• 수산해양기술연구
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• 제59권4호
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• pp.336-343
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• 2023

The aging fishery training vessels from the past have mostly been decommissioned, and many universities are introducing state-of-the-art large fishery training vessels. The purpose of these training vessels is to train marine professionals and above all, safety to prevent marine accidents should be of utmost priority as many students embark on the vessel. This study estimated the impact of the hydrodynamic interaction forces acting on the model vessel (fishery training vessel) from the bank when the vessel pass near the semi-circle bank wall in various conditions through the numerical calculation, especially concerning maneuvering motions of the vessel. For estimation, variables were mainly set as the size of the semi-circle shape, the lateral distance between the bank and the model vessel, and the depth near the bank. As a result, it was estimated that, in order for the model vessel to safely pass the semi-circle bank wall at a speed of 4 knots, the water depth to the vessel draft ratio should be 1.5 or more (approximately 8 m of water depth), and the lateral distance from the semi-circle bank wall should be 0.4 times the model vessel's length (L_pp) or more (a distance of 34 m or more). Under these conditions, it was expected that the model vessel would pass without significantly being affected by the bank wall.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4060-4066
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• 2021

Flow-accelerated corrosion (FAC) of carbon steel piping is a significant problem in nuclear power plants. The basic process of FAC is currently understood relatively well; however, the accuracy of prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a convolutional neural network, which is confined to a straight pipe without geometric changes. Furthermore, a methodology to generate training data is proposed to efficiently train the neural network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation.

• 한국환경교육학회지:환경교육
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• 제18권1호
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• pp.120-133
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• 2005

This study investigated the changes of consciousness and ambivalent attitudes about the important environmental issues among the teachers who enrolled the environmental education training. Experimental and control groups were composed with 47 and 30 person, respectively. Environmental issues were constructions of sea-wall, nuclear power plant and dam. Cronbach alpha of the self-developed questionnaire was $0.6909{\sim}0.8992$. Score were made with 5 Likert scales for consciousness, and with semantic differential half scale for ambivalent attitudes. Almost teachers(94.0% and 97.1%) have above 10 years teaching career. Strangers in environmental program were 53.2%. After environmental training, teachers' consciousness was significantly changed to negative about the construction of sea-wall for the farm field and industry complex area(p=0.019), and about the construction of dam for disaster like flood(p=0.026), and for adverse effects of citizen by fog(p=0.042). They were also significantly changed to negative about the construction of nuclear power plant for economical energy(p=0.004)', no-emission of greenhouse gases(p=0.033)', 'alternative energy(p=0.000)', 'destruction of ecology(p=0.052)' and 'social fear(p=0.009)'. The consciousness of teachers who have the experience of environmental training, were significantly changed to negative about the construction of nuclear power plant. Scores of teachers' ambivalent attitudes were made lower in experimental than control group about the construction of sea-wall and nuclear power plant. After education training, they were made lower so much as -10.0% in control, but higher much as +4.4% in experimental, and severely higher much as 86.5% in teachers who had experienced the environmental training about construction of sea-wall. Their scores were made lower so much as -3.3% in control, but much as -6.4% in control.

• 비파괴검사학회지
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• 제24권4호
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• pp.362-370
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• 2004

In this work, the collapse moment due to wall-thinning defects is estimated by using fuzzy neural networks. The developed fuzzy neural networks have been applied to the numerical data obtained from the finite element analysis. Principal component analysis is used to preprocess the input signals into the fuzzy neural network to reduce the sensitivity to the input change and the fuzzy neural networks are trained by using the data set prepared for training (training data) and verified by using another data set different (independent) from the training data. Also, two fuzzy neural networks are trained for two data sets divided into the two classes of extrados and intrados defects, which is because they have different characteristics. The relative 2-sigma errors of the estimated collapse moment are 3.07% for the training data and 4.12% for the test data. It is known from this result that the fuzzy neural networks are sufficiently accurate to be used in the wall-thinning monitoring of elbows.

