• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.87-94
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• 2008

In this paper, we propose a new vision-based fire detection method for a real-life application. Most previous vision-based methods using color information and temporal variation of pixel produce frequent false alarms because they used a lot of heuristic features. Furthermore there is also computation delay for accurate fire detection. To overcome these problems, we first detected candidated fire regions by using background modeling and color model of fire. Then we made probabilistic models of fire by using a fact that fire pixel values of consecutive frames are changed constantly and applied them to a Bayesian Network. In this paper we used two level Bayesian network, which contains the intermediate nodes and uses four skewnesses for evidence at each node. Skewness of R normalized with intensity and skewnesses of three high frequency components obtained through wavelet transform. The proposed system has been successfully applied to many fire detection tasks in real world environment and distinguishes fire from moving objects having fire color.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.173-187
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• 2018

This paper proposes a computer vision and deep learning-based technique for surveillance camera system for vehicle counting as one part of parking lot management system. We applied the You Only Look Once version 2 (YOLOv2) detector and come up with a deep convolutional neural network (CNN) based on YOLOv2 with a different architecture and two models. The effectiveness of the proposed architecture is illustrated using a publicly available Udacity's self-driving-car datasets. After training and testing, our proposed architecture with new models is able to obtain 64.30% mean average precision which is a better performance compare to the original architecture (YOLOv2) that achieved only 47.89% mean average precision on the detection of car, truck, and pedestrian.

• Journal of the Korean association of regional geographers
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• v.23 no.2
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• pp.269-287
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• 2017

While Jeju Free International City was promoted to overcome the economic crisis and build a new national competitiveness in the era of globalization, its development vision as 'the hub city of Northeast Asian economy in the $21^{st}$ century' has not been realized. This paper argues that Jeju Free International City to aim for the 'ideal free market model', 'neoliberal space of exception', and 'a new testing ground for neoliberal deregulation policies' has failed due to worsening of socioeconomic and environmental contradictions, growing conflicts in local community, and the logic of equity enforced by the central government. To support this claim, this article reviews the theoretical discussions of special economic zones, examines the shifts in the development visions of Jeju Free International City, and analyzes how Jeju has become a space of exception with the introduction of various exceptional policies and spatial mechanisms.

• Information Systems Review
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• v.13 no.1
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• pp.115-141
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• 2011

One consequence of the new economic imperative of developing an environment-friendly and sustainable economy has been the growing importance of green management as a corporate management strategy. In Korea, the government has put forth a green growth vision, and followed suit with policy measures reflecting this vision. Companies, in their turn, have announced various new management plans reflecting this green vision. But, thus far, no tangible progress has been made. This is mainly due to the lack of concreteness, both in the government's policy and green management plans by companies. The government's green IT vision, for instance, was formulated without an accurate assessment of the current level of maturity in terms of green IT in Korea. This study presents a model for assessing the level of green IT maturity, developed by taking into consideration the informatization and regulatory environments of Korean firms. We use this model to evaluate a sample of Korean firms selected as representatives of their respective industry sectors to assess the overall level of green IT maturity among Korean companies. Improvement strategies, based on the results of assessment, are presented as well.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.27-36
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• 2022

In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.

• Journal of Food Hygiene and Safety
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• v.33 no.3
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• pp.157-161
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• 2018

The objective of this study was to develop software to predict the kinetic behavior and the probability of foodborne bacterial growth on processed meat products. It is designed for rapid application by non-specialists in predictive microbiology. The software, named Foodborne bacteria Animal product Modeling Equipment (FAME), was developed using Javascript and HTML. FAME consists of a kinetic model and a probabilistic model, and it can be used to predict bacterial growth pattern and probability. In addition, validation and editing of model equation are available in FAME. The data used by the software were constructed with 5,400 frankfurter samples for the kinetic model and 345,600 samples for the probabilistic model using a variety of combinations including atmospheric conditions, temperature, NaCl concentrations and $NaNO_2$ concentrations. Using FAME, users can select the concentrations of NaCl and $NaNO_2$ meat products as well as storage conditions (atmosphere and temperature). The software displays bacterial growth patterns and growth probabilities, which facilitate the determination of optimal safety conditions for meat products. FAME is useful in predicting bacterial kinetic behavior and growth probability, especially for quick application, and is designed for use by non-specialists in predictive microbiology.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.537-544
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• 2005

