• 한국정보과학회논문지:데이타베이스
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• 제36권4호
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• pp.280-291
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• 2009

스카이라인 질의 처리는 센서 네트워크 응용에서 다차원 데이터를 효과적으로 활용할 수 있어서 그 역할이 중요하다. 센서 네트워크는 배터리 제약 사항을 가지고 있기 때문에, 센서 네트워크에서의 스카이라인에 관한 연구는 에너지 소비를 최소화 하는데 그 목표를 두고 있다. 이를 위해 기존연구에서 필터링 기법이 제안되었다. 하지만 기존 필터링 기법은 일회성 질의에 초점을 맞추고 있고, 상위 노드의 정보만을 활용하기 때문에 그 성능의 한계가 있다. 본 논문에서는 연속스카이라인 질의 처리를 위한 상향식 필터링 투플 선정 방법을 제안한다. 하위노드에서 생성된 이전 스카이라인 정보를 각 센서노드에 저장하고, 필터링 투플 선정에 활용함으로써 불필요한 데이터 통신을 감소시킬 수 있다, 이와 더불어 추가 필터링 투플을 선택할 때 사용될 수 있는 SFT(Support Filtering Tuple)방법을 제안한다. 센서 데이터의 경우, 이전 센싱된 데이터와 현재 데이터 간의 시간 관계성(temporal correlation)의 특징을 갖고 있다. SFT 방법은 저장된 과거 데이터를 기반으로 현재데이터를 예측하여 추가 필터링 투플을 선정하여 필터링 성능을 향상시킨다. 실험 결과를 통해, 제안하는 방법들이 기존 방법에 비해 데이터 감소율과 총 통신량 측면에서 효율적임을 보여준다.

• 한국측량학회지
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• 제30권1호
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• pp.39-48
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• 2012

수치표고모델(DEM : Digital Elevation Model)을 생성하거나 지상의 객체를 추출하기 위해서 라이다 자료에서 지면점과 비지면점을 분리하는 필터링(filtering) 과정은 중요하다. 본 연구에서는 라이다 자료에서 지면점을 추출하는 데 사용되는 기존의 필터링 방법을 대상으로 정성적 분석과 정량적 분석을 통해 가장 효과적인 필터링 알고리즘을 선정하는 것을 목적으로 하였다. 이를 위해 Adaptive TIN, Perspective Center Based Filtering Algorithm, Elevation Threshold with Expand Window, Progressive Morphology의 4가지 필터링 방법을 산악지역, 도시지역, 건물과 산이 공존하는 3가지 지역에 적용하여 각각의 방법에 대한 특징을 분석하였다. 실험에 사용된 4가지 필터링 방법의 정성적인 비교는 음영기복도를 생성한 후 시각적인 방법을 적용하였고 정량적인 비교는 GPS로 관측한 검사점을 이용한 절대적인 비교와 국토지리정보원의 수치표고모델을 이용하여 상대적인 비교를 수행하였다. 라이다 필터링 실험을 통하여 Adaptive TIN 알고리즘은 산악지역과 도시지역에서 지면점을 가장 효율적으로 추출하였고 건물과 산이 공존하는 지역에서는 Progressive Morphology 알고리즘이 가장 양호한 결과를 나타내었다. 또한 정성적, 정량적 비교 결과 전반적으로 지역적 특성에 관계없이 적용가능한 필터링 알고리즘은 ATIN 알고리즘으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1260-1272
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• 2024

This paper tackles the prevalent challenges faced by existing tourism route recommendation methods, including data sparsity, cold start, and low accuracy. To address these issues, a novel intelligent tourism route recommendation method based on collaborative filtering is introduced. The proposed method incorporates a series of key steps. Firstly, it calculates the interest level of users by analyzing the item attribute rating values. By leveraging this information, the method can effectively capture the preferences and interests of users. Additionally, a user attribute rating matrix is constructed by extracting implicit user behavior preferences, providing a comprehensive understanding of user preferences. Recognizing that user interests can evolve over time, a weight function is introduced to account for the possibility of interest shifting during product use. This weight function enhances the accuracy of recommendations by adapting to the changing preferences of users, improving the overall quality of the suggested tourism routes. The results demonstrate the significant advantages of the approach. Specifically, the proposed method successfully alleviates the problem of data sparsity, enhances neighbor selection, and generates tourism route recommendations that exhibit higher accuracy compared to existing methods.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 한국지능정보시스템학회
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• pp.218-225
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• 2007

Recommendation system is one way of implementing personalized service. The collaborative filtering is one of the major techniques that have been employed for recommendation systems. It has proven its effectiveness in the recommendation systems for such domain as motion picture or music. However, it has some limitations, i.e., sparsity and scalability. In this research, as one way of overcoming such limitations, we proposed the stepwise collaborative filtering method. To show the practicality of our proposed method, we designed and implemented a movie recommendation system which we shall call Step_CF, and its performance was evaluated using MovieLens data. The performance of Step_CF was better than that of Basic_CF that was implemented using the original collaborative filtering method.