• 대한물리의학회지
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• 제16권1호
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• pp.23-31
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• 2021

PURPOSE: This study was conducted to identify the effects of altering foot position on quadriceps femoris including vastus medialis obliques (VMO), vastus lateralis (VL) and rectus femoris (RF) activation during wall squat exercises. METHODS: All subjects (n = 15) were selected and randomly performed three kinds of wall squats: 1) GWS (General Wall Squat), 2) WSS1/4 (Wall Squat Short 1/4), and 3) WSS1/2 (Wall Squat Short 1/2). Each subject completed all three kinds of wall squatting exercises at three different times and recorded the muscle activity data of vastus medialis obliques, vastus lateralis and rectus femoris. RESULTS: Compared with GWS exercise, VMO and RF muscle activity significantly increased under WSS1/2 exercise (p < .05), while only RF muscle activity significantly increased under WSS1/4 exercise (p < .05). CONCLUSION: The results of the present study indicate that moving the foot toward the wall during wall squats has a positive effect on quadriceps activation. The exercise of wall squat short can not only be used as the lower limb muscle strengthening training for normal people, but also as the recovery training for patellofemoral pain syndrome patients in the rehabilitation stage. Besides, Anterior cruciate ligament patients can also try this exercise according to the advice of doctors and therapists.

• 한국해양공학회지
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• 제15권3호
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• pp.35-42
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• 2001

The characteristics of tidal circulation with the flow-control structures using the three-dimensional numerical model (POM, Princeton Ocean Model) of Chinhae Bay, Korea were investigated. To confirm th efficiencies of flow-control structures, the training wall and submerged training wall were constructed at the mouth and narrow channel in Chinhae Bay. On the basis of the present investigation, the tidal circulation induced by the construction of flow-control structures could enhance the water exchange improvement appropriately. And, th training wall at the central is more dominated than the other structures for the efficient of water exchange. The sites and types of structure and flow patterns seem to be very sensitive in tidal simulation and changes in flow fields.

• 상하수도학회지
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• 제23권5호
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• pp.655-667
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• 2009

An experimental study is performed on reducing the pollutants supplied by storm water through enhancing efficiency of SS from the detention storage tank where CSOs are kept temporarily before discharge to the receiving water system. SS removal efficiency is investigated in accordance with various conditions of the detention pond-such as its length, the existence of training wall, and the use of gravel filling. The removal efficiency is strongly affected by the detention pond's length until the critical falling distance of the suspended solids is reached. For cases where the tank has a length longer than this critical condition, the removal rate shows less sensitivity. To enhance the SS removal efficiency of tanks of shorter than the critical length, we studied alternative types of tank in which inside training walls are installed. The results showed improvement of 14 to 37% in removal efficiency in 2hours detention(2 training walls). The important factor in achieving a high SS removal rate is ensuring the critical length of the detention pond, but for the cases where the basin length cannot be guaranteed, baffles or a gravel filling scheme may be introduced to attain considerable efficiency. The results of studying and comparing different storage tank conditions show that, in terms of elimination efficiency, a storage tank with gravel filling and training walls > a storage tank with gravel filling > a storage tank with training walls > an empty tank. The experimental results should contribute to development of related further research, by empirically verifying the already assumed importance of critical falling distance, training walls, and gravel filling schemes.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.265-273
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• 2016

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.

• Journal of applied mathematics & informatics
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• 제20권1_2호
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• pp.171-180
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• 2006

Two infinite classes of special finite groups considered (The group G is special, if G' and Z(G) coincide). Using certain sequences of numbers we give explicit formulas for the Fibonacci lenghts of these classes which involve the well-known Wall numbers k(n).

• 한국건축시공학회지
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• 제7권3호
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• pp.131-137
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• 2007

To predict deep excavation wall movements is important in the urban areas considering the cost and the safety in construction. Failing to estimate deep excavation wall movements in advance causes too many problems in the projects. The purpose of this study is to propose the forecast model of deep excavation wall movements using artificial neural networks. The data of the Deep Excavation Wall Movements which were done form Long research is used of Artificial neural networks training and apply the real construction work measured data to the Artificial neural networks model. Applying the artificial neural networks to forecast the deep excavation wall movements can significantly contribute to identifying and preventing the accident in the overall construction work.