• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.351-360
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• 2018

Human emotion recognition is a research topic that is receiving continuous attention in computer vision and artificial intelligence domains. This paper proposes a method for classifying human emotions through multiple neural networks based on multi-modal signals which consist of image, landmark, and audio in a wild environment. The proposed method has the following features. First, the learning performance of the image-based network is greatly improved by employing both multi-task learning and semi-supervised learning using the spatio-temporal characteristic of videos. Second, a model for converting 1-dimensional (1D) landmark information of face into two-dimensional (2D) images, is newly proposed, and a CNN-LSTM network based on the model is proposed for better emotion recognition. Third, based on an observation that audio signals are often very effective for specific emotions, we propose an audio deep learning mechanism robust to the specific emotions. Finally, so-called emotion adaptive fusion is applied to enable synergy of multiple networks. The proposed network improves emotion classification performance by appropriately integrating existing supervised learning and semi-supervised learning networks. In the fifth attempt on the given test set in the EmotiW2017 challenge, the proposed method achieved a classification accuracy of 57.12%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.672-679
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• 2018

This paper describes a methodology for shape design using an optimal design system, whereas generally a three dimensional analysis is required for such designs. An automatic finite element mesh generation technique, which is based on fuzzy knowledge processing and computational geometry techniques, is incorporated into the system, together with a commercial FE analysis code and a commercial solid modeler. Also, with the aid of multilayer neural networks, the present system allows us to automatically obtain a design window, in which a number of satisfactory design solutions exist in a multi-dimensional design parameter space. The developed optimal design system is successfully applied to evaluate the structures that are used. This study used a stress gauge to measure the maximum stress affecting the parts of the side housing bracket which are most vulnerable to cracking. Thereafter, we used a tool to interpret the maximum stress value, while maintaining the same stress as that exerted on the spot. Furthermore, a stress analysis was performed with the typical shape maintained intact, SM490 used for the material and the minimizing weight safety coefficient set to 3, while keeping the maximum stress the same as or smaller than the allowable stress. In this paper, a side housing bracket with a comparably simple structure for 36 tons was optimized, however if the method developed in this study were applied to side housing brackets of different classes (tons), their quality would be greatly improved.

• Spatial Information Research
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• v.8 no.1
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• pp.141-153
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• 2000

The purpose of this study is to develop and apply spatial landslide information system using Geographic information system (GIS) in concerned with spatial data. Landslide locations detected from interpretation of aerial photo and field survey, and topographic , soil , forest , and geological maps of the study area, Yongin were collected and constructed into spatial database using GIS. As landslide occurrence factors, slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective thickness of soil were extracted from the soil database, and type, age, diameter and density of wood were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM satellite image. In addition, landslide damageable objects such as building, road, rail and other facility were extracted from the topographic database. Landslide susceptibility was analyzed using the landslide occurrence factors by probability, logistic regression and neural network methods. The spatial landslide information system was developed to retrieve the constructed GIS database and landslide susceptibility . The system was developed using Arc View script language(Avenue), and consisted of pull-down and icon menus for easy use. Also, the constructed database can be retrieved through Internet World Wide Web (WWW) using Internet GIS technology.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.145-154
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• 2012

Recently, the cardiovascular disease has increased by causes such as westernization dietary life, smoking, and obesity. In particular, the acute myocardial infarction (AMI) occupies 50% death rate in cardiovascular disease. Following this trend, the AMI has been carried out a research for discovery of risk factors based on national data. However, there is a lack of diagnosis minor suitable for Korean. The objective of this paper is to develop a classifier for short-term relapse mortality prediction of cardiovascular disease patient based on prognosis data which is supported by KAMIR(Korea Acute Myocardial Infarction). Through this study, we came to a conclusion that ANN is the most suitable method for predicting the short-term relapse mortality of patients who have ST-segment elevation myocardial infarction. Also, data set obtained by logistic regression analysis performed highly efficient performance than existing data set. So, it is expect to contribute to prognosis estimation through proper classification of high-risk patients.

• Journal of KIISE
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• v.43 no.10
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• pp.1085-1093
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• 2016

Community-based Question Answering system is a system which provides answers for each question from the documents uploaded on web communities. In order to enhance the capacity of question analysis, former methods have developed specific rules suitable for a target region or have applied machine learning to partial processes. However, these methods incur an excessive cost for expanding fields or lead to cases in which system is overfitted for a specific field. This paper proposes a multiple machine learning method which automates the overall process by adapting appropriate machine learning in each procedure for efficient processing of community-based Question Answering system. This system can be divided into question analysis part and answer selection part. The question analysis part consists of the question focus extractor, which analyzes the focused phrases in questions and uses conditional random fields, and the question type classifier, which classifies topics of questions and uses support vector machine. In the answer selection part, the we trains weights that are used by the similarity estimation models through an artificial neural network. Also these are a number of cases in which the results of morphological analysis are not reliable for the data uploaded on web communities. Therefore, we suggest a method that minimizes the impact of morphological analysis by using character features in the stage of question analysis. The proposed system outperforms the former system by showing a Mean Average Precision criteria of 0.765 and R-Precision criteria of 0.872.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.325-332
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• 2016

