• Proceedings of the Technology Innovation Conference
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• 2003.02a
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• pp.173-190
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• 2003

Demand forecasting is essential in establishing national and corporate strategy as well as the management of their resource. We forecast demand for multi-generation product using discrete choice model combining diffusion model The discrete choice model generally captures consumers'valuation of the product's qualify in the framework of a cross-sectional analysis. We incorporate diffusion effects into a discrete choice model in order to capture the dynamics of demand for multi-generation products. As an empirical application, we forecast demand for worldwide DRAM (dynamic random access memory) and each of its generations from 1999 to 2005. In so doing, we use the method of 'Technological Forecasting'for DRAM Density and Price of the generations based on the Moore's law and learning by doing, respectively. Since we perform our analysis at the market level, we adopt the inversion routine in using the discrete choice model and find that our model performs well in explaining the current market situation, and also in forecasting new product diffusion in multi-generation product markets.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.165-172
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• 2010

In clinical data minig, choosing the optimal subset of features is such important, not only to reduce the computational complexity but also to improve the usefulness of the model constructed from the given data. Moreover the threshold values (i.e., cut-off points) of selected features are used in a clinical decision criteria of experts for differential diagnosis of diseases. In this paper, we propose a fuzzy discretization approach, which is evaluated by measuring the degree of separation of redundant attribute values in overlapping region, based on spatial distribution of data with continuous attributes. The weighted average of the redundant attribute values is then used to determine the threshold value for each feature and rough set theory is utilized to select a subset of relevant features from the overall features. To verify the validity of the proposed method, we compared experimental results, which applied to classification problem using 668 patients with a chief complaint of dyspnea, based on three discretization methods (i.e., equal-width, equal-frequency, and entropy-based) and proposed discretization method. From the experimental results, we confirm that the discretization methods with fuzzy partition give better results in two evaluation measures, average classification accuracy and G-mean, than those with hard partition.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.853-855
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• 2005

은닉 마르코프(HMM)의 HMM의 구조 최적을 위한 모델 선택 방법에 많은 방법들이 연구되어지고 있다. HMM의 구조를 어떻게 최적으로 정해야 하는 가에 대해 HMM의 구조를 체계적인 방법으로 정함과 동시에 변별력의 단점을 개선 할 수 있는 방법으로 Anti-likelihood(ALC1)를 제안하였고 이를 모델 선택 기준인 BIC와의 결합(ALC2)하여 필기 데이터에 대해 실험한 결과 기존의 방법보다 파라미터의 수는 감소되고 인식률이 향상됨을 알 수 있었다. 이를 Discrete HMM에도 적용하여 제안된 ALC2가 HMM 구조를 최적화하는 모델 선택 기준임을 Continuous-Density HMM과 비교하여 실험 검증 한다.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.465-466
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• 2015

배터리 관리시스템(BMS;battery management system)의 중요 고려요소인 SOC(state-of-charge) 및 SOH(state-of-health)의 전기적 등가회로 모델 기반 고성능 추정의 전제 조건은 배터리 단자전압의 안정된 실험데이터 확보이다. 그러나, 예상치 않은 에러로 인해 배터리 단자전압에 노이즈 성분이 포함될 경우 SOC 및 SOH 추정알고리즘의 성능저하가 우려된다. 이를 위해, 본 논문은 이산 웨이블릿 변환(DWT;discrete wavelet transform)의 다해상도 분석(MRA;multi resolution analysis) 레벨에 따른 디노이징 최적 성능을 소개하고자 한다. 하드 임계화(hard-thresholding) 및 소프트 임계화(soft-thresholding) 기법에 따른 디노이징 성능 차이를 보이고, 각 임계화 기법 적용 시 디노이징 최적 성능을 보이는 레벨을 선택한다.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.9-17
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• 2023

Deep learning with large amount of computations is difficult to implement on micro-sized IoT devices or moblie devices. Recently, lightweight deep learning technologies have been introduced to make sure that deep learning can be implemented even on small devices by reducing the amount of computation of the model. Quantization is one of lightweight techniques that can be efficiently used to reduce the memory and size of the model by expressing parameter values with continuous distribution as discrete values of fixed bits. However, the accuracy of the model is reduced due to discrete value representation in quantization. In this paper, we introduce various quantization techniques to correct the accuracy. We selected APoT and EWGS from existing quantization techniques, and comparatively analyzed the results through experimentations The selected techniques were trained and tested with CIFAR-10 or CIFAR-100 datasets in the ResNet model. We found out problems with them through experimental results analysis and presented directions for future research.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.477-487
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• 2000

When volume data is visualized by the ray casting method, the color value of each pixel in the image is obtained by composing the color contributions of the sample points that lie on the ray cast from the pixel point. In most ray tracing methods including Levoy's classical method, the color composition is formulated as a summation of the color contributions of the discrete sample points. However, the more precise color composition is formulated as differential equations over the color contributions of the continuous sample points. The discrete formulation is used, because analytical solutions to the continuous formulations are hard to find. In this paper, however, we have discovered a semi-analytical solution to the continuous formulation of a typical ray tracing of volume data. We have applied both Levoy's method and ours to the same set of data, and compared the visual quality of both results. The comparison shows that our method produces a more fine-grained visualization of volume data.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.75-76
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• 2008

이산시간 모델로부터 시간응답 설계 명세를 만족시키도록 직접 디지털 제어기를 설계하는 새로운 방법을 제시한다 R-S-T형의 고정된 제어기 구조에서 폐루프 전달함수가 원하는 기준전달함수와 일치하도록 제어기 이득을 구한다. 시간 응답을 만족시키는 목표전달함수는 w-영역에서 CRA를 이용하여 선택되며 이 함수를 z-영역으로 변환시켜 얻는다. 예제를 통해 제시한 방법의 유용성을 보이고 극배치 방식과 비교한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.331-334
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• 2004

In this paper, we define signed interval-valued Choquet integrals and shows the signed interval-valued Choquet integrals can model violations of separability and monotonicity Furthermore, we discuss some applications to intertemporal preference, asset pricing, and welfare evauations.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.427-429
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• 2002

본 논문에서는 Marching Cubes(MC)의 알고리즘을 이용하여 3차원 모델을 빠르게 복원하기 위한 방법을 제안한다. MC 알고리즘은 대용량의 데이터를 복원하기 때문에 많은 수의 복셀로부터 등가면을 추출하는데 오랜 시간이 걸린다. 또한 많은 수의 삼각형 패치 생성은 빠른 렌더링을 수행할 수 없다는 단점이 있다. 본 논문에서는 등가면 추출 및 형성시간을 단축하고 형성된 등가면을 통한하는 과정을 동해 실행시간과 렌더링 시간을 단축할 수 있는 방법을 제안함으로써 MC의 문제점을 해결한다.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.279-291
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• 2012

When fitting a Cox proportional hazards model with missing covariates, it is inefficient to exclude observations with missing values in the analysis. Furthermore, if the missing-data mechanism is not Missing Completely At Random(MCAR), it may lead to biased parameter estimation. Many approaches have been suggested to handle the Cox proportional hazards model when covariates are sometimes missing, but they are based on the selection model. This paper suggest an approach to handle Cox proportional hazards model with missing covariates by using the pattern-mixture model (Little, 1993). The pattern-mixture model is expressed by the joint distribution of survival time and the missing-data mechanism. In the pattern-mixture model, many models can be considered by setting up various restrictions, and different results under various restrictions indicate the sensitivity of the model due to missing covariates. A simulation study was conducted to show the sensitivity of parameter estimation under different restrictions in a pattern-mixture model. The proposed approach was also applied to mouse leukemia data.