• Annual Conference of KIPS
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• 2013.11a
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• pp.1452-1454
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• 2013

최근 스마트 기기와 SNS가 대중화가 되면서, 이를 기반으로 사용자의 감정 분석을 통한 추천 연구가 활발하게 진행되어 지고 있다. 본 논문에서는 감정표현단어 500여개와 이모티콘을 활용하여 감정 범주를 9가지로 분류하고, 트위터광장의 여러 유명인사 트위터 타임라인의 텍스트를 가져온 후, 감정상태 분석으로 감정 범주를 카운트 한다. 이를 통해, 한사람의 감정상태의 수치를 나타내고, 추후 음악, 음식, 문화 활동 등을 다양하게 추천할 수 있는 감정상태분석모듈을 설계 및 구현한다.

• Annual Conference of KIPS
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• 2021.05a
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• pp.62-65
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• 2021

통신은 주로 통신의 시작, 데이터 전송, 오류 검사, 통신의 종료 4 가지 과정을 거쳐 이루어진다. 위 4 가지 과정에 따라 통신 모듈의 상태(state)를 분류하고 상태도(state diagram)를 그릴 수 있다. HDL 언어를 사용하여 상태도를 유한 상태 기계(finite-state machine)로 구현함으로써 통신 모듈을 쉽게 구현할 수 있다. 본 논문은 이러한 방법으로 FPGA 에 통신 모듈을 구현하는 방법을 다루고 있다. 나아가, 이 방법을 이용하여 UART 와 SPI 통신 모듈을 구현하는 실험을 소개한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.325-327
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• 2024

국내외에서 AI기반 의료 솔루션 시장은 빠른 속도로 확장 중이며 이에 따른 다양한 의학 분야에서 많은 기법을 통한 의료 AI 시스템이 등장하고 있다. 그러나 기존 다양한 AI 연구가 이뤄짐에도 아직 중환자의 징후 예측에는 많은 어려움이 있다. 또한, 중환자의 경우 현재 의료진만으로 모든 환자를 필요한 시기에 진료하기엔 어려움이 있고 환자 상태 조기 예측이 필수적임을 관련 다양한 의학 기사를 통해 쉽게 인지할 수 있다. 본 연구에서는 위와 같은 문제점을 해결하고자 중환자의 진료 결과 데이터를 활용하여 환자의 진료 후 상태를 예측하는 모델을 생성하였다. '용인시산업진흥원'에서 제공하는 60만여 건에 달하는 환자 데이터를 수집하여, 중환자 상태 징후를 조기에 예측할 수 있는 머신러닝/딥러닝 기반 알고리즘으로 구현한 여러 모델에 대해 비교했을 때 딥러닝(DNN) 기반 모델이 약 92%의 분류 정확도를 측정할 수 있었다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.04a
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• pp.72-72
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• 2019

간무봉(555.8m)은 강원도 인제군 남면에 위치하고 있다. 북한강의 지류인 소양강으로 대부분 둘러싸여 있으며 북서쪽에 봉화산(874.4m), 북동쪽에 기룡산(940.4m)과 이어진다. 현재까지 간무봉을 대상으로 한 식물상 조사 이력은 없었으나, DMZ 일원에 위치하는 산이기 때문에 DMZ의 식물상 연구를 위한 기초 자료로 보전하고 탐색하는 의미가 있다. 현지조사는 2018년 4월부터 10월까지 총 6회 수행하였다. 간무봉에는 굴참나무, 신갈나무, 소나무가 우점하고 있는 형태였으며, 조사 결과 관속식물은 73과 175속 211종 3아종 27변종 4품종으로 총 245분류군이 조사되었다. 그 중 양치식물은 고비, 고사리삼, 십자고사리, 퍼진고사리 4분류군이고, 나자식물은 소나무, 일본잎갈나무 2분류군, 피자식물은 노랑제비꽃, 은방울꽃, 꽃향유 등 205분류군이었다. 이는 우리나라 관속식물 4,494분류군(국립수목원, 2016)의 5.45%에 해당한다. 간무봉에서 확인한 특산식물은 진범, 할미밀망, 갈퀴아재비, 병꽃나무 등 6 분류군이며, 희귀식물은 미치광이풀(LC), 말나리(LC) 2 분류군 이외에 VU 이상에 해당하는 희귀식물은 발견하지 못하였다. 그리고 침입외래식물(국립수목원, 2016)의 경우 미국자리공, 수박풀, 미국쑥부쟁이, 단풍잎돼지풀 등 17 분류군이었다. 간무봉 주변에는 경작지, 군부대 훈련장소 등이 있으며, 서쪽 사면은 이미 벌목이 많이 된 상태이며, 북동쪽도 마을과 더불어 경작지로 개간되어 있다. 따라서 주변의 외부에서 가해지는 인위적인 간섭이 많은 상태라고 볼 수 있으며, 이미 유입된 단풍잎돼지풀 등 생태계교란종에 의한 변화에도 유의할 필요가 있다.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.494-507
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• 2021

