• 한국컴퓨터정보학회논문지
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• 제21권9호
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• pp.19-25
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• 2016

This paper is to recognize and play tab chords from guitar musical sheets. The musical chord area of an input image is segmented by changing the image in saturation and applying the Grabcut algorithm. Based on a template matching, our approach detects tab starting sections on a segmented musical area. The virtual block method is introduced to search blanks over chord lines and extract tab fret segments, which doesn't cause the computation loss to remove tab lines. In the experimental tests, the prototype based classification outperforms Bayesian method and the nearest neighbor rule with the whole set of training data and its performance is similar to that of the support vector machine. The experimental result shows that the prediction rate is about 99.0% and the number of selected prototypes is below 3.0%.

• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.809-818
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• 2009

협력적 필터링 기법은 전자상거래에서 거래되는 아이템에 대하여 고객들이 평가한 선호 정보를 이용하여 특정 상품에 대한 선호도 예측 대상 고객의 선호도를 예측하는 기법이다. 협력적 필터링 기법을 통한 예측 정확도를 향상시키기 위해서는 예측에 이용할 수 있는 고객들의 선호 정보를 충분히 확보하여야 한다. 그러나 과도한 이웃 고객의 선호 정보는 오히려 예측 정확도에 부정적 영향을 미치며 또한 과소 정보 역시 예측 정확도 감소에 영향을 미칠 수 있다. 본 연구에서는 협력적 필터링 알고리즘 적용에 있어 k명의 근접 이웃을 결정하는 이웃 선정방법을 개선하였으며 개별 고객의 선호도 평가 정보를 이용하여 적정 이웃 수를 결정할 수 있는 방법을 제시한다. 본 연구의 결과는 근접 이웃 수 결정을 위한 기존 방법인 탐색적 방법을 개선함과 동시에 선호도 예측 정확도를 향상시키는데 유용한 방법을 제공할 수 있다.

• 한국정보시스템학회지:정보시스템연구
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• 제26권3호
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• pp.63-90
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• 2017

Purpose This study proposes a novel system trading model using case-based reasoning (CBR) based on absolute similarity threshold. The proposed model is designed to optimize the absolute similarity threshold, feature selection, and instance selection of CBR by using genetic algorithm (GA). With these mechanisms, it enables us to yield higher returns from stock market trading. Design/Methodology/Approach The proposed CBR model uses the absolute similarity threshold varying from 0 to 1, which serves as a criterion for selecting appropriate neighbors in the nearest neighbor (NN) algorithm. Since it determines the nearest neighbors on an absolute basis, it fails to select the appropriate neighbors from time to time. In system trading, it is interpreted as the signal of 'hold'. That is, the system trading model proposed in this study makes trading decisions such as 'buy' or 'sell' only if the model produces a clear signal for stock market prediction. Also, in order to improve the prediction accuracy and the rate of return, the proposed model adopts optimal feature selection and instance selection, which are known to be very effective in enhancing the performance of CBR. To validate the usefulness of the proposed model, we applied it to the index trading of KOSPI200 from 2009 to 2016. Findings Experimental results showed that the proposed model with optimal feature or instance selection could yield higher returns compared to the benchmark as well as the various comparison models (including logistic regression, multiple discriminant analysis, artificial neural network, support vector machine, and traditional CBR). In particular, the proposed model with optimal instance selection showed the best rate of return among all the models. This implies that the application of CBR with the absolute similarity threshold as well as the optimal instance selection may be effective in system trading from the perspective of returns.

• 한국통신학회논문지
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• 제28권8A호
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• pp.581-590
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• 2003

본 논문은 ATM 시스템에서 동적 소스 기반의 점대다중점 호를 위한 라우팅 경로 택 방법을 제안한다. 특히 최적의 경로 선택보다는 리소스를 최대한 공유할 수 있고, 경로 계산 시간 단축 및 cycle 방지를 쉽게 보장할 수 있는 점대다중점 라우팅 경로 선택 방법을 제안하였다. 리소스를 최대한 공유할 수 있도록 하기 위해 착신지(destination) 노드로부터 가장 근거리의 branch point를 찾는 것이 이 알고리즘의 목표이다. 그러므로 목적(destination) 노드로부터 역 추적에 의해 인접한 노드중 같은 Call ID를 갖는 기존 경로상의 노드와 최초로 크로스(cross)되는 노드를 branch노드로 정하여 최적의 점대다중점 라우팅 경로를 구성하는 알고리즘이다. 크로스 노드를 찾을 때까지 역추적에 의해 선택되는 최적의 노트는 다익스트라 알고리즘을 이용하여 선택된다. 즉 점대다중점 라우팅 경로 선택은 역추적에 의한 인접 노드중 기존 경로와 크로스되는 노드 선택 단계와 각 인접 노드중 최적의 노드를 선택하는 단계를 수행한다. 이 기법은 경로 선정을 위한 라우팅 정보 테이블의 검색 및 경로 계산 과정을 단축시키고, 경로 설정시 cycle방지도 쉽게 해결한다.

