• KOREAN POULTRY JOURNAL
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• v.55 no.6
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• pp.131-135
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• 2023

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.309-311
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• 1998

동기화 전략을 명시하는 방법으로 시간 구간 명시, 시간축 명시, 레퍼런스 포인트를 두는 방법, 페트리넷을 이용하는 방법 등 매우 다양한 연구 결과가 소개되었다. 본 논문은 기존의 멀티미디어 시나리오의 동기화 명시를 위한 페트리넷 방법[1-3]을 확장하여, 페트리넷 동기화 명시를 실현한 멀티미디어 시나리오를 재생하여 주는 시스템을 구현하고, 자료구조, 멀티프로세싱, 동기화 기법 등을 중심으로 본 재생 시스템을 소개한다. 본 시스템의 특징은 미디어 단위의 시나리오 진행이 가능한 것이다. 멀티미디어 프로그램이 학습에 많이 이용되므로 물리의 '중력'을 간단하게 설명하는 예제와 더불어 어떻게 실행되는가를 설명한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.687-697
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• 2023

Botnets, whose attack patterns are becoming more sophisticated and diverse, are recognized as one of the most serious cybersecurity threats today. This paper revisits the experimental results of botnet detection using autoencoder, a semi-supervised deep learning model, for UGR and CTU-13 data sets. To prepare the input vectors of autoencoder, we create data points by grouping the NetFlow records into sliding windows based on source IP address and aggregating them to form features. In particular, we discover a simple power-law; that is the number of data points that have some flow-degree is proportional to the number of NetFlow records aggregated in them. Moreover, we show that our power-law fits the real data very well resulting in correlation coefficients of 97% or higher. We also show that this power-law has an impact on the learning of autoencoder and, as a result, influences the performance of botnet detection. Furthermore, we evaluate the performance of autoencoder using the area under the Receiver Operating Characteristic (ROC) curve.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8A
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• pp.613-619
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• 2003

Bluetooth is a communication technology enabling short-range devices to be wirelessly connected. A master and one or more slave devices are connected to form a piconet, and piconets are joined to form a scatternet. The units participating in two or more piconets in a scatternet, is called bridge or gateway nodes. In order to operate the scatternet efficiently, both piconet scheduling for the master and slaves of a piconet, and scatternet scheduling for the bridge nodes are playing important roles. In this paper, we propose a traffic-adaptive dynamic scatternet scheduling algorithm based on rendezvous points and rendezvous windows. The performance of the proposed algorithm is compared and analyzed with that of a static scheduling algorithm via simulations. Simulation results show that our algorithm can distribute wireless resources efficiently to bridge nodes depending on the traffic characteristics.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.5
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• pp.330-337
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• 2022

Research on object detection algorithms using 2D data has already progressed to the level of commercialization and is being applied to various manufacturing industries. Object detection technology using 2D data has an effective advantage, there are technical limitations to accurate data generation and analysis. Since 2D data is two-axis data without a sense of depth, ambiguity arises when approached from a practical point of view. Advanced countries such as the United States are leading 3D data collection and research using 3D laser scanners. Existing processing and detection algorithms such as ICP and RANSAC show high accuracy, but are used as a processing speed problem in the processing of large-scale point cloud data. In this study, PointNet a representative technique for detecting objects using widely used 3D point cloud data is analyzed and described. And RandLA-Net, which overcomes the limitations of PointNet's performance and object prediction accuracy, is described a review of detection technology using point cloud data was conducted.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.109-115
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• 2012

Humanities is very difficult area to use the computer. However, recently, convergence trend has increase widely at all of the academic area. Therefore, in this paper, we propose a technical method to use an IT for the popularity of humanities. For this purpose, we implement a path process that use Petri-net to apply digital storytelling to humanities. We also make a structure to connect an examples and questions from sentences or articles as digital storytelling. The digital storytelling consists of six factors; author, synopsis, background, construction, view point, and user's or reader's review. Proposed method provides a process to analyze the data path of a literary work using Petri-net.

• The Journal of Korean Association of Computer Education
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• v.5 no.3
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• pp.19-25
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• 2002

Bluetooth, in radio network environment, is a small local radio communication that is available with low expense and little power use. It is specified for the wireless connect in a small area between Network Excess Point, portable devices such as mobile phones. PDAs and portable PCs, and other per peripheral equipment. Bluetooth is consisted of a master and a piconet having seven slaves in a maximum and finally has a scatternet gathering lot of piconet. The thesis tries to design the most effective structure of a scatternet by the number of nodes and the type of scatternet such as linear and ring for its mechanism. then the performance evaluation is realized through the Bluehoc simulator based on the NS. Finally the most appropriate radio network environment is made by the comparison and analysis of the characteristics of liner and ring scatternets.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.363-369
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• 2021

High-definition road maps are used as the basic infrastructure for autonomous vehicles, so the latest road information must be quickly reflected. However, the current drawing and structural editing process of high-definition road maps are manually performed. In addition, it takes the longest time to generate road lanes, which are the main construction targets. In this study, the point cloud of the road lane markings, in which color types(white, blue, and yellow) were predicted through the PointNet model pre-trained in previous studies, were used as input data. Based on the point cloud, this study proposed a methodology for automatically drawing and structural editing of the layer of road lane markings. To verify the usability of the 3D vector data constructed through the proposed methodology, the accuracy was analyzed according to the quality inspection criteria of high-definition road maps. In the positional accuracy test of the vector data, the RMSE (Root Mean Square Error) for horizontal and vertical errors were within 0.1m to verify suitability. In the structural editing accuracy test of the vector data, the structural editing accuracy of the road lane markings type and kind were 88.235%, respectively, and the usability was verified. Therefore, it was found that the methodology proposed in this study can efficiently construct vector data of road lanes for high-definition road maps.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.303-313
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• 2021

Recently, quality control of the Liquefied Natural Gas Carrier (LNGC) cargo hold and block-erection interference areas using 3D scanners have been performed, focusing on large shipyards and the international association of classification societies. In this study, as a part of the research on LNGC cargo hold quality management advancement, a study on deep-learning-based scaffolding system 3D point cloud object detection and post-processing were conducted using a LNGC cargo hold 3D point cloud. The scaffolding system point cloud object detection is based on the PointNet deep learning architecture that detects objects using point clouds, achieving 70% prediction accuracy. In addition, the possibility of improving the accuracy of object detection through parameter adjustment is confirmed, and the standard of Intersection over Union (IoU), an index for determining whether the object is the same, is achieved. To avoid the manual post-processing work, the object detection architecture allows automatic task performance and can achieve stable prediction accuracy through supplementation and improvement of learning data. In the future, an improved study will be conducted on not only the flat surface of the LNGC cargo hold but also complex systems such as curved surfaces, and the results are expected to be applicable in process progress automation rate monitoring and ship quality control.