• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.217-217
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• 2023

상수도관망은 사용자에게 고품질의 물을 안정적으로 공급하는 것을 목적으로 하며, 이를 평가하기 위한 지표 중 하나로 압력을 활용한다. 최근 스마트 센서의 설치가 확장됨에 따라 기계학습기법을 이용한 실시간 데이터 기반의 분석이 활발하다. 따라서 어디에서 데이터를 수집하느냐에 대한 센서 위치 결정이 중요하다. 본 연구는 eXtreme Gradient Boosting(XGBoost) 모델을 활용하여 대규모 상수도관망 내 센서 위치를 최적화하는 방법론을 제안한다. XGBoost 모델은 여러 의사결정 나무(decision tree)를 활용하는 앙상블(ensemble) 모델이며, 오차에 따른 가중치를 부여하여 성능을 향상시키는 부스팅(boosting) 방식을 이용한다. 이는 분산 및 병렬 처리가 가능해 메모리리소스를 최적으로 사용하고, 학습 속도가 빠르며 결측치에 대한 전처리 과정을 모델 내에 포함하고 있다는 장점이 있다. 모델 구현을 위한 독립 변수 결정을 위해 압력 데이터의 변동성 및 평균압력 값을 고려하여 상수도관망을 대표하는 중요 절점(critical node)를 선정한다. 중요 절점의 압력 값을 예측하는 XGBoost 모델을 구축하고 모델의 성능과 요인 중요도(feature importance) 값을 고려하여 센서의 최적 위치를 선정한다. 이러한 방법론을 기반으로 상수도관망의 특성에 따른 경향성을 파악하기 위해 다양한 형태(예를 들어, 망형, 가지형)와 구성 절점의 수를 변화시키며 결과를 분석한다. 본 연구에서 구축한 XGBoost 모델은 추가적인 전처리 과정을 최소화하며 대규모 관망에 간편하게 사용할 수 있어 추후 다양한 입출력 데이터의 조합을 통해 센서 위치 외에도 상수도관망에서의 성능 최적화에 활용할 수 있을 것으로 기대한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1191-1194
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• 2011

스타의 대중에 대한 인기가 브랜드의 이미지 제고와 상업적 영향을 끄는 마케팅 전략을 스타 마케팅이라고 한다. 오늘날의 스타는 방송, 연예활동뿐만 아니라 스포츠, 정치활동, 사회기여활동 등 다양한 분야에서 활약하며 스타의 이미지는 그 활약상에 영향을 받는다. 스타의 이미지는 브랜드 및 기업의 이미지로 직결되므로 그에 대한 사전분석은 마케팅에서 중요한 요소이다. 그래서 일반적으로 스타들이 활약하는 도메인을 분류하여서 그 스타에 대해서 검색을 하였을 때 어떤 분야에서 활약하고 기여를 하는지 그 기여도를 도메인에 따라 랭킹을 매기는 방법을 제안한다. 뉴스기사에서 텍스트 마이닝 기술을 이용하여 스타의 이름과 활동 도메인들에 대해서 관련단어를 빈도에 따라 추출한다. 그리고 관련된 단어들을 이용하여 스타에 대한 뉴스 중 각 도메인과 관련된 기사들을 카운트하며 도메인에 대해서 긍정 혹은 부정적인 보도내용일 경우에는 극성을 부여하여 그 가중치를 달리한다. 빈도 및 극성을 고려한 점수화에 의해 스타가 기여하는 분야에 대한 순위를 매긴다.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.1031-1041
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• 2018

ANFIS-based methodology for improving GloSea5 ensemble weather forecast is developed and evaluated in this study. The proposed method consists of two steps: pre & post processing. For ensemble prediction of GloSea5, weights are assigned to the ensemble members based on Optimal Weighting Method (OWM) in the pre-processing. Then, the bias of the results of pre-processed is corrected based on Model Output Statistics (MOS) method in the post-processing. The watershed of the Chungju multi-purpose dam in South Korea is selected as a study area. The results of evaluation indicated that the pre-processing step (CASE1), the post-processing step (CASE2), pre & post processing step (CASE3) results were significantly improved than the original GloSea5 bias correction (BC_GS5). Correction performance is better the order of CASE3, CASE1, CASE2. Also, the accuracy of pre-processing was improved during the season with high variability of precipitation. The post-processing step reduced the error that could not be smoothed by pre-processing step. It could be concluded that this methodology improved the ability of GloSea5 ensemble weather forecast by using ANFIS, especially, for the summer season with high variability of precipitation when applied both pre- and post-processing steps.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.374-380
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• 2012

The k-Nearest Neighbor (kNN) technique is popularly applied to assess forest resources at the county level and to provide its spatial information by combining large area forest inventory data and remote sensing data. In this study, two approaches such as distance-weighting and stratification of training dataset, were compared to improve kNN-based forest growing stock estimates. When compared with five distance weights (0 to 2 by 0.5), the accuracy of kNN-based estimates was very similar ranged ${\pm}0.6m^3/ha$ in mean deviation. The training dataset were stratified by horizontal reference area (HRA) and forest cover type, which were applied by separately and combined. Even though the accuracy of estimates by combining forest cover type and HRA- 100 km was slightly improved, that by forest cover type was more efficient with sufficient number of training data. The mean of forest growing stock based kNN with HRA-100 and stratification by forest cover type when k=7 were somewhat underestimated ($5m^3/ha$) compared to statistical yearbook of forestry at 2011.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3C
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• pp.307-314
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• 2003