• 한국통신학회논문지
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• 제39B권12호
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• pp.870-872
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• 2014

본 논문에서는 피어의 소모 에너지와 잔여 에너지를 고려한 비용함수를 설계하고 시스템 내에 비용이 최소인 피어가 부모 피어로 선택될 수 있는 분산적 부모 피어 결정 방법을 제안한다. 각 피어가 자신의 이웃 피어 정보만을 이용하여 비용이 최소인 이웃 피어를 부모 피어로 선정하는 기존 기법과는 달리 제안 기법은 피어들 사이에 집단지성을 구축하고 이를 통해 부모 피어를 결정한다. 집단지성을 형성하여 부모 피어 검색 범위를 분산적으로 확장함으로써 제안기법은 기존 기법에 비해 최소 비용 피어가 부모 피어로 선택될 확률을 증가시키며 알고리즘 운영을 위한 시그널링 부하를 감소시킨 다는 것을 모의실험을 통해 검증하였다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.3-4
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• 2010

Map integration usually involves matching the common spatial objects in different datasets. There have been recent studies on object matching using relative location as defined by spatial relationships between the object and its neighbor landmark. Therefore the landmark selection process is an important part of map integration using relative location. In this research, we describe an approach to determine landmarks automatically in different geospatial datasets.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(4)
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• pp.167-170
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• 2000

In this paper, we present a fast algorithm for the motion estimation using the efficient selection of an initial search position. In the method, we select the initial search position using the motion vector from the subsmpled images, the predicted motion vector from the neighbor blocks, and the (0,0) motion vector. While searching the candidate blocks, we use the spiral search pattern with the successive elimination algorithm(SEA) and the partial distortion elimination(PDE). The experiment results show that the complexity of the proposed algorithm is about 2∼3 times faster than the three-step search(TSS) with the PSNR loss of just 0.05［dB］∼0.1［dB］ than the full search algorithm PSNR. The search complexity can be reduced with quite a few PSNR loss by controling the number of the depth in the spiral search pattern.

• Journal of Information Processing Systems
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• 제3권2호
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• pp.73-81
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• 2007

In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naive Bayesian classification algorithms. The architecture of our system is simple and modularized in that each sub-module of the system could be changed or improved efficiently. Moreover, it provides various feature selection mechanisms to be applied to optimize the general-purpose classifiers for a specific domain. As for the enhanced classification performance, our system provides conditional probability boosting (CPB) mechanism which could be used in various domains. In the mood classification domain, our optimized framework using the CPB algorithm showed 1% of improvement in precision and 2% in recall compared with the baseline.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.419-420
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• 2006

In the new video coding standard H.264/AVC, motion estimation (ME) is allowed to search multiple reference frames for improve the rate-distortion performance. The complexity of multi-frame motion estimation increases linearly with the number of used reference frame. However, the distortion gain given by each reference frame varies with the video sequence, and it is not efficient to search through all the candidate frames. In this paper, we propose a fast mult-frame selection method using all zero coefficient block (AZCB) prediction and sum of difference (SAD) of neighbor block. Simulation results show that the speed of the proposed algorithm is up to two times faster than exhaustive search of multiple reference frames with similar quality and bit-rate.

• 한국컴퓨터정보학회논문지
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• 제22권5호
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• pp.81-88
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• 2017

Solar-powered wireless sensor nodes can use extra energy to obtain additional data to increase the precision. However, if the amount of data sensed is increased indiscriminately, the overhead of relay nodes may increase, and their energy may be exhausted. In this paper, we introduce a sensing and compression rate selection scheme to increase the amount of data obtained while preventing energy exhaustion. In this scheme, the neighbor nodes of the sink node determine the limit of data to be transmitted according to the allocated energy and their descendant nodes, and the other nodes select a compression algorithm appropriate to the allocated energy and the limitation of data to be transmitted. A simulation result verifies that the proposed scheme gathers more data with a lower number of blackout nodes than other schemes. We also found that it adapts better to changes in node density and the amount of energy harvested.