• Journal of Biomedical Engineering Research
• /
• v.39 no.2
• /
• pp.63-68
• /
• 2018

In modern society, speech recognition technology is emerging as an important technology for identification in electronic commerce, forensics, law enforcement, and other systems. In this study, we aim to develop an age classification algorithm for extracting only MFCC(Mel Frequency Cepstral Coefficient) expressing the characteristics of speech in Korean and applying it to deep learning technology. The algorithm for extracting the 13th order MFCC from Korean data and constructing a data set, and using the artificial intelligence algorithm, deep artificial neural network, to classify males in their 20s, 30s, and 50s, and females in their 20s, 40s, and 50s. finally, our model confirmed the classification accuracy of 78.6% and 71.9% for males and females, respectively.

• International journal of advanced smart convergence
• /
• v.8 no.2
• /
• pp.8-17
• /
• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.442-444
• /
• 2019

Fabric defect classification is an important issue in fabric quality control. However, automated classification is difficult because it is hard to identify various types of defects in images. classification of fabric defects mostly rely on human ability. In this paper, to solve this problem we apply Convolutional Neural Networks (CNN) for fabric defect classification. To make training CNN easier, we propose a method that is manually attentive defect regions in images. we compare the proposed method with the original image and confirm that the proposed method is effective for learning.

• Nuclear Engineering and Technology
• /
• v.53 no.2
• /
• pp.522-531
• /
• 2021

We describe a new deep learning model - Convolutional eXtreme Gradient Boosting (ConvXGB) for classification problems based on convolutional neural nets and Chen et al.'s XGBoost. As well as image data, ConvXGB also supports the general classification problems, with a data preprocessing module. ConvXGB consists of several stacked convolutional layers to learn the features of the input and is able to learn features automatically, followed by XGBoost in the last layer for predicting the class labels. The ConvXGB model is simplified by reducing the number of parameters under appropriate conditions, since it is not necessary re-adjust the weight values in a back propagation cycle. Experiments on several data sets from UCL Repository, including images and general data sets, showed that our model handled the classification problems, for all the tested data sets, slightly better than CNN and XGBoost alone and was sometimes significantly better.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.655-657
• /
• 2021

It is challenging to find breast ultrasound image training dataset to develop an accurate machine learning model due to various regulations, personal information issues, and expensiveness of acquiring the images. However, studies targeting transfer learning for ultrasound breast cancer images classification have not been able to achieve high performance compared to radiologists. Here, we propose an improved transfer learning model for ultrasound breast cancer classification using publicly available dataset. We argue that with a proper combination of ImageNet pre-trained model and optimizer, a better performing model for ultrasound breast cancer image classification can be achieved. The proposed model provided a preliminary test accuracy of 99.5%. With more experiments involving various hyperparameters, the model is expected to achieve higher performance when subjected to new instances.

• Proceedings of the KIEE Conference
• /
• 2002.07b
• /
• pp.1283-1285
• /
• 2002

This study is developed vehicle information detector using loop and piezo sensors. This study would analyze the over all problems concerning our road conditions, environmental matters and unique features of our traffic matters; moreover, with these it would develope the hardware, software, car classification algorithm applied by artificial intelligence and traffic monitoring program which can be easily fixed. This can be divided into traffic detecting algorithm and car classification algorithm. Especially, we have developed the car classification algorithm used by C-means Fuzzy Clustering method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.7
• /
• pp.644-649
• /
• 2012

This paper proposes the gait phase classifier using backpropagation neural networks method which uses the angle of lower body's joints and ground reaction force as input signals. The classification of a gait phase is useful to understand the gait characteristics of pathologic gait and to control the gait rehabilitation systems. The classifier categorizes a gait cycle as 7 phases which are commonly used to classify the sub-phases of the gait in the literature. We verify the efficiency of the proposed method through experiments.

• Journal of Powder Materials
• /
• v.15 no.4
• /
• pp.320-330
• /
• 2008

On the base of experience in development of Magnetic Powder Composites, and particularly Soft Magnetic Composites, authors are trying to systematize classification and indicate possible development prospective of Magnetic Nanocomposites (MN) technology and their applications in electrical converters. Clear classification and systematization, at an early stage of any materials and technology development, are essential and lead for better understanding and communication between researchers and industry involved. This concern MN as well and it seems to be the right time to make it at present stage of their development. Presented proposal of classification distinguishes various types of MN by their magnetic properties and area of possible applications. It is not a close set of types, and can be extended due to increase of knowledge concern these nanocomposites.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2000.04a
• /
• pp.175-184
• /
• 2000

Recently Venture business in the area of information and communication industry is booming. Though Technology classification chart helps the potential entrepreneur through Survey paper and Internet Web Page, its service does not meet the customer demand. Hence Technology Classification system, which is proposed in this paper, will solve this problem by using virtual network among venture, technology experts and potential entrepreneurs. This system supports potential entrepreneurs' decision making for choice of venture business items by using dual client technology, and provides better services than existing systems by linking expert client and customer client, .

• The Journal of Korea Robotics Society
• /
• v.2 no.2
• /
• pp.191-195
• /
• 2007

Abstract MOCIE(Ministry of Commerce, Industry and Energy) handles 6 Projects for Intelligent Robot, whose budget is around 40 Million dollars per year. In this paper we have tried to analyze the state of robot technology of the projects. Each sub-projects has been divided according to the technological classification. Two major projects of Next Generation Growth Engine and 21C Frontier show different state each other. The former is focused on the product while the latter on the technology. Output of 21C Frontier should be linked to the Next Generation Growth Engine, otherwise, it will fail to advance. The project management handles only the quantitative performance such as business results, number of prototype, and number of patents and papers. Technological Capability is essential and it should be managed. This paper proposes efficient classification of robot technology and technology index.