• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1335-1349
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• 2019

Crowdsensing technologies can improve the efficiency of smart parking system in comparison with present sensor based smart parking system because of low install price and no restriction caused by sensor installation. A lot of sensing data is necessary to predict parking lot saturation in real-time. However in real world, it is hard to reach the required number of sensing data. In this paper, we model a saturation predication combining a time-based prediction model and a sensing data-based prediction model. The time-based model predicts saturation in aspects of parking lot location and time. The sensing data-based model predicts the degree of saturation of the parking lot with high accuracy based on the degree of saturation predicted from the first model, the saturation information in the sensing data, and the number of parking spaces in the sensing data. We perform prediction model learning with real sensing data gathered from a specific parking lot. We also evaluate the performance of the predictive model and show its efficiency and feasibility.

• Korean Journal of Materials Research
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• v.33 no.7
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• pp.273-278
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• 2023

In this study, based on the saturation magnetic flux density experimental values (B_s) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict B_s using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R² score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict B_s above the threshold value of B_s (1.4 T). After obtaining two ANN models optimized for the R² and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1365-1371
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• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model. The slope hazards data of Seoul and Kyonggi Province were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. The statistical analyses using the decision tree model were applied to the entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320m, respectively.

• Journal of Environmental Health Sciences
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• v.38 no.2
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• pp.136-141
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• 2012

Objectives: The objective of this study was to investigate whether the ammonia nitrogen concentration of aqueous samples such as drinking water can be determined by measuring the saturation of the samples colored by indophenol method. Methods: A color saturation measurement system was constructed by connecting a notebook computer to an image acquisition device composed of a PC camera and a light source, and was then used to measure the saturation of samples colored by blue indophenol complex. Results: Between two available light sources, a fluorescent lamp was selected due to its demonstrating better linearity between color saturation and ammonia nitrogen concentration. Prediction by quadratic regression was more accurate than by linear regression, and prediction by quadratic regression in the concentration range of 0.1-1.0 $mg/l$ was more accurate than in the concentration range of 0.0-1.0 $mg/l$. Regression-based predictions over 0.25 $mg/l$, 0.55 $mg/l$ and 0.75 $mg/l$ concentrations were implemented both by spectrophotometric method and by measuring color saturation. In the case of 0.25 $mg/l$, the predicted concentration by spectrophotometric method was $0.256{\pm}0.0076\;mg/l$ and the predicted concentration by measuring color saturation was $0.246{\pm}0.0086\;mg/l$ (p=0.051). In the case of 0.55 $mg/l$, they were $0.561{\pm}0.0068\;mg/l$ and $0.564{\pm}0.0166\;mg/l$ (p=0.660). In the case of 0.75 $mg/l$, they were $0.755{\pm}0.0139\;mg/l$ and $0.762{\pm}0.0088\;mg/l$ (p=0.215). Conclusions: There were no statistically significant differences (p>0.05) between the data from the two methods in all three of the concentrations. Therefore, the color saturation measurement method proposed in this paper may be considered applicable for determining the ammonia nitrogen concentration of aqueous samples such as drinking water.

• Environmental Engineering Research
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• v.14 no.2
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• Journal of the Korean GEO-environmental Society
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• v.8 no.4
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• pp.67-74
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• 2007

Many studies for slope stability studies have indicated that the infiltration of rainwater into a slope decrease the slope stability. In order to minimize damage caused by slope failure, most design codes suggest that the slope stability be analyzed by saturated condition during rainy season. However it would be excessively conservative condition that every soil slope is saturated in rainy season irrespective of rainfall intensity, soil type and slope geometry. In addition, because most soil slopes are in an unsaturated state, it is necessary to consider the unsaturated characteristics of slope. This paper suggests a prediction method of saturation time for the weathered granite soil slopes due to rainfalls. The finite element analysis of transient water flow through unsaturated slope was used to investigate effects of soil-water characteristics, permeability at saturation, slope geometry, and rainfall intensity. From the result of these analyses, the prediction charts considering soil-water characteristics, permeability at saturation, and slope height were proposed in this study. It is possible to the time required to be saturated slope after rainfall.

