• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.6
• /
• pp.134-143
• /
• 2020

With the worldwide spread of African swine fever, interest in livestock epidemics is growing. Livestock transport vehicles are the main cause of the spread of livestock epidemics, but no empirical quarantine procedures and standards related to the mobility of livestock transport vehicles in South Korea. This study extracted livestock-related vehicles' trajectory by utilizing the facility visit history data from the Korea Animal Health Integrated System and the DTG (Digital Tachograph) data from the Korea Transportation Safety Authority and presented them as exposure indexes aggregating the link-time occupancy of each vehicle. As a result, a total of 274,519 livestock-related vehicle trajectories were extracted, and exposure values by link and zone were quantitatively derived. Through this study, it is expected that prior monitoring of livestock transport vehicles and the establishment of post-disaster prevention policies would be provided.

• Journal of Advanced Navigation Technology
• /
• v.25 no.1
• /
• pp.52-59
• /
• 2021

According to the International Air Transport Association (IATA), 61% of the accidents occurred during the approach and landing phase of the flight, with 16% of the accidents caused by unstable access of the commercial aircraft. It was identified that the pilot's unstable approach and poor manipulation of correction led to accidents by continuing the excessive approach without go-around manuever. The causes of unstable access may vary, including airport approach procedures, pilot error, misplanning, workload, ATC (Air Traffic Contol) congestion, etc. In this study, we use the flight data analysis system to select domestic case airports and aircraft type where unstable approach events occur repeatedly. Through flight data analysis, including main events, airport approach procedures, pilot operations, as well as various environmental factors such as weather and geographical conditions at the airport. It aims to identify and eliminate the tendency of unstable approach events and the causes and risks of them to derive implications for mitigating unstable approach events and for developing navigation safety measures.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.913-916
• /
• 2010

Wireless sensor networks consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, energy efficiency is a key design issue that needs to be enhanced in order to improve the life span of the network. In BCDCP, all sensors send data from CH (Cluster Head) and then to BS (Base Station). BCDCP works well in small-scale network but in large scale network it is not appropriated since it uses much energy for long distance wireless communication. We propose a routing protocol - Triangular Clustering Routing Protocol (TCRP) - to prolong network life time through the balanced energy consumption. TCRP selects cluster head of triangular shape. The sensor field is divided into energy level and in every level we choose one node as a gate node. This gate node collects data and sends it to the leader node. Finally the leader node sends the aggregated data to the BS. We show TCRP outperforms BCDCP with several experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.24-34
• /
• 2021

Estimation of the presence of people in real time is extremely useful for businesses in providing better services. Many companies and researchers have attempted various researches in order to count the number of floating population in specific space. Recently, as part of smart cities and digital twins, commercialization of measuring floating populations using Wi-Fi signals has become active in the public and private sectors. This paper explains the floating population measuring system from the perspective of general consumers(non-experts) who uses current population data. Specifically, it presents a method of estimating the floating population based on MAC-address values collected from smartphones. By distinguishing Real MAC-address and Random MAC-address values, we compare the estimated number of smartphone devices and the actual number of people caught on CCTV screens to evaluate the accuracy of the proposed method. And it appeared to have a similar correlation between the two datas. As a result, we present a method of estimating the floating population based on analyzing Wi-Fi Probe Requests

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.6
• /
• pp.141-156
• /
• 2023

This study aims to improve understanding of freight movement, crucial for logistics facility investment and policy making. It addresses the limitations of traditional freight truck traffic data, aggregated only at city and county levels, by developing a new methodology. This method uses trip chain data for more detailed, facility-level analysis of freight truck movements. It employs DTG (Digital Tachograph) data to identify individual truck visit locations and creates H3 system-based polygons to represent these visits spatially. The study also involves an algorithm to dynamically determine the optimal spatial resolution of these polygons. Tested nationally, the approach resulted in polygons with 81.26% spatial fit and 14.8% error rate, offering insights into freight characteristics and enabling clustering based on traffic chain characteristics of freight trucks and visited facility types.

