• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.51-60
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• 2014

In this study, ICT medical service provider's level of knowledge fire fighting safety and methods on coping with fires in the regions of Gwangju and Jeonam Province of Korea were investigated to determine the elements affecting such levels and provide basic information on the manuals for educating how to cope with the fire fighting safety in medical facilities. The data were analyzed using SPSS Win 14.0. The scores of level of knowledge fire fighting safety of ICT medical service provider's were 7.06(10 point scale), and the scores of level of recognizing how to cope with fire fighting safety were 6.61(11 point scale). level of recognizing how to cope with fire fighting safety were significantly different according to gender(t=4.12, p<.001), age(${\chi}^2$=17.24, p<.001), length of career(${\chi}^2$=22.76, p<.001), experience with fire fighting safety education(t=6.10, p<.001), level of subjective knowledge on fire fighting safety(${\chi}^2$=53.83, p<.001). In order to enhance the level of understanding of fire fighting safety and methods of coping by the ICT medical service providers it is found that: self-directed learning through avoiding the education just conveying knowledge by lecture tailored learning for individuals fire fighting education focused on experiencing actual work by developing various contents emphasizing cooperative learning deploying patients by classification systems using simulations and a study on the implementation of digital anti-fire monitoring system with multipoint communication protocol, a design and development of the smoke detection system using infra-red laser for fire detection in the wide space, video based fire detection algorithm using gaussian mixture mode developing an education manual for coping with fire fighting safety through multi learning approach at the medical facilities are required.

• Proceedings of the Korea Society of Poultry Science Conference
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• 2002.11a
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• pp.59-63
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• 2002

1997년 홍콩 가금시장에서의 H5N1 조류독감바이러스의 발병은 18명의 감염된 사람 중에서 6명의 사람의 생명을 앗아갔다. 이 사건은 조류독감바이러스가 매개체를 통하지 않고 닭에서 바로 사람에게 감염한 처음 있는 사건이다. 홍콩가금시장에서의 역학조사는 H5Nl과 H9N2 조류독감바이러스가 함께 공존한다는 것을 밝혔다. 가금에서는 H5N1과 H9N2 조류독감바이러스가 검출되었다. 우리는 H5N1 조류독감바이러스로부터 자을 방어하는데 H9N2 조류독감바이러스의 역할에 대해 연구했다. H5N1과 H9N2 바이러스의 혼합바이러스를 동시에 자에 접종하면 자은 생존하지 못했다. 그러나, H5N1 조류 독감바이러스감염 이전에 H9N2 조류독감바이러스를 감염한 닭들은 생존할 수 있었다 H9N2 조류 독감바이러스로 감염된 닭으로부터 얻어진 혈청은 H5N1 조류독감바이러스와 교차반응을 일으키지 않는다. H9N2 조류독감바이러스로 감염시킨 닭으로부터 얻어진 T임파구 또는 CD8 T임파구를 감염하지 않은 닭에 주입할 때 닭은 H5N1 조류독감바이러스로부터 생존할 수 있었다. 실험실외 킬러임파구실험은 H9N2 조류독감바이러스로 감염된 닭으로부터 얻어진 T임파구는 H5N1과 H9N2 조류독감바이러스로 감염된 목표세포를 동시에 감지했다. 게다가, 생체내 T임파구의 제거실험은 교차보호면역은 a/b TCR를 가진 CD8 T임파구가 중요한 역할을 하며, a/b TCR (Vbl)형의 T임파구가 목표세포를 감지한다는 것을 증명했다. H9N2 조류독감바이러스에 의한 방어면역은 시간이 지남에 따라 감소를 했고, 감염 100일까지 방어력을 나타냈다. 1997년 조류독감바이러스인 H5N1의 홍콩에서의 발병에 대한 풀리지 않은 것 중의 하나는 약 20%의 조류들이 매우 치사율이 높은 H5N1 독감바이러스를 가지고 있음에도 홍콩가금시장에서의 대부분의 닭들은 건강했다. 얻을 수 있는 정보에 따르면 대부분의 자들은 H5N1조류독감바이러스를 변으로 방출했고, 단지 두 곳의 가금시장에 있는 자들이 질병증상을 보였다. 홍콩가금시장에서 분리된 모든 H5N1 조류독감바이러스를 닭에 감염하면 100%의 치사율을 나타낸다. 바이러스 측면에서의 연구에 따르면, H9N2 조류독감바이러스는 홍콩가금시장에서 두 번째로 많이 분리되었다. H9N2 조류독감바이러스에 대한 연구에 따르면 세 가지 형이 홍콩가금시장에서 검출되었다. 1997년에 가장 많이 분리된 H9N2 조류독감바이러스는 PB1과 PB2가 A/Chicken/HongKong /156/97 (H5N1)과 유전적으로 유사한 A/HongKong/G9/97 (H9N2)형이다. A/Chicken/Hong Kong/156/97(H5N1)의 나머지 유전자는 A/Chicken/HongKong/739/94 (H9N2)와 A/chicken /Hong Kong/G23/97의 유전자와 비슷하다. 하나의 A/Quail/Hong Kong/G1/97은 Quail에서 분리되었고, 두 개의 A/Duck/Hong Kong/Y280/97 (H9N2)은 오리에서 분리되었다. A/Quail/Hong Kong/G1/97 (H9N2)의 6개의 내부유전자는 A/HongKon9/156/97 (H5N1)에 유사하나, A/Duck/ Hongkong/Y280/97 (H9N2)의 유전자는 A/HongKong/156/97 (H5N1)과 유사하지 않다. 킬러임파구는 바이러스로 감염된 목표세포를 MHC에 의존하여 파괴한다. 독감바이러스 특이 킬러임파구는 독감바이러스로 감염된 mice의 폐로부터 독감바이러스를 제거하는데 중요하다고 알려져 있다. 독감바이러스의 HA단백질은 특이 킬러임파구의 주요 목표항원 단백질이 아니다. 내부단백질인 nucleoprotein, polymerase (PB1 PB2, PA), Matrix protein, 그리고 비 구조단백질인 NS1에 대한 특이 킬러임파구의 반응이 사람과 mice에서 보고되었다. 독감바이러스에 대한 mice의 킬러임파구의 인식영역은 제한되어 있다고 알려져 있다. 많은 mice MHC 1은 독감바이러스 단백질의 킬러임파구의 epitope를 표현하지 못한다. 사람 기억킬러임파구는 다양한 종류의 독감바이러스의 단백질을 인식한다고 알려져 있다. 지금까지, 닭에서의 독감바이러스의 킬러임파구에 대한 연구는 되지 않았다.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.249-254
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• 2022

