• Annual Conference of KIPS
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• 2008.11a
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• pp.992-995
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• 2008

최근 무선 센서 네트워크 기술은 환경 모니터링과 같은 분야에서 유용하게 사용된다. 일반적으로 Top-k 질의는 수집한 데이터 중에서 가장 높거나 낮은 k개의 값을 찾는 질의로써, 많은 센서 네트워크 응용 분야에서 널리 쓰이고 있다. 센서 네트워크에서 일정 시간동안 지속적인 모니터링을 위해 Top-k 질의를 주기적으로 수행해야 하는 경우, 인-네트워크 집계(In-Network Aggregation) 기법 또는 필터(Filter) 기법을 사용한 알고리즘이 제안되었다. 본 논문에서는 에너지 효율성을 지원하기 위해, 고정된 라우팅 트리에서 네트워크의 부하를 분산시키는 라우팅 트리 변경 기법을 제안한다. 아울러, 가장 효율이 좋은 필터 기반의 FILA를 기반으로, 질의 결과의 정확성 및 에너지 효율성을 효과적으로 제공하는 알고리즘을 제안한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1263-1268
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• 2018

In this paper, a video analysis is proposed to implement video surveillance system with deep learning object detection and probabilistic data association filter for tracking multiple objects, and suggests its implementation using GPU. The proposed video analysis technique involves object detection and object tracking sequentially. The deep learning network architecture uses ResNet for object detection and applies probabilistic data association filter for multiple objects tracking. The proposed video analysis technique can be used to detect intruders illegally trespassing any restricted area or to count the number of people entering a specified area. As a results of simulations and experiments, 48 channels of videos can be analyzed at a speed of about 27 fps and real-time video analysis is possible through RTSP protocol.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.91-100
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• 2006

A scheme is proposed for implementing the block LMS algorithm in a block floating point framework that permits processing of data over a wide dynamic range at a processor complexity and coat as low as that of a fixed point processor. The proposed scheme adopts appropriate formats for representing the filter coefficients and the data. Using these and a new upper bound on the step size, update relations for the filter weight mantissas and exponent are developed, taking care so that neither overflow occurs, nor are quantifies which are already very small multiplied directly. It is further shown how the mantissas of the filter coefficients and also the filter output can be evaluated faster by suitably modifying the approach of the fast block LMS algorithm

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.60-70
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• 2018

A software defined storage (SDS) is being deployed in cloud environment to allow multiple users to virtualize physical servers, but a solution for optimizing space efficiency with limited physical resources is needed. In the conventional data deduplication system, it is difficult to deduplicate redundant data uploaded to distributed storages. In this paper, we propose a distributed deduplication method using similarity-based clustering and multi-layer bloom filter. Rabin hash is applied to determine the degree of similarity between virtual machine servers and cluster similar virtual machines. Therefore, it improves the performance compared to deduplication efficiency for individual storage nodes. In addition, a multi-layer bloom filter incorporated into the deduplication process to shorten processing time by reducing the number of the false positives. Experimental results show that the proposed method improves the deduplication ratio by 9% compared to deduplication method using IP address based clusters without any difference in processing time.

• Annual Conference of KIPS
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• 2016.10a
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• pp.746-749
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• 2016

주제 기반 크롤링(Topical Crawling)으로 수집된 문서들은 서로 비슷한 단어들을 가지고 있기 때문에 정작 주어진 주제에 적합하지 않은 문서 들을 포함할 수 있다. 이를 해결하기 위해 특정 주제에 해당하는 문서만을 필터링하는 작업이 필요하다. 본 논문은 화재 뉴스 기사에 대한 필터링을 위해 단어 기반 속성과 어울려 화재 뉴스 기사의 특성을 고려한 메타 데이터 속성을 추출하여 이에 특화된 기계학습 메커니즘을 제안하였다. 제안 기법의 F1-측정치는 92.1 %로서, 현재 최고의 성능을 보이는 SVM, 나이브베이즈 알고리즘보다. 2~3% 개선된 것이다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.505-514
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• 2009

The researcher analyzes the relationship between the number of neighbors and the prediction accuracy in the preference prediction process using collaborative filtering system. The number of neighbors who are involved in the preference prediction process are divided into four groups. Each group shows a little difference in the preference prediction. By using prediction error averages in each group, linear functions are suggested. Through the result of this study, the accuracy of preference prediction can be raised when using linear functions by using the number of neighbors in the suggested system.

• Annual Conference of KIPS
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• 2003.11a
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• pp.505-508
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• 2003

디지털 영상 자천가 대중화됨에 따라 방대한 양의 자료를 효과적으로 이용 및 검색하기 하기 위해 영상 데이터의 색인과정이 필수적이다. 뉴스 데이터에서 자막 프레임은 뉴스의 내용을 한 눈에 파악할 수 있는 중요한 정보이다. 따라서 본 논문에서는 뉴스 데이터에서 색인과정을 위해 우선 자막 프레임을 검출하는 기법을 제안하고자 한다. 자막이 있는 프레임을 검출하기 위해 가변길이 프레임 생략법을 이용하여 키프레임을 검출한다. 영상보정을 위한 전처리 작업으로 BC(Brightness & Contrast) 필터기법을 제안하고 자막영역을 대상으로 IT(Invers & Threshold) 기법을 적용하여 자막프레임을 검출하는 방법을 제안한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.5
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• pp.94-104
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• 2000

The accuracy and reliability of the target tracking is very critical issue in the design of automotive collision warning radar A significant problem in multi-target tracking (MTT) is the target-to-measurement data association If an incorrect measurement is associated with a target, the target could diverge the track and be prematurely terminated or cause other targets to also diverge the track. Most methods for target-to-measurement data association tend to coalesce neighboring targets Therefore, many algorithms have been developed to solve this data association problem. In this paper, a new multi-target data association method based on order statistics is described The new approaches. called the order statistics probabilistic data association (OSPDA) and the order statistics joint probabilistic data association (OSJPDA), are formulated using the association probabilities of the probabilistic data association (PDA) and the joint probabilistic data association (JPDA) filters, respectively Using the decision logic. an optimal or near optimal target-to-measurement data association is made A computer simulation of the proposed method in a heavy cluttered condition is given, including a comparison With the nearest-neighbor CNN). the PDA, and the JPDA filters, Simulation results show that the performances of the OSPDA filter and the OSJPDA filter are superior to those of the PDA filter and the JPDA filter in terms of tracking accuracy about 18% and 19%, respectively In addition, the proposed method is implemented using a developed digital signal processing (DSP) board which can be interfaced with the engine control unit (ECU) of car engine and with the d?xer through the controller area network (CAN)

• The Journal of Bigdata
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• v.7 no.1
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• pp.187-192
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• 2022

Many schools are trying to improve students' competencies through many subjects and non-curricular activities, each students has different goals and different activities to prepare for employment. Accordingly, it is difficult to determine whether the programs offered in a comprehensive and comprehensive manner in the existing subject and non-curricular subjects systems are actually suitable for students, so it is necessary to introduce a personalized system. In this study, a method was proposed to classify non-departmental subjects that are uniformly provided to all students of Chungbuk National University by grade level and department. In addition, three types of collaborative filtering models are implemented using the evaluation score of students who participated in the non-curricular program, and personalized recommendations are proposed with the most accurate model by comparing performance.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.