• Journal of Digital Contents Society
• /
• v.10 no.2
• /
• pp.279-289
• /
• 2009

As the TCP is the protocol designed for the wired network that packet loss probability is very low, because TCP transmitter takes it for granted that the packet loss by the wireless network characteristics is occurred by the network congestion and lowers the transmitter's transmission rate, the performance is degraded. In this article, we suggest the newly improved algorithm using two parameters, the local retransmission time value and the local retransmission critical value to the BS based on the Snoop. This technique adjusts the base stations local retransmission timer effectively according to the wireless link status to recover the wireless packet loss rapidly. We checked that as a result of the suggested algorithm through various simulations, A-Snoop protocol improve more the wireless TCP transmission rate by recovering the packet loss effectively in the wireless link that the continuous packet loss occur than the Snoop protocol.

• Journal of KIISE:Information Networking
• /
• v.27 no.3
• /
• pp.323-330
• /
• 2000

이동통신망의 발전으로 인해 한정적인 주파수 자원을 효율적으로 사용하여 폭증하는 가입자를 수용하기 위해 셀의 반경은 점점 작아지고 있다. 가입자에게 신규호의 실패보다 더 민감한 핸드오프호가 자주 발생함에 따라 핸드오프호 처리의 중요성이 증대되었다. 따라서, 셀마다 핸드오프호를 위한 예약 채널을 두어 어느 정도 신규호의 블록킹율의 증가를 감수하더라도, 핸드오프호의 강제종료율을 낮추는 방법이 제안되었다. 이러한 예약 채널 할당 기법에서는 예약 채널을 몇 개로 할 것인가가 중요한 문제가 된다. 왜냐하면 예약 채널 수를 과다하게 설정하면 핸드오프가 빈번하지 않은 셀에서는 채널의 낭비를 초래하고, 적게 설정하면 핸드오프가 빈번한 셀은 핸드오프 강제종료율이 높아지게 되기 때문이다. 이러한 문제를 해결하기 위해 본 논문은 신경망 모델 중 다층 퍼셉트론을 이용하여 셀에서 요구되는 최적의 예약율을 구하여 셀의 환경이 변할 때마다 적용할 수 있는 방법을 제안한다. 본 논문에서는 모의 실험을 통해 이동통신 시스템에서의 핸드오프 예약율을 주기적으로 최적화 시킴으로써 핸드오프가 자주 발생하는 셀에서는 핸드오프 강제종료율을 낮추고, 핸드오프가 빈번하지 않은 셀은 채널의 손실을 막아 시스템의 전체적인 효율이 향상됨을 보인다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.228-228
• /
• 2019

오늘날 수자원 관리의 중요성과 관심이 매우 커지는 가운데 신뢰도 높은 수문자료의 생산은 매우 중요하다. 나아가 기후변화에 따른 집중호우 등을 고려했을 때 효율적인 수문자료 확보를 위해 수문관측소에 대한 객관적인 평가지표와 지점 중요도, 현장에서의 측정 및 하천환경 변화 등의 다양한 검토가 선행되어야 한다. 본 논문에서는 환경부 2019년 낙동강 수계 측정지점을 대상으로 관측소의 객관적인 평가지표, 즉 관측소 설치목적에 따른 지점 중요도(하천 대표 유역, 홍수 및 갈수 예 경보, 유지유량, 갈수모니터링 등)를 고려한 관측소 평가를 실시하였다. 아울러 각 지점에 대한 측정경험과 하천환경 변화에 대한 모니터링 자료를 바탕으로 측정 지점의 난이도와 관측소 변화 여부에 대한 평가 항목을 7가지로 분류, 평가하였다. 단순히 현장경험과 실용성에 중점을 둔 평가항목은 다소 주관적인 판단이 들어갈 수 있기 때문에 의사 결정 과정에서 데이터에 대한 가중치를 부여할 수 있는 엔트로피 기법을 적용하여 관측소 평가결과에 반영하였다. 그 결과 관측소 중요도가 높은 필수 지점 뿐 만 아니라 현장 여건이 고려되지 못한 지점들, 지점 특성에 따라 이설 및 관측망 조정이 필요한 지점도 다소 존재하였다. 효율적인 측정지점 선정을 위해서 관측소의 설치목적 뿐 만 아니라 설계홍수량이나 하천설계기준 수립 등의 객관적인 평가지표와 하천환경 변화 나아가 경제성 검토 등 다양한 요소의 추가적인 연구가 필요하다고 판단된다.

• The Journal of the Acoustical Society of Korea
• /
• v.37 no.6
• /
• pp.475-482
• /
• 2018

In this paper, we present a study on data augmentation methods for DNN (Deep Neural Network)-based audio tagging. In this system, an audio signal is converted into a mel-spectrogram and used as an input to the DNN for audio tagging. To cope with the problem associated with a small number of training data, we augment the training samples using time stretching, pitch shifting, dynamic range compression, and block mixing. In this paper, we derive optimal parameters and combinations for the augmentation methods through audio tagging simulations.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.340-340
• /
• 2019