With the rapid advance of the semiconductor and Information and communication technologies, remote environment monitoring technology, which can detect and analyze surrounding environmental conditions with various types of sensors and wireless communication technologies, is also drawing attention. However, since the conventional remote environmental monitoring systems require external power supplies, it causes time and space limitations on comfortable usage. In this study, we proposed the concept of the self-powered remote environmental monitoring system by supplying the power with the levitation-electromagnetic generator (L-EMG), which is rationally designed to effectively harvest biomechanical energy in consideration of the mechanical characteristics of biomechanical energy. In this regard, the proposed L-EMG is designed to effectively respond to the external vibration with the movable center magnet considering the mechanical characteristics of the biomechanical energy, such as relatively low-frequency and high amplitude of vibration. Hence the L-EMG based on the fragile force equilibrium can generate high-quality electrical energy to supply power. Additionally, the environmental detective sensor and wireless transmission module are composed of the micro control unit (MCU) to minimize the required power for electronic device operation by applying the sleep mode, resulting in the extension of operation time. Finally, in order to maximize user convenience, a mobile phone application was built to enable easy monitoring of the surrounding environment. Thus, the proposed concept not only verifies the possibility of establishing the self-powered remote environmental monitoring system using biomechanical energy but further suggests a design guideline.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.623-634
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• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.80-90
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• 2014

The objective of this research is to estimate the stand volume of Pinus koraiensis, by using the investigated volume and the information of remote sensing(RS), in the research forest of Kangwon National University. The average volume of the research forest per hectare was $307.7m^3/ha$ and standard deviation was $168.4m^3/ha$. Before and after carrying out 3 by 3 majority filtering on TM image, eleven indices were extracted each time. Independent variables needed for linear regression equation were selected using mean pixel values by indices. The number of indices were eleven: six Bands(except for thermal Band), NDVI, Band Ratio(BR1:Band4/Band3, BR2:Band5/Band4, BR3:Band7/Band4), Tasseled Cap-Greeness. As a result, NDVI and TC G were chosen as the most suitable indices for regression before and after filtering, and R-squared was high: 0.736 before filtering, 0.753 after filtering. As a result of error verification for an exact comparison, RMSE before and after filtering was about $69.1m^3/ha$, $67.5m^3/ha$, respectively, and bias was $-12.8m^3/ha$, $9.7m^3/ha$, respectively. Therefore, the regression conducted with filtering was selected as an appropriate model because of low RMSE and bias. The estimated stand volume applying the regression was $160,758m^3$, and the average volume was $314m^3/ha$. This estimation was 1.2 times higher than the actual stand volume of Pinus koraiensis.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• The Journal of Society for e-Business Studies
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• v.20 no.1
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• pp.183-199
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• 2015

Due to the Four Major Rivers Restoration Project, Nakdong River Estuary Barrage's designed flood quantity has been largely increased, and this has caused to construct several drainage gates at the right side of Eulsukdo island to secure the safety of downstream river area. For successful functioning of Nakdong River Estuary Barrage, such as flood control, disaster prevention, and the securing of sufficient water capacity, drainage gates at the both sides of island have to operate systematically and reliably. To manage this under restricted personnel and resources, we have implemented the IOS (Integrated Operation System) by integrating previous facilities and resources via information and communication technologies. The IOS has been designed to have higher availability and fault tolerance to function continuously even with the partial system's failure under the emergency situation like flood. Operators can use the system easily and acknowledge alarms of facilities through its IWS (Integrated Warning System) earlier. Preparing for Integrated Water Resources Management and Smart Water Grid, the architecture of IOS conformed to open system standards which will be helpful to link with the other systems easily.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.135-142
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• 2013

Contour detection is important for many computer vision applications, such as shape discrimination and object recognition. In many cases, local luminance changes turn out to be stronger in textured areas than on object contours. Therefore, local edge features, which only look at a small neighborhood of each pixel, cannot be reliable indicators of the presence of a contour, and some global analysis is needed. The novelty of this operator is that dilation is limited to Deluanary triangular. An efficient implementation is presented. The grouping algorithm is then embedded in a multi-threshold contour detector. At each threshold level, small groups of edges are removed, and contours are completed by means of a generalized reconstruction from markers. Both qualitative and quantitative comparison with existing approaches prove the superiority of the proposed contour detector in terms of larger amount of suppressed texture and more effective detection of low-contrast contour.