• KSCE Journal of Civil and Environmental Engineering Research
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• v.1 no.1
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• pp.53-68
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• 1981

Most clays under sustained load exhibit time-dependent deformation because of creep movement of soil particles and many investigators have attempted to relate their findings to the creep behavior of natural ground and to the long-term stability of slopes. Since the creep behavior of clays may assume a variety of forms depending on such factors as soil plasticity, activity and water content, it is difficult and complicated to analyse the creep behavior of clays. Rheological models composed of linear springs in combination with linear or nonlinear dashpots and sliders, are generally used for the mathematical description of the time-dependent behavior of soils. Most rheological models, however, have been proposed to simulate the behavior of secondary compression for saturated clays and few definitive data exist that can evaluate the behavior of non-saturated clays under the action of sustained stress. The clays change gradually from a solid state through plastic state to a liquid state with increasing water content, therefore, the rheological models also change. On the other hand, creep is time-dependent, and also the effect of thixotropy is time-function. Consequently, there may be certain correlations between creep behavior and the effects of thixotropy in compacted clays. In addition, the states of clay depend on water content and hence the height of the specimen under drained conditions. Futhermore, based on present and past studies, because immediate elastic deformation occurs instantly after the pressure increment without time-delayed behavior, the factor representing immediate elastic deformations in the rheological model is necessary. The investigation described in this paper, based on rheological model, is designed to identify the immediate elastic deformations and the effects of thixotropy and height of clay specimens with varing water content and stress level on creep deformations. For these purposes, the uniaxial drain-type creep tests were performed. Test results and data for three compacted clays have shown that a linear top spring is needed to account for immediate elastic deformations in the rheological model, and at lower water content below the visco-plastic limit, the effects of thixotropy and height of clay specimens can be represented by the proposed rheological model not considering the effects. Therefore, the rheological model does not necessitate the other factors representing these effects. On the other hand, at water content higher than the visco-plastic limit, although the state behavior of clays is visco-plastic or viscous flow at the beginning of the test, the state behavior, in the case of the lower height sample, does not represent the same behavior during the process of the test, because of rapid drainage. In these cases, the rheological model does not coincide with the model in the case of the higher specimens.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.6
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• pp.642-652
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• 2008

The aim of our research was to apply experimental design methodology in the optimization condition of Photo-Fenton oxidation of the residual livestock wastewater after the coagulation process. The reactions of Photo-Fenton oxidation were mathematically described as a function of parameters amount of Fe(II)($x_1$), $H_2O_2(x_2)$ and pH($x_3$) being modeled by the use of the Box-Behnken method, which was used for fitting 2nd order response surface models and was alternative to central composite designs. The application of RSM using the Box-Behnken method yielded the following regression equation, which is an empirical relationship between the removal(%) of livestock wastewater and test variables in coded unit: Y = 79.3 + 15.61x$_1$ - 7.31x$_2$ - 4.26x$_3$ - 18x$_1{^2}$ - 10x$_2{^2}$ - 11.9x$_3{^2}$ + 2.49x$_1$x$_2$ - 4.4x$_2$x$_3$ - 1.65x$_1$x$_3$. The model predicted also agreed with the experimentally observed result(R$^2$ = 0.96) The results show that the response of treatment removal(%) in Photo-Fenton oxidation of livestock wastewater were significantly affected by the synergistic effect of linear terms(Fe(II)($x_1$), $H_2O_2(x_2)$, pH(x$_3$)), whereas Fe(II) $\times$ Fe(II)(x$_1{^2}$), $H_2O_2$ $\times$ $H_2O_2$(x$_2{^2}$) and pH $\times$ pH(x$_3{^2}$) on the quadratic terms were significantly affected by the antagonistic effect. $H_2O_2$ $\times$ pH(x$_2$x$_3$) had also a antagonistic effect in the cross-product term. The estimated ridge of the expected maximum response and optimal conditions for Y using canonical analysis were 84 $\pm$ 0.95% and (Fe(II)(X$_1$) = 0.0146 mM, $H_2O_2$(X$_2$) = 0.0867 mM and pH(X$_3$) = 4.704, respectively. The optimal ratio of Fe/H$_2O_2$ was also 0.17 at the pH 4.7.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.742-749
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• 2009