지구온난화로 유발되는 기후변동성이 증가함에 따라서 정확한 수문인자의 예측은 전 세계적으로 주요 관심사항이 되고 있다. 최근에는 고성능 컴퓨터 자원의 증가로 수문기상학 연구에서 동일한 학습량에 비하여 정확도의 향상이 뚜렷한 기계학습 구조를 활용하여 위성영상 기반의 대기예측, 태풍위치 추적 및 강수량 예측 등의 연구가 활발하게 진행되고 있다. 본 연구에는 기계학습 중 시계열 분석에 널리 활용되고 있는 순환신경망(Recurrent Neural Network, RNN) 기법의 대표적인 LSTM(Long Short-Term Memory) 네트워크를 이용하여 수문인자를 예측하였다. LSTM 네트워크는 가중치 및 메모리 요소에 대한 추가정보를 셀 상태에 저장하고 시계열의 길이 조정하여 모형의 탄력적 활용이 가능하다. LSTM 네트워크를 이용한 다양한 수문인자 예측결과 RMSE의 개선을 확인하였다. 따라서 본 연구를 통하여 개발된 기계학습을 통한 수문인자 예측기술은 권역별 수계별 홍수 및 가뭄대응 계획을 능동적으로 수립하는데 활용될 것으로 판단된다. 향후 연구에서는 LSTM의 입력영역을 Bayesian 추론기법을 활용하여 구성함으로 학습과정의 불확실성을 정량적으로 제어하고자 한다.

• Journal of IKEEE
• /
• v.24 no.4
• /
• pp.991-997
• /
• 2020

Reinforcement learning has been applied mainly in sequential decision-making problems. Especially in recent years, reinforcement learning combined with neural networks has brought successful results in previously unsolved fields. However, reinforcement learning using deep neural networks has the disadvantage that it is too complex for immediate use in the field. In this paper, we implemented path planning algorithm for mobile robots using Q-learning, one of the easy-to-learn reinforcement learning algorithms. We used real-time Q-learning to update the Q-table in real-time since the Q-learning method of generating Q-tables in advance has obvious limitations. By adjusting the exploration strategy, we were able to obtain the learning speed required for real-time Q-learning. Finally, we compared the performance of real-time Q-learning and DQN.

• Journal of Korea Society of Industrial Information Systems
• /
• v.28 no.5
• /
• pp.15-30
• /
• 2023

Recently, generative models based on the Transformer architecture, such as ChatGPT, have been gaining significant attention. The Transformer architecture has been applied to various neural network models, including Google's BERT(Bidirectional Encoder Representations from Transformers) sentence generation model. In this paper, a method is proposed to create a text binary classification model for determining whether a comment on Korean movie review is positive or negative. To accomplish this, a pre-trained multilingual BERT sentence generation model is fine-tuned and transfer learned using a new Korean training dataset. To achieve this, a pre-trained BERT-Base model for multilingual sentence generation with 104 languages, 12 layers, 768 hidden, 12 attention heads, and 110M parameters is used. To change the pre-trained BERT-Base model into a text classification model, the input and output layers were fine-tuned, resulting in the creation of a new model with 178 million parameters. Using the fine-tuned model, with a maximum word count of 128, a batch size of 16, and 5 epochs, transfer learning is conducted with 10,000 training data and 5,000 testing data. A text sentiment binary classification model for Korean movie review with an accuracy of 0.9582, a loss of 0.1177, and an F1 score of 0.81 has been created. As a result of performing transfer learning with a dataset five times larger, a model with an accuracy of 0.9562, a loss of 0.1202, and an F1 score of 0.86 has been generated.

• Tunnel and Underground Space
• /
• v.19 no.3
• /
• pp.203-212
• /
• 2009

The prediction of near field behavior around an underground high-level radioactive waste repository is important for the repository design as well as the safety assessment. In this study, a sensitivity analysis for seven parameters consisted of design parameters and material properties was carried out using a three-dimensional finite difference code. From the sensitivity analysis, it was found that the effects of borehole spacing, tunnel spacing, cooling time and rock thermal conductivity were more significant than the other parameters. For getting a statistical distribution of buffer and rock temperatures around the repository, an artificial neural network, backpropagation, was applied. The reliability of the trained neural network was tested with the cases with randomly chosen input parameters. When the parameter variation is within ${\pm}10%$, the prediction from the network was found to be reliable with about a 1% error. It was possible to calculate the temperature distribution for many cases quickly with the trained neural network. The buffer and rock temperatures showed a normal distribution with means of $98^{\circ}C$ and $83.9^{\circ}C$ standard deviations of $3.82^{\circ}C$ and $3.67^{\circ}C$, respectively. Using the neural network, it was also possible to estimate the required change in design parameters for reducing the buffer and rock temperatures for $1^{\circ}C$.

• Journal of the Korea Computer Graphics Society
• /
• v.23 no.3
• /
• pp.31-38
• /
• 2017

In this paper, we propose a system which estimates Manhattan coordinate systems for urban scene images using a convolutional neural network (CNN). Estimating the Manhattan coordinate system from an image under the Manhattan world assumption is the basis for solving computer graphics and vision problems such as image adjustment and 3D scene reconstruction. We construct a CNN that estimates Manhattan coordinate systems based on GoogLeNet [1]. To train the CNN, we collect about 155,000 images under the Manhattan world assumption by using the Google Street View APIs and calculate Manhattan coordinate systems using existing calibration methods to generate dataset. In contrast to PoseNet [2] that trains per-scene CNNs, our method learns from images under the Manhattan world assumption and thus estimates Manhattan coordinate systems for new images that have not been learned. Experimental results show that our method estimates Manhattan coordinate systems with the median error of $3.157^{\circ}$ for the Google Street View images of non-trained scenes, as test set. In addition, compared to an existing calibration method [3], the proposed method shows lower intermediate errors for the test set.

• Journal of Navigation and Port Research
• /
• v.44 no.3
• /
• pp.187-194
• /
• 2020

It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.