• 한국정보통신학회논문지
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• 제15권2호
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• pp.279-285
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• 2011

차세대 무선통신에서는 현재 서비스 되고 있는 성능보다 높은 BER(Bit Error Rate)의 성능을 요구한다. 기존의 DVB-RCS(Digital Video Broadcasting - Return Channel via Satellite)에서 사용중인 이진 터보 코드(Double binary Turbo code)는 높은 SNR(에서 오류마루 현상이 발생하여 차세대 무선통신에서 사용하기가 어려움이 있다. 따라서 본 논문에서는 DVB-RCS NG에 적합한 부호화 방식으로 3D-터보 코드(Tthird-dimension Turbo code)의 부복호화기의 구조를 분석하고 성능분석 하였다. 3D-터보 코드는 기존의 DVB-RCS 방식에 rate-1인 post-encoder를 첨가시켜 오류마루 현상을 보완한 부호화기이다. 3D-터보 코드는 post-encoder의 형태, 인터리빙 기법, ${\lambda}$값의 변화에 따라 성능이 달라지므로 본 논문에서는 각 파라메타에 대한 최적의 값을 제시하였다. 전체적으로 3D-터보 코드가 기존의 DVB-RCS 터보 코드에 비해 성능이 우수하고 기존의 문제점인 오류마루 현상을 해결할 수 있음을 알 수 있다.

• 한국음향학회지
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• 제20권7호
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• pp.47-54
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• 2001

본 논문은 스펙트럴 크기 파라미터들에 대한 효율적인 가변 차원 양자화 기법을 제안한다. 특히, 하모닉 부호화 기에서의 스펙트럴 크기값 계수들은 가변차원이기 때문에 가변 차원의 양자화를 필요로 한다. 따라서, 본 논문에서는 스펙트럴 크기값 계수들에 대해 가변 이산 코사인 변환(DCT: Discrete Cosine Transform) 및 가변 차원에 적합한 훈련구조를 가지는 비정방형 변환 벡터 양자화 (NSTVQ: Nonsquare Transform Vector Quantization)를 홀수/짝수 구조 및 분할(Split) 구조 그리고 다단계(Multi-stage) 구조 등과 결합시킨 효율적인 양자화 기법을 제안한다. 제안된 양자화 기법의 성능평가는 스펙트럴의 크기값에 대한 주파수 왜곡(SD: Spectral Distortion) 값을 사용하였으며, 다단계 비정방형 변환 벡터 양자화(MSNSTVQ: Multi-Stage Nonsquare Transform Vector Quantization)가 가장 좋은 성능을 나타내었다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권8호
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• pp.361-368
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• 2016

고차원데이터에 대한 차원 감소 기법들은 많이 연구되어져 온 반면, 개념 변동을 가진 고차원 스트리밍 데이터에서 적용할 수 있는 차원 감소 기법에 대한 연구는 제한적이다. 이 논문에서는 스트리밍 데이터에서 적용할 수 있는 점층적 차원 감소 기법들을 살펴보고, 개념 변동 고차원 스트리밍 데이터에 대해 분류 성능을 향상시킬 수 있도록 차원 감소를 효과적으로 적용하는 방법을 제안한다.

• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.721-733
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• 2022

In this paper we compare parameter estimation by Grassmann manifold optimization and sequential candidate set algorithm in a structured principal fitted component (PFC) model. The structured PFC model extends the form of the covariance matrix of a random error to relieve the limits that occur due to too simple form of the matrix. However, unlike other PFC models, structured PFC model does not have a closed form for parameter estimation in dimension reduction which signals the need of numerical computation. The numerical computation can be done through Grassmann manifold optimization and sequential candidate set algorithm. We conducted numerical studies to compare the two methods by computing the results of sequential dimension testing and trace correlation values where we can compare the performance in determining dimension and estimating the basis. We could conclude that Grassmann manifold optimization outperforms sequential candidate set algorithm in dimension determination, while sequential candidate set algorithm is better in basis estimation when conducting dimension reduction. We also applied the methods in real data which derived the same result.

• 설비공학논문집
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• 제28권4호
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• pp.165-170
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• 2016

Long-term performance of the enthalpy exchange element is a topic of current interest due to the concern of possible performance degradation over time. In this study, a 350 CMH enthalpy recovery ventilator equipped with an enthalpy exchange element was installed in an office room, and the performance has been traced over the past 5 years. The appearance, overall dimension, thermal performance, leakage ratio and anti-bacterial performance were checked annually. Results showed that the change in thermal performance (sensible, latent and enthalpy efficiency) was negligible with periodic cleaning with an air gun. However, the leakage ratio increased with time, measuring 7.3% after 5 years. Anti-bacterial test revealed that no bacteria were found during the test period. The largest change in the dimension occurred at the middle location of the element, although the change was less than 2% of the initial value.

• 성인간호학회지
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• 제19권1호
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• pp.155-165
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• 2007

Purpose: The purpose of this study was to determine the relationships of pain cognition, performance status, and hope with health-related quality of life. Methods: Patients(n=149) with various cancer diagnoses completed the SF-36 standard Korean Version and the Herth Hope Index. The Perceived Meanings of Cancer Pain Inventory was used to measure the cognition dimension of pain, whereas the Brief Pain Inventory Korean version was used to represent the sensory dimension of pain. Results: The patients in the pain group had significant differences in the three dimensions(loss, threat, spiritual awareness) of pain cognition. There were statistically significant negative correlations between the three dimensions(loss, threat, and spiritual awareness) of pain cognitions and SF-36 dimension, and the positive correlations between challenge dimension and SF-36 dimension. Hope had the positive correlation with SF-36 dimensions. Conclusion: Pain has a negative impact on health-related quality of life, especially on physical health. However, patients who ascribed more positive meaning to their pain, tended to have a higher quality of life. Therefore, nursing intervention to reinforce the positive aspects of pain cognition is to empower patients to create a sense of control and assume an active role in pain management and quality of life.

• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 춘계학술대회 논문집
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• pp.283-284
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• 2007

• 한국산업융합학회 논문집
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• 제5권1호
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• pp.33-38
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• 2002

This paper proposes an efficient method for improving the performance of polynomial adaline using the dimension reduction of independent variables. The adaptive principal component analysis is applied for reducing the dimension by extracting efficiently the features of the given independent variables. It can be solved the problems due to high dimensional input data in the polynomial adaline that the principal component analysis converts input data into set of statistically independent features. The proposed polynomial adaline has been applied to classify the patterns. The simulation results shows that the proposed polynomial adaline has better performances of the classification for test patterns, in comparison with those using the conventional polynomial adaline. Also, it is affected less by the scope of the smoothing factor.

• 한국경영과학회지
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• 제30권4호
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• pp.83-97
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• 2005

We could confirm through this study that in the company which has secured its agility in an information technology dimension, a process dimension and an organization behavior dimension as a real time enterprise, various agility competences of that company are used more effectively for productivity Improvement, development on new products and customer satisfaction. Therefore, based on these determinant factors, it has been proved that an individually differentiated investment of technical and organizational resources for development and innovation of new products has contributed affirmatively to get a more efficient enterprise performance : and, in this point, it is verified again that the agility secured through a conversion toward the real time enterprise (RTE) is a more effective plan to increase the enterprise performance.