• Journal of Korean Society of Disaster and Security
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• 제15권4호
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• pp.39-46
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• 2022

The rainfall characteristics such as heavy rains are changing differently from the past, and uncertainties are also greatly increasing due to climate change. In addition, urban development and population concentration are aggravating flood damage. Since the causes of urban inundation are generally complex, it is very important to establish an appropriate flood prevention plan. Thus, the government in Korea is establishing standards for disaster prevention performance for each local government. Since the concept of the disaster prevention performance target was first presented in 2010, the setting standards have changed several times, but the overall technology, methodology, and procedures have been maintained. Therefore, in this study, studies and technologies related to urban disaster prevention performance were reviewed using the scientometric analysis method to review them. This analysis is a method of identifying trends in the field and deriving new knowledge and information based on data such as papers and literature. In this study, papers related to the disaster prevention performance of the Web of Science for the last 30 years from 1990 to 2021 were collected. Citespace, scientometric software, was used to identify authors, research institutes, countries, and research trends, including citation analysis. As a result of the analysis, consideration factors such as the the concept of asset evaluation were identified when making decisions related to urban disaster prevention performance. In the future, it is expected that prevention performance standards and procedures can be upgraded if the keywords are specified and the review of each technology is conducted.

• KIPS Transactions on Software and Data Engineering
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• 제12권11호
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• pp.481-492
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• 2023

This study explores how to build a Korean dataset to extract information from text using generative large language models. In modern society, mixed information circulates rapidly, and effectively categorizing and extracting it is crucial to the decision-making process. However, there is still a lack of Korean datasets for training. To overcome this, this study attempts to extract information using text-based zero-shot learning using a generative large language model to build a purposeful Korean dataset. In this study, the language model is instructed to output the desired result through prompt engineering in the form of "system"-"instruction"-"source input"-"output format", and the dataset is built by utilizing the in-context learning characteristics of the language model through input sentences. We validate our approach by comparing the generated dataset with the existing benchmark dataset, and achieve 25.47% higher performance compared to the KLUE-RoBERTa-large model for the relation information extraction task. The results of this study are expected to contribute to AI research by showing the feasibility of extracting knowledge elements from Korean text. Furthermore, this methodology can be utilized for various fields and purposes, and has potential for building various Korean datasets.

• Korean Journal of Optics and Photonics
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• 제35권3호
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• pp.107-114
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• 2024

In this study, the feasibility of using the covariance-matrix-adaptation-evolution-strategy (CMA-ES) algorithm in a multichannel coherent-beam-combining (CBC) system was experimentally verified. We constructed a multichannel CBC system utilizing a spatial light modulator (SLM) as a multichannel phase-modulator array, along with a coherent light source at 635 nm, implemented the stochastic-parallel-gradient-descent (SPGD) and CMA-ES algorithms on it, and compared their performances. In particular, we evaluated the characteristics of the CMA-ES and SPGD algorithms in the CBC system in both 16-channel rectangular and 19-channel honeycomb formats. The results of the evaluation showed that the performances of the two algorithms were similar on average, under the given conditions; However, it was verified that under the given conditions the CMA-ES algorithm was able to operate with more stable performance than the SPGD algorithm, as the former had less operational variation with the initial phase setting than the latter. It is emphasized that this study is the first proof-of-principle demonstration of the CMA-ES phase-control algorithm in a multichannel CBC system, to the best of our knowledge, and is expected to be useful for future experimental studies of the effects of additional channel-number increments, or external-phase-noise effects, in multichannel CBC systems based on the CMA-ES phase-control algorithm.

