• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.6
• /
• pp.821-827
• /
• 2009

Human emotion can be reflected by their facial expressions. So, it is one of good ways to understand people's emotions by recognizing their facial expressions. General recognition system of facial expressions had selected interesting points, and then only extracted features without analyzing physical meanings. They takes a long time to find interesting points, and it is hard to estimate accurate positions of these feature points. And in order to implement a recognition system of facial expressions on real-time embedded system, it is needed to simplify the algorithm and reduce the using resources. In this paper, we propose a real-time recognition algorithm of facial expressions that project the grid points on an expression space based on Gabor wavelet feature. Facial expression is simply described by feature vectors on the expression space, and is classified by an neural network with its resources dramatically reduced. The proposed system deals 5 expressions: anger, happiness, neutral, sadness, and surprise. In experiment, average execution time is 10.251 ms and recognition rate is measured as 87~93%.

• Journal of Digital Contents Society
• /
• v.19 no.2
• /
• pp.415-423
• /
• 2018

In recent years, Computer-based learning, such as machine learning and deep learning in the computer field, is attracting attention. They start learning from the lowest level and propagate the result to the highest level to calculate the final result. Research literature has shown that systematic learning and growth can yield good results. However, systematic models based on systematic models are hard to find, compared to various and extensive research attempts. To this end, this paper proposes the first TNT(Transitive Nested Triangle)model, which is a growth and fusion model that can be used in various aspects. This model can be said to be a recursive model in which each function formed through geometric forms an organic hierarchical relationship, and the result is used again as they grow and converge to the top. That is, it is an analytical method called 'Horizontal Sibling Merges and Upward Convergence'. This model is applicable to various aspects. In this study, we focus on explaining the TNT model.

• The Journal of Korean Association of Computer Education
• /
• v.23 no.1
• /
• pp.37-44
• /
• 2020

As the importance of artificial intelligence(AI) education is emphasized recently, policies and researches are being promoted to develop the AI curriculum or courses for K-12 students in worldwide. In this study, researcher analysed a synthesis of contents and standards on AI education curriculum to present implications for AI education in the elementary and secondary schools. As a result, Korea and the United States are proposing national curriculum standards to provide the basis for AI curriculum establishment in school sites and to provide guidelines for various related policies such as teacher training programs. The EU's AI education is characterized by its curriculum and online courses to ensure that all citizens of the EU have AI literacy, rather than designating students or subjects at specific school levels. In terms of educational contents and levels, Korea, United States, and EU's curriculum or standards includes basics and applications related to machine learning and neural network based on the fundamental concepts and principles of artificial intelligence.

• Journal of Digital Contents Society
• /
• v.10 no.2
• /
• pp.327-332
• /
• 2009

In this paper, we propose the diagnosis system that can predict pet's state of health for pet lovers lacking a technical knowledge of dog-diseases. The proposed system deduces diseases of dogs from input symptoms by our database constructed with 105 kinds of diseases and symptoms. First, a disease is clustered by ART2, the self-learning method in neural network and secondly, the result values, outputs and the weight values clustered by the algorithm are stored to database. Finally, our system diagnoses the state of health by means of comparing the learned information of diseases with the input vectors of each symptom and the related results of questions on diseases. The correct information of diseases and symptom diagnosing is important to predict the state of health of dogs. Therefore, in this paper, the proposed system can manage symptoms and diseases efficiently by database and ART2. We ask veterinary specialist with the efficiency of our system. As a result, we could confirm the possibility as the auxiliary diagnosis system for dog diseases.

• Nuclear Engineering and Technology
• /
• v.52 no.10
• /
• pp.2250-2261
• /
• 2020

Accurate localization of radioactive materials is crucial in homeland security and radiological emergencies. Coded-aperture gamma camera is an interesting solution for such applications and can be developed into portable real-time imaging devices. However, traditional reconstruction methods cannot effectively deal with signal-independent noise, thereby hindering low-noise real-time imaging. In this study, a novel reconstruction method with excellent noise-suppression capability based on a multi-layer perceptron (MLP) is proposed. A coded-aperture gamma camera based on pixel detector and coded-aperture mask was constructed, and the process of radioactive source imaging was simulated. Results showed that the MLP method performs better in noise suppression than the traditional correlation analysis method. When the Co-57 source with an activity of 1 MBq was at 289 different positions within the field of view which correspond to 289 different pixels in the reconstructed image, the average contrast-to-noise ratio (CNR) obtained by the MLP method was 21.82, whereas that obtained by the correlation analysis method was 5.85. The variance in CNR of the MLP method is larger than that of correlation analysis, which means the MLP method has some instability in certain conditions.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.4
• /
• pp.725-732
• /
• 2020

