• Tunnel and Underground Space
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• 제5권4호
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• pp.323-335
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• 1995

지열에너지 개발의 기본적 개념은 지하 심부의 고온건조암체에 시추공을 이용한 수압파쇄를 실시하여 고온건조암체내에 인공파쇄대를 형성함으로써 유체의 유동회로를 구축하여 지열에너지의 회수를 도모하는 것이다. 본 논문에서는 수압파쇄균열의 발전방향 조절문제와 관련하여, 초고압수 절삭장치를 이용, 수압파쇄공 내에 인공슬롯을 형성하여 수압파쇄를 실시함으로써 균열의 발전방향을 조사하였으며, 수압파쇄에 의한 파쇄대내로의 유체순환실험을 통해 지열수의 유동특성을 규명하였다. 이를 위해 모델에 종균열과 횡균열을 형성시키고 균열내에 주입되는 물의 주입률, 정상류압력, 흐름저항을 조사하고, 이 결과를 이용하여 전산모델링을 수행하였다. 인공절리면에 대한 투수시험에서는 10$0^{\circ}C$까지의 온도변화에 따라 투수계수가 증가하였으며, 봉압 증가에 따라 증가율이 현저히 감소하였다.

• Journal of Biomedical Engineering Research
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• 제19권2호
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• pp.153-162
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• 1998

It is needless to say that the nonlinear hemodynamic variables estimation is a very important study for the artificial heart. Even though it is important, there have not been satisfactory results which can be applied to the real world situations, In this paper, the problem of hemodynamic variables estimation for the moving-actuator type total artificial heart(MA-TAH) was studed. Multidimensional linear interpolation(MDI)scheme was used for the estimation. Proposed method was verified by in vitro test and showed good performance.

• Journal of Yeungnam Medical Science
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• 제4권1호
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• pp.65-74
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• 1987

This study deals with the changes in the concentrations of blood glucose and electrolytes during open heart surgery. Blood glucose and electrolytes in connection with age, disease and anesthetic period were measured in 25 patients about to undergo heart surgery which were performed between June 1986 and August 1986 in Yeungnam University Hospital. Because glucose solution is commonly used as priming solution, and the priming solution is hyperglycemic and hyperosmolar, glucose level of priming solution in this study was adjusted to 100-200mg% level to minimize hyperglycemic and hyperosmolar effect. The following results were obtained. 1. Glucose level of priming solution before extracorporeal circulation was $151.6{\pm}3.13mg%$. 2. With body cooling, blood glucose level was elevated. As duration of extracorporeal circulation is prolonged, blood glucose level was elevated more, but no difference between age and diseases were observed. On warming, blood glucose level was progressively lowered. 3. Despite the low serum potassium level during by pass, the potassium level was progressively elevated following by-pass, cut the serum potassium level was low compared to control values. Elevated calcium level was maintained during by pass.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2004년도 학술발표회
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• pp.620-623
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• 2004

도시하천인 청계천 유역의 물순환 해석을 위한 기초자료로 강우량, 유출량, 상하수도랑, 지하수이용량, 지하수위 변화에 의한 유역 저류량 등의 관측자료를 이용하여 연간 물수지 분석을 수행하였다. 청계천 유역의 물순환 구조는 자연계 유출과 인공계 배수의 구조를 지니고 있으며, 이들의 수문성분을 규명하기 위해서는 각각의 수문성분들의 관측 및 해석이 필요하며, 각 수문성분들의 물수지 분석을 통하여 정량적인 합의 결과를 가시적으로 확보함이 매우 중요하다. 신뢰도와 정확성에 근거한 관측자료를 이용한 물수지 분석은 수문성분들의 총체적 표현이라 할 수 있는 모형(model)의 중요 입력자료이며, 모형의 모의 분석결과를 검증할 수 있는 중요한 기준이 된다. 청계천 유역에 기 설치된 수문모니터링 자료로 물수지 분석을 수행하는 데는 많은 제약과 한계성이 따르므로, 지속적인 수문관측 및 모니터링이 수행된다면 복원 이후 장래의 건전한 물순환 대책 수립에 기여 할 것이다.

• KIISE Transactions on Computing Practices
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• 제21권8호
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• pp.567-571
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• 2015

Transition-based dependency parsing requires much time and efforts to design and select features from a very large number of possible combinations. Recent studies have successfully applied Multi-Layer Perceptrons (MLP) to find solutions to this problem and to reduce the data sparseness. However, most of these methods have adopted greedy search and can only consider a limited amount of information from the context window. In this study, we use a Recurrent Neural Network to handle long dependencies between sub dependency trees of current state and current transition action. The results indicate that our method provided a higher accuracy (UAS) than an MLP based model.

• Ecology and Resilient Infrastructure
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• 제3권2호
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• pp.100-109
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• 2016

Low impact development (LID) facilities are established for the purpose of restoring the natural hydrologic cycle as well as the removal of pollutants from stormwater runoff. Improved efficiency of LID facilities can be obtained through the optimized interaction of their major components (i.e., plant, soil, filter media, microorganisms, etc.). Therefore, this study was performed to evaluate the performances of LID facilities in terms of runoff and pollutant reduction and also to provide an optimal maintenance method. The monitoring was conducted on four LID technologies (e.g., bioretention, small wetlands, rain garden and tree box filter). The optimal SA/CA (facility surface area / catchment area) ratio for runoff reduction greater than 40% is determined to be 1 - 5%. Since runoff reduction affects the pollutant removal efficiency in LID facilities, SA/CA ratio is derived as an important factor in designing LID facilities. The LID facilities that are found to be effective in reducing stormwater runoff are in the following order: rain garden > tree box filter > bioretention> small wetland. Meanwhile, in terms of removal of particulate matter (TSS), the effectiveness of the facilities are in the following order: rain garden > tree box filter > small wetland > bioretention; rain gardens > tree box filter > bioretention > small wetland were determined for the removal of organic matter (COD, TOC), nutrients (TN, TP) and heavy metals (Cu, Pb, Cd, Zn). These results can be used as an important material for the design of LID facilities in runoff volume and pollutant reduction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제16권6호
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.

• Journal of Biomedical Engineering Research
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• 제10권2호
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• pp.117-124
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• 1989

The intelligent trajectory control method that controls moving direction and average velocity for a prosthetic arm is proposed by pattern recognition and force estimations using EMG signals. Also, we propose the real time trajectory planning method which generates continuous accelleration paths using 3 stage linear filters to minimize the impact to human body induced by arm motions and to reduce the muscle fatigue. We use combination of MLP and fuzzy filter for pattern recognition to estimate the direction of a muscle and Hogan`s method for the force estimation. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. The simulation results of proposed prosthetic arm control system using the EMf signals show that the arm is effectively followed the desired trajectory depended on estimated force and direction of muscle movements.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제25권10호
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• pp.1435-1440
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• 2021

With the advent of the low interest rate era, many investors are flocking to the stock market. In the past stock market, people invested in stocks labor-intensively through company analysis and their own investment techniques. However, in recent years, stock investment using artificial intelligence and data has been widely used. The success rate of stock prediction through artificial intelligence is currently not high, so various artificial intelligence models are trying to increase the stock prediction rate. In this study, we will look at various artificial intelligence models and examine the pros and cons and prediction rates between each model. This study investigated as stock prediction programs using artificial intelligence artificial neural network (ANN), deep learning or hierarchical learning (DNN), k-nearest neighbor algorithm(k-NN), convolutional neural network (CNN), recurrent neural network (RNN), and LSTMs.

• The Journal of the Korea institute of electronic communication sciences
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• 제17권5호
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• pp.825-832
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• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.