• Journal of Soil and Groundwater Environment
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• v.19 no.4
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• pp.12-22
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• 2014

This study evaluated the pattern of groundwater fluctuation in cyrstalline rock using time series and factor analyses. From the results, groundwater level for the 18 wells was classified into 4 types reflecting the hydrogeological properties and rainfall event. Type 1 (DB1-5, DB1-6, DB2-2, KB-10, KB-13) was significantly influenced by groundwater flow through water-conducting features, whereas type 2 (DB1-3, DB1-7, KB-1~KB-3, KB-7, KB-11, KB-14, KB-15) was affected by minor fracture network as well as rainfall event. Type 3 (DB1-1, DB1-2) was mainly influenced by surface infiltration of rainfall event. Type 4 (DB1-8, KB-9) was reflected by the irregular variation of groundwater level caused by anisotropy and heterogeneity of crystalline rock.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.19-27
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• 2022

In this study, we propose a language and platform to describe and manage the MLOps(Machine Learning Operations) workflow for time series data anomaly detection. Time series data is collected in many fields, such as IoT sensors, system performance indicators, and user access. In addition, it is used in many applications such as system monitoring and anomaly detection. In order to perform prediction and anomaly detection of time series data, the MLOps platform that can quickly and flexibly apply the analyzed model to the production environment is required. Thus, we developed Python-based AI/ML Modeling Language (AMML) to easily configure and execute MLOps workflows. Python is widely used in data analysis. The proposed MLOps platform can extract and preprocess time series data from various data sources (R-DB, NoSql DB, Log File, etc.) using AMML and predict it through a deep learning model. To verify the applicability of AMML, the workflow for generating a transformer oil temperature prediction deep learning model was configured with AMML and it was confirmed that the training was performed normally.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.211-212
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• 2021

Recently, as interest in health increases, the wearable market that can collect various biometric information is expanding. In addition, telemedicine and healthcare services through these bio-signals are expected to become common. In this paper, we introduce a service that can store bio-signals collected through IoT equipment in a database and monitor them in real time through the web. By implementing a system for collecting and storing biometric data and real-time monitoring, it can be utilized for various health management diagnosis.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.11 no.3
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• pp.179-192
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• 2013

This study is characterized the groundwater flow pattern near crystalline site of Yangbook-Myeon, Gyeong-ju City, South Korea. From the time series analyses, groundwater level could be classified into 4 types reflecting the hydrogeological characteristics and rainfall pattern. The type I (DB1-1, DB1-2) may be directly influenced by rainfall pattern. The type II (DB1-3, DB1-7, KB-1, KB-2, KB-3, KB-7, KB-14, KB-15) may be influenced by rainfall event as well as groundwater flow through water-conducting features. The type III (DB-5, DB1-6, DB2-2, KB-10, KB-11, KB-13) may be predominantly happens in the crystaline rock mass, groundwater in this type flows through the minor fracture networks rather than direct effect of rainfall event. The type IV (DB1-8, KB-9) may be influenced by irregular variation of the groundwater level due to anisotropy and heterogeneity of crystalline rock.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.911-917
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• 2023

In today's world, we find ourselves facing energy crises due to factors such as war and climate crises. To prepare for these energy crises, many researchers continue to study systems related to energy monitoring and conservation, such as energy management systems, energy monitoring, and energy conservation. In line with these efforts, nations are making it mandatory for energy-consuming facilities to implement these systems. However, these facilities, limited by space and energy constraints, are exploring ways to improve. This research explores the operation of a data collection system using low-performance embedded devices. In this context, it proves that an optimized version of RocksDB, a Key-Value store, outperforms traditional databases when it comes to time-series data. Furthermore, a comprehensive database evaluation tool was employed to assess various databases, including optimized RocksDB and regular RocksDB. In addition, heterogeneous databases and evaluations are conducted using a UD Benchmark tool to evaluate them. As a result, we were able to see that on devices with low performance, the time required was up to 11 times shorter than that of other databases.

• Journal of the Korean GEO-environmental Society
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• v.16 no.3
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• pp.15-21
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• 2015

Since the introduction of 'the rule for computerizing subsoil investigation results and its application' in 2007, the DB construction of the national geotechnical information by Ministry of Land, Transport and Maritime Affairs (MOLTMA) has been performed. According to the Integrated DB Center of National Geotechnical Information, there have been 180 thousands borehole information stored in the system. In this study, the time-series analyses of precision on the most used information, the depth of stratum and ground water level, were performed to evaluate the effect of the initiation of the rule established in 2007. The precisions were evaluated based on the statistical analyses using kurtosis and normal distribution. Based on the results, the increase of precision after 2007 and the affirmative effects of the rule established in 2007 are confirmed. Furthermore, the precision of the regional information can be achieved with the precision analyses on the information from various areas.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.60.1-60.1
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• 2015

We present the current operation status of StarDB web services by showing its user access statistics. The StarDB web services started its operation in late November, allowing world-wide users to access results of new variability analysis for Northern Sky Variability Survey light curves. New analysis results of various time-series data have been added to the StarDB services. Importantly, our services have supported a simple cone search, which is an internationally well-defined catalog search interface in the international Virtual Observatory systems. We have collected user access statistics such as how users find our analysis data since its operation in later November. We expect our analysis of the StarDB operation to help Korean community members who plan and operate their own web services preparing for a future era of big survey data.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.25-31
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• 2017

In this paper, we propose a study on DB-based automatic crop cultivation that obtains crop cultivation data using IOT and automatically controls the cultivation environment using it. A system for DB-based automatic crop cultivation that automatically controls the cultivation environment is composed of a management server and a local controller. The management server was implemented using the MySQL DB in the Linux server system, and the local controller was designed and manufactured using the WiFi module and ARM Coretax-3 series MCU and confirmed its operation in the laboratory. The purpose of this study is to provide the optimal cultivation data and to grasp the cultivation status in real time when the knowledge of professional cultivation is needed like the farmers of ear farm villages. Research should continue to enable the cultivation of crops to reflect the requirements of each user.

• Journal of Korea Technology Innovation Society
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• v.7 no.3
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• pp.681-709
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• 2004

The purpose of this study is to classify customers by e-mail responsiveness on time-series analysis and testify the effectiveness of grouping by ROI analysis. Response recency, response frequency and Activity(RFA) of e-mailing systems are adapted for Customer segmentations. ROI analysis are consisted of open, click-through, duration time, personalization, conversion rate and email loyalty index of email systems. Major findings are as follows: RFA analysis is used for customer segmentations that is fundamental process of e-CRM applications. Customers can be grouped into loyal customers, odds customers, dormant customers, secession customers, and observation customers by RFA grouping. Loyal customer group has high point in all ROI index compared to other groups. These results indicated that customer responsiveness of e-mail systems were appropriate methods to group the customer with demographic variables. Therefore, effective e-mail marketing strategy of e-Biz should have suitable active DB and Behavior targeting is best approach to enforce the target e-mail marketing.