The purpose of this study is to investigate the attributes influencing the prevalence of metabolic syndrome and develop the prediction model for metabolic syndrome over 40-aged people from Korea Health and Nutrition Examination Study 2012. The researcher chose the attributes for prediction model through literature review. Also, we used the decision tree, logistic regression, artificial neural network of data mining algorithm through Weka 3.6. As results, social economic status factors of input attributes were ranked higher than health-related factors. Additionally, prediction model using decision tree algorithm showed finally the highest accuracy. This study suggests that, first of all, prevention and management of metabolic syndrome will be approached by aspect of social economic status and health-related factors. Also, decision tree algorithms known from other research are useful in the field of public health due to their usefulness of interpretation.

• Geophysics and Geophysical Exploration
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• v.23 no.2
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• pp.97-114
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• 2020

Recently, many studies have been actively conducted on the application of machine learning in all branches of science and engineering. Studies applying machine learning are also rapidly increasing in all sectors of seismic exploration, including interpretation, processing, and acquisition. Among them, fault detection is a critical technology in seismic interpretation and also the most suitable area for applying machine learning. In this study, we introduced various machine learning techniques, described techniques suitable for fault detection, and discussed the reasons for their suitability. We collected papers published in renowned international journals and abstracts presented at international conferences, summarized the current status of the research by year and field, and intensively analyzed studies on fault detection using machine learning. Based on the type of input data and machine learning model, fault detection techniques were divided into seismic attribute-, image-, and raw data-based technologies; their pros and cons were also discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.15-23
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• 2017

The person Re-identification is the most challenging part of computer vision due to the significant changes in human pose and background clutter with occlusions. The picture from non-overlapping cameras enhance the difficulty to distinguish some person from the other. To reach a better performance match, most methods use feature selection and distance metrics separately to get discriminative representations and proper distance to describe the similarity between person and kind of ignoring some significant features. This situation has encouraged us to consider a novel method to deal with this problem. In this paper, we proposed an enhanced recurrent neural network with three-tier hierarchical network for person re-identification. Specifically, the proposed recurrent neural network (RNN) model contain an iterative expectation maximum (EM) algorithm and three-tier Hierarchical network to jointly learn both the discriminative features and metrics distance. The iterative EM algorithm can fully use of the feature extraction ability of convolutional neural network (CNN) which is in series before the RNN. By unsupervised learning, the EM framework can change the labels of the patches and train larger datasets. Through the three-tier hierarchical network, the convolutional neural network, recurrent network and pooling layer can jointly be a feature extractor to better train the network. The experimental result shows that comparing with other researchers' approaches in this field, this method also can get a competitive accuracy. The influence of different component of this method will be analyzed and evaluated in the future research.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.1
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• pp.75-83
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• 2008

We propose a welfare interface using multiple fecial features tracking, which can efficiently implement various mouse operations. The proposed system consist of five modules: face detection, eye detection, mouth detection, facial feature tracking, and mouse control. The facial region is first obtained using skin-color model and connected-component analysis(CCs). Thereafter the eye regions are localized using neutral network(NN)-based texture classifier that discriminates the facial region into eye class and non-eye class, and then mouth region is localized using edge detector. Once eye and mouth regions are localized they are continuously and correctly tracking by mean-shift algorithm and template matching, respectively. Based on the tracking results, mouse operations such as movement or click are implemented. To assess the validity of the proposed system, it was applied to the interface system for web browser and was tested on a group of 25 users. The results show that our system have the accuracy of 99% and process more than 21 frame/sec on PC for the $320{\times}240$ size input image, as such it can supply a user-friendly and convenient access to a computer in real-time operation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.134-139
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• 2021

Currently, the autonomous vehicle market is commercializing a level 3 autonomous vehicle, but it still requires the attention of the driver. After the level 3 autonomous driving, the most notable aspect of level 4 autonomous vehicles is vehicle stability. This is because, unlike Level 3, autonomous vehicles after level 4 must perform autonomous driving, including the driver's carelessness. Therefore, in this paper, we propose the Emergency-notification and Driver-response Confirmation System(EDCS) for an autonomousvehicle safety that notifies the driver of an emergency situation and recognizes the driver's reaction in a situation where the driver is careless. The EDCS uses the emergency situation delivery module to make the emergency situation to text and transmits it to the driver by voice, and the driver response confirmation module recognizes the driver's reaction to the emergency situation and gives the driver permission Decide whether to pass. As a result of the experiment, the HMM of the emergency delivery module learned speech at 25% faster than RNN and 42.86% faster than LSTM. The Tacotron2 of the driver's response confirmation module converted text to speech about 20ms faster than deep voice and 50ms faster than deep mind. Therefore, the emergency notification and driver response confirmation system can efficiently learn the neural network model and check the driver's response in real time.