Earticle

확장된 데이터 표현의 주요 목표는 유비쿼터스 환경에서 일반적인 문제에 적합한 데이터 구조를 개발하는 것이다. 이 방법의 가장 큰 특징은 속성 값을 확률로 표현할 수 있다는 것이다. 다음 특성은 훈련 데이터의 각 이벤트가 중요도를 나타내는 가중치 값을 갖도록 한다는 것이다. 데이터 구조가 개발된 후에 이를 학습할 수 있는 알고리즘이 고안된다. 그 동안 이 알고리즘은 여러 분야에서 여러 문제에 적용하여 좋은 결과를 산출해 왔다. 본 논문은 먼저 데이터 표현 기법인 UChoo를 소개하고 이론적인 배경이 되는 규칙 개선 문제를 소개한다. 그리고 규칙 개선, 손실 데이터 처리, BEWS 문제, 앙상블 시스템과 같은 응용 분야의 예를 소개한다.

The main goal of extended data expression is to develop a data structure suitable for common problems in ubiquitous environments. The greatest feature of this method is that the attribute values can be represented with probability. The next feature is that each event in the training data has a weight value that represents its importance. After this data structure has been developed, an algorithm has been devised that can learn it. In the meantime, this algorithm has been applied to various problems in various fields to obtain good results. This paper first introduces the extended data expression technique, UChoo, and rule refinement method, which are the theoretical basis. Next, this paper introduces some examples of application areas such as rule refinement, missing data processing, BEWS problem, and ensemble system.

Recently most universities are suffering from students leaving their majors. In order to make a countermeasure for reducing major separation rate, many universities are trying to find a proper solution. As a similar endeavor, this paper uses decision tree algorithm which is one of the data mining techniques which conduct grouping or prediction into several sub-groups from interested groups. This technique can analyze a feature of type on students leaving their majors. The dataset consists of 5,115 features through data selection from total data of 13,346 collected from a university in Kangwon-Do during seven years(2000.3.1 2006.6.30). The main objective of this study is to evaluate performance of algorithms including CHAID, CART and C4.5 for classification of students leaving their majors with ROC Chart, Lift Chart and Gains Chart. Also, this study provides values about accuracy, sensitivity, specificity using classification table. According to the analysis result, CART showed the best performance for classification of students leaving their majors.

A plethora of big data applications are emerging and being researched in the computer science community which require online classification and pattern recognition of huge data pools collected from sensor networks, image and video systems, online forum platforms, medical agencies etc. However, as an NP hard issue data mining techniques are facing with lots of difficulties. To deal with the hardship, we conduct research on the novel algorithm for data mining and knowledge discovery through network entropy. We firstly introduce necessary data analysis techniques such as support vector machine, neural network and decision tree methods. Later, we analyze the organizational structure of network graphical pattern with the knowledge of machine learning methodology and graph theory. Eventually, our modified method is finalized with decision and validation implementation. The simulation results of our approach on different databases show the feasibility and effectiveness of our proposed framework. As the final part, we provide our conclusion and prospect.

This paper indirectly created high density point cloud data using unmanned aerial vehicle image. Then, we tried to suggest new concept of classification technique where particular objects from point cloud data can be selectively classified. For this, we established the classification technique that can be used as search factor in classifying color information in point cloud data. Then, using suggested classification technique, we implemented object classification and analyzed classification accuracy by relative comparison with self-created proof resource. As a result, the possibility of point cloud data classification was observable using the image's information. Furthermore, it was possible to classify particular object's point cloud data in high classification accuracy.

This research aimed to develop forest type classification technique for the mixed forest with coniferous and broad-leaved species using the high resolution satellite data. QuickBird data was used as satellite data. The method of this research was to extract satellite data for every single tree crown using image segmentation technique, then to evaluate the accuracy of classification by changing grouping criteria such as tree species, families, coniferous or broad-leaved species, and timber prices. As a result, the classification of tree species and families level was inaccurate, on the other hand, coniferous or broad-leaved species and timber price level was high accurate.

