• The Korean Journal of Archival Studies
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• no.25
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• pp.3-46
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• 2010

Digital records management is one whole system in which many social and technical elements are interacting. To maintain the trustworthiness, the repository needs periodical audit and certification. Thus, individual electronic records management agency needs toolkit that can be used to self-evaluate their trustworthiness continuously, and self-assess their atmosphere and system to recognize deficiencies. The purpose of this study is development of self-certification toolkit for repositories, which synthesized and analysed such four international standard and best practices as OAIS Reference Model(ISO 14721), TRAC, DRAMBORA, and the assessment report conducted and published by TNA/UKDA, as well as MoRe2 and current national laws and standards. As this paper describes and demonstrate the development process and the framework of this self-certification toolkit, other electronic records management agencies could follow the process and develop their own toolkit reflecting their situation, and utilize the self-assessment results in-house. As a result of this research, 12 areas for assessment were set, which include (organizational) operation management, classification system and master data management, acquisition, registration and description, storage and preservation, disposal, services, providing finding aids, system management, access control and security, monitoring/audit trail/statistics, and risk management. In each 12 area, the process map or functional charts were drawn and business functions were analyzed, and 54 'evaluation criteria', consisted of main business functional unit in each area were drawn. Under each 'evaluation criteria', 208 'specific evaluation criteria', which supposed to be implementable, measurable, and provable for self-evaluation in each area, were drawn. The audit and certification toolkit developed by this research could be used by digital repositories to conduct periodical self-assessment of the organization, which would be used to supplement any found deficiencies and be used to reflect the organizational development strategy.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.168-181
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• 2024

Constructed wetlands (CWs) deliver a range of ecosystem services, including the removal of contaminants, sequestration and storage of carbon, and enhancement of biodiversity. These services are facilitated through hydrological and ecological processes such as infiltration, adsorption, water retention, and evapotranspiration by plants and microorganisms. This study investigated the correlations between microbial populations, soil physicochemical properties, and treatment efficiency in a horizontal subsurface flow constructed wetland (HSSF CW) treating runoff from roads and parking lots. The methods employed included storm event monitoring, water quality analysis, soil sampling, soil quality parameter analysis, and microbial analysis. The facility achieved its highest pollutant removal efficiencies during the warm season (>15℃), with rates ranging from 33% to 74% for TSS, COD, TN, TP, and specific heavy metals including Fe, Zn, and Cd. Meanwhile, the highest removal efficiency was 35% for TOC during the cold season (≤15℃). These high removal rates can be attributed to sedimentation, adsorption, precipitation, plant uptake, and microbial transformations within the CW. Soil analysis revealed that the soil from HSSF CW had a soil organic carbon content 3.3 times higher than that of soil collected from a nearby landscape. Stoichiometric ratios of carbon (C), nitrogen (N), and phosphorus (P) in the inflow and outflow were recorded as C:N:P of 120:1.5:1 and 135.2:0.4:1, respectively, indicating an extremely low proportion of N and P compared to C, which may challenge microbial remediation efficiency. Additionally, microbial analyses indicated that the warm season was more conducive to microorganism growth, with higher abundance, richness, diversity, homogeneity, and evenness of the microbial community, as manifested in the biodiversity indices, compared to the cold season. Pollutants in stormwater runoff entering the HSSF CW fostered microbial growth, particularly for dominant phyla such as Proteobacteria, Actinobacteria, Acidobacteria, and Bacteroidetes, which have shown moderate to strong correlations with specific soil properties and changes in influent-effluent concentrations of water quality parameters.

• Journal of Nutrition and Health
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• v.42 no.7
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• pp.650-663
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• 2009

The purposes of this study were to investigate one portion size of menus served and to evaluate nutrient intake of lunch at three elderly care facility food services located in Seoul. A weighed plate method was employed to measure plate wastes and consumption of the menus served. Yield factors were calculated from cooking experiments based on standardized recipes, and were used to evaluate nutrient intake. One hundred elderly participated in this study for measuring plate waste and were asked to complete questionnaire. Nutrient analyses for the served and consumed meal were performed using CAN program. The yield factors of rice dishes after cooking are 2.4 regardless of rice dish types, 1.58 for thick soups, 0.60 to 0.70 for meat dishes, and 1.0 to 1.25 branched vegetable. Average consumption quantity of dishes were 235.97 g for rice, 248.53 g for soup, 72.83 g for meat dishes, 39.80 g for vegetables and 28.36 g for Kimchi. On average the food waste rate is 14.0%, indicating the second highest plate waste percentage of Kimchi (26.2%), and meat/fish dish (17.3%). The evaluation results of NAR (Nutrition Adequacy Ratio) showed that iron (0.12), calcium (0.64), riboflavin (0.80), and folic acid (0.97) were less than 1.0 in both male and female elderly groups, indicating significant differences of NAR among three facilities. Compared to the 1/3 Dietary Reference Intake (DRIs) for the elderly groups, nutrient intake analysis demonstrated that calcium (100%) and iron (100%), followed by riboflavin, vitamin A, and Vitamin B6 did not met of the 1/3 EAR (Estimated Average Requirement). For the nutritious meal management, a professional dietitian should be placed at the elderly care center to develop standardized recipes in consideration of yield factors and the elderly's health and nutrition status.

• 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.