• 전기학회논문지
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• 제64권7호
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• pp.1064-1073
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• 2015

In this study, we introduce the pedestrian detection system by using the feature of HOG-PCA and RBFNNs pattern classifier. HOG(Histogram of Oriented Gradient) feature is extracted from input image to identify and recognize a object. And a dimension is reduced for improving performance as well as processing speed by using PCA which is a typical dimensional reduction algorithm. So, the feature of HOG-PCA through the dimensional reduction by using PCA leads to the improvement of the detection rate. FCM clustering algorithm is used instead of gaussian function to apply the characteristic of input data as well and connection weight is used by polynomial expression such as constant, linear, quadratic and modified quadratic. Finally, INRIA person database known as one of the benchmark dataset used for pedestrian detection is applied for the performance evaluation of the proposed classifier. The experimental result of the proposed classifier are compared with those studied by Dalal.

• 전기전자학회논문지
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• 제21권3호
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• pp.309-319
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• 2017

The research investigates the inhibition of fatty acid biosynthesis of the human Acetyl-CoA Carboxylase enzyme by the aromatic-structure inhibitors (also known as ligands) containing variables of substituents, contributing an important role in the treatment of fatty-acid metabolic syndrome expressed by the group of cardiovascular risk factors increasing the incidence of coronary heart disease and type-2 diabetes. The effective interoperability between ligand and enzyme is characterized by a 50% concentration of enzyme inhibitor ($IC_{50}$) which was determined by experiment, and the factor of geometry structure of the ligands which are modeled by quantum mechanical methods using HyperChem 8.0.10 and Gaussian 09W softwares, combining with the calculation of quantum chemical and chemico-physical structural parameters using HyperChem 8.0.10 and Padel Descriptor 2.21 softwares. The result data are processed with the combination of classical statistical methods and modern bioinformatics methods using the statistical softwares of Department of Pharmaceutical Technology - Jadavpur University - India and R v3.3.1 software in order to accomplish a model of the quantitative structure - activity relationship between aromatic-structure ligands inhibiting fatty acid biosynthesis of the human Acetyl-CoA Carboxylase.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권9호
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• pp.430-438
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• 2001

In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 춘계학술대회
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• pp.981-984
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• 2010

우리는 본 연구에서 학습방법으로서 연속음성을 초성, 중성, 종성의 음소단위로 분할하기 위하여 인공 신경회로망의 하나인 SVMs을 사용하였으며 분할한 음소단위의 음성으로 연속음성인식에 적용하여 그 성능을 살펴보았다. 음소경계는 단 구간에서의 최대 주파수를 가진 알고리듬에 의하여 결정되며 또한 음성인식처리는 CHMM에 의하여 이루어지며 목측에 의한 분할결과와도 비교하여 살펴보았다. 시뮬레이션 결과로부터 초성의 분할성능에서 제안한 SVMs를 적용한 결과가 GMMs보다 효율적인을 알 수 있었다.

• Structural Engineering and Mechanics
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• 제71권6호
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• pp.739-749
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• 2019

This article deals with the application of reliability analysis for determining the safety of simply supported beam under the uniformly distributed load. The uncertainties of the existing methods were taken into account and hence reliability analysis has been adopted. To accomplish this aim, Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR) models are developed. Reliability analysis is the probabilistic style to determine the possibility of failure free operation of a structure. The application of probabilistic mathematics into the quantitative aspects of a structure and improve the qualitative aspects of a structure. In order to construct the GRNN, ELM and GPR models, the dataset contains Modulus of Elasticity (E), Load intensity (w) and performance function (${\delta}$) in which E and w are inputs and ${\delta}$ is the output. The achievement of the developed models was weighed by various statistical parameters; one among the most primitive parameter is Coefficient of Determination ($R^2$) which has 0.998 for training and 0.989 for testing. The GRNN outperforms the other ELM and GPR models. Other different statistical computations have been carried out, which speaks out the errors and prediction performance in order to justify the capability of the developed models.