IT 기술의 발전으로 인해 데이터 처리 시스템은 많은 양의 데이터를 처리 및 가공해야 한다. 하지만, 기존에 사용되던 온디스크(On-Disk) 시스템으로는 급증하는 데이터를 빠르게 처리하는 데 한계점을 가졌다. 이로 인해 많은 분야에서 하드디스크에 데이터를 저장하는 것이 아닌 속도가 빠른 메모리에 데이터를 저장 및 관리하는 인메모리(In-Memory) 시스템이 도입되고 있다. 하지만, 메모리에 데이터를 관리하는 것은 메모리의 특성 중 하나인 휘발성으로 인해 데이터 손실이라는 위험을 갖기 때문에 항상 결함 허용 기법이 뒤따라야 한다. 결함 허용 기법은 인메모리 시스템의 처리 속도를 낮추는 성능 저하 원인이 된다. 따라서 본 논문에서는 인메모리 시스템의 데이터 사용 특성을 고려하여 핫콜드 데이터로 분류하고, 데이터 영속성 보장을 위한 복합 백업 기법을 제안한다. 제안하는 기법은 영속성을 높이고, 성능 저하 원인을 보완한다.

As the IT technology advances, data processing system is required to handle and process large amounts of data. However, the existing On-Disk system has limit to process data which increase rapidly. For that reason, the In-Memory system is being used which saves and manages data on the fast memory not saving data into hard disk. Although it has fast processing capability, it is necessary to use the fault tolerance techniques in the In-Memory system because it has a risk of data loss due to volatility which is one of the memory characteristics. These fault tolerance techniques lead to performance degradation of In-Memory system. In this paper, we classify the data into Hot and Cold data in consideration of the data usage characteristics in the In-Memory system and propose compound backup technique to ensure data persistence. The proposed technique increases the persistence and improves performance degradation.

본 논문은 원격탐사 Data를 이용한 분야에서 보다 효과적인 좌상처리 기법 및 보다 정확한 분류화상을 얻는 것을 목적으로 하고 있다. 이의 실행을 위해 여름 좌상과 겨울 화상을 합성한 토지이용 분류결과와 여름 화상만의 분류결과를 비교분석 하였다. 위의 분석결과로부터 Bitemporal Classification 기법과 $tan^{-1}$변환이 유효함을 알아내었다. 특히 Bitemporal Classification 기법을 적용함으로써 농경지를 논과 밭으로 구별하여 분류하는 것이 보다 가능하였다.

This study aims at obtaining more effective image processing techniques and more accurately classified image in the sphere which uses remotely sensed data. For this practice, the result of land use classification compounding summer scene with winter scene and the classified result of summer scene were compared, analyzed. From the upper analysed results, we found that Bitemporal Classification technique and $tan^{-1}$transformation were effective. Particularly, dividing crop class into two classes of farmland and field was more possible by appling Bitemporal Classification technique.

본 연구는 인공신경망의 한 종류인 LSTM(Long Short-Term Memory) 기법을 활용하기 위하여 진행하였다. UTD(University of Texas at Dallas)가 공개한 27종 동작 데이터 중 3축 가속도 및 각속도 데이터를 기본 LSTM 및 Deep Residual Bidir-LSTM 기법에 적용하여 행동을 분류해 보았다.

This study was carried out to utilize LSTM(Long Short-Term Memory) technique which is one kind of artificial neural network. Among the 27 types of motion data released by the UTD(University of Texas at Dallas), 3-axis acceleration and angular velocity data were applied to the basic LSTM and Deep Residual Bidir-LSTM techniques to classify the behavior.

최근 머신러닝은 빅데이터에 대한 분석방법으로서 학습을 통한 지능화된 문제해결 방안으로서 관심이 증가하고 있다. 본 논문은 LBSN 데이터와 머신러닝 방식을 이용하여 토지이용현황을 파악하는 분석을 시도하였다. 도시계획에 있어서 토지이용현황의 파악은 직접적인 현장 조사에 의존해 왔다. 최근 스마트폰 사용자가 증가하면서 등장하고 있는 위치기반 소셜미디어의 자료들은 토지이용의 상황을 반영하는 빅데이터로서, 머신러닝 방법론은 이들에 대한 자동화된 분석을 할 수 있게 한다. 본 연구에서는LBSN 자료와 머신러닝 기법을 이용하여 토지이용을 예측하는 모델을 개발하여 실제 토지이용현황 자료와의 비교분석을 수행하였다. 이러한 분석을 통해 LBSN자료를 이용한 토지이용현황의 자동화된 분석 방안에 대해 연구하였다.

