• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.62.2-62.2
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• 2018

From the gaussian, near scale-invariant density perturbations observed in the CMB to the late time clustering of galaxies, CDM provides a minimal theoretical explanation for a variety of cosmological data. However accepting this explanation, requires that we include within our cosmic ontology a vacuum energy that is ~122 orders of magnitude lower than QM predictions, or alternatively a new scalar field (dark energy) that has negative pressure. Alternatively, modifications to Einstein's General Relativity have been proposed as a model for cosmic acceleration. Recently there have been many works attempting to test for modified gravity using the large scale clustering of galaxies, ISW, cluster abundance, RSD, 21cm observations, and weak lensing. In this work, we compare various modified gravity models using cosmic shear data from the Deep Lens Survey as well as data from CMB, SNe Ia, and BAO. We use the Bayesian Evidence to quantify the comparison robustly, which naturally penalizes complex models with weak data support. In this poster we present our methodology and preliminary constraints on f(R) gravity.

• Journal of the korean Society of Automotive Engineers
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• v.5 no.3
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• pp.45-55
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• 1983

This paper discussed about Application Technique of Numerical Methods for large structure. The dynamic behaviours of a vehicle were investigated through finite element modelling. After dividing a vehicle body into three substructures, Basic Mass System was composed of 60 flexual modes which was obtained from the dynamic characteristics of each substructure using Modal Synthesis Method. Engine, transmission and rear axle, etc. were added to Basic Mass Model, consequently Full Mass System was constructed by 72 degree of freedoms. Full Mass System was analyzed over the frequency range 0.5-50.0 Hz under the loading conditions which were Stationary Gaussian Random Process. Results and discussions provided the guidelines to eliminate resonances among the parts and to improve the Ride Quality. The Absorbed Power was used as a standard to determine the Ride Quality. The RMS value of driver's vertical acceleration was obtained 0.423g from the basic model and 0.415g from the modified model.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.270-272
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• 2006

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2946-2948
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• 2000

In this paper, we proposed the Fuzzy Polynomial Neural Networks(FPNN) model with fuzzy activation node. The proposed FPNN structure is generated from the mutual combination of PNN(Polynomial Neural Networks) structure and fuzzy inference system. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. The structure of FPNN is not fixed like in conventional Neural Networks and can be generated. The design procedure to obtain an optimal model structure utilizing FPNN algorithm is shown in each stage. Gas furnace time series data used to evaluate the performance of our proposed model.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• Journal of Korean Society of Steel Construction
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• v.13 no.5
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• pp.525-533
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• 2001

The wind resistant design of long-span bridges has urged a special attention to the prevention of the flutter occurrence Therefore calculation of flutter derivatives is indispensable to this prediction. A used system identification method must identify all the flutter derivatives from noisy experimental data In this paper MITD(Modified Ibrahim Tim Domain) method and AKF (Adaptive Kalman Filter) method are applied to extract flutter derivatives from section-model tests. The robustness and reliability of proposal SI methods under a high signal-to-noise ratio is demonstrated through numerical simulation for windtunnel test.

• Smart Structures and Systems
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• v.33 no.6
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• pp.385-398
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• 2024

To improve the accuracy of time-frequency analysis (TFA) and instantaneous frequency (IF) extraction of structural dynamic response signals, this paper improves the spline-kernelled chirplet transform, and a new form of modified spline-kernelled chirplet transform (MSCT) based on revised Gaussian window function and energy concentration principle is put forward. The effectiveness of the proposed method is verified by numerical examples of single-component signal, multicomponent signal, single-degree-of-freedom Duffing nonlinear system and two-layer shear frame structure model. Then, a time-varying cable test is designed to collect the acceleration response signals under linear changing tension, and the IF extraction of these signals is performed by using MSCT, which further verifies the effectiveness and accuracy of this method. Through numerical simulation and experimental verification, it is proved that the proposed method can effectively extract the IF of nonlinear structure and time-varying structure.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.40-41
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• 2011

