• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.441-450
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• 2003

Collaborative filtering is one of the most widely used methodologies for recommendation system. Collaborative filtering is based on a data matrix of each customer's preferences and frequently, there exits missing data problem. We introduced two imputation approach (multiple imputation via Markov Chain Monte Carlo method and multiple imputation via bootstrap method) to improve the prediction performance of collaborative filtering and evaluated the performance using EachMovie data.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2008년도 정보통신설비 학술대회
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• pp.141-145
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• 2008

Wireless sensor network (WSN) is expected to be used in many applications. However, sensor nodes still have some secure problems to use them in the real applications. They are typically deployed on open, wide, and unattended environments. An adversary using these features can easily compromise the deployed sensor nodes and use compromised sensor nodes to inject fabricated data to the sensor network (false data injection attack). The injected fabricated data drains much energy of them and causes a false alarm. To detect and drop the injected fabricated data, a filtering-based security method and adaptive methods are proposed. The number of different partitions is important to make event report since they can make a correctness event report if the representative node does not receive message authentication codes made by the different partition keys. The proposed methods cannot guarantee the detection power since they do not consider the filtering scheme. We proposed clustering method for filtering-based secure methods. Our proposed method uses fuzzy system to enhance the detection power of a cluster.

• 경영과학
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• 제29권2호
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• pp.79-90
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• 2012

Focusing on the rapid market penetration of smart phones, the importance of LBS (Location Based Service) is drastically increased. However, traditional GPS method has critical weakness caused by limited availability, such as indoor environment. WPS is newly attractive method as a widely applicable positioning method. In WPS, RSSI (Received Signal Strength Indication) data of all Wi-Fi APs (Access Point) are measured and stored into a huge database. The stored RSSI data in database make single radio fingerprint map. By the radio fingerprint map, we can estimate the actual position of target point. The essential factor of radio fingerprint database is data integrity of RSSI. Because of millions of APs in urban area, RSSI measurement data are seriously contaminated. Therefore, we present the unified filtering method for RSSI measurement data. As the results of filtering, we can show the effectiveness of suggested method in practical positioning system of mobile operator.

• 대한설비관리학회지
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• 제23권4호
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• pp.57-64
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• 2018

User-based collaborative filtering is a method of recommending an item to a user based on the preference of the neighbor users who have similar purchasing history to the target user. User-based collaborative filtering is based on the fact that users are strongly influenced by the opinions of other users with similar interests. Item-based collaborative filtering is a method of recommending an item by comparing the similarity of the user's previously preferred items. In this study, we create a recommendation model using user-based collaborative filtering and item-based collaborative filtering with consumer's consumption data. Collaborative filtering is performed by using RFM (recency, frequency, and monetary) technique with purchasing data to recommend items with high purchase potential. We compared the performance of the recommendation system with the purchase amount and the performance when applying the RFM method. The performance of recommendation system using RFM technique is better.

• 제어로봇시스템학회논문지
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• 제6권12호
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• 한국멀티미디어학회논문지
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• 제24권2호
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• pp.274-284
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• 2021

Customized services to a sleep induction for better sleepcare are more effective because of different satisfaction levels to users. The EOG data measured at the frontal lobe when a person blinks his eyes can be used as biometric data because it has different values for each person. The accuracy of measurement is degraded by a noise source, such as toss and turn. Therefore, it is necessary to analyze the noisy data and remove them from normal EOG by filtering. There are low-pass filtering and high-pass filtering as filtering using a frequency band. However, since filtering within a frequency band range is also required for more effective performance, we propose a machine learning model for the filtering of EOG data in this paper as the second filtering method. In addition, optimal values of parameters such as the depth of the hidden layer, the number of nodes of the hidden layer, the activation function, and the dropout were found through experiments, to improve the performance of the machine learning filtering model, and the filtering performance of 95.7% was obtained. Eventually, it is expected that it can be used for effective user identification services by using filtering model for EOG data.