• Journal of Intelligence and Information Systems
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• 제18권3호
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• pp.97-117
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• 2012

Nowadays, Internet users tend to do a variety of actions at the same time such as web browsing, social networking and multimedia consumption. While watching a video, once a user is interested in any product, the user has to do information searches to get to know more about the product. With a conventional approach, user has to search it separately with search engines like Bing or Google, which might be inconvenient and time-consuming. For this reason, a video annotation platform has been developed in order to provide users more convenient and more interactive ways with video content. In the future of smart TV environment, users can follow annotated information, for example, a link to a vendor to buy the product of interest. It is even better to enable users to search for information by directly discussing with friends. Users can effectively get useful and relevant information about the product from friends who share common interests or might have experienced it before, which is more reliable than the results from search engines. Social networking services provide an appropriate environment for people to share products so that they can show new things to their friends and to share their personal experiences on any specific product. Meanwhile, they can also absorb the most relevant information about the product that they are interested in by either comments or discussion amongst friends. However, within a very huge graph of friends, determining the most appropriate persons to ask for information about a specific product has still a limitation within the existing conventional approach. Once users want to share or discuss a product, they simply share it to all friends as new feeds. This means a newly posted article is blindly spread to all friends without considering their background interests or knowledge. In this way, the number of responses back will be huge. Users cannot easily absorb the relevant and useful responses from friends, since they are from various fields of interest and knowledge. In order to overcome this limitation, we propose a method to filter a user's friends for information search, which leverages semantic video annotation and social networking services. Our method filters and brings out who can give user useful information about a specific product. By examining the existing Facebook information regarding users and their social graph, we construct a user profile of product interest. With user's permission and authentication, user's particular activities are enriched with the domain-specific ontology such as GoodRelations and BestBuy Data sources. Besides, we assume that the object in the video is already annotated using Linked Data. Thus, the detail information of the product that user would like to ask for more information is retrieved via product URI. Our system calculates the similarities among them in order to identify the most suitable friends for seeking information about the mentioned product. The system filters a user's friends according to their score which tells the order of whom can highly likely give the user useful information about a specific product of interest. We have conducted an experiment with a group of respondents in order to verify and evaluate our system. First, the user profile accuracy evaluation is conducted to demonstrate how much our system constructed user profile of product interest represents user's interest correctly. Then, the evaluation on filtering method is made by inspecting the ranked results with human judgment. The results show that our method works effectively and efficiently in filtering. Our system fulfills user needs by supporting user to select appropriate friends for seeking useful information about a specific product that user is curious about. As a result, it helps to influence and convince user in purchase decisions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• 제9권2호
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• pp.133-140
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• 2016

Software reliability in the software development process is an important issue. Software process improvement helps in finishing with reliable software product. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, we were proposed the reliability model with the exponential and inverse exponential distribution, which made out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on mean square error (MSE) and coefficient of determination($R^2$), for the sake of efficient model, were employed. Analysis of failure, using real data set for the sake of proposing the exponential and inverse exponential distribution, was employed. This analysis of failure data compared with the exponential and inverse exponential distribution property. In order to insurance for the reliability of data, Laplace trend test was employed. In this study, the inverse exponential distribution model is also efficient in terms of reliability because it (the coefficient of determination is 80% or more) in the field of the conventional model can be used as an alternative could be confirmed. From this paper, the software developers have to consider life distribution by prior knowledge of the software to identify failure modes which can be able to help.

• The Journal of Society for e-Business Studies
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• 제20권4호
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• pp.193-208
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• 2015

In knowledge based society, information technology (IT) has been playing a key role in economic growth. In recent years, it is surprisingly notable that the source of value creation moved from hardware to software in IT industry. Especially, among many kinds of software products, the economic potential of open source was realized by many government agencies. Open source means software codes made by voluntary and open participation of worldwide IT developers, and many policies to promote open source activities were implemented for the purpose of fast growth in IT industry. But in many cases, especially in Korea, the policies promoting open source industry and its ecosystem were not considered successful. Therefore, this study provides the practical reasons for the low performance of Korean open source industry and suggests the pragmatic requisites for effective open source policy. For this purpose, this study applies soft system model (SSM) which is frequently used in academy and industry as a methodology for problem-solving and we link the problems with corresponding policy solutions based on SSM. Given concerns which Korean open source faces now, this study suggests needs for the three different kinds of government policies promoting multiple dimensions of industry: research and development (R&D)-side, supply-side, and computing environment-side. The implications suggested by this research will contribute to implement the practical policy solutions to boost open source industry in Korea.