• Geomechanics and Engineering
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• v.11 no.6
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• pp.771-785
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• 2016

The thermal conductivity of soils is an important property in energy-related geotechnical structures, such as underground heat pumps and underground electric power cable tunnels. This study explores the effects of geotechnical engineering properties on the thermal conductivity of soils. The thermal conductivities of quartz sands and Korean weathered silty sands were documented via a series of laboratory experiments, and its variations with effective stress, porosity, and water saturation were examined. While thermal conductivity was found to increase with an increase in the effective stress and water saturation and with a decrease in porosity, replacing air by water in pores the most predominantly enhanced the thermal conductivity by almost one order of magnitude. In addition, we have suggested an improved model for thermal conductivity prediction, based on water saturation, dry thermal conductivity, saturated thermal conductivity, and a fitting parameter that represents the curvature of the thermal conductivity-water saturation relation.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.55-57
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• 1998

A finite element analysis for a synchronous reluctance motor (SynRM) is presented with emphasis on the effect of saturation and iron losses. Preisachs model, which allows accurate prediction of iron losses, is adopted in this procedure to provide a nonlinear solution. This technique provide significant properties of proposed SynRM under the magnetic saturation and iron losses effect.

• Journal of the Korean Geotechnical Society
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• v.30 no.1
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• pp.37-47
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• 2014

Unsaturated hydraulic conductivity (HC) is integrated theoretically from soil water retention curves (SWRC) by Mualem capillary model, but the prediction of HC is extremely sensitive to small variation of matric suction near saturation. Near saturation, the Mualem HC based on smooth SWRC decreases abruptly and has problems in the reliability of hydraulic behavior and the stability of numerical solutions. To improve van Genuchten-Mualem (VGM) HC, the van Genuchten SWRC model is modified within range of low matric suction (arbitrary air entry pressure). At an arbitrary air entry pressure, the VG SWRC is linearized in log scale until full saturation. The modified VG SWRC does not affect the fit of actual retention behavior and either the parameters of original VG SWRC fit. Using the modified VG SWRC, the VGM HC is modified to integrate for each interval decomposed by arbitrary air entry pressure. An analytical solution on modified VGM HC is proposed each interval, to protect the rapid change in HC near saturation. For silty soils, VGM models of HC function underestimate the unsaturated permeability characteristics and especially show rapid reduction near saturation. The modified VGM model predicts more accurate HC functions for Korean weathered soils. Furthermore, near saturation, the saturated HC is conserved by the modified VGM model. After 2-D infiltration analysis of an actual slope, the hydraulic behaviors are compared for VGM and the modified models. The prediction by the proposed model conserved the convergence of solutions on various rainfall conditions. However, the solution by VGM model did not converge since the conductivity near saturation reduced abruptly for heavy rainfall condition. Using VGM model, the factor of safety is overestimated in both initial and final stage during heavy rainfall. Stability analysis based on infiltration analysis could simulate the actual slope failure by the proposed model on HC.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.6
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• pp.61-69
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• 2004

The aim of the work described in this paper was to confirm the application of the equation of the soil-water characteristic curves on an unsaturated soil. A series of suction test for unsaturated soils was conducted on the selected 4 kinds of soil using modified pressure extractor apparatus. And it was carried out to analyse The experimental parameters which can be describe the soil-water characteristics, were determined by using the data obtained from the experiment. From the results, it was found that The matric suction varied according to the grain size distribution, amount of fine grain particles and void ratio. Also it was found that the residual degree of saturation was decreased with in crease of the void ratio, but the pore size distribution index and air entry value were increased with in crease of the void ratio. And The application of the soil-water characteristic curve equation was confirmed for the various conditions and the various state by the comparison between the measured degree of saturation and the predicted degree of saturation.