정보화 환경에 적합한 인재의 Personality 유형 분류 및 주요 특징 분석을 통하여 이에 대한 모델을 제시하는 것이 본 연구의 목적이다. 기존 심리검사 및 정보화 성격 관련 연구의 한계점을 제시하고 조직 내 각 계층의 업무 수행에 필요한 성격 및 주요 성공 역할을 도출하여 이를 정보화 환경에 맞게 정리함으로써 정보화 성격 유형을 분류하고자 한다. 이러한 성격 유형들은 세부적으로 IT 창조자, Communicator, IT 리더, 정보 공유자, IT 감독자, 비전 제시자, 동기 부여자 등 7가지 수평적 유형으로 분류되며, 이러한 유형들의 분석을 위하여 성격 검사 연구인 Big 5 Model의 분석 방법 및 설문 문항을 적용하고자 한다. 이러한 정보화 성격 분류 및 각 유형에 대한 특성 제시는 개인의 정보화 성향 및 잠재성격을 파악하고 이를 개인적, 조직적으로 더욱 발전시킬 수 있는 방향을 제시할 수 있다.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.131-132
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• 2011

국가 R&D 특허성과를 높이기 위해서는 원천 핵심특허 선점이 가능한 미래 유망기술에 대한 집중적인 R&D 투자가 필요하다. 따라서 본 연구는 국가 R&D선정단계에서부터 최종관리까지 지식재산 관점에서의 국가 R&D 목표값을 제시하여 관리함으로써 R&D 투자 성과를 극대화할 수 있는 비전을 제시하고 이를 좀 더 구체화하기 위한 지재권 전략 추진을 위한 모델을 제시하였다.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2006.05a
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• pp.129-145
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• 2006

본 연구는 과학기술연합대학원대학교(UST)를 중심으로 한 과학기술계의 새로운 대학원모델을 평가하는 기법에 주안점을 두고 있다. UST와 일본의 SOKENDAI(The Graduate University for Advanced Studios)를 비교함으로써 새로운 대학원모델의 필요성을 말하고 있다. UST는 커리큘럼과 도제식 수업, 맞춤형 교육체계, 현장 중심형 인력양성체계들이 아직 높은 수준에 도달하지 못한 반면 교육목적과 비전이 상당 수준 만족스럽다는 결과를 얻고 있는 조사를 바탕으로 한 UST의 평가에 관해 다루고 있다. 본 연구에서는 대학평가체계의 네 가지 유형과 함께 토론회와 모든 평가에 부합되는 고급 평론을 통하여 UST에 방법론적으로 적용할 수 있는 평가방식을 제안한다. UST를 위한 새로운 평가방식은 대학원평가를 위한 특수성과 보편성으로 서로 동일하다. UST는 다 학제적이며 신생 융합기술분야에서 현장중심형인 리더십을 갖춘 인재양성을 위한 교육체계를 가지고 있으며 동시에 세계적인 특성을 가지고 있다. 두 요소는 특수성이 더욱 강조된 5:5, 6:4의 비율을 보이고 있다. 앞으로 후속적인 보완연구가 추가되면 더욱 활용성이 높아질 것으로 기대된다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.505-509
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• 2007

색 변환(Color Transfer) 기법은 컴퓨터 비전 및 영상 처리 분야에서 점점 더 많은 연구가 되고 있는 분야이다. 이는 참조 영상의 분위기를 원본 영상에 반영하는 기법이다. 본 논문에서는 채도가 낮은 색상에서 나타나는 잘못된 연산 결과를 해결하기 위해 채도 문턱치에 따라 유채색과 무채색으로 분류하여 인덱싱 하고, Lab색 모델에서 색상 채널인 a, b를 사용하여 그림 영상에서의 색 변환하는 방법을 제안한다. 제안된 방법은 영상의 화소들의 채도 문턱치를 이용하여 유채색과 무채색으로 분류하는 단계, 분류된 화소들의 색 특성을 이용한 cylindrical metric를 이용한 인덱싱 하는 단계, 각 인덱스 내의 위치적 표준편차와 화소수를 이용하여 인덱스들의 우열을 가리는 단계, 인덱스들의 우세한 순서로 Lab 색 모델에서 a 채널과 b 채널을 이용하여 색 변환하는 단계 등 4단계로 구성된다. 실험결과는 제안한 방법이 무채색과 유채색이 잘 분류되어 인덱싱 되었음을 보이고 원본 영상의 색이 참조영상의 색으로 잘 변환된 것을 보인다.