• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.940-943
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• 2022

This study aims to analyze the Korean dataset by performing Korean sentence Emotion Analysis in the radio listeners' text messages collected personally. Currently, in Korea, research on the Emotion Analysis of Korean sentences is variously continuing. However, it is difficult to expect high accuracy of Emotion Analysis due to the linguistic characteristics of Korean. In addition, a lot of research has been done on Binary Sentiment Analysis that allows positive/negative classification only, but Multi-class Emotion Analysis that is classified into three or more emotions requires more research. In this regard, it is necessary to consider and analyze the Korean dataset to increase the accuracy of Multi-class Emotion Analysis for Korean. In this paper, we analyzed why Korean Emotion Analysis is difficult in the process of conducting Emotion Analysis through surveys and experiments, proposed a method for creating a dataset that can improve accuracy and can be used as a basis for Emotion Analysis of Korean sentences.

• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.63-72
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• 2017

In this paper, we propose a security architecture for HDLC-based T4 class common data link. The common data links are composed of point-to-point, multi-to-point, and point-to-multi mode. For multi-to-point mode, one node has a bundle of point-to-point links with different end-point on the other side of the links. Thus multi-to-point mode can be considered as a bundle of point-to-point mode. Point-to-multi mode is broadcasting link. For point-to-point mode we adopted robust security network scheme to establish a secure data link, and for multi-to-point mode we use broadcast encryption scheme based on ID-based cryptography to distribute encryption key for broadcasting message encryption. We also included MACsec technology for point-to-point data link security. Computational and communicational complexity analysis on the broadcast encryption have been done.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.297-304
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• 2011

This study was to analyze the learning effectiveness of multi-media based class by comparing with traditional classroom method. The "Distribution Working Subject" course that is one of the required courses of Vocational high school was selected and its contents were digitalized on MS Powerpoint for multi-media based class. The thirty students were sampled for each experimental and control groups. The homogeneity and learning achievement of sample groups were tested for experiment. Same teacher took the classes of two groups and delivered same contents of course. Only difference between two groups was the delivery method, one is traditional classroom teaching method and the other was the multi-media based class. The learning achievements and satisfaction of sample were post-tested in order to analyze the learning effectiveness by comparing two teaching methods. The results showed that there was a significant difference between experimental and control group in learning achievement after ANCOVA controlled pre-test as covariance(F=5.08, p<.05). It means that the learning achievement of multi-media based class was higher than that of traditional classroom group. The results also showed that a significant difference in students' satisfaction between two groups (t=5.57, p<.001). This study concluded that using multi-media in class could produce more learning achievements and satisfaction of students than traditional classroom method.

• Current Optics and Photonics
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• v.1 no.3
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• pp.207-213
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• 2017

The Adaptive Front-lighting System (AFS) is a part of the active safety system, providing optimized vision to the driver during night time and other poor-sight conditions of the road by automatic adaptation of lighting to environmental and traffic conditions. Basically, an AFS provides four different modes of the passing beam as designated in an United Nations Economic Commission for Europe regulation (ECE324-R123): neutral state or country light (Class C), urban light (Class V), highway light (Class E), and adverse weather light (Class W). In this paper, we first present an optics design for an AFS system capable of producing the Class C/V/E/W patterns requiring only on/off modulation of multi-array LEDs with no need for any additional mechanical components. The AFS optics consists of two separated modules, cutoff and spread; the cutoff module lights a narrow central area with high luminous intensity, satisfying the cutoff regulation, and the spread module forms a wide spread beam of low luminous intensity. Each module consists of two major parts; the first converts a discretely positioned LED array into a full-filled area emitting light source plane, and the second projects the light source plane to a 25 m away target plane. With the combination of these two optics modules, the four beam patterns are formed by simple on/off modulation of multi-array LEDs. Then we report the development of a prototype that was demonstrated to provide the four beam patterns.

• ETRI Journal
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• v.29 no.5
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• pp.700-702
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• 2007

We present a relevance feedback approach based on multi-class support vector machine (SVM) learning and cluster-merging which can significantly improve the retrieval performance in region-based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low-level features and high-level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re-clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two-class SVM and multi-class relevance feedback methods.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.6
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• pp.323-331
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• 2013

An extreme learning machine (ELM) is an efficient learning algorithm that is based on the generalized single, hidden-layer feed-forward networks (SLFNs), which perform well in classification applications. Many studies have demonstrated its superiority over the existing classical algorithms: support vector machine (SVM) and BP neural network. This paper presents a novel face recognition approach based on a multi-class project extreme learning machine (MPELM) classifier and 2D Gabor transform. First, all face image features were extracted using 2D Gabor filters, and the MPELM classifier was used to determine the final face classification. Two well-known face databases (CMU-PIE and ORL) were used to evaluate the performance. The experimental results showed that the MPELM-based method outperformed the ELM-based method as well as other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.262-267
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• 2013

The recognition of human emotional state is one of the most important components for efficient human-human and human- computer interaction. In this paper, four emotions such as fear, disgust, joy, and neutral was a main problem of classifying emotion recognition and an approach of visual-stimuli for eliciting emotion based on physiological signals of skin conductance (SC), skin temperature (SKT), and blood volume pulse (BVP) was used to design the experiment. In order to reach the goal of solving this problem, half-against-half (HAH) multi-class support vector machine (SVM) with Gaussian radial basis function (RBF) kernel was proposed showing the effective techniques to improve the accuracy rate of emotion classification. The experimental results proved that the proposed was an efficient method for solving the emotion recognition problems with the accuracy rate of 90% of neutral, 86.67% of joy, 85% of disgust, and 80% of fear.

• Journal of the Korean School Mathematics Society
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• v.9 no.3
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• pp.363-384
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• 2006

In general, teachers have opened a specially supplementary course for the underachivers in mathematics. But because of a lot of problems, the class has not been activated. So in this paper, for the purpose of maximizing the effect of the class, we introduce mathematical games and puzzles in the class for causing the students' interest in mathematics and adopt multi-step test, which is a test with multi level problems in a problem, for both selecting the underachivers in mathematics and supplementing learning deficiency. As a result of the process, the atmosphere of learning is positive and learning activities are voluntary and the altitude to the mathematics is improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.383-386
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• 2007

Under the circumstance of Ubiquitous Society, service should be provided according to the users' location and environment. When specific service is demanded by user, the technology, which can help to detect whether there are devices performing the requested service in the ubiquitous network, is mandatory. This technology also should guarantee that users have the appropriate service through the most suitable device which is selected by this technology. The priority is an essential factor when multi service are asked by multi users simultaneouslyin the ubiquitous network, The paper focuses on realization of Smart Classroom which provides multi users, such as the professor and the students, with active services based on the Zigbee network and the Mobile platform.