• Journal of the Institute of Electronics and Information Engineers
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• 제54권1호
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• pp.55-69
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• 2017

Recently, the IoT (Internet of Things) environment is being developed rapidly through network which is linked to intellectual objects. Through the IoT, it is possible for human to intercommunicate with objects and objects to objects. Also, the IoT provides artificial intelligent service mixed with knowledge of situational awareness. One of the industries based on the IoT is a car industry. Nowadays, a self-driving vehicle which is not only fuel-efficient, smooth for traffic, but also puts top priority on eventual safety for humans became the most important conversation topic. Since several years ago, a research on the recognition of the surrounding environment for self-driving vehicles using sensors, lidar, camera, and radar techniques has been progressed actively. Currently, based on the WAVE (Wireless Access in Vehicular Environment), the research is being boosted by forming networking between vehicles, vehicle and infrastructures. In this paper, a research on the recognition of a traffic signs on highway was processed as a part of the awareness of the surrounding environment for self-driving vehicles. Through the traffic signs which have features of fixed standard and installation location, we provided a learning theory and a corresponding results of experiment about the way that a vehicle is aware of traffic signs and additional informations on it.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제11권4호
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• pp.1318-1324
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• 2010

Recently, U-Healthcare is receiving attentions as a research for reducing the manpower, time in treatment, and etc. Although fundamental technologies, such as sensing, measuring, and etc. are sufficiently investigated. However, Technologies of IT/Medical convergence, which graft IT technologies to medical area, are still in germ. For this, we present a novel healthcare system, which can be applied to the middle sized medical environment, such as private hospital, home, or etc., by means of pre-verified technologies and the expert system. There exist IT element technologies are sufficiently developed in the fields, such as network, database or etc. due to the remarkable developments in IT technologies, and the healthcare is a mission-critical environment. Therefore, it is important not only to investigate novel approaches but also to utilize verified technologies for the U-Healthcare solution. Presented solution provisions automated medical services based on expert system by utilizing the measured data, such as body fat, blood pressure, blood glucose, and etc., in order to provide convenient treatment environment to doctors and nurses. In addition, since people, who do not have medical knowledge, can self-diagnose themselves, it is expected to cut medical costs in various areas. Especially, since each devices communicate with each other through standardized Bluetooth technology, Presented healthcare system is an extensible solution which can easily accept various medical devices. As a result of this, we can safely say that the self measurement and diagnosis services in U-Healthcare are now enhanced by reducing medical cost through our healthcare system.

• Journal of Intelligence and Information Systems
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• 제17권3호
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• pp.147-168
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• 2011

As personalized recommendation of products and services is rapidly growing in importance, a number of studies provided fundamental knowledge and techniques for developing recommendation systems. Among them, the CF technique has been most widely used and has proven to be useful in many practices. However, current collaborative filtering (CF) technique has still considerable rooms for improving the effectiveness of recommendation systems: 1) a similarity function most systems use to find so-called like-minded people is not well defined in that similarity is computed from a single perspective of similarity concept; and 2) temporal information that contains the changing preference of customers needs to be taken into account when making recommendations. We hypothesize that integration of multiple aspects of similarity and utilization of temporal information will improve the accuracy of recommendations. The objective of this paper is to test the hypothesis through a series of experiments using MovieLens data. The experimental results show that the proposed recommendation system highly outperforms the conventional CF-based systems, confirming our hypothesis.

• The Journal of Society for e-Business Studies
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• 제11권4호
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• pp.1-20
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• 2006

Many government offices have been proceeding a development of Enterprise Architecture(EA) according to apply Government-wide Enterprise Architecture'. Each office and working-level officials have had a hard time because of no guides related to a EA development method such as the Framework, Standards, Principle, Reference Model, Etc. This paper propose a method for developing e-Government Enterprise Architecture considered a characteristic of public institutes through analyzing existing cases. The method for development e-Government EA includes the EA Performance Management Model to monitor objectively each office's long-term business promotion because the e-Government EA development is a job of long duration and cooperation with many institutes. This method also combines the EA Change Management Activities for the officials to improve general knowledge about EA's idea and EA's value, etc. We show the EA case study of the Ministry of Government Administration and Home Affairs to demonstrate feasibility of our approach. As a result, public offices will carry out their BPR(Business Process Re-engineering) and ISP(Information Strategy Planning) more efficiently based on this EA development method.