• Journal of Fisheries and Marine Sciences Education
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• v.16 no.2
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• pp.156-162
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• 2004

This study aimed to know the effects of three marine training items, swimming, rowing and yachting on pulse, lung capacity and weight. The experiment subjects were composed of ten each item and were tested for six days. The experiment groups were strictly controlled in eating time, food amount, sleeping time and training intensity. The level of training intensity was 70~80% of maximal pulse rate. In the training intensity of each item the speed was decided after examination in advance, and the trainees kept the speed during training. The contents of training were made up through enough examination. The conclusions were as follows. 1. The effect on pulse in average value showed the decrease of 1.80round/min swimming, 1.51round/min rowing, and 0.11round/min yachting, but it was not admitted as significant difference. And in average value, swimming showed the decrease of 0.26round/m than rowing and 1.69round/m than yachting. 2. The effect on lung capacity showed the increase of 66.66cc swimming, 42.97cc rowing, and 4.22cc yachting, but there was no significant difference. And the average value of swimming showed the increase of 23.66cc than rowing, and 62.44cc than yachting. 3. The effect on weight showed decrease of 3.45g in swimming, 3.24g in rowing, and 2.07g in yachting. Swimming and rowing proved to have significant difference (p<.05). And in average value, swimming showed the decrease of 1.175g than rowing, and 1.38g than yachting. On the whole, in all experiment items, pulse, lung capacity and weight, the change was in the order of swimming, rowing and yachting after experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2900-2922
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• 2021

In this paper, the power spectral density(PSD) for Multilevel Minimum Shift Keyed signal with modulation index h = 1/2 (M-ary MSK) are derived using the mathematical method of the Markov Chain model. At first, according to an essential requirement of the phase continuity characteristics of MSK signals, a complete model of the whole process of signal generation is built. Then, the derivations for autocorrelation functions are carried out precisely. After that, we verified the correctness and accuracy of the theoretical derivation by comparing the derived results with numerical simulations using MATLAB. We also divided the spectrum into four components according to the derivation. By analyzing these figures in the graphic, each component determines the characteristics of the spectrum. It is vital for enhanced spectral characteristics. To more visually represent the energy concentration of the main flap and the roll-down speed of the side flap, the specific out-of-band power of M-ary MSK is given. OMLCD(Optimum Maximum Likelihood Coherent Detection) of M-ary MSK is adopted to compare the signal received with prepared in advance in a code element T to go for the best. And M-ary MSK BER(Bit Error Rate) is compared with the same ary PSK (Phase Shift Keying) with M=2,4,6,8. The results show the detection method could improve performance by increasing the length of L(memory inherent) in the phase continuity.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.347-363
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• 2019

Malware has its own unique behavior characteristics, like DNA for living things. To respond APT (Advanced Persistent Threat) attacks in advance, it needs to extract behavioral characteristics from malware. To this end, it needs to do classification for each malware based on its behavioral similarity. In this paper, various similarity of Windows malware is estimated; and based on these similarity values, malware's family is predicted. The similarity measures used in this paper are as follows: 'TF-IDF cosine similarity', 'Nilsimsa similarity', 'malware function cosine similarity' and 'Jaccard similarity'. As a result, we find the prediction rate for each similarity measure is widely different. Although, there is no similarity measure which can be applied to malware classification with high accuracy, this result can be helpful to select a similarity measure to classify specific malware family.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• Journal of the Semiconductor & Display Technology
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• v.16 no.2
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• pp.32-38
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• 2017

This paper is about the analysis on the vibration characteristic of tooling units on the precision bed in high-speed automatic lathe for precision machining. An automatic lathe operating at about 25,000 RPM is a critical factor in the self-weight stress and deformation of the bed. Especially, the resonance frequency should be grasped in advance to prevent abnormal vibration that may occur during processing. If the wrong bed is used, the resonant frequency can have a fatal influence on the precision machining and increase the defective rate of precision machined parts such as semiconductor parts. In this paper, vibration characteristics were evaluated through static load and resonance frequency analysis of automatic lathe bed. As a result, the maximum stress was 0.14MPa, the maximum deformation amount was $17.9{\mu}m$, and the natural frequency was 364.72Hz. The resonance frequency was calculated as 718Hz, and the stability was confirmed by being in the range of 400Hz or more, which is the processing condition.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.66-70
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• 2018

