• 방송공학회논문지
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• 제28권3호
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• pp.275-284
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• 2023

유전자 발현 데이터는 질병의 예후 예측, 약물 반응성 예측 등 질병에 대한 이해와 정밀 의료 실현을 위한 연구들에 활용될 수 있지만 충분한 양의 데이터를 수집하는 데 많은 비용적 문제가 있다. 본 논문에서는 Conditional VAE에 기반한 유전자 발현 데이터 생성 모델을 제안하였다. 이전 연구인 WGAN-GP기반의 유전자 발현 생성 모델과 정형 데이터 생성 모델인 CTGAN, TVAE와 비교하여 본 논문의 Conditional VAE기반 모델이 생물학적, 통계학적으로 더 유의미한 합성 데이터를 생성할 수 있음을 보였다.

• 스마트미디어저널
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• 제10권1호
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• pp.79-87
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• 2021

In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.

• 한국경영과학회지
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• 제37권3호
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.

• 로봇학회논문지
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• 제4권1호
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• pp.56-61
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• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.764-767
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• 2021

While Generative Adversarial Networks (GANs) have seen huge success in image synthesis tasks, synthesizing high-quality images from text descriptions is a challenging problem in computer vision. This paper proposes a method named Text-to-Face Generation Using Multi-Scale Gradients for Conditional Generative Adversarial Networks (T2F-MSGGANs) that combines GANs and a natural language processing model to create human faces has features found in the input text. The proposed method addresses two problems of GANs: model collapse and training instability by investigating how gradients at multiple scales can be used to generate high-resolution images. We show that T2F-MSGGANs converge stably and generate good-quality images.

• 응용통계연구
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• 제10권1호
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• pp.151-161
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• 1997

순서적 다항자료의 누적로짓 모형에 대한 베이지안 사후추론을 위하여 몬테칼로 깁스표본기법을 제안하였다. 원래의 모형에서는 깁스표본기법 적용에 필수적으로 요구되는 각 원소모수의 조건부 확률분포가 난수생성에 편리한 형태로 주어지지 않으므로 Albert and Chib(1993)과 Oh(1997)에서 이항 로짓모형에 사용한 바와 같이 적절한 잠재변수를 도입하여 깁스표본기법 적용에 매우 편리한 형태를 갖도록 한다.

• 대한임베디드공학회논문지
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• 제18권5호
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• pp.217-223
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• 2023

In this paper, we propose a new model for the conditional generation of music, considering key and rhythm, fundamental elements of music. MIDI sheet music is converted into a WAV format, which is then transformed into a Mel Spectrogram using the Short-Time Fourier Transform (STFT). Using this information, key and rhythm details are classified by passing through two Convolutional Neural Networks (CNNs), and this information is again fed into the Music Transformer. The key and rhythm details are combined by differentially multiplying the weights and the embedding vectors of the MIDI events. Several experiments are conducted, including a process for determining the optimal weights. This research represents a new effort to integrate essential elements into music generation and explains the detailed structure and operating principles of the model, verifying its effects and potentials through experiments. In this study, the accuracy for rhythm classification reached 94.7%, the accuracy for key classification reached 92.1%, and the Negative Likelihood based on the weights of the embedding vector resulted in 3.01.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권10호
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• pp.407-420
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• 2014

한국어 형태소 분석 및 태깅은 크게 2가지 단계로 나뉜다. 첫 번째 단계는 어절을 분석하여 후보들을 생성하는 것으로, 여러 의미를 가진 어절은 이 단계에서 다양한 후보들이 생성된다. 두 번째는 문맥 정보를 이용하여 후보 중에 가장 적절한 하나를 선택하는 단계로, 흔히 태깅이라 한다. 일반적으로 두 번째 단계에서는 은닉 마르코프 모델(Hidden Markov Model, 이하 HMM)을 자주 사용하지만, 본 논문에서는 처리속도를 향상시킨 부분어절 조건부확률 모델을 제안한다. 이 모델은 우선적으로 인접 어절 정보를 이용하여 현재 처리 중인 어절의 의미를 결정하고, 예외적으로 용언이 인접한 경우에만 후보 정보의 극히 일부분을 이용한다. 실험 결과 정확률은 HMM의 96.49%보다 0.07% 낮았지만, 처리 소요 시간을 약 53% 감소시켰다.

• 한국지능시스템학회논문지
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• 제2권3호
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• pp.29-39
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• 1992

We investigate a systematic design procedure of automatic rule generation of fuzzy logic based controllers for highly nonlinear dynamic systems such as an engine dynamic modle. By "automatic rule generation" we mean autonomous clustering or collection of such meaningful transitional relations from one conditional subspace to another. During the design procedure, we also consider optimaly control strategies such as minimum squared error, near minimum time, minimum energy or combined performance critiera. Fuzzy feedback control systems designed by our method have the properties of closed-loop stability, robustness under parameter variabitions, and a certain degree of optimality. Most of all, the main advantage of the proposed approach is that reliability can be potentially increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which we apply our strategic fuzzy controller dwsign to a highly nonlinear model of engine idling speed control.d control.

• 한국정보처리학회논문지
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• 제2권2호
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• pp.254-263
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• 1995

본 논문은 파이프라인 방식의 ASIC 데이타 경로를 제어하기 위한 무어 및 밀리식 의 시간 정지형 콘트롤러에 관한 연구이다. 조건분기(conditional branches)를 가진 데이타흐름도로 부터 무어 및 밀리식의 유한상태기(finite state machine) 콘트롤러 를 합성하는 방법을 소개한다. 콘트롤 합성은 콘트롤 명세서의 작성과 유한상태기의 합성으로 구성된다. 콘트롤 명세서를 작성하기 위한 과정들을 통해 상태표(state table)의 형태로 표현되는 유한상태기의 내역이 작성된다. 이 유한상태기를 여러 가지 다른 방식의 분할 과정과 축소화 과정을 거쳐 최소 면적을 가진 콘트롤러가 합성된다. 실험을 통해 두가지 콜트롤 방식의 특성을 비교하며 또한 두 모델의 비용과 성능의 영향 관계를 보여준다.