• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.591-600
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• 2006

This paper describes the development and implementation of a algorithm to decompose word images from image regions mixed text/graphics in document images using statistical analyses. To decompose word images from image regions, the character components need to be separated from graphic components. For this process, we propose a method to separate them with an analysis of box-plot using a statistics of structural components. An accuracy of this method is not sensitive to the changes of images because the criterion of separation is defined by the statistics of components. And then the character regions are determined by analyzing a local crowdedness of the separated character components. finally, we devide the character regions into text lines and word images using projection profile analysis, gap clustering, special symbol detection, etc. The proposed system could reduce the influence resulted from the changes of images because it uses the criterion based on the statistics of image regions. Also, we made an experiment with the proposed method in document image processing system for keyword spotting and showed the necessity of studying for the proposed method.

• Journal of Korea Spatial Information System Society
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• v.10 no.1
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• pp.41-49
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• 2008

This paper presents a data analysis method for the taxi telematics system which generates a greate deal of location history data. By the record consist of the basic GPS receiver-generated fields, device-added fields such as taxi operation status, and framework-attached fields such as matched link Identifier and position ratio in a link, each taxi can be represented by a state diagram. The transition and the state definition enable us to efficiently extract such information as pick-up time, pick-up distance, dispatch time, and dispatch distance. The analysis result can help to verify the efficiency of a specific taxi dispatch algorithm, while the analysis framework can invite a new challenging service including future traffic estimation, trajectory clustering, and so on.

• Convergence Security Journal
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• v.12 no.3
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• pp.107-113
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• 2012

Due to the development of communications technology, it is now possible to be offered online from remote places. This kind of communications technology can be applied to the medical field. The medical treatment appointments in hospitals can be its typical example. But still, in most of hospitals, patient or guardian have to physically visit or call to the hospital to set up an appointment for the medical treatment. In addition, they have to wait in line in order to pay after receiving the medical treatment. The patient or guardian, after paying, receive a paper prescription and they go to a nearby pharmacy to take the medicines. They must wait in line again there in order to receive the medicine from the pharmacy. In this paper, we would like to suggest a smart medical treatment system in order to solve the problems discussed above. With this proposed system, the user will be able to make an appointment, make payments and receive medication quickly and easily without spending extra time. Also, there will be no need for paper prescriptions with this system. We discuss about the security of medical information for this proposed smart medical treatment system proposed.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.391-404
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• 2019

Customers' fixed characteristics have often been used to predict customer behavior. It has recently become possible to track customer web logs as customer activities move from offline to online. It has become possible to collect large amounts of web log data; however, the researchers only focused on organizing the log data or describing the technical characteristics. In this study, we predict the decision-making time until each customer makes the first reservation, using Airbnb customer data provided by the Kaggle website. This data set includes basic customer information such as gender, age, and web logs. We use various methodologies to find the optimal model and compare prediction errors for cases with web log data and without it. We consider six models such as Lasso, SVM, Random Forest, and XGBoost to explore the effectiveness of the web log data. As a result, we choose Random Forest as our optimal model with a misclassification rate of about 20%. In addition, we confirm that using web log data in our study doubles the prediction accuracy in predicting customer behavior compared to not using it.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.66-77
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• 2011

We propose a framework for grouping photographic advertising images that employs a hierarchical indexing scheme based on efficient feature combinations. The study provides one specific application of effective tools for monitoring photographic advertising information through online and offline channels. Specifically, it develops a preprocessor for advertising image information tracking. We consider both global features that contain general information on the overall image and local features that are based on local image characteristics. The developed local features are invariant under image rotation and scale, the addition of noise, and change in illumination. Thus, they successfully achieve reliable matching between different views of a scene across affine transformations and exhibit high accuracy in the search for matched pairs of identical images. The method works with global features in advance to organize coarse clusters that consist of several image groups among the image data and then executes fine matching with local features within each cluster to construct elaborate clusters that are separated by identical image groups. In order to decrease the computational time, we apply a conventional clustering method to group images together that are similar in their global characteristics in order to overcome the drawback of excessive time for fine matching time by using local features between identical images.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.159-159
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• 2017