This paper uses variables following as : to follow me well(0-9), it takes a lot of time to make a decision (0-9), lethargy(0-9) during physical activity in the exercise learning program of the children in the marginalized class. This paper classifies 'gender', 'physical education classroom', and 'upper, middle and lower' of age, and observe changes in ego-resiliency and self-control through sports rehabilitation therapy to find out changes in mental health. To achieve this, the data acquired was merged and the characteristics of large and small numbers were removed using the Label encoder and One-hot encoding. Then, to evaluate the performance by applying each algorithm of MLP, SVM, Dicesion tree, RNN, and LSTM, the train and test data were divided by 75% and 25%, and then the algorithm was learned with train data and the accuracy of the algorithm was measured with the Test data. As a result of the measurement, LSTM was the most effective in sex, MLP and LSTM in physical education classroom, and SVM was the most effective in age.

• Journal of Broadcast Engineering
• /
• v.24 no.6
• /
• pp.1134-1142
• /
• 2019

Detection of double compression is one of the most efficient ways of remarking the validity of videos. Many methods have been introduced to detect HEVC double compression with different coding parameters. However, HEVC double compression detection under the same coding environments is still a challenging task in video forensic. In this paper, we introduce a novel method based on the frame partitioning information in intra prediction mode for detecting double compression in with the same coding environments. We propose to extract statistical feature and Deep Convolution Neural Network (DCNN) feature from the difference of partitioning picture including Coding Unit (CU) and Transform Unit (TU) information. Finally, a softmax layer is integrated to perform the classification of the videos into single and double compression by combing the statistical and the DCNN features. Experimental results show the effectiveness of the statistical and the DCNN features with an average accuracy of 87.5% for WVGA and 84.1% for HD dataset.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.10
• /
• pp.2180-2188
• /
• 2009

This paper is study on real-time hand gesture recognition system based on vision for intelligent robot control. We are proposed a recognition system using PCA and BP algorithm. Recognition of hand gestures consists of two steps which are preprocessing step using PCA algorithm and classification step using BP algorithm. The PCA algorithm is a technique used to reduce multidimensional data sets to lower dimensions for effective analysis. In our simulation, the PCA is applied to calculate feature projection vectors for the image of a given hand. The BP algorithm is capable of doing parallel distributed processing and expedite processing since it take parallel structure. The BP algorithm recognized in real time hand gestures by self learning of trained eigen hand gesture. The proposed PCA and BP algorithm show improvement on the recognition compared to PCA algorithm.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.22 no.2
• /
• pp.76-82
• /
• 2018

In this present study, in order to update the finite element model considering the boundary conditions, a method has been proposed. The conventional finite element model updating method, updates the finite element model by using the dynamic characteristics (natural frequency, mode shape) which can be estimated from the ambient vibration test. Therefore, prediction of the static response of an actual structure is difficult. Furthermore, accurate estimation of the physical properties is relatively hard. A novel method has been proposed to overcome the limitations of conventional method. Initially, the proposed method estimates the rotational spring constant of a finite element model using the deflection of structure and the rotational displacement of support measurements. The final updated finite element model is constructed by estimating the material properties of the structure using the finite element model with updated rotational spring constant and the dynamic characteristics of the structure. The proposed finite element model updating method is validated through numerical simulation and compared with the conventional finite element model updating method.

• Journal of KIISE:Software and Applications
• /
• v.30 no.12
• /
• pp.1124-1134
• /
• 2003

The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. Generally combining classifiers gives high performance and high confidence. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features using three benchmark datasets to precisely classify cancer, and systematically evaluate the performances of the proposed method. Experimental results show that the ensemble classifier with negatively correlated features produces the best recognition rate on the three benchmark datasets.