• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.539-544
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• 2023

The subject of this study is the Maae Buddha statue in granodiorite of the Mesozoic Cretaceous period, which is concerned about stability as a standing stone cultural property located in ◯◯-dong, Gyeongsangbuk-do. For stability analysis, three-dimensional face mapping, geological properties of joints, three-dimensional scanning, ultrasonic velocity, polarization microscopy, electron microscopy analysis and XRD analysis were performed. In addition, the safety factor of the Maaebul was calculated by analyzing the damage status investigation, stereographic projection analysis, rock classification, and limit equilibrium analysis. The types and scales of damage and possible collapse by section depend on the degree of weathering of the rock and the orientation and characteristics of the joints, but wedge-failure and toppling-failure are expected to be small-scale. The safety factor of Maaebul in dry and wet conditions is less than 1.2, so stability is concerned. The types of damage were mainly observed, such as exfoliation, cracking, granular decomposition, and vegetation growth. The Maaebul rock is granodiorite, and the surface discoloration materials are K, Fe, and Mg. The 4 sets of joints are developed, J1 is tensile joint and the others are shear joint. The uniaxial compressive strength estimated by ultrasonic exploration is 514kgf/cm2, which corresponds to most soft rocks and some weathered rocks. Rock classification(RMR) is estimated to be grade 5, very poor rock mass. These technique along with the existing methods of safety diagnosis of cultural properties are expected to be a reasonable tool for objective interpretation and stability review of stone cultural properties.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.815-822
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• 2023

Organic pollution is one of the most common forms of water contamination. Under the Water Quality Conservation Act, indicators for measuring organic substances include BOD, COD, and TOC. Analysis of BOD and COD is labor-intensive, and in the case of organic substances where biological decomposition is not feasible or toxic substances are present, the accuracy is often low. Therefore, the Ministry of Environment is shifting towards TOC-centric management. With advancements in sensor technology today, various parameters can be monitored using sensors. In this study, digital monitoring of river TOC using a spectrophotometer called Spectro::lyser V3 was conducted. Initially, experiments were carried out at the Andong River Experiment Center to assess the applicability of the measurement equipment. Subsequently, data collected at the confluence of the Nakdong River was analyzed for the spatial distribution of TOC using the Kriging technique. This research proposes the utilization of sensors for river TOC monitoring and spatial distribution analysis. Real-time monitoring of changes in river TOC concentration can serve as fundamental data for pollution monitoring and response. Sensor-based river monitoring offers advantages in terms of temporal resolution and real-time data acquisition. When various spatial information interpretation methods are applied, it is expected to contribute to diverse studies such as aquatic ecological health, river water source selection, and stratification analysis in the future.

• Food Science and Preservation
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• v.30 no.6
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• pp.983-990
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• 2023

This study was conducted to develop a preservation technology that can induce changes in physicochemical properties to effectively utilize of persimmon leaves. The application effects of steaming or freezing technique were investigated. Astringent persimmon leaves were steam-blanched (100℃, 30 sec) or frozen (-20℃, 15 d), followed by hot-air drying (50℃). The physicochemical properties of the extract obtained by hot-water leaching from the dried leaves were compared. The extract of leaves dried without pretreatment was used as a control. L* value was higher in steamed than in control and frozen. a* value was highest in the control. The browning index was higher in the frozen and lower in the steamed than in the control. Soluble solids were the highest in the steamed and the lowest in the frozen. Sucrose content was relatively high in the steamed, and the glucose and fructose contents were relatively high in the frozen. Total polyphenol content and DPPH radical scavenging activity were higher in steamed and lower in frozen than in control. Thus, it was confirmed that steam or freeze pretreatment after harvesting persimmon leaves affects the extraction yield, color, antioxidant capacity and component changes of dried persimmon leaves. Unlike steaming, freezing pretreatment showed the effect of promoting decomposition and browning reactions, and it is considered useful when such an effect is needed.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.47-75
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• 2009

