• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.405-406
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• 2012

A sensor network is composed of a large number of sensor nodes which have sensing, computation and wireless communication capabilities. The sensor node sends such collected data, usually via radio transmitter, to a command center (sink) either directly or through a data concentration center (a gateway). These sensor networks can be used for various application areas such as health, military, home network, managing inventory, monitoring disaster areas and so on. Moreover, owing to the rapid growth of mobile technology, high-performance smartphones are widespread and in increasing cases are utilized as a terminal device. In this paper, we propose a home management system using smartphone and sensor networks.

• Journal of KIISE:Databases
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• v.29 no.6
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• pp.477-491
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• 2002

The broadcast environment has asymmetric communication aspect that is typically much greater communication bandwidth available from server to clients than in the opposite direction. In addition, mobile computing systems generate mostly read-only transactions from mobile clients for retrieving different types of information such as stock data, traffic information and news updates. Since previous concurrency control protocols, however, do not consider such a particular characteristics, the performance degradation occurs when previous schemes are applied to the broadcast environment. In this paper, we propose optimistic concurrency control based on timestamp interval for broadcast environment. The following requirements are satisfied by adapting weak consistency that is the appropriate correctness criterion of read-only transactions: (1) the mutual consistency of data maintained by the server and read by clients (2) the currency of data read by clients. We also adopt the timestamp Interval protocol to check the weak consistency efficiently. As a result, we improved a performance by reducing unnecessary aborts and restarts of read-only transactions caused when global serializability was adopted.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.69-77
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• 2017

In this paper, a disaster response scheme based on deep learning and augmented reality technology is proposed and a network resource reservation scheme is presented accordingly. The features of deep learning, augmented reality technology and its relevance to the disaster areas are explained. Deep learning technology can be used to accurately recognize disaster situations and to implement related disaster information as augmented reality, and to enhance disaster response capabilities by providing disaster response On-site disaster response agent, ICS (Incident Command System) and MCS (Multi-agency Coordination Systems). In the case of various disasters, the fire situation is focused on and it is proposed that a plan to strengthen disaster response capability effectively by providing fire situation recognition based on deep learning and augmented reality information. Finally, a scheme to secure network resources to utilize the disaster response method of this paper is proposed.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.5
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• pp.1-9
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• 2021

Children's drawings are widely used in the diagnosis of children's psychology as a means of expressing inner feelings. This paper proposes a children's drawings-based object detection algorithm applicable to children's psychology analysis. First, the sketch area from the picture was extracted and the data labeling process was also performed. Then, we trained and evaluated a Faster R-CNN based object detection model using the labeled datasets. Based on the detection results, information about the drawing's area, position, or color histogram is calculated to analyze primitive information about the drawings quickly and easily. The results of this paper show that Artificial Intelligence-based object detection algorithms were helpful in terms of psychological analysis using children's drawings.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.30 no.1
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• pp.29-51
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• 2019

Most fields of society have constructed and utilized various name identifier systems such and International Standard Name Identifier(ISNI), Open Researcher and Contributor ID(ORCID), and Interested Parties Information System(IPI) in order to uniquely identify individual authors and institutions and to associate them to data related to creative works. Although it might be inevitable to apply name identifier systems in the current data environment with rapid association and integration of data across fields, there are many problems to be addressed when utilizing those systems. In order to overcome these problems and construct better information ecological system by associating and linking data from various fields, this research analyzed advanced cases for data integration based on ISNI. Through the analysis, it suggested managemental refinements for efficiently utilizing ISNI in data integration and association.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.298-300
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• 2021

The Ministry of National Defense decided to build a realistic combat simulation training system based on virtual reality and augmented reality in accordance with the expansion of the scientific training system of "Defense Reform 2.0". The realistic combat simulation training system should be able to maximize the tension and training effect as in actual combat through engagement between trainers. In addition, it should be possible to increase the effectiveness of survival training at the same time as shooting training similar to actual combat through cover training. Previous studies are suitable techniques to improve the shooting precision of the trainee, but it is difficult to practice bilateral engagement like in actual combat, and it is particularly insufficient for combat shooting training using cover. Therefore, in this paper, we propose a S/W algorithm for generating a virtual avatar by recognizing the shooting posture of the opponent on the screen of the virtual shooting trainer. This S/W algorithm can recognize the trainer and the cover based on the depth information acquired through the depth sensor and estimate the trainer's posture.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.3-3
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• 2022

