• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.667-671
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• 2024

Professional job training for government officers emphasizes establishing desirable values as public officials and improving professionalism in public service. To provide customized education, some studies are analyzed factors affecting education satisfaction. However, there is a lack of research predicting education satisfaction with educational contents. Therefore, we propose a deep learning-based regression model that predicts government officer education satisfaction with educational contents. We use education information data for government officer. We use one-hot encoding to categorize variables collected in text format, such as education targets, education classifications, and education types. We quantify the education contents stored in text format as TF-IDF. We train our deep learning-based regression model and validate model performance with 10-Fold Cross Validation. Our proposed model showed 99.87% accuracy on test sets. We expect that customized education recommendations based on our model will help provide and improve optimized education content.

• Proceedings of The KACE
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• 2017.08a
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• pp.209-211
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• 2017

기계학습은 인공지능(AI, Artificial Intelligence)의 일종으로 다른 인공지능 알고리즘이 정해진 규칙을 기반으로 주어진 임무(Task)를 해결하는 것과는 달리, 기계학습은 수집된 Data를 기반으로 최적의 솔루션을 학습한 후 미래의 값들을 예측하거나 해석하는 방법을 사용하고 있다. 더욱이 인터넷을 통한 연결성의 확대와 컴퓨터의 연산능력 발전으로 가능하게 된 Big-Data를 기반으로 하고 있어 이전의 인공지능 알고리즘에 비해 월등한 성능을 보여주고 있다. 그러나 기계학습 알고리즘이 Data를 학습할 때 학습 결과를 사람이 해석하기에 너무 복잡하여 사람이 그 내부 구조를 이해하는 것은 사실상 불가능하고, 이에 따라 학습된 기계학습 모델의 단점 또는 한계 등을 알지 못하는 문제가 있다. 본 연구에서는 이러한 블랙박스화된 기계학습 알고리즘의 특성을 이해하기 위해, 기계학습 알고리즘이 특정 입력에 대한 결과를 예측할 때 어떤 입력들로 부터 영향을 많이 받는지 그리고 어떤 입력으로부터 영향을 적게 받는지를 알아보는 방법을 소개하고 기존 연구의 단점을 개선하기 위한 방법을 제시한다.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.620-628
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• 2020

Automatically predicting video highlight is an important task for media industry and streaming platform providers to save time and cost of manual video editing process. We propose a new ensemble model that combines multiple highlight detectors with each focusing on different parts of highlight events. Therefore, our model can capture more information-rich sections of events. Furthermore, the proposed model can extract improved features for highlight detection particularly when the train video set is small. We evaluate our model on e-sports and baseball videos.

• Explosives and Blasting
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• v.22 no.3
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• pp.43-55
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• 2004

Use term of tower style construction exceeds recently. Accordingly, according to construction safety diagnosis result, achieve removal or Improvement construction. But when work removal, must shorten shut down time. Therefore, removal method of construction to use blasting demolition of construction is very profitable. Influence construction and equipment by blasting vibration and occurrence vibration caused by felling impact. Is using disadvantageous machine removal method of construction by and economic performance by effect of such vibartion. Therefore, this research studied techniques to forecast vibartion level happened at blasting demolition and vibration reduction techniques by use a scaled model test.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.88-98
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• 2016

The Korean summer is hot and humid, and air-conditioners consume considerable amounts of electricity. In such cases, the simultaneous use of indirect evaporative coolers may help reduce the sensible heat and save electricity. In this study, heat transfer and pressure drop characteristics of indirect or regenerative evaporative coolers made from plastic/paper are investigated. The results showed that heat and mass transfer model based on the ${\epsilon}-NTU$ method predicted the indirect evaporation efficiencies, cooling capacities and pressure drops adequately. Both for indirect or regenerative evaporative cooler, the indirect evaporation efficiency increased with increasing dry channel inlet temperature or relative humidity. The indirect evaporation efficiency of the regenerative evaporative cooler was larger than that of the indirect evaporative cooler.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1788-1796
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• 1997

In this paper, we propose a new connection admission control(CAC) algorithm for traffic control in ATM network in which traffic estimation is performed based on user-specified parameters at every moment of connection request or connection release by recursive formula which makes real-time calculation possible. And traffic estimation using cell flow measurement is carried out when the number of connectioned calls does not change during a measurement reflection period. Performance analysis of the proposed method is carried out using several aspects for homogeneous and heterogeneous bursty traffic. The results showed that the proposed CAC method revealed better performance, than conventional CAC method for burst model in both utilization and QoS point of view.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.895-900
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• 2011