With the increasing use of TBM, research has recently been conducted in Korea to analyze TBM data with machine learning techniques to predict the ground in front of TBM, predict the exchange cycle of disk cutters, and predict the advance rate of TBM. In this study, classification prediction of rock characteristics of slurry shield TBM sites was made by combining traditional rock classification techniques and machine learning techniques widely used in various fields with machine data during TBM excavation. The items of rock characteristic classification criteria were set as RQD, uniaxial compression strength, and elastic wave speed, and the rock conditions for each item were classified into three classes: class 0 (good), 1 (normal), and 2 (poor), and machine learning was performed on six class algorithms. As a result, the ensemble model showed good performance, and the LigthtGBM model, which showed excellent results in learning speed as well as learning performance, was found to be optimal in the target site ground. Using the classification model for the three rock characteristics set in this study, it is believed that it will be possible to provide rock conditions for sections where ground information is not provided, which will help during excavation work.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.354-360
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• 2022

Metabolic disease is a disease with a prevalence of 26% in Korean, and has three of the five states of abdominal obesity, hypertension, hunger glycemic disorder, high neutral fat, and low HDL cholesterol at the same time. This paper links the consumer panel data of the Rural Development Agency(RDA) and the medical care data of the National Health Insurance Service(NHIS) to generate a classification model that can be divided into a metabolic disease group and a control group through food consumption characteristics, and attempts to compare the differences. Many existing domestic and foreign studies related to metabolic diseases and food consumption characteristics are disease correlation studies of specific food groups and specific ingredients, and this paper is logistic considering all food groups included in the general diet. We created a classification model using regression, a decision tree-based classification model, and a classification model using XGBoost. Of the three models, the high-precision model is the XGBoost classification model, but the accuracy was not high at less than 0.7. As a future study, it is necessary to extend the observation period for food consumption in the patient group to more than 5 years and to study the metabolic disease classification model after converting the food consumed into nutritional characteristics.

• Journal of the Korean GEO-environmental Society
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• v.18 no.6
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• pp.5-19
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• 2017

In this study, the effect of rock mass curtain grouting was investigated by analyzing the correlation between the parameters of the RMR & grout injection volume, Lugeon value & RQD, Lugeon value & cement injection volume. In order to investigate the effect of rock mass curtain grouting, we analyzed the grout injection volume of 315 curtain grouting holes at 9 tunnel face of NATM Subsea tunnels in gneiss area. The total grout injection volume in the Subsea tunnels study was slightly changed in some tunnels face but decreased with increasing the rating of parameters in spacing of discontinuity (R3, Js) and groundwater condition (R5). The geological anomalies of seismic survey (3D, TSP) and the inflow of probe hole were found to be more correlated of relative than the parameters of RMR. The unit injection volume was found to decrease with higher ratings in the parameters of the RMR except the weathering degree of the discontinuity (Jc, R4). The correlation between RQD and Lugeon values is not significant, but it can be confirmed that the Lugeon value tends to decrease as the RQD value increases.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.263-271
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• 2014

Corrective thermal performance analysis is required for thermal power plants to determine performance status of turbine cycle. We developed classification method for main feed water flow to make precise correction for performance analysis based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). The classification is based on feature identification of status of main water flow. Also we developed predictive algorithms for corrected main feed-water through Support Vector Machine (SVM) Model for each classified feature area. The results was compared to estimations using Neural Network(NN) and Kernel Regression(KR). The feature classification and predictive model of main feed-water flow provides more practical methods for corrective thermal performance analysis of turbine cycle.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.107-114
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• 2008

In this paper, we constructed a speech emotion recognition system that classifies four emotions - neutral, happy, sad, and anger from speech based on male/female gender discrimination. At first, the proposed system distinguish between male and female from a queried speech, then the system performance can be improved by using separate optimized feature vectors for each gender for the emotion classification. As a emotion feature vector, this paper adopts ZCPA(Zero Crossings with Peak Amplitudes) which is well known for its noise-robustic characteristic from the speech recognition area and the features are optimized using SFS method. For a pattern classification of emotion, k-NN and SVM classifiers are compared experimentally. From the computer simulation results, the proposed system was proven to be highly efficient for speech emotion classification about 85.3% regarding four emotion states. This might promise the use the proposed system in various applications such as call-center, humanoid robots, ubiquitous, and etc.