• Journal of information and communication convergence engineering
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• 제9권4호
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• pp.452-456
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• 2011

Context-awareness system can provide an optimized services to users. Analyzing physical and complex circumstance elements which give direct or indirect influence to users can tell what users want. However, there are various situation informations around users and it requires high level technology to extract the service what users really want among those informations. The circumstance of the user can be changed from moment to moment, even the service what users want also can be changed in every minutes. Recently the researches to provide the service which a user demands has been progressed actively. Web based filtering method which reaches commercialization is a one of good examples. This method extracts necessary data according to users' demands from the documents on the Web or multimedia informations. However, there is a limit to use it to provide Context-awareness service because it extracts static data, not dynamic data. There is also other researches with a rule based filtering method in progress to filter situation information but this method doesn't have mechanism for dynamic data as well. We would like to solve these problems by providing a dynamic situation information filtering mechanism applying an weighted value about each property of objects and also applying Web based dynamic categories in this paper when unnecessary data should be filtered.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권7호
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• pp.680-693
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• 2008

XML 메시지 필터링에 관한 최근의 연구들은 모든 출판되는 데이타 소스들이 필터링 시스템에 정의된 유일한 전역 스키마를 따르는 것을 가정한다. 하지만 이러한 가정을 넘어서, 데이타 제공자들이 그들 자신의 스키마를 자유롭게 사용할 수 있는 서비스를 고려할 수 있다. 즉, 데이타 소스들이 이질적인 환경이다. 하지만 XML 필터링 시스템에서 데이타 소스는 다수이며, 또한 출판되는 데이타들은 수시로 생성되고, 갱신되며, 사라진다. 즉, 매우 다이내믹한 환경이다. 본 논문에서는 그러한 다이내믹한 환경을 고려하여 고안된 단순하며 효율적인 의미적 XPath 질의 번역 구현을 소개한다. 특별히 제안되는 질의 번역 기법은 어떤 비주얼한 데이타 가이드가 제공되지 않는 환경에서 사용자가 자신의 지식과 경험에만 의존하여 작성한 질의를 번역하는 것에 초점을 맞춘다. 이러한 환경에서, 사용자는 다수의 이질적인 데이타를 질의하기 때문에, 사용자의 기억상의 스키마에 의존하여 작성된 질의는 실제 스키마와 불일치할 수 있다. 본 연구에서는 제안하는 의미적 XPath 질의 기법이 이러한 문제를 고려하도록 설계한다. 몇 가지 실험 결과는 제안된 질의 번역 기법이 수용할 만한 질의 번역시간을 제공하며, 기존의 방법과 비교하여 실제적임을 보여 준다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권6호
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• pp.542-553
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• 2005

추천 시스템은 사용자의 선호도를 분석하고, 아이템에 대한 사용자의 선호도를 예측하여 아이템을 추천하는 시스템이다. 다양한 추천 기법 중에 협동적 여과(collaborative filtering)는 상용화된 시스템에성공적인 적용이 이루어진 기법이다. 그러나 협동적 여과는 데이타의 희소성 문제(sparseness problem)와초기 추천 문제(cold-start problem)에 대해 취약점을 가 고 있다. 만약 매우 적은 양외 선호도 데이타가존재하면 많은 유사 사용자를 찾기 어려우며, 이것은 추천 성능을 저하시키는 요인으로 작용한다. 또한 선호도 정보가 없는 새로운 사용자에게는 아이템을 전혀 추천할 수 없는 문제가 발생한다. 본 논문에서는 사용자와 아이템에 대한 추가 속성 정보를 통합하여 협동적 여과의 희소성 문제와 초기 추천 문제를 해결하 고 추천 성능을 향상시키는 기법을 제안한다. 본 논문에서 제안하는 기법은 추가 속성 정보의 확률분포를 이용하여 알려지지 않은 선호도 값을 예측함으로써 선호도 데이타를 변경 고, 변경된 선호도 데이타에 협동적 여과를 적용하여 top-N 추천을 생성하는 것이다. 이와 같은 선호도 데이타 변경 기법을 데이타 블러링(data blurring)이라 한다. 몇 가지 실험 결과를 통해 제안된 기법의 효과를 확인하였다.

• 정보처리학회논문지D
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• 제17D권4호
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• pp.245-252
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• 2010

본 연구에서는 MANET(Mobile Ad hoc NETwork) 환경에서 스카이라인 질의를 하기 위한 효과적인 필터링 방법을 제안한다. 기존의 MANET 환경에서의 스카이라인 질의 처리는 데이터가 균등하게 분포한다고 가정한다. 이러한 가정하에서 제한된 배터리 용량을 위한 에너지 소모 최소화에 중점을 두어 스카이라인 질의를 처리하는 방법을 연구한다. 그러나 실제 환경에서는 특정한 영역에 데이터가 편향되는 분포를 가진다. 배터리의 에너지 소비를 감소하기 위해서 본 논문에서는 데이터 분포를 고려한 새로운 필터링 방법을 제안한다. 그리고 기존의 필터링 방법과 본 논문에서 제안하는 필터링 방법을 비교 실험한다. 실험 결과는 본 논문에서 제안하는 방법이 기존의 방법보다 통신 오버헤드와 실행시간이 감소하는 것을 보여준다.