the impact from climate risk on water resource management of the country, i.e., water resources management under new risk based on development scenarios, relationship with area-based problems, priority definition by viewpoints of risk, vulnerability (impact and occurrence probability in past and future), water management system in emergency case and water reserve system, use of information, knowledge and technology in management, network cooperation and exchange of experiences, knowledge, technique for sustainable development with mitigation and adaptation, education and communication systems in risk, new impact, and emergency-reserve system. These issues will be described and discussed.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.76-76
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• 2021

물정보 중 가뭄 정보가 상대적으로 부족한 원인은 무엇을 가뭄으로 볼 것인지 정의하기 어렵기 때문이다. 특히 우리나라와 같이 댐 및 저수지, 광역상수도 등 수자원시스템 네트워크를 기반으로 물공급이 이루어지는 경우, 개별 요소만을 고려한 기존 가뭄모니터링 및 전망은 현실적이지 못하며, 가뭄 위험도 관리 측면에서도 부족한 부분이 있다. 가뭄 현상의 경우 기상학적 영향인 강수의 부족이 가장 큰 요소로 기여하지만 실질적으로 국민에 필요한 양보다 적은 양의 물이 공급될 때 국민들은 가뭄을 체감한다. 이러한 점을 보완하기 위하여 지역별로 사용하는 수원 및 물수급 시설 등을 세분화하고, 실적기반 분석을 통해 분석대상 지역의 가뭄을 정확히 판단하기 위한 합리적인 물수급 분석 모형 개발이 필요하다. 즉, 공간분석단위를 표준유역 단위 이하의 취방류 시설물을 기준으로 구성하고, 이들 시설물의 운영정보와 수문기상 빅데이터를 연계한 물순환 모형을 구현함으로써 댐, 저수지, 하천 등 다양한 수원을 가지는 유역 내 가용 수자원량을 준실시간 개념으로 평가하는 시스템의 개발이 필요하다. 본 연구에서는 하천을 중심으로 물수급 관련 수요·공급 시설의 위치를 절점으로 부여하고 연결하는 물수급 네트워크 알고리즘을 통해 빅데이터 기반 물수급 분석 모형을 개발하였다. 주요 모니터링 지점 및 모든 이수 시설의 위치를 유역분석 기법을 통하여 점(point), 선(line), 면(shape)으로 구성된 지형공간정보의 위상(topology) 관계를 설정하여 물수급 분석의 계산순서를 선정하고, 시계열 DB를 입력하여 지점별 물수급 분석 결과를 도출하였다. 권역별 주요 수위-유량관측소 1:1 Nash 계수를 검증한 결과 저유량에서 0.8 이상의 높은 재현 성능을 보이는 것으로 나타났다. 이에 따라 본 연구에서 개발된 물수급 분석 모형은 향후 물관련 이슈 지역의 용수공급능력 평가 및 수자원장기종합계획 등 다양한 수자원 정책평가에 활용될 것으로 기대된다.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.61-67
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• 2017

Recently, CCTV has been combined with areas such as big data, artificial intelligence, and image analysis to detect various abnormal behaviors and to detect and analyze the overall situation of objects such as people. Image analysis research for this intelligent video surveillance function is progressing actively. However, CCTV images using 2D information generally have limitations such as object misrecognition due to lack of topological information. This problem can be solved by adding the depth information of the object created by using two cameras to the image. In this paper, we perform background modeling using Mixture of Gaussian technique and detect whether there are moving objects by segmenting the foreground from the modeled background. In order to perform the depth information-based segmentation using the RGB information-based segmentation results, stereo-based depth maps are generated using two cameras. Next, the RGB-based segmented region is set as a domain for extracting depth information, and depth-based segmentation is performed within the domain. In order to detect the center point of a robustly segmented object and to track the direction, the movement of the object is tracked by applying the CAMShift technique, which is the most basic object tracking method. From the experiments, we prove the efficiency of the proposed object detection and tracking method using the RGB-D model.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.17-24
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• 2019

Since the information of $4^{th}$ Industrial Revolution is introduced in WEF (World Economic Forum) in 2016, IoT, AI, Big Data, 5G, Cloud Computing, 3D/4DPrinting, Robotics, Nano Technology, and Bio Engineering have been rapidly developed as business applications as well as technologies themselves. Among the diverse business applications for IoT, wearable devices are recognized as the leading application devices for final customers. This longitudinal study is compared to the results of the 1st study conducted to identify customer needs of activity trackers, and links the identified users' needs with the well-known marketing frame of marketing mix. For this longitudinal study, a survey was applied to university students in June, 2018, and ANOVA were applied for major variables on usable features. Further, potential customer needs were identified and visualized by Word Cloud Technique. According to the analysis results, different from other high tech IT devices, activity trackers have diverse and unique potential needs. The results of this longitudinal study contribute primarily to understand usable features and their changes according to product maturity. It would provide some valuable implications in dynamic manner to activity tracker designers as well as researchers in this arena.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.91-102
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• 2007

To anticipate and respond to harmful algae produced in a big artificial lake like Daecheong reservoir, development of a regional analysis computer system using GIS or RS technique is needed in addition to biological and chemical research. The purpose of this study is to develop a cyanobacterial blooming prediction model to prevent harmful algae produced in Daecheong reservoir and construct an early forecasting system based on GIS. For this purpose this paper examines previous studies related to the relationship between cyanobacteria and environmental factors in Daecheong reservoir and selects precipitation and air temperature as two important environmental factors for the development of cyanobacterial blooming prediction model. Data used in this study are water quality and weather data for three water regions in Daecheong reservoir between 2000 and 2004. Based on qualitative correlation analysis between cyanobacteria and environmental factors, this paper presents a Rump model which enables us to predict cyanobacteria in water regions of Daecheong reservoir. Under this model the prediction of initial occurrence time and growth period of cyanobacteria are possible. The model is also applied to the GIS-based early forecasting system for cyanobacteria, and finally a GIS which can predict cyanobacteria produced in Daecheong reservoir and can manage the related data is developed.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.77-84
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• 2022

This study aims to present a method for implementing a decision support system that can be used for selecting emerging technologies by applying a machine learning-based automatic classification technique. To conduct the research, the architecture of the entire system was built and detailed research steps were conducted. First, emerging technology candidate items were selected and trend data was automatically generated using a big data system. After defining the conceptual model and pattern classification structure of technological development, an efficient machine learning method was presented through an automatic classification experiment. Finally, the analysis results of the system were interpreted and methods for utilization were derived. In a DTW-kNN-based classification experiment that combines the Dynamic Time Warping(DTW) method and the k-Nearest Neighbors(kNN) classification model proposed in this study, the identification performance was up to 87.7%, and particularly in the 'eventual' section where the trend highly fluctuates, the maximum performance difference was 39.4% points compared to the Euclidean Distance(ED) algorithm. In addition, through the analysis results presented by the system, it was confirmed that this decision support system can be effectively utilized in the process of automatically classifying and filtering by type with a large amount of trend data.