• 한국컴퓨터정보학회논문지
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• 제20권11호
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• pp.25-30
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• 2015

The enormous increase of data with the development of the information technology make internet users to be hard to find suitable information tailored to their needs. In the face of changing environment, the information filtering method, which provide sorted-out information to users, is becoming important. The data on the internet exists as various type. However, similarity calculation algorithm frequently used in existing collaborative filtering method is tend to be suitable to the numeric data. In addition, in the case of the categorical data, it shows the extreme similarity like Boolean Algebra. In this paper, We get the similarity in SNS user's information which consist of the mixed data using the Gower's similarity coefficient. And we suggest a method that is softer than radical expression such as 0 or 1 in categorical data. The clustering method using this algorithm can be utilized in SNS or various recommendation system.

• 한국수산과학회지
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• 제44권3호
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• pp.308-317
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• 2011

A MBES (multibeam echo sounder) survey around Yokji Island, Korea, was conducted to find an effective method for removing error data. Two post-processing software programs, PDS2000 (RESON) and HIPS (CARIS), were used to remove the error data using an interactive editing method and the CUBE algorithm filter. The post-processing with the PDS2000 and HIPS programs, using the interactive editing method, took 120 and 168 hours, respectively, and there was little difference in the seafloor images. The processing time of the PDS2000 and HIPS programs using the CUBE algorithm filter was 36 and 60 hours, respectively. Nevertheless, there was little difference in the seafloor images because of differences in the factor parameters in each of the post-processing programs. Therefore, post-processing using CUBE filtering can save time in data processing and provide consistent results, excluding the subjective decisions of the operator. This method is more effective than other methods for rejecting erroneous multibeam echo sounder data.

• 디지털산업정보학회논문지
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• 제15권3호
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• pp.69-78
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• 2019

The amount of data is increasing significantly as information and communication technology advances, mobile, cloud computing, the Internet of Things and social network services become commonplace. As the data grows exponentially, there is a growing demand for services that recommend the information that users want from large amounts of data. Collaborative filtering method is commonly used in information recommendation methods. One of the problems with collaborative filtering-based recommendation method is the cold start problem. In this paper, we propose a method to improve the cold start problem. That is, it solves the cold start problem by mapping the item evaluation data that does not exist to the initial user to the automatically generated data from the mobile app. We describe the main contents of the proposed method and explain the proposed method through the book recommendation scenario. We show the superiority of the proposed method through comparison with existing methods.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 컴퓨터소사이어티 추계학술대회논문집
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• pp.175-178
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• 2003

Two defects have been pointed out in existing user-based collaborative filtering such as sparsity and scalability, and the research has been also made progress, which tries to improve these defects using item-based collaborative filtering. Actually there were many results, but the problem of sparsity still remains because of being based on an explicit data. In addition, the issue has been pointed out. which attributes of item arenot reflected in the recommendation. This paper suggests a recommendation method using nave Bayesian algorithm in hybrid user and item-based collaborative filtering to improve above-mentioned defects of existing item-based collaborative filtering. This method generates a similarity table for each user and item, then it improves the accuracy of prediction and recommendation item using naive Bayesianalgorithm. It was compared and evaluated with existing item-based collaborative filtering technique to estimate the accuracy.

• 대한원격탐사학회지
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• 제2권2호
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• pp.79-94
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• 1986

TM data, which have better resolution in spatial and spectral than MSS data, were used for this study, and several Image Processing Techniques (IPT) were examined for finding the best IPT to fit to lineament extraction and mineralized zone mapping. The Ryeongnam area was selected as test area, because the area is one of major mineralized zones in Korea and its hydrothermal alteration zone is wider and deeper than other areas. The spatial filtering method is most optimum one for limeament extraction: that is, the directional spatial filtering is most efficient to detect N-S, E-W direction lineaments on the image, and the high boost filtering can be applied for mapping all direction lineaments. The ratio method was selected for detecting altered zone. It is possible to make several tens combinations in ratio with 7 bands of TM data, but considering spectral characteristics of each band of TM to the geological meterials and vegetation, the band 4/band 3(A), band 5/band 7(B), and B/A ratio methods were chosen among them. The 5/7 ratio image did not show clearly the altered area due to noise from vegetation cover, so the 4/3 ratio imae was used for trying to decrease the effect of vegetation. As a result the B/A ratio image showed quite nicely the altered zone of the test area. In conclusion, the spatial filtering is the best image processing techniques for lineament mapping, and the B/A ratio image in TM data is useful for the mineralized zone mapping.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2002년도 총회 및 춘계학술발표회
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• pp.93-97
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• 2002

This research introduces some preliminary results of bank filtering intake method adopted in Dalseo area, Nakdong River. This intake method has been planned to supply water resources of 41,000 ㎥/day to Goryeong-Gun and Seongju-Gun in 2016. It is believed that the bank filtering intake method can afford to supply 41,000 ㎥/day amount of water resources and that the raw water quality using the method has more advantages in water treatment than direct surface water intake. Even though the safety yield about individual vertical well is roughly estimated to about 2,000 ㎥/day, it is desirable to decrease the safety yield to about 1,000 ㎥/day in the consideration of long term and simultaneous well pumpings and other unknown factors. Ongoing study will give basic data and new techniques to solve the problems appearing in application of bank filtering method as well.