• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.101-109
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• 2008

In this paper, we introduce clustering scheme to calculate probability of detection which is practically required for conventional weighted-collaborative sensing technique. We also propose an improved weighted-collaborative spectrum sensing scheme using new weight generation algorithm to achieve better performance in Cognitive Radio systems. We calculate Pd in each cluster which is a CR users group with similar channel situation. New weight factor is generated using square sum of all cluster's Pds. Simulations under slow fading show that we can get better total detection probability and lower false alarm rate when PU (Primary User) suddenly terminates their transmission.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.520-525
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• 1997

This paper presents a fault detection and diagnosis methodology based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. In the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model is used to generate those candidates. The weight is determined from dynamic simulation. Using WSM, the methodology can generate the cause candidates and rank them according to the probability. Second, the fault propagation trends identified from the partial or complete sequence of measurements are compared with the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies, and the results showed satisfactory diagnostic resolution.

• ETRI Journal
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• v.31 no.4
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• pp.438-447
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• 2009

In this paper, a novel image projection technique for face recognition application is proposed which is based on linear discriminant analysis (LDA) combined with the relevance-weighted (RW) method. The projection is performed through 2-directional and 2-dimensional LDA, or $(2D)^2$LDA, which simultaneously works in row and column directions to solve the small sample size problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and an RW method is used in the within-class scatter matrix to weigh the information to resolve confusable data in these classes. This technique is called the relevance-weighted $(2D)^2$LDA, or RW$(2D)^2$LDA, which is used for a more accurate discriminant decision than that produced by the conventional LDA or 2DLDA. The proposed technique has been successfully tested on four face databases. Experimental results indicate that the proposed RW$(2D)^2$LDA algorithm is more computationally efficient than the conventional algorithms because it has fewer features and faster times. It can also improve performance and has a maximum recognition rate of over 97%.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.357-361
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• 2010

미래의 연별 최대 강수량 예측의 정확성을 향상시키는데 역사적 자료가 도움이 된다는 많은 연구 결과가 있었다. 관측의 오차와 자료의 손실로 역사자료를 이용한 강수 예측 방법은 절단자료의 분석을 중심으로 연구되었다. 대표적인 역사자료의 이용방법으로 조건부 적률을 이용한 B17B [Interagency Committee in Water Data, 1982], 조건부적률과적률 관계식을 이용한 Expected Moment Algorithm(EMA) [Cohn et al.;1997], 조건부 확률가중적률을 이용한 Partial Probability Weighted Moment (PPWM)[Wang ; 1991] 방법이 있다. 본 연구에서는 역사적 자료를 반영하는 방법에 있어 B17B와 EMA의 관계를 밝히고 그러한 관계가 PPWM에 동일하게 적용할 수 있음을 보였다. 우리는 B17B와 EMA의 관계를 적률방정식으로 표현하였고 PPWM에서 확률가중 적률 방정식을 정의함으로써 PPWM을 확장하였다. 본 연구에서 제안한 새로운 역사 자료를 이용한 강수예측 방법론을 Expected Probability Weighted Momemt (EPWM) 방법이라고 부르고 그 예측 방법의 성능을 다른 예측방법과 시뮬레이션 결과를 통해 비교하였다. 역사 자료 방법론의 비교는 Generalized Extreme Value (GEV) 분포를 이용하여 이루어졌으며, 각 방법론은 GEV분포의 형태모수(shape parameter)따라 다른 특성을 나타난다는 것을 보였다. 뿐만 아니라 여기서 제안한 EPWM 방법은 대부분의 경우에 좋은 추정량을 준다는 것을 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.252-255
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• 2014

Image treatment is becoming mainstream as the demand for image restoration has drastically increased in the digital era. But in the process of acquiring, transmitting and treating video data, the salt and pepper noise damages the image. One of the major methods used for restoring images are SMF(standard median filter), CWMF(center weighted median filter) and SWMF(switching weighted median filter), but these filters all leave a bit to be desired in terms of removing noise and preserving edge. Therefore, a transformed median filter is suggested through the algorithm presented for the restoration of damaged images.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.73-83
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• 2005

This paper proposes both a novel tactile human-computer interface method and an improved algorithm for the wearable PC. Under the condition of Ubiquitous computing, the next generation PC aims at effective representation and integration of colors, brightness of light. sound, odor, taste and feelings. Also, it aims at human being centered man-machine interface. In spite of various functions of the wearable PC, for the convenience of possessing, hardware platform for the wearable PC should be small-sized and light weighted one. The main problems of making small sized PC are user interfaces, like keyboard, monitor and so on. The traditional user interfaces have critical limitations for reducing their size. In this paper, we propose a novel user input method and improved algorithm to constructing small sized, light weighted and wearable PC. And, we verify the effectiveness of suggested method and algorithm compared to the traditional algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.700-713
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• 2009

Most of any existing classification algorithm in data mining area have focused on goals improving efficiency, which is to generate decision tree more rapidly by utilizing just less computing resources. In this paper, we focused on the efficiency as well as effectiveness that is able to generate more meaningful classification rules in application area, which might consist of the ontology automatic generation, business environment and so on. For this, we proposed not only novel function with the weight of support and confidence degree but also analyzed the characteristics of the weighted function in theoretical viewpoint. Furthermore, we proposed novel classification algorithm based on the weighted function and the characteristics. In the result of evaluating the proposed algorithm, we could perceive that the novel algorithm generates more classification rules with significance more rapidly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8A
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• pp.679-688
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• 2012

In this paper, performance improved hole-filling algorithm including boundary noise removing pre-process which can be used for an arbitrary view synthesis with given two views is proposed. Boundary noise usually occurs because of the boundary mismatch between the reference image and depth map and common-hole is defined as the occluded region. These boundary noise and common-hole created while synthesizing a virtual view result in some defects and they are usually very difficult to be completely recovered by using only given two images as references. The spiral weighted average algorithm gives a clear boundary of each object by using depth information and the gradient searching algorithm is able to preserve details. In this paper, we combine these two algorithms by using a weighting factor ${\alpha}$ to reflect the strong point of each algorithm effectively in the virtual view synthesis process. The experimental results show that the proposed algorithm performs much better than conventional algorithms.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.437-445
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• 2009

In this paper, we present the performance of the LOB(line of bearing) - based emitter localization algorithm. The linear LSE(least-squared error) algorithm, nonlinear LSE algorithm and Stansfield algorithm are considered. In addition, we focus on the performance improvement of the weighted estimation compared with the unweighted estimation. Each estimation algorithm is briefly introduced, and the performance of the algorithm is illustrated using the numerical results.

• Journal of Astronomy and Space Sciences
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• v.21 no.4
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• pp.371-382
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• 2004

To improve the precision of relative positioning for GPS single frequency(L1) receiver, we accomplished the GPS data processing using the weighted smoothing techniques. The weighted phase smoothing technique is used to minimize the measurement error of pseudorange and position domain smoothing technique is adopted to make the complement of cycle-slip affection. we also considered some component errors like as ionospheric error, which are related with baseline length, and processed for several baselines (5, 10, 30, 40, and 150 km) to check the coverage area of this algorithm. This paper shows that weighted phase smoothing technique give more stable results after using this technique and the position domain smoothing technique can reduce the errors which are sensitive to the observational environment. Based on the results, we could find out that this algorithm is available for post-time and real-time applications and these techniques can be substitution methods which is able to get the high accuracy and precision without resolving the Integer ambiguity.