• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.241-244
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• 2014

Wireless Sensor Network(WSN) has been attracting lots of interest in recent years for smart surveillance systems. WSN-based surveillance systems need to figure out the occurrence or existence of events or objects and to find out where the events have occurred or the objects are present. In our surveillance system, it is needed to give an alarm only when the detected object is human (not pets or rodents) for reducing false alarms and improving the system reliability. In this paper, we propose a height prediction mechanism to determine if the detected object is human using Heron's formula. Finally, we verify the performance of our proposed mechanism through various experiments.

• Journal of electromagnetic engineering and science
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• v.11 no.3
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• pp.220-226
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• 2011

We have proposed a new method to accurately predict the resonance of Fabry-Perot Cavity (FPC) antennas enclosed with conducting side walls. When lateral directions of an FPC antenna are not blocked with metallic walls, the conventional technique is accurate enough to predict the resonance of the FPC antenna. However, when the FPC antenna has side walls, especially for case with only a short distance between the walls, the conventional prediction method yields an inaccurate result, inevitably requiring a tedious, time-consuming tuning process to determine the correct resonant height to provide the maximum antenna gain in a target frequency band using three-dimensional full-wave computer simulations. To solve that problem, we have proposed a new resonance prediction method to provide a more accurate resonant height calculation of FPC antennas by using the well-known resonance behavior of a rectangular resonant cavity. For a more physically insightful explanation of the new prediction formula, we have reinvestigated our proposal using a wave propagation characteristic in a hollow rectangular waveguide, which clearly confirms our approach. By applying the proposed technique to an FPC antenna covered with a partially reflecting superstrate consisting of continuously tapered meander loops, we have proved that our method is very accurate and readily applicable to various types of FPC antennas with lateral walls. Experimental result confirms the validness of our approach.

• Journal of Power System Engineering
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• v.13 no.2
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• pp.63-70
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• 2009

This paper discusses the influence on long-tenn predictions of the ship response in ocean by using the Global Wave Statistics data, GWS, and wave information from the remote sensing satellites. GWS's standard scatter diagrams of significant wave height and zero-crossing wave period are suggested to be corrected to a round number of 0.01/1000 fitted with a statistical analytic model of the conditional lognormal distribution for zero-crossing wave period. The GEOSAT satellite data are utilized which presented by I. R. Young and G. J. Holland (1996, named as GEOSAT data). At first, qualities of this data are investigated, and statistical characteristic trends are studied by means of applying known probability distribution functions. The wave height data of GEOSAT are compared to the data observed onboard merchant ships, the data observed by measure instrument installed on the ocean-going container ship and so on. To execute a long-tenn prediction of ship response, joint probability functions between wave height and wave period are introduced, therefore long-term statistical predictions are executed by using the functions.

• Journal of Coastal Disaster Prevention
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• v.6 no.3
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• pp.111-120
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• 2019

In coastal morphological modelling, there are a number of input factors: wave height, water depth, sand particle size, bed friction coefficients, coastal structures and so forth. Measurements or estimates of these input data may include uncertainties due to errors by the measurement or hind-casting methods. Therefore, it is necessary to consider the uncertainty of each input data and the range of the uncertainty during the evaluation of numerical results. In this study, three uncertainty factors are considered with regard to the prediction of coastal erosion in Ilsan beach located in Ilsan-dong, Ulsan metropolitan city. Those are wave diffraction effect of XBeach model, wave input scenario and the specification of the coastal structure. For this purpose, the values of mean wave direction, significant wave height and the height of the submerged breakwater were adjusted respectively and the followed numerical results of morphological changes are analyzed. There were erosion dominant patterns as the wave direction is perpendicular to Ilsan beach, the higher significant wave height, and the lower height of the submerged breakwater. Furthermore, the rate of uncertainty impacts among mean wave direction, significant wave height and the height of the submerged breakwater are compared. In the study area, the uncertainty influence by the wave input scenario was the largest, followed by the height of the submerged breakwater and the mean wave direction.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.87-92
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• 2007

The full automation welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed models using three different training algorithms in order to select an adequate neural network model for prediction of top-bead height.

• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.178-182
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• 2009

A new mixed-effects model was developed that predicts individual-tree total height for Pinus densiflora trees in Gangwon province as a function of individual-tree diameter (cm). The mixed-effects model contains two random-effects parameters. Maximum likelihood estimation was used to fit the model to 560 height-diameter observations of individual trees measured throughout Gwangwon province in 2007 as part of the National Forest Inventory Program in Korea. The new model is an improvement over fixed-effects models because it can be calibrated to a local area, such as an inventory plot or individual stand. The new model also appears to be an improvement over the Forest Resources Evaluation and Prediction Program for the ten calibration trees used in this study. An example is provided that describes how to estimate the random-effects parameters using ten calibration trees.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.19
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• pp.100-131
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• 1975

To obtain basic information on the improvement of naked barley production. and to clarify the relation-ships between yield or yield components and some meteorogical factors for yield prediction were the objectives of this study. The basic data used in this study were obtained from the experiments carried out for 16 years from 1958 to 1974 at the Chon-nam Provincial Office of Rural development. The simple correlation coefficients and multiple regression coefficients among the yield or yield components and meteorogical factors were calculated for the study. Days to emergence ranged from 8 to 26 days were reduced under conditions of mean minimum air temperature were high. The early emergence contributed to increasing plant height and number of tillers as well as to earlier maximum tillering and heading date. The plant height before wintering showed positive correlations with the hours of sunshine. On the other hand, plant height measured on march 1st and March 20th showed positive correlation with the amount of precipitation and negative correlation with the hours of sunshine during the wintering or regrowth stage. Kernel weights were affected by the hours of sunshine and rainfall after heading, and kernel weights were less variable when the hours of sunshine were relatively long and rainfalls in May were around 80 to 10mm. It seemed that grain yields were mostly affected by the climatic condition in March. showing the negative correlation between yield and mean air temperature, minimum air temperature during the period. In the other hand, the yield was shown to have positive correlation with hours of sunshine. Some yield prediction equations were obtained from the data of mean air temperature, mean minimum temperature and accumulated air temperature in March. Yield prediction was also possible by using multiple regression equations, which were derived from yield data and the number of spikes and plant height as observed at May 20th.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1314-1319
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• 2018

The current TW3 - based maximum height prediction technique used in KMAA(Korean Medical Academy of Auxology) is manual and subjective, and it requires a lot of time and effort in the medical treatment, while the interest in the child's growth is very high. In addition, the technique of classifying images using deep learning, especially convolutional neural networks, is used in many fields at a more accurate level than the human eyes, also there is no exception in the medical field. In this paper, we introduce a TW3 algorithm using deep learning, that uses the convolutional neural network to predict the growth level of the left hand bone, to predict the maximum height of child and youth in order to increase the reliability of predictions and improve the convenience of the doctor.

• Journal of ILASS-Korea
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• v.18 no.1
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• pp.35-43
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• 2013

The empirical correlations for the prediction of breakup length of liquid jet in uniform cross flow are reviewed and classified in this study. The breakup length of liquid jets in cross flow was normally discussed in terms of the distances from the nozzle exit to the column breakup location in the x and y directions, called as column fracture distance and column fracture height, respectively. The empirical correlations for the prediction of column fracture distance can be classified as constant form, momentum flux ratio form, Weber number form and other parameter form, respectively. In addition, the empirical correlations for the prediction of column fracture height can be grouped as momentum flux ratio form, Weber number form and other parameter form, respectively. It can be summarized that the breakup length of liquid jet in a cross flow is a basically function of the liquid to air momentum flux ratio. However, Weber number, liquid-to-air viscosity ratio and density ratio, Reynolds number or Ohnesorge number were incorporated in the empirical correlations depending on the investigators. It is clear that there exist the remarkable discrepancies of predicted values by the existing correlations even though many correlations have the same functional form. The possible reasons for discrepancies can be summarized as the different experimental conditions including jet operating condition and nozzle geometry, measurement and image processing techniques introduced in the experiment, difficulties in defining the breakup location etc. The evaluation of the existing empirical correlations for the prediction of breakup length of liquid jet in a uniform cross flow is required.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.313-325
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• 2022

Recently around the world, coastal erosion is paying attention as a social issue. Various constructions using low-crested and submerged structures are being performed to deal with the problems. In addition, a prediction study was researched using machine learning techniques to determine the wave attenuation characteristics of low crested structure to develop prediction matrix for wave attenuation coefficient prediction matrix consisting of weights and biases for ease access of engineers. In this study, a deep neural network model was constructed to predict the wave height transmission rate of low crested structures using Tensor flow, an open source platform. The neural network model shows a reliable prediction performance and is expected to be applied to a wide range of practical application in the field of coastal engineering. As a result of predicting the wave height transmission coefficient of the low crested structure depends on various input variable combinations, the combination of 5 condition showed relatively high accuracy with a small number of input variables defined as 0.961. In terms of the time cost of the model, it is considered that the method using the combination 5 conditions can be a good alternative. As a result of predicting the wave transmission rate of the trained deep neural network model, MSE was 1.3×10^-3, I was 0.995, SI was 0.078, and I was 0.979, which have very good prediction accuracy. It is judged that the proposed model can be used as a design tool by engineers and scientists to predict the wave transmission coefficient behind the low crested structure.