• Geomechanics and Engineering
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• 제31권3호
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• pp.265-279
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• 2022

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

• Journal of Astronomy and Space Sciences
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• 제41권3호
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• pp.149-158
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• 2024

This study developed a machine learning-based methodology to classify gravitational wave (GW) signals from black hol-eneutron star (BH-NS) mergers by combining convolutional neural network (CNN) with conditional information for feature extraction. The model was trained and validated on a dataset of simulated GW signals injected to Gaussian noise to mimic real world signals. We considered all three types of merger: binary black hole (BBH), binary neutron star (BNS) and neutron starblack hole (NSBH). We achieved up to 96% correct classification of GW signals sources. Incorporating our novel conditional information approach improved classification accuracy by 10% compared to standard time series training. Additionally, to show the effectiveness of our method, we tested the model with real GW data from the Gravitational Wave Transient Catalog (GWTC-3) and successfully classified ~90% of signals. These results are an important step towards low-latency real-time GW detection.

• 디지털콘텐츠학회 논문지
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• 제18권6호
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• pp.1165-1173
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• 2017

가장 대표적인 기계학습 알고리즘인 딥러닝 방법은 여러 응용 분야에서 활용성이 입증돼 디지털신호처리에 널리 사용되고 있다. 그러나 많은 학습데이터를 사용해 학습하는 과정에서 많은 메모리와 학습시간이 필요하기 때문에 CPU 성능과 메모리 용량이 제한된 IoT 디바이스에 딥러닝 기술을 적용하기는 어렵다. 특히 메모리 용량이 2K~8K 로 극히 적은 아두이노 기반의 디바이스를 사용한다면 알고리즘 구현에 많은 한계가 발생한다. 본 논문에서는 정확성과 효율성이 입증돼 여러 분야에서 활용되고 있는 ELM 알고리즘을 아두이노에서 최적화하는 방법을 제안하고, 실험을 통해 메모리 용량이 2KB인 아두이노 UNO와 메모리 용량이 8KB인 아두이노 MEGA에서 각각 15차원, 42차원의 다중 클래스 학습이 가능함을 보였다. 실험을 입증하기 위해 가우시안 혼합 모델링을 사용해 생성한 데이터셋과 범용적으로 사용하는 UCI 데이터셋을 사용해 제안한 알고리즘의 효율성을 입증하였다.

• 한국위성정보통신학회논문지
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• 제12권3호
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• pp.74-79
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• 2017

본 논문에서는 머신러닝 알고리즘 중 하나인 다층 퍼셉트론을 기반으로 전력선통신 시스템에서의 수신 신호를 이용하여 송신단에서 전송한 원신호를 예측하는 시스템 모델을 제안한다. 전력망을 활용한 통신 방식을 사용하는 전력선통신 시스템은 일반적인 통신설로를 활용하는 통신 방식에 비해 잡음이 많다. 이 때문에 전력선통신 시스템의 성능이 저하가 되는 문제가 발생한다. 이를 해결하기 위해 본 논문에서 제안하는 통신 시스템 모델을 이용하면 원신호 예측을 통해 잡음의 영향이 최소화되어 전력선통신 시스템의 성능저하를 완화시킨다. 본 논문에서는 제안한 통신 시스템 모델을 백색 잡음 환경에 적용하여 시뮬레이션을 해봄으로써 원신호가 예측 되는지를 입증한다.

• 예술인문사회 융합 멀티미디어 논문지
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• 제8권3호
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• pp.57-67
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• 2018

앙상블 기법은 기계학습에서 다수의 알고리즘을 사용하여 더 좋은 성능을 내기 위해 사용하는 방법이다. 본 논문에서는 앙상블 기법에서 많이 사용되는 부스팅과 배깅에 대해 소개를 하고, 서포트벡터 회귀, 방사기저함수 네트워크, 가우시안 프로세스, 다층 퍼셉트론을 이용하여 설계한다. 추가적으로 순환신경망과 MOHID 수치모델을 추가하여 실험을 진행한다. 실험적 검증를 위해 사용하는 뜰개 데이터는 7 개의 지역에서 관측된 683 개의 관측 자료다. 뜰개 관측 자료를 이용하여 6 개의 알고리즘과의 비교를 통해 앙상블 기법의 성능을 검증한다. 검증 방법으로는 평균절대오차를 사용한다. 실험 방법은 배깅, 부스팅, 기계학습을 이용한 앙상블 모델을 이용하여 진행한다. 각 앙상블 모델마다 동일한 가중치를 부여한 방법, 차등한 가중치를 부여한 방법을 이용하여 오류율을 계산한다. 가장 좋은 오류율을 나타낸 방법은 기계학습을 이용한 앙상블 모델로서 6 개의 기계학습의 평균에 비해 61.7%가 개선된 결과를 보였다.