스마트 시설환경은 대표적으로 원예, 축산 분야 등 여러 형태의 농업현장에 정보 통신 및 데이터 분석 기술을 도입하고 있는 시설화된 생산 환경이라 할 수 있다. 근래에 하드웨어적으로 급증한 스마트 시설환경에서 생산되는 방대한 생육/환경 데이터를 올바르고 적합하게 사용하기 위해서는 일반 산업 현장과는 차별화 된 분석기법이 요구된다고 할 수 있다. 소프트웨어 공학 분야에서 연구된 빅데이터 처리 기술을 기계적으로 농업 분야의 빅데이터에 적용하기에는 한계가 있을 수 있다. 시설환경 내/외부의 다양한 환경 변수는 시계열 데이터의 난해성, 비가역성, 불특정성, 비정형 패턴 등에 기인하여 예측 모델 연구가 매우 난해한 대상이기 때문이라 할 수 있다. 본 연구에서는 근래에 관심이 급증하고 있는 인공신경망 연구 소프트웨어인 Tensorflow (www.tensorflow.org)와 대표적인 Open source인 OpenNN (www.openn.net)을 스마트 시설환경 환경변수 상호간 상관성 분석에 응용하였다. 해당 소프트웨어 라이브러리의 운영환경을 살펴보면 Tensorflow 는 Linux(Ubuntu 16.04.4), Max OS X(EL capitan 10.11), Windows (x86 compatible)에서 활용가능하고, OpenNN은 별도의 운영환경에 대한 바이너리를 제공하지 않고 소스코드 전체를 제공하므로, 해당 운영환경에서 바이너리 컴파일 후 활용이 가능하다. 소프트웨어 개발 언어의 경우 Tensorflow는 python이 기본 언어이며 python(v2.7 or v3.N) 가상 환경 내에서 개발이 수행이 된다. 주의 깊게 살펴볼 부분은 이러한 개발 환경의 제약으로 인하여 Tensorflow의 주요한 장점 중에 하나인 고속 연산 기능 수행이 일부 운영 환경에 국한이 되어 제공이 된다는 점이다. GPU(Graphics Processing Unit)의 제공하는 하드웨어 가속기능은 Linux 운영체제에서 활용이 가능하다. 가상 개발 환경에 운영되는 한계로 인하여 실시간 정보 처리에는 한계가 따르므로 이에 대한 고려가 필요하다. 한편 근래(2017.03)에 공개된 Tensorflow API r1.0의 경우 python, C++, Java언어와 함께 Go라는 언어를 새로 지원하여 개발자의 활용 범위를 매우 높였다. OpenNN의 경우 C++ 언어를 기본으로 제공하며 C++ 컴파일러를 지원하는 임의의 개발 환경에서 모두 활용이 가능하다. 특징은 클러스터링 플랫폼과 연동을 통해 하드웨어 가속 기능의 부재를 일부 극복했다는 점이다. 상기 두 가지 패키지를 이용하여 2016년 2월부터 5월 까지 충북 음성군 소재 딸기 온실 내부에서 취득한 온도, 습도, 조도, CO2에 대하여 Large-scale linear model을 실험적(시간단위, 일단위, 주단위 분할)으로 적용하고, 인접한 세그먼트의 환경변수 예측 모델링을 수행하였다. 동일한 조건의 학습을 수행함에 있어, Tensorflow가 개발 소요 시간과 학습 실행 속도 측면에서 매우 우세하였다. OpenNN을 이용하여 대등한 성능을 보이기 위해선 병렬 클러스터링 기술을 활용해야 할 것이다. 오프라인 일괄(Offline batch)처리 방식의 한계가 있는 인공신경망 모델링 기법과 현장 보급이 불가능한 고성능 하드웨어 연산 장치에 대한 대안 마련을 위한 연구가 필요하다.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.777-785
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• 2009

Collaborative filtering is a technique used to predict whether a particular user will like a particular item. User-based or item-based collaborative techniques have been used extensively in many commercial recommender systems. In this paper, a hybrid collaborative filtering method that combines user-based and item-based methods using a low-dimensional linear model is proposed. The proposed method solves the problems of sparsity and a large database by using NMF among the low-dimensional linear models. In collaborative filtering systems the methods using the NMF are useful in expressing users as semantic relations. However, they are model-based methods and the process of computation is complex, so they can not recommend items dynamically. In order to complement the shortcomings, the proposed method clusters users into groups by using NMF and selects features of groups by using TF-IDF. Mutual information is then used to compute similarities between items. The proposed method clusters users into groups and extracts features of groups on offline and determines the most suitable group for an active user using the features of groups on online. Finally, the proposed method reduces the time required to classify an active user into a group and outperforms previous methods by combining user-based and item-based collaborative filtering methods.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.05a
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• pp.126-142
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• 2007

