• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.241-248
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• 2018

A Recommender System (RS) is a system that provides optimized information to users in an over-supply situation. The key to RS is to accurately predict the behavior of the user. The Matrix Factorization (MF) method was used for this prediction in the early stage, and according to the recent SNS development, social information is additionally utilized to improve prediction accuracy. In this paper, we use RS internal trust cluster, which was overlooked in previous studies, to further improve performance and analyze the characteristics of trust clusters.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.809-814
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• 2022

ADHD is a disorder showing inattentiveness and hyperactivity. Since symptoms diagnosed in childhood continue to the adulthood, it is important to diagnose ADHD and start treatments in early stages. However, it has the problems to acquire enough and accurate data for the diagnosis because the mental state of children is immature using the self-diagnosis method or the computerized test. In this paper, we present the classification method based on the CNN model and execute experiment using the EEG data to improve the objectiveness and the accuracy of ADHD diagnosis. For the experiment, we build the 3D convolutional networks model and exploit the 5-folds cross validation method. The result shows the 97% accuracy on average.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.230-230
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• 2023

딥러닝 알고리즘 중 과거의 정보를 저장하는 문제(장기종속성 문제)가 있는 단순 RNN(Simple Recurrent Neural Network)의 단점을 해결한 LSTM(Long short-term memory)이 등장하면서 특정한 유역의 강우-유출 모형을 구축하는 연구가 증가하고 있다. 그러나 하나의 모형으로 모든 유역에 대한 유출을 예측하는 지역화 강우-유출 모형은 서로 다른 유역의 식생, 지형 등의 차이에서 발생하는 수문학적 행동의 차이를 학습해야 하므로 모형 구축에 어려움이 있다. 따라서, 본 연구에서는 국내 12개의 유역에 대하여 LSTM 기반 분포형 지역화 강우-유출 모형을 구축한 이후 강우 이외의 보조 자료에 따른 정확도를 살펴보았다. 국내 12개 유역의 7년 (2012.01.01-2018.12.31) 동안의 49개 격자(4km²)에 대한 10분 간격 레이더 강우, MODIS 위성 이미지 영상을 활용한 식생지수 (Normalized Difference Vegetation Index), 10분 간격 기온, 유역 평균 경사, 단순 하천 경사를 입력자료로 활용하였으며 10분 간격 유량 자료를 출력 자료로 사용하여 LSTM 기반 분포형 지역화 강우-유출 모형을 구축하였다. 이후 구축된 모형의 성능을 검증하기 위해 학습에 사용되지 않은 3개의 유역에 대한 자료를 활용하여 Nash-Sutcliffe Model Efficiency Coefficient (NSE)를 확인하였다. 식생지수를 보조 자료를 활용하였을 경우 제안한 모형은 3개의 검증 유역에 대하여 하천 흐름을 높은 정확도로 예측하였으며 딥러닝 모형이 위성 자료를 통하여 식생에 의한 차단 및 토양 침투와 같은 동적 요소의 학습이 가능함을 나타낸다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.68-71
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• 2008

웹 상에 정보가 폭발적으로 증가함에 따라 각 사용자에게 맞는 정보를 선별하여 제공하는 개인화 서비스는 매우 중요한 이슈가 되었다. 기존 추천시스템들은 컨텐츠 기반 필터링과 협업 필터링 기법을 기반으로 한다. 그러나 이러한 방법들은 충분히 수집된 사용자 정보를 필요로 하기 때문에, 적절한 추천이 이루어지기 까지 다소 시간이 소요되는 문제를 가지고 있다. 또한 사용자의 성향이 지나치게 편중되는 경우, 사용자의 취향변화를 반영하여 새로운 상품을 추천하는 것은 어렵다. 실제로 사용자들은 웹 사이트의 방문 목적에 따라 개인화된 상품추천을 원하기도 하고, 많은 사용자들에게 인기 있는 상품을 원하기도 한다. 본 논문에서는 사용자의 행동분석을 기반으로, 협업 필터링을 기반으로 하는 개인화된 추천과 다수의 사용자들에게 공통적으로 인기 있는 상품의 추천 비율을 동적으로 조합하여 최종 추천 상품들을 선별하는 새로운 적응형 추천 시스템을 제안한다. 본 논문에서는 MovieLens의 데이터 셋을 이용하여 기존 추천기법들과 추천결과에 대한 정확도를 비교 실험하였으며, 보다 높은 정확도를 보이는 실험결과를 통해 제안시스템의 유효성을 확인하였다.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.29-36
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• 2017

In this paper, we propose a method for recognizing eating behavior with almost no motion acceleration. First, by using the acceleration of gravity acting on the wrist direction, we calculate the angle between the gravity and the wrist direction. After that, detect wrist reciprocating motion when peak and vally exist in specific angle band. And then, when accumulate the number of wrist reciprocating motion occurrences are up to 10, then regard as the meal time 5 minutes before the detection time. Also, estimate the meal time only if its duration is more than 7 minutes. Using the data of 2128 minutes, which was collected from four graduate student, the result of the meal time estimation shows 95.63% accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.441-446
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• 2010

Each robot decides and behaviors themselves surrounding circumstances in the swarm robot system. Robots have to conduct tasks allowed through cooperation with other robots. Therefore each robot should have the ability to learn and evolve in order to adapt to a changing environment. In this paper, we proposed learning based on Q-learning algorithm and evolutionary using Harmony Search algorithm and are trying to improve the accuracy using Harmony Search Algorithm, not the Genetic Algorithm. We verify that swarm robot has improved the ability to perform the task.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.334-337
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• 2019

In this study, we built the data collection system to learn user's habit data by deep learning and to create an indoor environment according to the situation. The system consists of a data collection server and several sensor nodes, which creates the environment according to the data collected. We used Google Inception v3 network to analyze the photographs and hand-designed second DNN (Deep Neural Network) to infer behaviors. As a result of the DNN learning, we gained 98.4% of Testing Accuracy. Through this results, we were be able to prove that DNN is capable of extrapolating the situation.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.161-170
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• 2017

Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.1-10
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• 2018

Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. Recently, some studies have applied information technology to a livestock management system to minimize the damage resulting from such anomalies. Nonetheless, detecting each pig in a crowed pigsty is still challenging problem. In this paper, we propose a new Kinect camera and deep learning-based monitoring system for the detection of the individual pigs. The proposed system is characterized as follows. 1) The background subtraction method and depth-threshold are used to detect only standing-pigs in the Kinect-depth image. 2) The standing-pigs are detected by using YOLO (You Only Look Once) which is the fastest and most accurate model in deep learning algorithms. Our experimental results show that this method is effective for detecting individual pigs in real time in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (average 99.40% detection accuracies).

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.774-779
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• 2012

Brain-computer interface based on motor imagery is a system that transforms a subject's intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject's limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).