The use of cone-beam computed tomography(CBCT) has been proposed for guiding the delivery of radiation therapy. A kilovoltage imaging system capable of radiography, fluoroscopy, and cone-beam computed tomography(CT) has been integrated with a medical linear accelerator. A standard clinical linear accelerator, operating in arc therapy mode, and an amorphous-silicon (a-Si) with an on-board electronic portal imager can be used to treat palliative patient and verify the patient's position prior to treatment. On-board CBCT images are used to generate patient geometric models to assist patient setup. The image data can also, potentially, be used for dose reconstruction in combination with the fluence maps from treatment plan. In this study, the accuracy of Hounsfield Units of CBCT images as well as the accuracy of dose calculations based on CBCT images of a phantom and compared the results with those of using CT simulator images. Phantom and patient studies were carried out to evaluate the achievable accuracy in using CBCT and CT stimulator for dose calculation. Relative electron density as a function of HU was obtained for both planning CT stimulator and CBCT using a Catphan-600 (The Phantom Laboratory, USA) calibration phantom. A clinical treatment planning system was employed for CT stimulator and CBCT based dose calculations and subsequent comparisons. The dosimetric consequence as the result of HU variation in CBCT was evaluated by comparing MU/cCy. The differences were about 2.7% (3-4MU/100cGy) in phantom and 2.5% (1-3MU/100cGy) in patients. The difference in HU values in Catphan was small. However, the magnitude of scatter and artifacts in CBCT images are affected by limitation of detector's FOV and patient's involuntary motions. CBCT images included scatters and artifacts due to In addition to guide the patient setup process, CBCT data acquired prior to the treatment be used to recalculate or verify the treatment plan based on the patient anatomy of the treatment area. And the CBCT has potential to become a very useful tool for on-line ART.)

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.6
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• pp.57-65
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• 2010

Physical activity of the people has decreased due to a sedentary lifestyle according to developing the economy throughout the world. It is thought to increase the risk of chronic diseases, including obesity, diabetes, etc. People are interested in walking, which is an easy activity to engage in as an antidote to chronic diseases. The aim of this study is to increase the diminishing physical activity of modem society by inducing walking as part of everyday life through building a walking-based activity-friendly city where people can live merrily, safely and pleasantly. For this purpose, this study conducted a satisfaction survey to dwellers of Jinhae on the physical pedestrian environments which affect determining walking participation and intentions of people, and also provided a valid model to evaluate the effects of the physical environmental factors on walking satisfaction using factor analysis and multiple linear regression analysis. The results are summarized as follows. The 18 variables of the physical pedestrian environments were selected based on pre-literature reviews. The results of the satisfaction surveys showed that the satisfaction of crossing aids in segments was highest, while the building feature was the lowest. Factor analysis was run through a two-step process. The first analysis was conducted to examine the adequacy of this factor analysis on the selected 18 variables. As a result, two variables were removed and the remaining 16 variables were extracted to the four factors by second analysis. Each factor was named function of path, effect of traffic, amenity and safety based on the each factor's commonality. Each factor score of the extracted four factors was set as the independent variable, while the overall walking satisfaction was set as the dependent variable. Then, the multiple linear regression analysis was conducted and showed that all four factors had a positive influence on the overall satisfaction of walking, especially the 'function of path' and 'amenity' factors, followed by 'effect of traffic' and 'safety'. The results of this research will be used as foundational data for creating a walking-based activity-friendly city.

• Journal of the Korean Society of Radiology
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• v.7 no.2
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• pp.145-149
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• 2013

Helical Tomotherapy is an innovative means of delivering intensity modulated radiation therapy (IMRT) using a device that merges features of a linear accelerator and helical computed tomography (CT) scanner. Hereat, during helical tomotherapy process, megavoltage computed tomography (MVCT) image are usually used for guiding the precise set-up of patient before/after treatment delivery. But which would certainly increase the total dose for patients, this study was to investigate the imaging dose of MVCT using the cylindrical "Cheese" phantom on a tomotherapy machine. A set of cylindrical "Cheese" phantom was adopted for scanning with respectively pitch value (1, 2, 3 mm) with same number slice (10 slice), same length (approximately 9 cm) and phantom set-ups on the couch of tomotherapy system. The average MVCT imaging dose were measured using A1SL ion chamber inserted in the phantom with preset geometry. The MVCT scanning average dose for the cylindrical "Cheese" phantom was 2.24 cGy, 1.02 cGy, 0.81 cGy during respectively pitch value (pitch 1, 2, 3 mm) with same number slice (10 slice), and same length's average dose was 2.47 cGy, 1.28 cGy, 0.88 cGy respectively (pitch 1, 2, 3 mm). Two major parameters, the assigned pitch numbers and scanning length, where the most important impacts to the dose variation. The MVCT dose was inversely proportional to the CT pitch value. The results may provide a reliable guidance for proper planning design of the scanning region, which is valuable to help minimize the extra dose to patient. Questionnaires were distributed to Radiology departments at hospitals with 300 sickbeds throughout the Pohang region of North Gyeongsang Province concerning awareness and performance levels of infection control. The investigation included measurements of the pollution levels of imaging equipment and assistive apparatuses in order to prepare a plan for the activation of prevention and management of hospital infections. The survey was designed to question respondents in regards to personal data, infection management prevention education, and infection management guidelines.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1159-1166
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• 2009