인터넷은 학교 교육은 물론 기업의 교육을 변화시키고 있다. 인터넷을 학습에 활용하는 이러닝(electronic learning)은 1990년대 말에 도입되어 7년 이상 경과되었다. 이러닝은 오프라인교육(집체교육)에 비해 시간과 공간의 제약을 받지 않고 비용이 저렴하며 반복학습이 가능하다는 장점이 있다. 이러한 특성 때문에 이러닝은 지역간, 계층간 교육 불평등을 해소하면서 사회 구성원의 학습욕구를 충족시킬 수 있다. 이러닝은 평생교육의 일환으로 학교 교육은 물론 기업 교육과 사회 교육을 변화시키면서 점차 확산되고 있다. 지금까지 이러닝에 대해 연구된 논문들은 주로 이러닝을 사용하게 하는 주요 요인에 대해 초점이 맞춰져 있었다. 그러나 아쉽게도 이러닝은 초기에 기대했던 만큼 크게 확산되지는 않은 실정이다. 따라서 확산되지 않은 요인을 확인하고 이러닝 보급을 확산시킬 방안을 찾는 것이 매우 중요하게 부각되고 있다. 따라서 향후에는 이러닝에 대한 연구의 초점이 이러닝을 왜 채택하지 않고 있는가에 맞춰져야 한다. 이와 같은 연구의 필요성에 따라 본 연구에서는 이러닝을 왜 사용하지 않는가라는 기존의 입장과는 정반대의 관점에서 이러닝을 연구하였다. 본 연구에서 규명하고자 하는 주요 가설은 다음과 같다. 가설1: 이러닝 단점은 채택의도에 부의 영향을 미친다. 가설2-1: 이러닝장점은 채택의도에 정의 영향을 미친다. 가설2-2: 이러닝장점은 용이성을 통해 채택의도에 정의 영향을 미친다. 가설2-3: 이러닝장점은 유용성을 통해 채택의도에 정의 영향을 미친다. 가설3-1: 재미는 채택의도에 정의 영향을 미친다. 가설3-2: 재미는 유용성을 통해 채택의도에 정의 영향을 미친다. 가설3-3: 재미는 용이성을 통해 채택의도에 정의 영향을 미친다. 가설4: 유용성은 채택의도에 정의 영향을 미친다. 가설5: 용이성은 채택의도에 정의 영향을 미친다. 가설6: 용이성은 유용성에 정의 영향을 미친다. 본 연구의 대상은 자발적으로 이러닝을 채택할 수 있는 대학생을 대상으로 하였고, 설문 데이터 분석을 통한 실증연구를 수행하였다. 분석방법으로는 PLS 분석도구를 사용하였다. 분석결과 가설6을 제외하고는 모두 유용한 것으로 입증되었다.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.473-483
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• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.

• Journal of the Korean Applied Science and Technology
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• v.38 no.2
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• pp.465-475
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• 2021

This study conducted an online and offline survey of 210 people from March 11 to 27, 2021 for the purpose of investigating and analyzing the current status of skin stress recognition and beauty care behavior due to wearing masks. The collected data were analyzed using SPSS 25.0 with Cronbach's α, Frequency Analysis, Chi-square test, and One way Anova. The average daily mask wearing time of more than 7 hours during the Covid-19 period was 43.8%, and skin stress recognition by wearing masks was highest among those in their 30s (M=4.27) and service workers (M=4.64), and those with acne and skin troubles (M=4.47) perceived high stress. The most important factor for home care treatment was cleansing(67.6%) and for beauty care was skin care(36.7%). Considerations factors on beauty care were 54.3% for service and customer care capabilities, and on body shape management method 45.7% for exercise. According to this study, respondents are recognizing skin stress due to the long-term use of masks, and home care treatment has been increasing as the esthetic salon has become unstable to visit due to the Covid-19 epidemic.