• Journal of Science Education
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• v.37 no.1
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• pp.170-185
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• 2013

The purpose of this study is to investigate a cognitive model of task commitment on biology classification inquiry. To achieve this goal, first, this study analyzed several literatures on task commitment in biology inquiry, and invented the tentative model of the task commitment. To investigate a tentative model invented, 2 main tasks were developed. These tasks were administered to 8 high-school students, first grade. Raw protocols were collected by thinking aloud method and a retrospective interview method. Collected protocols were converted to segmented protocols and coded by analyzing frame based invented model. The codes were analyzed. As a result, some problems were discovered, tentative model were revised. New analyzing frame based on Improved model were composed, and raw protocols were re-analyzed. Finally, a cognitive model of task commitment on biology classification inquiry was investigated. The investigated cognitive model of task commitment on biology classification inquiry was constructed 3 steps, 'Task commitment Induction', 'Task commitment Reinforcement', 'Task commitment Maintenance'. And each steps were consisted of several sub-factor. And commitment component were changed in each steps. Through this results, base information for strategy that improvement task commitment on biology classification inquiry is provided. Furthermore, the cognitive model of task commitment on biology classification inquiry will assist on evaluation and feedback by stage on task commitment.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.295-304
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• 2017

The purpose of the study was to develop welfare technology classification frame according to welfare needs, function, and ICT technology and to explore current trends in Korean welfare technology application for older adults and people with disabilities. A systematic literature review and descriptive statistics were used for data analyses. Korean welfare technology services were categorized by a new welfare technology classification frame with five components for welfare needs and function and eight ICT technologies. Self-reliance and self-help emerged as the most frequent welfare needs and function. The use of ICT devices was frequently applied to welfare technology services. Our findings suggest that it is important to use a new welfare technology classification frame and to apply it to welfare technology in Korea. Further research is necessary to seek for future directions in Korean welfare technology.

• Journal of architectural history
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• v.32 no.1
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• pp.61-72
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• 2023

Korean architecture classifies Banja (the decorated flat of the ceiling visible from the inside) of Royal Palaces into two types: Woomul(water-well, 井) banja, which inserts rectangular wooden board into lattice frame, and paper banja, which applies paper to the flat ceiling. Such classification was established in the 19th century. Before that, Banja was classified according to what was inserted into the lattice frame, either wooden or paper board. At first, the banja that used paper board was widely installed regardless of the purpose or nobility of the building. However, since the 17th century, the use of paper board banja became mostly restricted to Ondol (Korean floor heating system) rooms which are characterized by private usage and the importance of heating, and it was considered inferior to wooden board banja in terms of rank or grace. The contemporary paper banja was mainly installed in low-rank ondol rooms until the late 19th century to early 20th century, when roll-type wallpaper was introduced from the West and the paper banja came to decorate the King's and Queen's bedrooms. The traditional paper board banja benefits heat reservation, reduces the weight of the ceiling, and allows the adjustment of the lattice frame size. Furthermore, it can feature unique artistry if covered with blue, white, or red Neung-hwa-ji (traditional flower pattered paper).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.1-11
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• 2006

In this paper, we propose the novel video retrieval algorithm using non-parametric based motion classification in the shot-based video indexing structure. The proposed system firstly gets the key frame and motion information from each shot segmented by scene change detection method, and then extracts visual features and non-parametric based motion information from them. Finally, we construct real-time retrieval system supporting similarity comparison of these spatio-temporal features. After the normalized motion vector fields is created from MPEG compressed stream, the extraction of non-parametric based motion feature is effectively achieved by discretizing each normalized motion vectors into various angle bins, and considering a mean, a variance, and a direction of these bins. We use the edge-based spatial descriptor to extract the visual feature in key frames. Experimental evidence shows that our algorithm outperforms other video retrieval methods for image indexing and retrieval. To index the feature vectors, we use R*-tree structures.

