• Korean Journal of Soil Science and Fertilizer
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• v.32 no.4
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• pp.344-356
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• 1999

A new and relatively simple equation for the soil water content-pressure head curve, ${\theta}$(h) is described in this paper. The particular form of the equation enables one to derive closed-form analytical expressions for the relative hydraulic conductivity, Kr, when substituted in the predictive conductivity models of Y. Mualem. Hopmans' equation is presented as an experimental method. The experienced method, $ET_a=K_sK_cET_o$ is introduced to estimate the actual evapotranspiration, $ET_a$(or $ET_c$). Using $ET_c$ and coil water data measured automatically in a weighing lusimeter, $K_s$ and $K_c$ values are estimated. Recently, FAO has introduced calculation procedures for the soil water(stress) coefficient, Ks in "Guidelines for computing crop water requirements".

• Membrane and Water Treatment
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• v.10 no.4
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• pp.313-320
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• 2019

Increasing the concentration of nitrate ions in the soil solution and then leaching it to underground aquifers increases the concentration of nitrate in the water, and can cause many health and ecological problems. This study was conducted to evaluate the vulnerability of Meymeh aquifer to nitrate pollution. In this research, sampling of 10 wells was performed according to standard sampling principles and analyzed in the laboratory by spectrophotometric method, then; the nitrate concentration zonation map was drawn by using intermediate models. In the drastic model, the effective parameters for assessing the vulnerability of groundwater aquifers, including the depth of ground water, pure feeding, aquifer environment, soil type, topography slope, non-saturated area and hydraulic conductivity. Which were prepared in the form of seven layers in the ARC GIS software, and by weighting and ranking and integrating these seven layers, the final map of groundwater vulnerability to contamination was prepared. Drastic index estimated for the region between 75-128. For verification of the model, nitrate concentration data in groundwater of the region were used, which showed a relative correlation between the concentration of nitrate and the prepared version of the model. A combination of two vulnerability map and nitrate concentration zonation was provided a qualitative aquifer classification map. According to this map, most of the study areas are within safe and low risk, and only a small portion of the Meymeh Aquifer, which has a nitrate concentration of more than 50 mg / L in groundwater, is classified in a hazardous area.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.65-70
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• 2022

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.

• Journal of Korean Society on Water Environment
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• v.26 no.2
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• pp.191-199
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• 2010

Introduction of Technology-Based Effluent Limitations (TBELs) concepts into Korea requires extensive and through analyses on the expected pollutants reduction effects and the cost factor for the industry side. In addition, the TEBLs should be optimized for the regulatory environments of Korea and be applied in a progressive manner to minimize the undesirable effects. It is also necessary to assess the contribution of each industrial categories's to the pollution of natural water bodies to find the priority of TEBLs application. For these purposes, the pollution loads of various industrial categories were analyzed using risk assessment indicator based on Toxic Weighting Factors (TWFs). First, the TWFs were calculated for the pollutants regulated using the method adopted by USEPA. And the effluent characteristics of the eighty two categories of industry in Korea were investigated. Although the analytical data on the wastewater from different industrial categories are relatively limited, the results from two previous studies were used. The first study, conducted by the National Institute of Environmental Research in 2001~2004, investigated the wastewater characteristics from 255 industrial sources covering the major 20 industrial categories. The second study includes more recent analytical data for the wastewater from 500 industrial sources, covering all the 82 industrial categories. In the result of the pollution loads analyses, the category of 'Synthetic and Chemical manufacture' was found to show the highest raw pollution load. On the other hand the category of 'Ion & Steel manufacture' was found to show the highest effluent pollution loads, which can be considered as the real impacts on natural water streams. The top five categories occupied 62.2% of the total effluent pollution loads. Through the analyses, the relative importances of each industrial categories and the priorities of TEBL-based pollution reduction were determined.

• Journal of the Korean association of regional geographers
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• v.14 no.6
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• pp.736-751
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• 2008

Although a growing number of studies have commonly used a simple areal weighting interpolation method to quantify demographic characteristics of impacted areas in environmental equity analysis, the results obtained are inevitably imprecise because of the method's unrealistic assumption that population is evenly distributed within a census enumeration unit. Two alternative areal interpolation methods such as intelligent areal weighting and regression methods can account for the distributional biases in the estimation of impacted populations by making use of additional information about the geographic distribution of population. This research explores five areal interpolation methods for estimating the population characteristics of impacted areas in environmental equity analysis and evaluates the sensitivity of the outcomes of environmental equity analysis to areal interpolation methods. This study used GIS techniques to allow areal interpolation to be informed by the distribution of land cover types, as inferred from a satellite image. in both the source and target units. Independent samples t-test statistics were measured to verify the environmental equity hypothesis while coefficients of variation were calculated to compare the relative variability and consistency in the socioeconomic characteristics of populations at risk over different areal interpolation methods. Results show that the outcomes of environmental equity analysis in the study area are not sensitive to the areal interpolation methods used in estimating affected populations, but the population estimates within the impacted areas are largely variable as different areal interpolation methods are used. This implies that the use of different areal interpolation methods may to some degree alter the statistical results of environmental equity analysis.