Recently, machine learning is an analytical method for big data, and interest is increasing as an intelligent problem solving method through learning. In this paper, we tried to analyze land use status using LBSN data and machine learning method. The identification of land use status in urban planning has relied on direct field surveys. Recently, the data of location - based social media emerging as users of smartphone are big data reflecting the situation of land use, and machine learning methodology enables automated analysis of these. In this study, a model for predicting land use was developed using LBSN data and machine learning technique and compared with actual land use data. Through this analysis, an automated analysis method of land use status using LBSN data was studied.

습지는 탄소 흡수 및 생태계 보전 기능이 뛰어나며 기후변화 대응과 지속가능성 측면에서 그 중요성이 높아지고 있다. 이에 따라 고해상도 이미지와 딥러닝 모델을 활용하여 정확한 습지 유형 분류 연구가 수행되고 있다. 그러나 딥러닝 모델의 분류 성능을 향상시키기 위해서는 습지 유형별로 충분하고 균형적인 학습 데이터를 확보하는 것이 필수적이다. 그런데 실제 습지 환경은 식생 유형의 다양성, 계절 및 기후 변화에 따른 빠른 환경적 변화 등 복잡한 특성을 가지고 있어 데이터를 균형적으로 획득하는데 한계가 있다. 이에 따라 본 연구에서는 드론 기반의 고해상도 내륙습지 데이터셋을 활용하여 데이터 균형화 기법의 적용을 통한 내륙 습지 분류 성능을 비교·분석하였다. 분류 모델로는 TransUNet을 활용하였다. 원본 데이터만을 활용한 모델의 경우 비대상지, 초본우점, 목본우점, 수체, 나지 클래스에 대한 F1-score가 0.849, 0.945, 0.878, 0.985 그리고 0.769였으며 데이터 균형화를 적용한 모델의 경우 각각 0.862, 0.951, 0.889, 0.985 그리고 0.796으로 모든 클래스에 대해서 성능 향상을 확인할 수 있었다. 특히, 데이터 비율이 적은 비대상지, 목본 우점, 나지 클래스에 대해서 0.013, 0.011 그리고 0.027의 성능 향상을 확인할 수 있었다. 그러나 균형화 적용 이후에도 나지 클래스의 성능은 여전히 상대적으로 낮았으며, 이는 나지 클래스의 다양성과 복잡성을 충분히 반영하지 못했기 때문이라 판단된다. 본 연구 결과는 내륙습지 분류뿐만 아니라 다양한 불균형한 다중 클래스 환경에서도 효과적으로 적용될 것으로 기대된다.

Wetlands are becoming increasingly important for climate change mitigation and sustainability due to their ability to absorb carbon and preserve ecosystems. As a result, research is being conducted to accurately classify wetland types using high-resolution imagery and deep learning models. However, in order to improve the classification performance of deep learning models, it is essential to acquire sufficient and balanced training data for each wetland type. However, the actual wetland environment has complex characteristics such as diversity of vegetation types and rapid environmental changes due to seasonal and climatic changes, which limit the acquisition of balanced data. Therefore, in this study, we compared and analyzed the performance of inland wetland classification by applying data balancing techniques using a drone-based high-resolution inland wetland dataset. TransUNet was utilized as a classification model. The F1-scores for the unclassified, herbaceous dominated, woody dominated, water, and bare land classes were 0.849, 0.945, 0.878, 0.985, and 0.769 for the model using only the original data, respectively, and 0.862, 0.951, 0.889, 0.985, and 0.796 for the model applying data balancing, respectively, showing performance improvement for all classes. In particular, we found performance improvements of 0.013, 0.011, and 0.027 for the unclassified, woody-dominated, and bare land classes with small proportions of data. However, even after balancing, the performance of the bare land class was still relatively low, which is likely due to the fact that it does not fully reflect the diversity and complexity of the bare land class. The results of this study are expected to be effectively applied not only to inland wetland classification but also to various unbalanced multi-class environments.