동해를 전파하는 지진해일은 세계적으로 다른 지역에서 발생하는 지진해일과 비교하였을 때 상대적으로 파장이 짧고, 이에 비해 먼 거리를 전파한다. 그러므로 동해에서 발생한 지진해일의 전파에 대한 해석을 수행할 때 물리적인 분산효과가 매우 중요하다. 따라서 지배방정식으로 분산 효과가 충분히 고려된 선형 Boussinesq 방정식을 사용한다. 기존의 연구에서는 leap-frog 기법을 사용하여 선형 천수방정식을 차분할 때 발생하는 수치분산항에 분산 보정계수를 이용하여 선형 Boussinesq 방정식의 물리적 분산항과 같은 형태로 나타나도록 유도하여 수치모의를 수행하였다. 그러나 기존에 사용한 지배방정식은 수심이 일정하다는 가정을 통하여 유도된 것으로, 수심에 변화가 있는 실제 지형을 통과하는 지진해일에 대한 수치모의를 수행한 결과의 정확도에 문제가 생길 수 있다. 본 연구에서는 기존의 연구에서 발생할 수 있는 수심 변화에 따른 오류를 개선하기 위하여 바닥 지형이 1차원으로 변한다는 가정을 이용하여 지배방정식을 유도하였으며, 이로 인해 발생하는 수심 변화가 고려된 항을 기존의 분산보정기법에 추가하였다. 그리고 적용성을 높이기 위하여 수치모의 기법의 제한을 최소화하는 연구를 진행하였다. 본 연구에서 제안한 수정 기법이 수심이 변화하는 지형을 전파하는 지진해일 수치모의 과정에서 경사에 대한 분산효과가 충분히 고려되는지 확인하기 위하여 Gaussian hump를 이용한 가상 지진해일을 원형 천퇴 지형에 통과시켰다. 본 연구에서 사용한 지형을 통과하는 Gaussian hump에 대한 해석해를 구하는 방법이 존재하지 않으므로, Boussinesq 방정식을 직접 차분하여 푸는 FUNWAVE를 사용하여 동일한 조건 하에서 수치모의를 수행하였다. 비교 결과를 통하여 본 연구에서 제안한 기법의 정확도 향상을 확인하게 되면, 실제 지형을 통과하는 지진해일의 수치모의에 대한 활용성을 높일 수 있을 것이다.

• Structural Engineering and Mechanics
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• v.77 no.3
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• pp.355-368
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• 2021

The time-varying mean (TVM) component of non-stationary wind speeds is commonly extracted utilizing empirical mode decomposition (EMD) in practice, whereas the accuracy of the extracted TVM is difficult to be quantified. To deal with this problem, this paper proposes an approach to identify and extract the optimal TVM from several TVM results obtained by the EMD. It is suggested that the optimal TVM of a 10-min time history of wind speeds should meet both the following conditions: (1) the probability density function (PDF) of fluctuating wind component agrees well with the modified Gaussian function (MGF). At this stage, a coefficient p is newly defined as an evaluation index to quantify the correlation between PDF and MGF. The smaller the p is, the better the derived TVM is; (2) the number of local maxima of obtained optimal TVM within a 10-min time interval is less than 6. The proposed approach is validated by a numerical example, and it is also adopted to extract the optimal TVM from the field measurement records of wind speeds collected during a sandstorm event.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.443-446
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• 2007

음성신호는 잡음 또는 전송 채널의 특성에 의하여 왜곡되고, 왜곡된 음성은 음성인식 및 화자인식의 성능을 크게 저하시킨다. 이러한 문제점을 극복하기 위해 본 논문에서는 Gaussian mixture model (GMM)에 적용된 신호대잡음비 (SNR)기반 신뢰도 가중 기법[1][2]을 Hidden Markov model(HMM) 디코더에 변형하여 적용하였다. HMM 디코더 변형은 HMM 상태별 관측확률을 논문 [1]에서 제시된 신뢰도로 가중함으로써 이루어졌다. 제안한 방법의 성능을 확인하기 위해 ETRI에서 만든 한국어 화자인식용 휴대폰 음성 DB를 사용하여 문맥종속 화자식별 실험을 하였다. 실험결과 기존 방법에 비해 제안한 방법의 화자인식률이 크게 향상됨을 확인 할 수 있었다.