To improve drawback of existing modulation method, in this paper, we propose the channel forecast method using adaptive modulation which can improve throughput of channel. This method adaptively changes modulation method to the change of channel environments. In proposed method, channel's characteristics are measured in realtime to determine code rate to the changes of demanded channel's bit error rate. If bit error rate is increased, this method reduce code rate to maintain maximum throughput. We analysis performance of proposed method by Matlab.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.167-167
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• 2022

최근 빅데이터에 관련된 연구에 있어 데이터의 품질관리에 대한 논의가 꾸준히 이뤄져 오고 있다. 특히 이미지 처리 및 분석에 활용되어온 딥러닝 기술의 경우, 분류 작업 및 패턴인식 등으로부터 데이터의 특징을 추출함으로써 비지도학습(Unsupervised Learning)을 가능하게 한다는 장점이 있음에도 불구하고 빅데이터를 다루는 과정에 있어 용량, 다양성, 속도 및 신뢰성 측면에서의 한계가 있었다. 본 연구에서는 CCTV 영상을 활용한 강수량 산정 모델 개발에 있어 예측 정확도 향상 및 성능 개선을 도모할 수 있는 데이터 전처리 방법을 제안하였다. 서울 근린 AWS 4개소 지역(김포장기, 하남덕풍, 강동, 성남) 및 중앙대학교 지점 내 CCTV를 설치한 후, 최대 9개월의 영상을 확보하여 강수량 산정을 위한 딥러닝 모델을 개발하였다. 배경분리, 조도조정, 영역설정, 데이터증진, 이상데이터 분류 등이 가능한 알고리즘을 개발함으로써 데이터셋 자체에 대한 전처리 작업을 수행한 후, 이에 대한 결과를 기존 관측자료와 비교·분석하였다. 본 연구에서 제안한 전처리 방법들을 적용한 결과, 강수량 산정 모델의 예측 정확도를 평가하는 지표로 선정한 평균 제곱근 편차(Root Mean Square Error; RMSE)가 약 30% 감소함을 확인하였다. 본 연구의 결과로부터 CCTV 영상 데이터를 활용한 강수량 산정의 가능성을 확인할 수 있었으며 특히, 딥러닝 모델 개발시 필요한 적정 전처리 방법들에 대한 기준을 제시할 수 있을 것으로 판단된다.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.7
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• pp.36-45
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• 1999

In order to control the flow of traffics in ATM networks and optimize the usage of network resources, an efficient control mechanism is necessary to cope with congestion and prevent the degradation of network performance caused by congestion. In this paper, Buffered Leaky Bucket which applies the same control scheme to a variety of traffics requiring the different QoS(Quality of Service) and Neural Networks lead to the effective buffer utilization and QoS enhancement in aspects of cell loss rate and mean transfer delay. And the cell scheduling algorithms such as DWRR and DWEDF for multiplexing the incoming traffics are enhanced to get the better fair delay. The network congestion information from cell scheduler is used to control the predicted traffic loss rate of Neural Leaky Bucket, and token generation rate and buffer threshold are changed by the predicted values. The prediction of traffic loss rate by neural networks can enhance efficiency in controlling the cell loss rate and cell transfer delay of next incoming cells and also be applied for other traffic controlling schemes. Computer simulation results performed for random cell generation and traffic prediction show that QoSs of the various kinds of traffcis are increased.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.1
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• pp.129-137
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• 2016

In the present study we verified performance of feed-forward control algorithm using short term prediction of ship motion information by taking advantage of developed numerical simulation model of FPSO motion. Up until now, various studies have been conducted about thrust control and allocation for dynamic positioning systems maintaining positions of ships or marine structures in diverse sea environmental conditions. In the existing studies, however, the dynamic positioning systems consist of only feedback control gains using a motion of vessel derived from environmental loads such as current, wind and wave. This study addresses dynamic positioning systems which have feedforward control gain derived from forecasted value of a motion of vessel occurred by current, wind and wave force. In this study, the future motion of vessel is forecasted via Brown's Exponential Smoothing after calculating the vessel motion via a selected mathematical model, and the control force for maintaining the position and heading angle of a vessel is decided by the feedback controller and the feedforward controller using PID theory and forecasted vessel motion respectively. For the allocation of thrusts, the Lagrange Multiplier Method is exploited. By constructing a simulation code for a dynamic positioning system of FPSO, the performance of feedforward control system which has feedback controller and feedforward controller was assessed. According to the result of this study, in case of using feedforward control system, it shows smaller maximum thrust power than using conventional feedback control system.