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.858-865
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• 2011

In order to allow the user to efficiently browse, select, and retrieve a desired video part without having to deal directly with GBytes of compressed data, classification of shot motion characteristic has to be carried out as a preparation for such user interaction. The organization of video information for video database requires segmentation of a video into its constituent shots and their subsequent characterization in terms of content and camera movement in shot. In order to classify shot motion, it is a conventional way to use element of motion vector. However, there is a limit to estimate global camera motion because the way that uses motion vectors only represents local movement. For shot classification in terms of motion information, we propose a new scheme consisting of partial decoding of INTRA pictures and comparing the x, y displacement vector curve between the decoded I-frame and next P-frame in compressed video data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.167-177
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• 2018

As the number of attacks on vehicles has increased, studies on CAN-based security technologies are actively being carried out. However, since the upper layer protocol of CAN differs for each vehicle manufacturer and model, there is a great difficulty in researches such as developing anomaly detection for CAN or finding vulnerabilities of ECUs. In this paper, we propose a method to infer the detailed structure of the data field of CAN frame by analyzing CAN trace to mitigate this problem. In the existing Internet environment, many researches for reverse engineering proprietary protocols have already been carried out. However, CAN bus has a structure difficult to apply the existing protocol reverse engineering technology as it is. In this paper, we propose new field classification methods with low computation-cost based on the characteristics of data in CAN frame and existing field classification method. The proposed methods are verified through implementation that analyze CAN traces generated by simulations of CAN communication and actual vehicles. They show higher accuracy of field classification with lower computational cost compared to the existing method.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.37-44
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• 2017

While IoT (Internet of Things) industry has been spreading, it becomes very important for object to recognize user's information by itself without any control. Above all, gender (male, female) is dominant factor to analyze user's information on account of social and biological difference between male and female. However since each gender consists of diverse face feature, face-based gender classification research is still in challengeable research field. Also to apply gender classification system to IoT, size of device should be reduced and device should be operated with low power. Consequently, To port the function that can classify gender in real-world, this paper contributes two things. The first one is new gender classification algorithm based on deep learning and the second one is to implement real-time gender classification system in embedded board operated by low power. In our experiment, we measured frame per second for gender classification processing and power consumption in PC circumstance and mobile GPU circumstance. Therefore we verified that gender classification system based on deep learning works well with low power in mobile GPU circumstance comparing to in PC circumstance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2748-2760
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• 2018

This paper proposes an audio event classification method based on convolutional neural networks (CNNs). CNN has great advantages of distinguishing complex shapes of image. Proposed system uses the features of audio sound as an input image of CNN. Mel scale filter bank features are extracted from each frame, then the features are concatenated over 40 consecutive frames and as a result, the concatenated frames are regarded as an input image. The output layer of CNN generates probabilities of audio event (e.g. dogs bark, siren, forest). The event probabilities for all images in an audio segment are accumulated, then the audio event having the highest accumulated probability is determined to be the classification result. This proposed method classified thirty audio events with the accuracy of 81.5% for the UrbanSound8K, BBC Sound FX, DCASE2016, and FREESOUND dataset.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1287-1292
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• 2005

This paper presents an effective pattern classification model by designing an artificial neural network based pattern classifiers for face recognition. First, a RGB image inputted from a frame grabber is converted into a HSV image which is similar to the human beings' vision system. Then, the coarse facial region is extracted using the hue(H) and saturation(S) components except intensity(V) component which is sensitive to the environmental illumination. Next, the fine facial region extraction process is performed by matching with the edge and gray based templates. To make a light-invariant and qualified facial image, histogram equalization and intensity compensation processing using illumination plane are performed. The finally extracted and enhanced facial images are used for training the pattern classification models. The proposed H-ART2 model which has the hierarchical ART2 layers and F-LVQ model which is optimized by fuzzy membership make it possible to classify facial patterns by optimizing relations of clusters and searching clustered reference patterns effectively. Experimental results show that the proposed face recognition system is as good as the SVM model which is famous for face recognition field in recognition rate and even better in classification speed. Moreover high recognition rate could be acquired by combining the proposed neural classification models.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.2 s.36
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• pp.69-75
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• 2006

This study aims to standardize the work classification system in spatial data. It is the base frame to classify the general information systematically in spatial data construction process. Work process of the surveying firm and rules for basic surveying which is being accomplished in the NGII(National Geographic Information Institute) are investigated and analysed. Therefore, types, individual process, and results of surveying work is standardized. If the work classification system from this study is adopted as the national standard and is also advanced by construction methodology, the spatial data will be managed futuristically and systematically.