• Journal of the Korean association of regional geographers
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• v.13 no.2
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• pp.143-155
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• 2007

There are many conflicts or interests among various stakeholders on the development of the coastal area. The integrated methodology, which is reflective of physical conditions, socio-economic circumstances, and people's sense of values, is thus needed to solve the problems. In this study, geographical information systems(GIS) and analytic hierarchy process(AHP) that arc one of multi-criteria analysis methodologies are loosely coupled to develop better analytic procedures for coastal assessment. Socio-economic and environmental parameters of the study area, Hampyung Bay area, are converted to a GIS system-applicable format, while AHP is used to assess the relative importance level of each parameter by calculating weighting factors. After standardizing and rasterizing spatial data from various sources. the weighting factors are applied to produce the layers for each parameter. Map algebra and overlay analyses are used to create the final layer according to the decision making logic or model proposed here. Cell values of that layer could be considered as spatial alternatives. In addition to this finding, the flexibility with the weighing factors enable decision-makers to understand the procedures and alternatives in relevance with selective strategies for coastal management.

• Journal of KIISE
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• v.42 no.5
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• pp.642-651
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• 2015

A vanishing point is a point where parallel lines converge, and they become evident when a camera's lenses are used to project 3D space onto a 2D image plane. Vanishing point detection is the use of the information contained within an image to detect the vanishing point, and can be utilized to infer the relative distance between certain points in the image or for understanding the geometry of a 3D scene. Since parallel lines generally exist for the artificial structures within images, line-detection-based vanishing point-detection techniques aim to find the point where the parallel lines of artificial structures converge. To detect parallel lines in an image, we detect edge pixels through edge detection and then find the lines by using the Hough transform. However, the various textures and noise in an image can hamper the line-detection process so that not all of the lines converging toward the vanishing point are obvious. To overcome this difficulty, it is necessary to assign a different weight to each line according to the degree of possibility that the line passes through the vanishing point. While previous research studies assigned equal weight or adopted a simple weighting calculation, in this paper, we are proposing a new method of assigning weights to lines after noticing that the lines that pass through vanishing points typically belong to artificial structures. Experimental results show that our proposed method reduces the vanishing point-estimation error rate by 65% when compared to existing methods.

• Journal of the Korean Society of Industry Convergence
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• v.1 no.1
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• pp.21-30
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• 1998

The purpose of this study is to clarify the floor temperature on the human body and to estimate thermal comfort zone in a heated room. In order to evaluate the effects of floor heating, a series of experiments were carried out using Korean subjects. The following experiments were conducted: 1) to obtain the effective radiation area and configuration factors of the person in the sitting posture on a floor to get the mean radiant temperature, 2) to measure contacted area of the person to the floor to calculate conduction heat rate, 3) to measure convective heat transfer coefficient of the body and 4) to know the thermal comfort zone of indoor environment heated by ON-DOL. Subjects were exposed to the following conditions: combinations of air temperature $20^{\circ}C$, $22.5^{\circ}C$, $25^{\circ}C$, and floor temperature $20^{\circ}C$, $22.5^{\circ}C$, $25^{\circ}C$, $27.5^{\circ}C$, $30^{\circ}C$, $32.5^{\circ}C$, $35^{\circ}C$, $37.5^{\circ}C$, $40^{\circ}C$ under still air and 50% relative humidity in the controllable artificial climate chamber. To evaluate the effect of heat conduction between the body and a floor modified mean skin temperature was defined. Weighting coefficient to calculate mean skin temperature were modified with the contacted area. The experiments revealed a positive correlation between the modified operative temperature and the modified mean skin temperature. The modified mean skin temperature can indicate the effect of heat conduction between body and a floor surface.

• Journal of Broadcast Engineering
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• v.5 no.2
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• pp.281-289
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• 2000

While acquiring images, the relative motion between the imaging device and the object scene seriously damages the image quality. This phenomenon is called motion blur. The peak-trace approach, which is our recent previous work, identifies important parameters to characterize the point spread function (PSF) of the blur, given only the blurred image itself. With the peak-trace approach the direction of the motion blur can be extracted regardless of the noise corruption and does not need much Processing time. In this paper stochastic peak-trace approaches are introduced. The erroneous data can be selected through the ML classification, and can be made small through weighting. Therefore the distortion of the direction in the low frequency region can be prevented. Using the linear prediction method, the irregular data are prohibited from being selected as the peak point. The detection of the second peak using the proposed moving average least mean (MALM) method is used in the Identification of the motion extent. The MALM method itself includes a noise removal process, so it is possible to extract the parameters even an environment of heavy noise. In the experiment, we could efficiently restore the degraded image using the information obtained by the proposed algorithm.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.