• Journal of The Korean Association For Science Education
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• v.35 no.6
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• pp.997-1006
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• 2015

Perception of the experiment is one of the most important factors of students' understanding of scientific inquiry and the nature of science. This study examined the perception of middle and high school students of the meaning of 'experiment' in the biological sciences. Semantic network analysis (SNA) was especially used to visualize students' perception structure in this study. One hundred and ninety middle school students and 200 high school students participated in this study. Students responded to two questions on the meaning of 'experiment' in science and biology. This study constructed four semantic networks based on the collected response. As a result, middle school students about the 'experiment' in science are 'we', 'direct', 'principle' of such words was aware of the experiments from the center to the active side. The high school students' 'theory', 'true', 'information' were recognized as an experiment that explores the process of creating a knowledge center including the word. In addition, middle school students relative to 'experiment' of the creature around the 'dissection', 'body', high school students were recognized as 'life', 'observation' observation activities dealing with the living organisms and recognized as a core. The results of this study will be used as important evidence in the future to map out an experiment in biological science curriculum.

• Korean Journal of Cognitive Science
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• v.19 no.2
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• pp.143-161
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• 2008

The goal of this paper is to extract the hyponymy relation between words in the corpus. Adopting the basic algorithm of Hearst (1992), I propose a method of pattern-based extraction of semantic relations from the corpus. To this end, I set up a list of hypernym-hyponym pairs from Sejong Electronic Dictionary. This list is supplemented with the superordinate-subordinate terms of CoroNet. Then, I extracted all the sentences from the corpus that include hypemym-hyponym pairs of the list. From these extracted sentences, I collected all the sentences that contain meaningful constructions that occur systematically in the corpus. As a result, we could obtain 21 generalized patterns. Using the PERL program, we collected sentences of each of the 21 patterns. 57% of the sentences are turned out to have hyponymy relation. The proposed method in this paper is simpler and more advanced than that in Cederberg and Widdows (2003), in that using a word net or an electronic dictionary is generally considered to be efficient for information retrieval. The patterns extracted by this method are helpful when we look fer appropriate documents during information retrieval, and they are used to expand the concept networks like ontologies or thesauruses. However, the word order of Korean is relatively free and it is difficult to capture various expressions of a fired pattern. In the future, we should investigate more semantic relations than hyponymy, so that we can extract various patterns from the corpus.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.161-167
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• 2018

The purpose of this study is to investigate how the advertising campaign is spreading in social networks, and how the advertising model plays an important role in advertisement diffusion. In order to grasp the diffusion patterns of advertising, a text mining and social network analysis were conducted using the beer brand 'Kloud' as a collection keyword. After analyzing the social data for two months since the on-air of 'Good Body' advertisement, which was the first ad that "Sulhyun" appeared in. After the launch of the ad, Kloud has been mainly associated with keywords such as 'yavis & trendy style', 'beer brand', 'beer matching food', 'luxury beer drinking place', 'leisure trend', and 'SNS activity', etc. In addition, "Sul Hyun" also showed that an advertising model contributes to the spread of advertisement on social media in terms of image transition as well as brand's name and unique selling point.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.240-247
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• 2020

There have been many efforts to understand the trends of IT environments that have been rapidly changed. In a view point of management, it needs to prepare the social systems in advance by using Big-data these days. This research is for the implementation of Issue Analysis System for the Big-data based on Artificial Intelligence. This paper aims to confirm the possibility of new technology for Big-data processing through the proposed Issue Analysis System using. We propose a technique for semantic reasoning and pattern analysis based on the AI and show the proposed method is feasible to handle the Big-data. We want to verify that the proposed method can be useful in dealing with Big-data by applying latest security issues into the system. The experiments show the potentials for the proposed method to use it as a base technology for dealing with Big-data for various purposes.

• Journal of KIISE:Databases
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• v.29 no.6
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• pp.492-501
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• 2002

An asynchronous trigger processor (ATP) is a oftware system that processes triggers after update transactions to databases are complete. In an ATP, discrimination networks are used to check the trigger conditions efficiently. Discrimination networks store their internal states in memory nodes. TriggerMan is an ATP and uses Gator network as the .discrimination network. The changes in databases are delivered to TriggerMan in the form of tokens. Processing tokens against a Gator network updates the memory nodes of the network and checks the condition of a trigger for which the network is built. Parallel token processing is one of the methods that can improve the system performance. However, uncontrolled parallel processing breaks trigger processing semantic consistency. In this paper, we propose four trigger processing consistency levels that allow parallel token processing with minimal anomalies. For each consistency level, a parallel token processing technique is developed. The techniques are proven to be valid and are also applicable to materialized view maintenance.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.419-428
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• 2021

Shadow removal plays a key role for the pre-processing of image processing techniques such as object tracking and detection. With the advances of image recognition based on deep convolution neural networks, researches for shadow removal have been actively conducted. In this paper, we propose a novel method for shadow removal, which utilizes self attention distillation to extract semantic features. The proposed method gradually refines results of shadow detection, which are extracted from each layer of the proposed network, via top-down distillation. Specifically, the training procedure can be efficiently performed by learning the contextual information for shadow removal without shadow masks. Experimental results on various datasets show the effectiveness of the proposed method for shadow removal under real world environments.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.12
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• pp.83-92
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• 2019

The purpose of this study is to analyze the continuous forms of time and space that work as architectural design principles of the Memorial to the Murdered Jews of Europe (Jewish Memorial). Continuity is divided into two, physical and non-physical attributes. The former extends from small installations to furnitures, finishes, spatial composition, and even spatial networks that complete architecture, and the latter is tied to time, from traditional to historical, developmental, commemorative or memorial elements. They are inherent in architecture as continuous forms. The Jewish Memorial is analyzed by the analysing framework of these two items. The analysis of the two layers can be summarized as follows; physical continuity is found in the space networks of the Memorial's inside and outside, the undulating spaces, the finishes, the small furnitures and installations, the entrances of staircases, the expanded underground of the ground order, and non-physical continuity manifests in the topological variation of spatiality, the morphological development of memorial architecture, the connection of semantic sense of space, and the superposition of historicity and modernity. These forms of continuity do not aestheticize the German enormity history, but make the meaning of the Memorial into non-superficial, in-depth architecture as a monument. Thus, the results of this study show that physical and non-physical continuity should be considered as the important design principles of architecture that makes the Memorial architecture possible.

• The Journal of Korean Institute of Information Technology
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• v.17 no.7
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• pp.31-45
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• 2019

The core technologies for intelligent services today are deep learning, that is neural networks, and parallel and distributed processing technologies such as GPU parallel computing and big data. However, for intelligent services and knowledge sharing services through globally shared ontologies in the future, there is a technology that is better than the neural networks for representing and reasoning knowledge. It is a knowledge representation of IF-THEN in RIF or SWRL, which is the standard rule language of the Semantic Web, and can be inferred efficiently using the rete algorithm. However, when the number of rules processed by the rete algorithm running on a single computer is 100,000, its performance becomes very poor with several tens of minutes, and there is an obvious limitation. Therefore, in this paper, we analyze the past and current studies on parallel and distributed processing of rete algorithm, and examine what aspects should be considered to implement an efficient rete algorithm.