• Digital Contents
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• no.2 s.69
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• pp.70-73
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• 1999

KIMS NET은 월간지에 수록된 기사를 제공하는 온라인 뉴스판 성격과 각종행사 진행시 필요한 안내요원을 채용할 수 있도록 도우미 관련 정보를 다양하게 제공하는 인력정보 시장 역할을 중심으로 서비스가 구성돼 있다. 필요한 관련 정보를 살펴 볼 수 있는 KIMS NET을 살펴봤다.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.87-94
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• 2006

For realizing an ubiquitous home network(uHome-net), various sensors should be able to be connected to an integrated wire/wireless sensor network. This paper describes an application case of applying LonWorks technology being widely used in control network to wire/wireless sensor network in uHome-net and the design and application of LonRF device that consists of a neuron chip including LonTalk protocol, a 433.92MHz RF transceiver, a sensor, and application programs. As an application example of the LonRF device, the LonRF smart badge that can measure the 3D location of objects in indoor environment and interwork with the uHome-net was developed. LonRF device based home network services were realized on the uHome-net testbed such as indoor positioning service, remote surveillance service and remote metering service were realized. This research shows that LonWorks technology based sensor network could be applicable to the control network in an ubiquitous home network and the LonRF device can be used as a wireless node in various sensor networks.

• Journal of the Korean GEO-environmental Society
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• v.21 no.12
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• pp.11-16
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• 2020

It is necessary to assess the proper performance of the existing rockfall prevention net in order to minimize the damage to human lives and property in the event of rockfall. However, there is no standard for performance evaluation of rockfall prevention net in Korea, and only the design of rockfall prevention net exists by calculating energy that can be absorbed energy. Therefore, laboratory pullout test was conducted for the performance evaluation of the rockfall prevention net, cuts and load-displacement characteristics of the PVC coating net used in the laboratory pullout test are determined to identify the performance of the rockfall prevention net.

• The Journal of Korean Association of Computer Education
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• v.18 no.3
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• pp.1-14
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• 2015

The aim of this paper is to explore and understand the gifted student's characteristics such as NetLogo programming patterns, attitudes, his/her interest in problems solving. Based on transcripts and coding video frames, we explored the meaningful scenes to come up with thinking patterns, NetLogo programming patterns, attitudes, behaviors on tasks such as drawing regular starlike shapes. This case study contrasts with two other students revealing their unique characteristics both in computational thinking patterns and coding activities. The participant reveals his own ways of finding a clue and elaborating it further for coming up with concise NetLogo coding. This paper provides cross-case discussion and future research direction on how to improve gifted education in terms of problem solving in creative ways.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.53-62
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• 2018

It is necessary to analyze the character that are a key element of the story in order to analyze the story. Current character analysis methods such as Character-net and RoleNet are not sufficient to classify the roles of supporting characters by only analyzing the results of the final accumulated stories. It is necessary to study the time series analysis method according to the story progress in order to analyze the role of supporting characters rather than the accumulated story analysis method. In this paper, we propose a method to classify helpers as a mentor and a best friend through 3-D visualization of Character-net and evaluate the accuracy of the method. WebGL is used to configure the interface for 3D visualization so that anyone can see the results on the web browser. It is also proposed that rules to distinguish mentors and best friends and evaluated their performance. The results of the evaluation of 10 characters selected for 7 films confirms that they are 90% accurate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1657-1665
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• 2016

Various fields are being researched through Deep Learning using CNN(Convolutional Neural Network) and these researches show excellent performance in the image recognition. In this paper, we provide streamlined GoogLeNet of CNN architecture that is capable of learning a large-scale Korean character database. The experimental data used in this paper is PHD08 that is the large-scale of Korean character database. PHD08 has 2,187 samples for each character and there are 2,350 Korean characters that make total 5,139,450 sample data. As a training result, streamlined GoogLeNet showed over 99% of test accuracy at PHD08. Also, we made additional Korean character data that have fonts that are not in the PHD08 in order to ensure objectivity and we compared the performance of classification between streamlined GoogLeNet and other OCR programs. While other OCR programs showed a classification success rate of 66.95% to 83.16%, streamlined GoogLeNet showed 89.14% of the classification success rate that is higher than other OCR program's rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.453-460
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• 2013

This study proposes a framework that enables efficient integration and usage of enterprise data using semantic based concept net. Integration of enterprise information that has been increasing geometrically in cloud environment. The concept net is very similar in approaching way to existing ontology. However, it builds correlation between object and concept to help user's information integration retrieval more efficiently. In this study, concept nets are divided into 3 kinds and are applied to the proposed framework independently. The concept net in this study is built in ontology format based on master information concept net, keyword concept net and business process concept net. This concept net enables retrieval and usage of data based on correlation among data according to user's request. Then, through combination of master information concept and keyword concept, it provides frequency trace of keyword and category thus improving convenience and speed of retrieval.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.17 no.1
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• pp.9-14
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• 1984

To study the correlation of the net drag force, net height, and towing speed, three kinds of similiar size model trawl nets were experimented in the still watertank ($60m{\times}4m{\times}3m$). The scale ratios of model nets, 2 seam, 4 seam, and 6 scam net were 1/31.3, 1/20.0, and 1/44.4 respectively, The maximum streched circumferences of the bag net were same length, i. e. 140cm. Net drags were propotional to the $1.75{\sim}1.98th$ order of towing speed and showed similar result as Koyama's net drag equation. Net heights were propotional to the $-0.85{\sim}-0.72th$ order of towing speed. It could observe that the towing nets showed normal shape in $3.0{\sim}3.5$ knot full scale towing speed but bad shape below $1.0{\sim}1.5$ knot. And it showed tendency to lift the bag net and codend with increasing speed.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.51 no.3
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• pp.375-386
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• 2015

The study aims at a selective effect of the Grid and Window net in the shrimp beam trawl. The experimental trawling of the Proto type, Grid type and Grid and Window net type was performed in a sea area of Geo-je and Tong-yeong from Mar, 2006 to Apr, 2010. The obtained results are as follows; Catch per unit area (Number) of the Proto type net and Grid type net were $0.18/m^2$, $0.23/m^2$, respectively. The Grid type demonstrated 2.4% lower bycatch rate than the proto type (6.6% vs 4.2%, respectively). In addition, in terms of total weight, the bycatch rate of Grid type was 7.6% lower than the proto type (50.2% vs 42.6%, respectively). In the comparison of shrimp catch, the Proto type demonstrated better haul outcome ($0.02case/m^2$) than the Grid & Window type ($0.02case/m^2$). The Grid & Window net type demonstrated 16.4% lower bycatch rate than the Proto type (32.2% vs 48.6%, respectively). In addition, in terms of total weight, the bycatch rate of Grid & Window net type was 8.3% lower than the Proto type (85.9% vs 94.2% respectively).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.77-83
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• 2020

The technologies of 3D spatial information, such as Smart City and Digital Twins, are developing rapidly for managing land and solving urban problems scientifically. In this construction of 3D spatial information, an object using aerial photo images is built as a digital DB. Realistically, the task of extracting a texturing image, which is an actual image of the object wall, and attaching an image to the object wall are important. On the other hand, occluded areas occur in the texturing image. In this study, the ResNet algorithm in deep learning technologies was tested to solve these problems. A dataset was constructed, and the street tree was detected using the ResNet algorithm. The ability of the ResNet algorithm to detect the street tree was dependent on the brightness of the image. The ResNet algorithm can detect the street tree in an image with side and inclination angles.