Recently, researches on Management Information Systems (MIS) have laid out theoretical foundation and academic paradigms by introducing diverse theories, themes, and methodologies. Especially, academic paradigms of MIS encourage a user-friendly approach by developing the technologies from the users' perspectives, which reflects the existence of strong causal relationships between information systems and user's behavior. As in other areas in social science the use of structural equation modeling (SEM) has rapidly increased in recent years especially in the MIS area. The SEM technique is important because it provides powerful ways to address key IS research problems. It also has a unique ability to simultaneously examine a series of casual relationships while analyzing multiple independent and dependent variables all at the same time. In spite of providing many benefits to the MIS researchers, there are some potential pitfalls with the analytical technique. The research objective of this study is to provide some guidelines for an appropriate use of SEM based on the assessment of current practice of using SEM in the MIS research. This study focuses on several statistical issues related to the use of SEM in the MIS research. Selected articles are assessed in three parts through the meta analysis. The first part is related to the initial specification of theoretical model of interest. The second is about data screening prior to model estimation and testing. And the last part concerns estimation and testing of theoretical models based on empirical data. This study reviewed the use of SEM in 164 empirical research articles published in four major MIS journals in Korea (APJIS, ISR, JIS and JITAM) from 1991 to 2007. APJIS, ISR, JIS and JITAM accounted for 73, 17, 58, and 16 of the total number of applications, respectively. The number of published applications has been increased over time. LISREL was the most frequently used SEM software among MIS researchers (97 studies (59.15%)), followed by AMOS (45 studies (27.44%)). In the first part, regarding issues related to the initial specification of theoretical model of interest, all of the studies have used cross-sectional data. The studies that use cross-sectional data may be able to better explain their structural model as a set of relationships. Most of SEM studies, meanwhile, have employed. confirmatory-type analysis (146 articles (89%)). For the model specification issue about model formulation, 159 (96.9%) of the studies were the full structural equation model. For only 5 researches, SEM was used for the measurement model with a set of observed variables. The average sample size for all models was 365.41, with some models retaining a sample as small as 50 and as large as 500. The second part of the issue is related to data screening prior to model estimation and testing. Data screening is important for researchers particularly in defining how they deal with missing values. Overall, discussion of data screening was reported in 118 (71.95%) of the studies while there was no study discussing evidence of multivariate normality for the models. On the third part, issues related to the estimation and testing of theoretical models on empirical data, assessing model fit is one of most important issues because it provides adequate statistical power for research models. There were multiple fit indices used in the SEM applications. The test was reported in the most of studies (146 (89%)), whereas normed-test was reported less frequently (65 studies (39.64%)). It is important that normed- of 3 or lower is required for adequate model fit. The most popular model fit indices were GFI (109 (66.46%)), AGFI (84 (51.22%)), NFI (44 (47.56%)), RMR (42 (25.61%)), CFI (59 (35.98%)), RMSEA (62 (37.80)), and NNFI (48 (29.27%)). Regarding the test of construct validity, convergent validity has been examined in 109 studies (66.46%) and discriminant validity in 98 (59.76%). 81 studies (49.39%) have reported the average variance extracted (AVE). However, there was little discussion of direct (47 (28.66%)), indirect, and total effect in the SEM models. Based on these findings, we suggest general guidelines for the use of SEM and propose some recommendations on concerning issues of latent variables models, raw data, sample size, data screening, reporting parameter estimated, model fit statistics, multivariate normality, confirmatory factor analysis, reliabilities and the decomposition of effects.

• 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.28 no.4
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• pp.368-375
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• 2006

Single and complex metal oxide catalysts supported onto a commercial DT51D $TiO_2$ have been investigated for gas-phase TCE oxidation in a continuous flow type fixed-bed reaction system to develop a better design approach to catalysts for this reaction. Among the $TiO_2$-supported single metal oxides used, i.e., $CrO_x,\;FeO_x,\;MnO_x,\;LaO_x,\;CoO_x,\;NiO_x,\;CeO_x\;and\;CuO_x$, with the respective metal contents of 5 wt.%, the $CrO_x/TiO_2$ catalyst was shown to be most active for the oxidative TCE decomposition, depending significantly on amounts of $CrO_x\;on\;TiO_2$. The use of high $CrO_x$ loadings greater than 10 wt.% caused lower activity in the catalytic TCE oxidation, which is probably due to production of $Cr_2O_3$ crystallites on the surface of $TiO_2$. $CrO_x/TiO_2$-supported $CrO_x$-based bimetallic oxide catalysts were of particular interest in removal efficiency for this TCE oxidation reaction at reaction temperatures above $200^{\circ}C$, compared to that obtained with $CrO_x$-free complex metal oxides and a 10 wt.% $CrO_x/TiO_2$ catalyst. Catalytic activity of 5 wt.% $CrO_x-5$ wt.% $LaO_x$ in the removal reaction was similar to or slightly higher than that acquired for the $CrO_x$-only catalyst. Similar observation was revealed for 5 wt.% $CrO_x$-based bimetallic oxides consisting of either 5 wt.% $MnO_x,\;CoO_x,\;NiO_x\;or\;FeO_x$. These results represent that such $CrO_x$-based bimetallic systems for the catalytic TCE oxidation on significantly minimize the usage of $CrO_x$ that is well known to be one of very toxic heavy metals, and offer a very useful technique to design new type catalysts for reducing chlorinated volatile organic substances.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.2
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• pp.95-109
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• 1993

In this study, the optimal configuration of arch structure has been tested by a decomposition technique. The object of this study is to provide the method of optimizing the shapes of both two hinged and fixed arches. The problem of optimal configuration of arch structures includes the interaction formulas, the working stress, and the buckling stress constraints on the assumption that arch ribs can be approximated by a finite number of straight members. On the first level, buckling loads are calculated from the relation of the stiffness matrix and the geometric stiffness matrix by using Rayleigh-Ritz method, and the number of the structural analyses can be decreased by approximating member forces through sensitivity analysis using the design space approach. The objective function is formulated as the total weight of the structures, and the constraints are derived by including the working stress, the buckling stress, and the side limit. On the second level, the nodal point coordinates of the arch structures are used as design variables and the objective function has been taken as the weight function. By treating the nodal point coordinates as design variable, the problem of optimization can be reduced to unconstrained optimal design problem which is easy to solve. Numerical comparisons with results which are obtained from numerical tests for several arch structures with various shapes and constraints show that convergence rate is very fast regardless of constraint types and configuration of arch structures. And the optimal configuration or the arch structures obtained in this study is almost the identical one from other results. The total weight could be decreased by 17.7%-91.7% when an optimal configuration is accomplished.