부산 에코델타시티는 세종 5-1 생활권과 더불어 스마트시티 국가시범도시이며, 사업대상지역은 평강천이 관류하여 맥도강, 서낙동강 본류로 흐르는 세물머리지역이 그 중심에 위치하고 있다. 하천을 중심으로 스마트시티가 조성되고 있으나 하천관리는 유역내 홍수관리, 수질관리 등등에 관한 정부주체 상위계획에 종속성이 강한 공공분야로서 단위사업에서 스마트시티에 걸맞는 하천관리계획을 수립하고 시행하는 것은 많은 어려움이 존재한다. 하천관리의 주 관심 대상은 이수를 제외하면 치수, 하천환경, 친수, 하천시설물로 대별할 수 있으며, 스마트 하천관리란 이들에 대한 현황 정보구축 및 지속적인 현행화, 그리고 이들 실제 정보를 토대로 한 합리적 운영과 실시간 연계운영 실현으로 공공서비스를 제공하는 것이라 할 수 있을 것이다. 본 연구는 국가연구개발사업에서 연구한 각 분야별 하천관리기법을 에코델타시티에 시범적용하여 스마트하천관리를 구현할 수 있는 방법을 제시하고자 하였다. 에코델타시티 조성사업의 진행수준으로 인해 취득하기 어려운 정보들은 수립된 계획을 참조하여 적용하였다. 유역내 주 대상 하천들은 델타지역에서 수문으로 통제되므로 일반적인 하천과 달리 정수역에 가까운 특이한 수리특성을 보이고 있지만, 우선 스마트 하천관리를 위한 일반적인 과정들을 시범적용 해보고 필요에 따라 특성 고려 방안을 찾고자 하였다. 유역내 정량적 홍수위험도 평가, 하천환경 평가 적용방안, 하천시설물 관리방안, 주요 지점의 실시간 모니터링 방안 등이 에코델타시티 조성사업 진행단계를 고려하여 수행되고 있다. 사용편의성과 지속가능성을 담보하기 위한 하천연계플랫폼을 구축하여, 개발된 기법들을 각각 내부 모듈로 장착하거나 완성도에 따라 외부모듈로 연계할 수 있도록 하였다. 현재 개발된 기법들의 시스템화 완성 및 관련 정보들의 데이터베이스 확대를 위한 노력이 진행되고 있으며, 향후 하천관리연계플랫폼의 실제 운영을 통하여 완성도를 높여갈 계획이다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.51-59
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• 2024

This study examines the relationship between the release of the news and the individual stock returns. Investors utilize a variety of information sources to maximize stock returns when establishing investment strategies. News companies publish their articles based on stock recommendation reports of analysts, enhancing the reliability of the information. Defining release of a stock-recommendation news article as an event, we examine its economic impacts and propose a binary classification model that predicts the stock return 10 days after the event. XGBoost and LightGBM models are applied for the study with accuracy of 75%, 71% respectively. In addition, after categorizing the recommended stocks based on the listed market(KOSPI/KOSDAQ) and market capitalization(Big/Small), this study verifies difference in the accuracy of models across four sub-datasets. Finally, by conducting SHAP(Shapley Additive exPlanations) analysis, we identify the key variables in each model, reinforcing the interpretability of models.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.1
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• pp.115-137
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• 2008

As stock market gets bigger, use of HTS(Home Trading System) is getting increased in stock exchange. HTS provides lots of functions such as inquiry about stock quotations, investment counsel and so on. Thus, despite the fact that the functions fur convenience and usefulness are developed and used, security functions for privacy and trade safety are insufficient. In this paper, we analyze the security system of HTS service through the key-logging and sniffing and suggest that many private information is unintentionally exposed. We also find out a vulnerable point of the system, and show the advisable criteria of secure HTS.