• Journal of Digital Policy
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• v.2 no.4
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• pp.25-32
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• 2023

This paper is a study to develop a deep neural network (DNN) blood glucose prediction model based on heart rate (HR) and heart rate variability (HRV) data measured by PPG-based sensors. MLP deep learning consists of an input layer, a hidden layer, and an output layer with 11 independent variables. The learning results of the blood glucose prediction model are MAE=0.3781, MSE=0.8518, and RMSE=0.9229, and the coefficient of determination (R²) is 0.9994. The study was able to verify the feasibility of glycemic control using non-blood vital signs using PPG-based digital devices. In conclusion, a standardized method of acquiring and interpreting PPG-based vital signs, a large data set for deep learning, and a study to demonstrate the accuracy of the method may provide convenience and an alternative method for blood glucose management in dogs.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.903-911
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• 2022

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.181-186
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• 2020

The purpose of this study is to empirically examine the factors affecting the binge-watching behaviors of OTT service users by using a multi-layer perceptron (MLP) artificial neural network. All samples (n=1,000) were collected from 'A survey on user awareness in OTT service' published by a Media Research Center of the Korea Press Foundation in 2018. Our research model includes one dependent variable which is binge-watching behaviors on OTT service and five independent variables such as gender, age, frequency of service usage, users' satisfaction with content recommendation algorithm, and content types mainly consumed. Our findings demonstrate that age, frequency of service usage, users' satisfaction with content recommendation algorithms, and certain types of contents (e.g., Korean dramas, Korean films, and foreign dramas) were found to be highly related to binge-watching behavior on OTT services.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.447-455
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• 2019

Since the forest type map in Korea has been mostly constructed every five years, the forest information from the map lacks up-to-date information. Forest research has been carried out by aerial photogrammetry and field surveys, and hence it took a lot of times and money. The vertical structure of forests is an important factor in evaluating forest diversity and environment. The vertical structure is essential information, but the observation of the vertical structure is not easy because the vertical structure indicates the internal structure of forests. In this study, the index map and texture map produced from KOMPSAT-3/3A/5 satellite images and the canopy information generated by the difference between DSM (Digital Surface Model) and DTM (Digital Terrain Model) were classified using the artificial neural network. The vertical structure of forests of single and multi-layer forests was classified to identify 81.59% of the final classification result.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.494-503
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• 2011

In this paper, we propose a recognition application of facial expression for laughter theraphy on smartphone. It detects face region by using AdaBoost face detection algorithm from the front camera image of a smartphone. After detecting the face image, it detects the lip region from the detected face image. From the next frame, it doesn't detect the face image but tracks the lip region which were detected in the previous frame by using the three step block matching algorithm. The size of the detected lip image varies according to the distance between camera and user. So, it scales the detected lip image with a fixed size. After that, it minimizes the effect of illumination variation by applying the bilateral symmetry and histogram matching illumination normalization. After that, it computes lip eigen vector by using PCA(Principal Component Analysis) and recognizes laughter expression by using a multilayer perceptron artificial network. The experiment results show that the proposed method could deal with 16.7 frame/s and the proposed illumination normalization method could reduce the variations of illumination better than the existing methods for better recognition performance.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.304-306
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• 2001

많은 실세계의 문제에서 일반적인 패턴 분류 알고리즘들은 데이터의 불균형 문제에 어려움을 겪는다. 각각의 학습 예제에 균등한 중요도를 부여하는 기존의 기법들은 문제의 특징을 제대로 파악하지 못하는 경우가 많다. 본 논문에서는 불균형 데이터 문제를 해결하기 위해 퍼셉트론에 기반한 부스팅 기법을 제안한다. 부스팅 기법은 학습을 어렵게 하는 데이터에 집중하여 앙상블 머신을 구축하는 기법이다. 부스팅 기법에서는 약학습기를 필요로 하는데 기존 퍼셉트론의 경우 문제에 따라 약학습기(weak learner)의 조건을 만족시키지 못하는 경우가 있을 수 있다. 이에 커널을 도입한 커널 퍼셉트론을 사용하여 학습기의 표현 능력을 높였다. Reuters-21578 문서 집합을 대상으로 한 문서 여과 문제에서 부스팅 기법은 다층신경망이나 나이브 베이스 분류기보다 우수한 성능을 보였으며, 인공 데이터 실험을 통하여 부스팅의 샘플링 경향을 분석하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.881-884
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• 2017

스마트 디바이스가 대중화 되면서 각종 사건 사고에 대한 데이터가 SNS 상에 실시간으로 업데이트 된다. SNS의 이런 특성을 이용하여 이용자 개개인이 사고감지센서의 역할을 하면 빠른 사고감지가 가능하다. 하지만 기존 연구들은 단순히 키워드의 출현 빈도로 사고를 판단하는 방식과, 문법파괴 요소가 많은 트위터의 특성으로 인해 정확성에서 한계를 보인다. 본 연구에서는 사고감지의 정확도를 높이기 위해 형태소로 분석한 트윗을 벡터화하여 다층퍼셉트론신경망으로 학습시키는 모델을 구현하였다. 연구 결과 일반명사로 이루어진 40개의 단어를 사용했을 때 가장 높은 82.58%의 정확도를 얻었다.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.2
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• pp.81-86
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• 2022

Given that time series are used in various fields, such as finance, IoT, and manufacturing, data analytical methods for accurate time-series forecasting can serve to increase operational efficiency. Among time-series analysis methods, multi-horizon forecasting provides a better understanding of data because it can extract meaningful statistics and other characteristics of the entire time-series. Furthermore, time-series data with exogenous information can be accurately predicted by using multi-horizon forecasting methods. However, traditional deep learning-based models for time-series do not account for the heterogeneity of inputs. We proposed an improved time-series predicting method, called the temporal fusion transformer method, which combines multi-horizon forecasting with interpretable insights into temporal dynamics. Various real-world data such as stock prices, fine dust concentrates and electricity consumption were considered in experiments. Experimental results showed that our temporal fusion transformer method has better time-series forecasting performance than existing models.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.217-218
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• 2021

본 논문에서는 다층 퍼셉트론을 활용하여 League of Legends 게임의 승패를 예측하는 Deep Neural Network 프로그램을 설계하는 방법을 제안한다. 연구 방법으로 한국 서버의 챌린저 리그에서 행해진 약 26000 경기 데이터 셋을 분석하여, 경기 도중 15분 데이터 중 드래곤 처치 수, 챔피언 레벨, 정령, 타워 처치 수가 게임 결과에 유의미한 영향을 끼치는 것을 확인하였다. 모델 설계는 softmax 함수보다 sigmoid 함수를 사용했을 때 더 높은 정확도를 얻을 수 있었다. 실제 LOL의 프로 게임 16경기를 예측한 결과 93.75%의 정확도를 도출했다. 게임 평균시간이 34분인 것을 고려하였을 때, 게임 중반 정도에 게임의 승패를 예측할 수 있음이 증명되었다. 본 논문에서 설계한 이 프로그램은 전 세계 E-sports 프로리그의 승패예측과 프로팀의 유용한 훈련지표로 활용 가능하다고 사료된다.