• English Language & Literature Teaching
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• v.11 no.4
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• pp.335-363
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• 2005

One of the most important features in the 7th National Curriculum of English is the introduction of the middle school multi-purpose English classes. Despite the importance of the classes, there doesn't seem to be enough studies of developing learning materials for them. The purpose of the current study is, therefore, to develop English learning materials for the multi-purpose English classes based on the Task-Based Learning framework. To do so, various tasks were collected and adapted for the classes, and different teaching techniques suitable for the tasks were designed. It is hoped that this research will help teachers prepare for teaching materials for the classes, and students recognize their interests in English and to improve their English abilities.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.219-225
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• 2022

In this paper, we propose a CAC exploring a RA based dynamic spectrum hole grouping for secondary users' QoS enhancement in multi-classes cognitive radio networks. The RA based dynamic spectrum hole grouping uses SU multi-classes overlaying spectrum structure suggested here. Multiclass SUs are divided into real and non real, and real SUs have a priority for resource utilization against non real. The amount of resource required by real SUs is supported by Wiener prediction and the dynamic spectrum hole grouping, and that required by non real SU is supported by the remained available amount without prediction. In the simulations, we compare the proposed CAC performances using the dynamic spectrum hole grouping in terms of SU connection's blocking(dropping) rate and resource utilization efficiency according to multi-classes traffic characteristics, and then we show the proposed CAC can guarantee the desired QoS of multi-classes secondary users.

• Journal of Korean Elementary Science Education
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• v.38 no.3
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• pp.406-425
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• 2019

The purpose of this study is to analyze The Effect of Teacher Participation-oriented Education Program (TPEP) centered on Multi-Faceted Analysis of Elementary Science Classes on the Class Expertise of novice teacher. First, in order to develop the TPEP, lectures and exploratory science classes were analyzed using imaging and eye-tracking techniques. In this study, the TPEP was developed in five stages: image analysis, eye analysis, teaching language analysis, gesture analysis, and class development. Participants directly analyzed the classes of experienced and novice teachers at each stage. The TPEP developed in this study is different from the existing teacher education program in that it reflected the human performance technology aspects. The participants analyzed actual elementary science classes in a multi-faceted way and developed better classes based on them. The results of this study are as follows. First, at the teacher training institutions and the school sites, pre-service teachers and novice teachers should be provided with various experiences in class analysis and multi-faceted analysis of their own classes. Second, through this study, we were able to identify the limitations of existing class observations and video analysis. Third, the TPEP should be developed to improve the novice teachers' class expertise. Finally, we hope that the results of this study are used as basic data in developing programs to improve teachers' class expertise in teacher training institutions and education policy institutions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.43-49
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• 2004

The classification time required by conventional multi-class SVMs(Support Vector Machines) greatly increases as the number of pattern classes increases. This is due to the fact that the needed set of binary class SVMs gets quite large. In this paper, we propose a method to reduce the number of classes by using nearest neighbor rule (NNR) in the principle component analysis and linear discriminant analysis (PCA+LDA) feature subspace. The proposed method reduces the number of face classes by selecting a few classes closest to the test data projected in the PCA+LDA feature subspace. Results of experiment show that our proposed method has a lower error rate than nearest neighbor classification (NNC) method. Though our error rate is comparable to the conventional multi-class SVMs, the classification process of our method is much faster.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.462-470
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• 2005

We propose a multi-rate and multi-BEP transmission scheme using adaptive overlapping pulse-position modulator (OPPM) and optical power controller in optical code division multiple access (CDMA) networks. The proposed system achieves the multi-rate and multi-BEP transmission by accommodating users with different values of OPPM parameter and transmitted power in the same network. The proposed scheme has advantages that the system is not required to change the code length and number of weight depending on the required bit rate of a user and the difference of bit rates does not have so much effect on the bit error probabilities (BEPs). Moreover, the difference of transmitted powers does not cause the change of bit rate. We analyze the BEPs of the four multimedia service classes corresponding to the com­binations of high/low-rates and low/high-BEPs and show that the proposed scheme can easily achieve distinct differentiation of the service classes with the simple system configuration.

• 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.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.381-392
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• 2003

Multi-temporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land-cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. A harmonic model that can represent seasonal variability is characterized by four components: mean level, frequency, phase and amplitude. The trigonometric components of the harmonic function inherently contain temporal information about changes in land-cover characteristics. Using the estimates which are obtained from sequential images through spectral analysis, seasonal periodicity can be incorporates into multi-temporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 ～ 2000 using a dynamic technique. Land-cover types were then classified both with the estimated harmonic components using an unsupervised classification approach based on a hierarchical clustering algorithm. The results of the classification using the harmonic components show that the new approach is potentially very effective for identifying land-cover types by the analysis of its multi-temporal behavior.

• Korean Journal of Childcare and Education
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• v.10 no.6
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• pp.5-23
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• 2014

The purpose of this study was to investigate the teachers' multi-media application ability, their perception on the use of multi-media for story-telling, and actual use in class. The survey, using a questionnaire, was conducted targeting 225 teachers in Seoul, Gyeonggi and Incheon. The results of the study are as follows: Firstly, teachers who were younger, had a higher level of education, were experienced in taking multi-media courses, and took charge of larger classes showed better ability of multi-media application. And teachers who were trained on multi media as well as kindergarten teachers indicated a more positive perception about applying multi-media for story-telling. In addition, teachers who had higher levels of education, were experienced in taking multi-media courses, and teaching larger classes at national/public institutions applied multi-media more frequently. Secondly, the teachers' ability of applying multi-media, their perception on the use of multi-media for story-telling, and their multi-media use in class indicated strong positive correlations. Thirdly, a teacher's perception on the significance of the multi-media application for story-telling, their skills for multi-media use, the size of classes, and the training experiences on multi-media affected their actual application of multi-media for story-telling.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1968-1971
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• 2003

We propose a knowledge-based algorithm for extracting an object boundary from low-quality image like the forward looking infrared image. With the multi-classes training data set, the global shape is modeled by multispace KL(MKL)[1] and curvature model. And the objective function for fitting the deformable boundary template represented by the shape model to true boundary in an input image is formulated by Bales rule. Simulation results show that our method has more accurateness in case of multi-classes training set and performs better in the sense of computation cost than point distribution model(PDM)[2]. It works well in distortion under the noise, pose variation and some kinds of occlusions.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.863-870
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• 2002

The current class scheduling has difficulties in reflecting students' preferences for the classes that they want to take and forecasting the demands of classes. Also, it is usually a repetitive and tedious work to allocate classes to limited time and cesourres Although many research studios in task allocation and meeting scheduling intend to solve similar problems, they have limitations to be directly applied to the class-scheduling problem. In this paper. a class scheduling system using multi agents-based negotiation is suggested. This system consists of student agents, professor agents and negotiation agents each agent arts in accordance with its respective human user's preference and performs the repetitive and tedious process instead of the user The suggested system utilizes negotiation cost concept to derive coalition in the agent's negotiation. The negotiation cost is derived from users' bidding prices on classes, where each biding price represents a user's preference on a selected class. The experiments were performed to verify the negotiation model in the scheduling system. The result of the experiment showed that it could produce a feasible scheduling solution minimizing the negotiation cost and reflecting the users' performance. The performance of the experiments was evaluated by a class success ratio.