In the general multiple video object coder, more interested objects such as speaker or moving object is consistently coded with higher priority. Since the priority of each object may not be fixed in the whole sequence and be variable on frame basis, it must be adjusted in a frame. In this paper, we analyze the independent rate control algorithm and global algorithm that the QP value is controled by the static parameters, object importance or priority, target PSNR, weighted distortion. The priority among static parameters is analyzed and adjusted into dynamic parameters according to the visual interests or importance obtained by camera interface. Target PSNR and weighted distortion are proportionally derived by using magnitude, motion, and distortion. We apply those parameters for the weighted distortion control and the priority-based control resulting in the efficient bit-rate distribution. As results of this paper, we achieved that fewer bits are allocated for video objects which has less importance and more bits for those which has higher visual importance. The duration of stability in the visual quality is reduced to less than 15 frames of the coded sequence. In the aspect of PSNR, the proposed scheme shows higher quality of more than 2d13 against the conventional schemes. Thus the coding scheme interfaced to human- eye proves an efficient video coder dealing with the multiple number of video objects.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.121-129
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• 2015

With the explosive growth of information about books, there is a growing number of customers who find it difficult to pick a book. Against the backdrop, the importance of a book recommendation system becomes greater, through which appropriate information about books could be offered then to encourage customers to buy a book in the end. However, existing recommendation systems based on the bibliographical information or user data reveal the reliability issue found in their recommendation results. This is why it is necessary to reflect semantic information extracted from the texts of a book's main body in a recommendation system. Accordingly, this paper suggests a method for extracting keywords from the main body of novels, as a preceding research, by using TF-IDF method as well as the text structure. To this end, the texts of 100 novels have been collected then to divide them into four structural elements of preface, dialogue, non-dialogue and closing. Then, the TF-IDF weight of each keyword has been calculated. The calculation results show that the extraction accuracy of keywords improves by 42.1% in performance when more weight is given to dialogue while including preface and closing instead of using just the main body.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.361-368
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• 2010

It is general that mostly congested sections of national backbone networks have been improved based on the national network expansion plan. However, in case of intermodal terminals which are origins of logistics, it is still so congested that travel time between origin and destination is long. Therefore, intermodal transportation systems plan of major intermodal terminals for the intermodal connector networks between intermodal terminal and national backbone network or intermodal terminal was established. With the limitation of priority methodology applying to intermodal connector facility under existing methodology, this study suggests an improved priority methodology. This study includes characteristics of terminal on the hierarchical structure and assessment list, but it does not concentrate on the specific terminal type through survey. To avoid a certain concentration, budget constraint for each terminal type was considered ahead of priority. Finally priority methodology was developed with two-step assessment under consideration that specific terminal is not involved in intermodal connector facility project. As a result of calculating weights by survey, effects such as d/c and accessibility fluctuations index through project implementation gain high weight, and degree of region underdevelopment gets next. Although the methodology in this study could not yields the priority by assessment list, it will be useful for setting the direction on policy related to intermodal connector facility projects.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.54-67
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• 2010

Applying the conventional machine-learning method which has been frequently used in health-care area has several fundamental problems for modern U-health service analysis. First of all, we are still lack of application examples of the traditional method for our modern U-health environment because of its short term history of U-health study. Second, it is difficult to apply the machine-learning method to our U-health service environment which requires real-time management of disease because the method spends a lot of time in the process of learning. Third, we cannot implement a personalized U-health diagnosis system using the conventional method because there is no way to assign weights on the disease-related variables although various kinds of machine-learning schemes have been proposed. In this paper, a novel diagnosis scheme PCADP is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method and it makes the bio-data analysis just a 'process' in the U-health service system. In addition, we offer a semantics modeling of the U-health ontology framework in order to describe U-health data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring of decision process. Upto the best of authors' knowledge, the PCADP scheme and ontology framework proposed in this paper reveals one of the best characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring among recently developed U-health schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1506-1514
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• 2015

In this paper, we proposed a forest fires prediction and detection system. It could provide a situation of fire prediction and detection methods using context awareness sensor. A fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire in complex situations. In addition, it is possible to differential management of intensive fire detection and prediction for required dividing the state of fire zone. Therefore we propose an algorithm to determine the prediction and detection from the fire parameters as an temperature, humidity, Co2 and the flame in real-time by using a context awareness sensor and also suggest algorithm that provide the path of fire diffusion and service the secure safety zone prediction.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.71-81
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• 2004

Minutiae-based fingerprint identification systems use minutiae points, which cannot completely characterize local ridge structures. Further, this method requires many methods for matching two fingerprint images containing different number of minutiae points. Therefore, to represent the fired length information for one fingerprint image, the filterbank-based method was proposed as an alternative to minutiae-based fingerprint representation. However, it has two shortcomings. One shortcoming is that similar feature vectors are extracted from the different fingerprints which have the same fingerprint type. Another shortcoming is that this method has overload to reduce the rotation error in the fingerprint image acquisition. In this paper, we propose the minutia-weighted feature vector extraction method that gives more weight in extracting feature value, if the region has minutiae points. Also, we Propose new fingerprint alignment method that uses the average local orientations around the reference point. These methods improve the fingerprint system's Performance and speed, respectively. Experimental results indicate that the proposed methods can reduce the FRR of the filterbank-based fingerprint matcher by approximately 0.524% at a FAR of 0.967%, and improve the matching performance by 5% in ERR. The system speed is over 1.28 times faster.