The use of cone-beam computed tomography(CBCT) has been proposed for guiding the delivery of radiation therapy. A kilovoltage imaging system capable of radiography, fluoroscopy, and cone-beam computed tomography(CT) has been integrated with a medical linear accelerator. A standard clinical linear accelerator, operating in arc therapy mode, and an amorphous-silicon (a-Si) with an on-board electronic portal imager can be used to treat palliative patient and verify the patient's position prior to treatment. On-board CBCT images are used to generate patient geometric models to assist patient setup. The image data can also, potentially, be used for dose reconstruction in combination with the fluence maps from treatment plan. In this study, the accuracy of Hounsfield Units of CBCT images as well as the accuracy of dose calculations based on CBCT images of a phantom and compared the results with those of using CT simulator images. Phantom and patient studies were carried out to evaluate the achievable accuracy in using CBCT and CT stimulator for dose calculation. Relative electron density as a function of HU was obtained for both planning CT stimulator and CBCT using a Catphan-600 (The Phantom Laboratory, USA) calibration phantom. A clinical treatment planning system was employed for CT stimulator and CBCT based dose calculations and subsequent comparisons. The dosimetric consequence as the result of HU variation in CBCT was evaluated by comparing MU/cCy. The differences were about 2.7% (3-4MU/100cGy) in phantom and 2.5% (1-3MU/100cGy) in patients. The difference in HU values in Catphan was small. However, the magnitude of scatter and artifacts in CBCT images are affected by limitation of detector's FOV and patient's involuntary motions. CBCT images included scatters and artifacts due to In addition to guide the patient setup process, CBCT data acquired prior to the treatment be used to recalculate or verify the treatment plan based on the patient anatomy of the treatment area. And the CBCT has potential to become a very useful tool for on-line ART.)

• Journal of the Korean Electrochemical Society
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• v.19 no.3
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• pp.80-86
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• 2016

High Ni content layered oxide materials for the positive electrode in lithium-ion batteries have high specific capacity. However, their poor electrochemical and thermal stability at elevated temperature restrict the practical use. A small amount of $Al_2O_3$ was added to the mixture of transition metal hydroxide and lithium hydroxide. The $LiNi_{0.6}Co_{0.2}Mn_{0.2}O_2$ was simultaneously doped and coated with $Al_2O_3$ during heat-treatment. Electrochemical characteristics of modified $LiNi_{0.6}Co_{0.2}Mn_{0.2}O_2$ were evaluated by the galvanostatic cycling and the LSTA(linear sweep thermmametry) at the constant voltage conditions. The nano-sized $Al_2O_3$ added materials show better cycle performance at elevated temperature than that of micro-sized $Al_2O_3$. As the added amount of nano-$Al_2O_3$ increased, the thermal stability of electrode also enhanced, but the use of 2.5 mol% Al showed the best high temperature performance.

• Fire Science and Engineering
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• v.15 no.1
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• pp.23-33
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• 2001

The utility tunnels are the important facility as a mainstay of country because of the latest communication developments. However, the utilities tunnel is difficult to deal with in case of a fire accident. When a cable burns, the black smoke containing poisonous gas will be reduced. This black smoke goes into the tunnel, and makes it difficult to extinguish the fire. Therefore, when there was a fire in the utility tunnel, the central nerves of the country had been paralyzed, such as property damage, communication interruption, in addition to inconvenience for people. This paper is based on the fire occurred in the past, and reenacting the fire by making the real utilities tunnel model. The aim of this paper is the scientific analysis of the character image of the fire, and the verification of each fire protection system whether it works well after process of setting up a fire protection system in the utilities tunnel at a constant temperature. The fire experiment was equipped with the linear heat detector, the fire door, the connection water spray system and the ventilation system in the utilities tunnel. Fixed portion of an electric power supply cable was coated with a fire retardant coating, and a heating tube was covered with a fireproof. The result showed that the highest temperature was $932^{\circ}c$ and the linear heat detector was working at the constant temperature, and it pointed at the place of the fire on the receiving board, and Fixed portion of the electric power supply cable coated with the fire retardant coating did not work as the fireproof. The heating tube was covered with the fireproof about 30 minutes.