• Journal of Korea Multimedia Society
• /
• v.12 no.10
• /
• pp.1386-1396
• /
• 2009

Most decision support functions of spatial data warehouse rely on the OLAP operations upon a spatial cube. Meanwhile, higher performance is always guaranteed by indexing the cube, which stores huge amount of pre-aggregated information. Hierarchical Dwarf was proposed as a solution, which can be taken as an extension of the Dwarf, a compressed index for cube structures. However, it does not consider the spatial dimension and even aggregates incorrectly if there are redundant values at the lower levels. OLAP-favored Searching was proposed as a spatial hierarchy based OLAP operation, which employs the advantages of R-tree. Although it supports aggregating functions well against specified areas, it ignores the operations on the spatial dimensions. In this paper, an indexing approach, which aims at utilizing the concept hierarchy of the spatial cube for decision support, is proposed. The index consists of concept hierarchy trees of all dimensions, which are linked according to the tuples stored in the fact table. It saves storage cost by preventing identical trees from being created redundantly. Also, it reduces the OLAP operation cost by integrating the spatial and aspatial dimensions in the virtual concept hierarchy.

• Journal of The Korean Association of Information Education
• /
• v.9 no.3
• /
• pp.453-462
• /
• 2005

Recently, due to technical improvements of storage devices and networks, the amount of data increase rapidly. In addition, it is required to find the knowledge embedded in a data stream as fast as possible. Huge data in a data stream are created continuously and changed fast. Various algorithms for finding frequent itemsets in a data stream are actively proposed. Current researches do not offer appropriate method to find frequent itemsets in which flow of time is reflected but provide only frequent items using total aggregation values. In this paper we proposes a novel algorithm for finding the relative frequent itemsets according to the time in a data stream. We also propose the method to save frequent items and sub-frequent items in order to take limited memory into account and the method to update time variant frequent items. The performance of the proposed method is analyzed through a series of experiments. The proposed method can search both frequent itemsets and relative frequent itemsets only using the action patterns of the students at each time slot. Thus, our method can enhance the effectiveness of learning and make the best plan for individual learning.

• Journal of Convergence for Information Technology
• /
• v.11 no.11
• /
• pp.57-65
• /
• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• Journal of Convergence Society for SMB
• /
• v.6 no.4
• /
• pp.1-8
• /
• 2016

In this study, we must investigate what the major success factors that should be considered before introducing to successfully achieve the goal of building enterprise information systems. To evaluate the factors significantly affecting among the success factors. This study is to present by analyzing the typical success factors affecting successful introduction of the system information to companies that lack sufficient information and reviews for the introduction of enterprise information systems.

• Journal of the Korean Geographical Society
• /
• v.34 no.5
• /
• pp.473-491
• /
• 1999

세균성 이질은 국내 제1종 법정 전염병으로 분류되어 관리되고 있는 질환으로서 1998년 이후 그 발병 사례가 급속히 증가하고 있다. 본 연구는 1999년 3월 부산시 사상구에서 집단 발병한 세균성 이질을 대상으로 하여, 각 환자들의 발병 시점과 장소의 분포패턴에 대한 지리학적 고찰을 목적으로 한다. 환자분포의 특징적 공간패턴과 그들의 시계열적 확산 양상 등을 탐색하기 위한 방법론은 보건지리학과 지도학 및 공간통계학에 기반을 둔 공간분석기법을 중심으로 설정하였다. 분석자료는 해당 지역의 수치지형도, 지적도, 인구 센서스 자료를 포함한 GIS 데이터베이스로 구축되었다. 인구분포를 감안한 밀도구분도를 바탕으로 개별환자의 위치자료와 동 단위로 집계된 자료를 자료의 형태에 따라 분석기법을 달리하였으며, 환자 발생 밀도, 상대적 위험지수 등을 지도화하여 역학자료의 시각적 통계적 분석을 수행하였다. 환자분포의 공간적 중심위치와 분산의 변화 등 기술적 통계분석과 함께 제1차 공간속성을 커널추정법으로 찾아보았다. 이와 더불어 ‘공간적 의존성’과 관련된 제2차 공간속성은 K-함수와 시뮬레이션을 통해 분석하여 군집성 등이 통계적으로 확인되었다. 본 연구를 통해 역학조사시 GIS의 활용사례가 제시되었으며, 모집단 인구를 고려한 확률지도 작성 기법과 다양한 데이터 가시화 방법, 그리고 시계열별 발생 환자들의 지리적 변이를 분석 하는데 따르는 문제들이 논의되었다.