This paper is about the analysis on the vibration characteristic of tooling units on the precision bed in turning and hobbing automatic lathe for precision machining. An automatic lathe operating at about 12,000 RPM is a critical factor in the self-weight stress and deformation of the bed. Especially, the resonance frequency should be grasped in advance to prevent abnormal vibration that may occur during processing. If the wrong bed is used, the resonant frequency can have a fatal influence on the precision machining and increase the defective rate of precision machined parts such as semiconductor parts. In this paper, vibration characteristics were evaluated through static load and resonance frequency analysis of automatic lathe bed. As a result, the maximum stress was 14.52 MPa, the maximum deformation amount was $12.15{\mu}m$, and the natural frequency was 189.43 Hz. The resonance frequency was calculated as 500 Hz, and the stability was confirmed by being in the range of 200 Hz or more, which is the processing condition.

• Journal of the Korean Society of Safety
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• v.34 no.2
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• pp.56-62
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• 2019

Secondary accident is common type of accident which occurs in Korean highway tunnels. Fatality rate of secondary accidents in highway tunnels is six time higher than primary accidents. Video type VMS is a new way of providing information to road users which was recently introduced by Korean government to prevent secondary accidents in highway tunnels. In this study we compared changes in driver's behavior when information is provided by Text type and Video Type VMS. In addition to analyze effects of secondary accident reduction, driving behavior was analyzed based on providing advance information by video type VMS at tunnel entrance. Analysis showed that both text type and video type VMS has similar effect on driver behavior. Video type VMS showed positive effect on driver's behavior to prevent secondary accident when information is provided 1km ahead of accident. Considering there results and the short-term memory characteristics of driver, it was determined that information should be provide at about 650m from the entrance of the tunnel. The results of this study are consistent with the requirement that VMS should be installed at least 500m ahead of tunnel and produce more accurate providing information points. 650m is also appropriate interval for providing information in tunnel to cope with an accident ahead.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.195-200
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• 2021

With the development of transportation and mobiles demand for tourism travel is increasing and related industries are also developing significantly. The combination of augmented reality and tourism contents one of the areas of digital media technology, is also actively being studied, and artificial intelligence is already combined with the tourism industry in various directions, enriching tourists' travel experiences. In this paper, we propose a system that scans miniature models produced by reducing tourist areas, finds the relevant tourist sites based on models learned using deep learning in advance, and provides relevant information and 3D models as AR services. Because model learning and object detection are carried out using YOLOv3 neural networks, one of various deep learning neural networks, object detection can be performed at a fast rate to provide real-time service.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.8
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• pp.157-168
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• 2019

Modular Construction is regarded as having enhanced safety compared to traditional construction since most of modular manufacturing process in plants. Unlike general consideration for safety in modular construction, several industrial accident data and studies have pointed out that the accident rate of modular construction is not enough less as much as the practitioners have expected. It means that there is a clear need for improvement of safety management in modular construction. To enhance safety, it is necessary to identify the type and cause of accident through accident cases in order to prevent safety accident in advance. In this consideration, this study analyzed the types and causes of accidents through root cause analysis procedure with accident cases of U.S. OSHA. The classification was carried out in the order of process type, accident type and cause of accident. By following the classification criteria in this study, the causal factor was derived and the root cause map was created. Based on the analysis results, cross-analysis was conducted and it is shown that activity characteristics of modular construction are related to safety accidents. In addition, prevention methods to reduce safety accident by major activity are presented in terms of organizational, educational and technical aspects. This study contributes that the result can be used as the basic safety management in the manufacturing and construction process of modular construction.

• Tunnel and Underground Space
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• v.31 no.1
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• pp.25-40
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• 2021

As the era of Industry 4.0 arrives, the researches using artificial intelligence in the field of rock engineering as well have increased. For a better understanding and availability of AI, this paper analyzed the types of algorithms and how to apply them to the research papers where AI is applied among domestic and international studies related to tunnels, blasting and mines that are major objects in which rock engineering techniques are applied. The analysis results show that the main specific fields in which AI is applied are rock mass classification and prediction of TBM advance rate as well as geological condition ahead of TBM in a tunnel field, prediction of fragmentation and flyrock in a blasting field, and the evaluation of subsidence risk in abandoned mines. Of various AI algorithms, an artificial neural network is overwhelmingly applied among investigated fields. To enhance the credibility and accuracy of a study result, an accurate and thorough understanding on AI algorithms that a researcher wants to use is essential, and it is expected that to solve various problems in the rock engineering fields which have difficulty in approaching or analyzing at present, research ideas using not only machine learning but also deep learning such as CNN or RNN will increase.