• ETRI Journal
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• v.24 no.3
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• pp.226-238
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• 2002

A spatial object changes its states over time. However, existing spatial and temporal database systems cannot fully manage time-varying data with both spatial and non-spatial attributes. To overcome this limitation, we present a framework for spatio-temporal databases that can manage all time-varying historical information and integrate spatial and temporal relationship operators into the select statement in SQL3. For the purpose of our framework, we define three referencing macros and a history aggregate operator and classify the existing spatial and temporal relationship operators into three groups: exclusively spatial relationship operators, exclusively temporal relationship operators, and spatio-temporal common relationship operators. Finally, we believe the integration of spatial and temporal relationship operators into SQL3 will provide a useful framework for the history management of time-varying spatial objects in a uniform manner.

• The Journal of Information Technology and Database
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• v.4 no.1
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• pp.19-30
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• 1997

시간지원 데이터 모델은 시간 의미를 데이터 모델에 추가하여 시간에 따라 변화된 정보를 처리할 수 있는 데이터 모델이다. 시간지원 데이터 모델은 실세계에서 사건이 발생한 시간인 유효시간을 지원하는 데이터 모델과 데이터가 수록된 시간을 지원하는 거래시간 데이터 모델 그리고 거래시간과 유효시간을 모두 지원하는 이원시간 데이터 모델이 있다. 대부분이 시간지원 데이터 모델은 관계형 모델을 확장하여 시간지원 데이터를 처리할 수 있도록 설계된다. 시간지원 데이터 모델의 두 부류는 시간을 결합하는 단위에 따라 튜플 타임스탬프와 속성 타임스탬프의 두 가지 형식이 있다. 본 논문은 데이터 모델에서 시간 추가를 위한 기본적인 시간 개념과 시간지원 데이터 모델을 위한 고려사항을 나타낸다. 그리고 시간지원 데이터 모델을 지원시간에 따라 비교하였으며, 유효시간이 지원되는 시간지원 집계에 적합한 데이터 모델을 제안하였다.

• YAKHAK HOEJI
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• v.56 no.1
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• pp.66-69
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• 2012

This study aims to assess the aggregate contribution of new drugs to the increase in life expectancy. We constructed a panel data combining mortality data in KOSIS and a drug dataset generated by assigning new drugs listed in 2000~2009 to their respective ICD codes. We found that 10% increase in stock of new drug led to 0.13~0.27% increase in the probability of survival to age 65. Due to lack of disease-specific life table, we used indirect approach to estimate the effect of new drugs on longevity. Using ordinary least squares, the estimate of the probability of survival to age 65 (logarithm) on life expectancy for all ages was 24.92. In conclusion, the increase in life expectancy of the entire population in Korea between 2000 and 2009 resulting from NMEs is 1.95 years, which explains 46.6% of real increase in life expectancy.

• Proceedings of the KOR-KST Conference
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• 1998.09a
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• pp.152-159
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• 1998

This paper presents a method evaluating benefits of ramp metering strategies on freeway safety. Based on the traffic and the accident data collected on a 4.2 km (2.6 mile) section of Interstate highway 35-West in Minneapolis, U.S.A., the relationship between traffic variables and safety measures is investigated. An aggregate specification that could be used to predict accident frequencies on freeways is proposed as a multiple regression form. The specification includes 15 minutes volume and occupancy data, which are commonly available from surveillance and control systems. The primary variables that appear to affect the frequencies of freeway accident are: vehicle-miles of travel, entrance ramp volumes and the dynamic effect of queue building. A simulation method evaluating the dynamic effect of control strategies on safety is proposed next. The potential benefits of freeway ramp metering on freeway safety are finally investigated via a proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3689-3696
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• 2011

In decision support system, knowledge workers require aggregation operations of the large data and are more interested in the trend analysis rather than in the punctual analysis. Therefore, it is necessary to provide fast approximate answers rather than exact answers, and to research approximate query answering techniques. In this paper, we propose a new approximation query answering method which is based on Fuzzy C-means clustering (FCM) method and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed method using FCM-ANFIS can compute aggregate queries without accessing massive multidimensional data cube by producing the KR model of multidimensional data cube. In our experiments, we show that our method using the KR model outperforms the NMF method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5427-5445
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• 2019

While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks. In this paper, we propose a new representation learning method, namely generative adversarial networks (GAN) based bagging deep convolutional autoencoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion. To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks. We focus on combining those three techniques. To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning. The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems. We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.

• The korean journal of orthodontics
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• v.54 no.4
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• pp.210-228
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• 2024

Objective: Understanding the orofacial characteristics and growth patterns in children is essential for both orthodontics and research on children with orofacial abnormalities. However, a concise resource of normative data on the size and relative position of these structures in different populations is not available. Our objective was to aggregate normative data to assess the growth of the orofacial skeletal structures in children with a well-balanced face and normal occlusion. Methods: The MEDLINE, Embase, and Scopus databases were searched. Inclusion criteria included longitudinal and cross-sectional studies on cephalometric measurement of skeletal tissues and a study population ≤ 18 years with a well-balanced face and normal occlusion. Key study parameters were extracted, and knowledge was synthesized. A quality appraisal was performed using a 10-point scale. Results: The final selection comprised of 12 longitudinal and 33 cross-sectional studies, the quality of which ranged from good to excellent. Our results showed that from childhood to adulthood, the length of the cranial base increased significantly while the cranial base angle remained constant; both the maxilla and mandible moved forward and downward. The profile becomes straighter with age. Conclusions: Growth patterns in children with a well-balanced face and normal occlusion follow accepted theories of growth.

• Journal of the Korean Society for information Management
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• v.37 no.3
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• pp.77-106
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• 2020

In this study, we proposed a method to systematically manage research data in the field of condensed matter physics, which is the most active and interdisciplinary field. In the course of the research, a questionnaire was conducted for researchers in the field of condensed matter physics. The questionnaire was constructed based on the research data management tool Data Asset Framework (DAF) and the FAIR principle for data sharing and reuse. The current status of research data management in the field of aggregated material physics was collected from 14 researchers. The collected data consisted of data on the characteristics and basic information of researchers who answered the questionnaire, data preservation and management, and data sharing and access. By analyzing the collected questionnaire results, nine problems were drawn about the characteristics of research data in the field of aggregate material physics, data collection and production, data preservation and management, data sharing and access. In this study, suggestions were made to improve the problems derived from each aspect.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1107-1114
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• 2004

System Packet Interface Level 4 Phase 2(SPI-4.2) is an interface for packet and cell transfer between a physical layer(PHY) device and a link layer device, for aggregate bandwidths of OC-192 ATM and Packet Over Sonet/SDH(POS), as well as 10Gbps Ethernet applications. SPI-4.2 core consists of Tx and Rx modules and supports full duplex communication. Tx module of SPI-4.2 core writes 64-bit data word and 14-bit header information from the user interface into asynchronous FIFO and transmits DDR(Double Data Rate) data over PL4 interface. Rx module of SPI-4.2 core operates in vice versa. Tx and Rx modules of SPI-4.2 core are designed to support maximum 256-channel and control the bandwidth allocation by configuring the calendar memory. Automatic DIP4 and DIP-2 parity generation and checking are implemented within the designed core. The designed core uses Xilinx ISE 5.li tool and is described in VHDL Language and is simulated by Model_SIM 5.6a. The designed core operates at 720Mbps data rate per line, which provides an aggregate bandwidth of 11.52Gbps. SPI-4.2 interface core is suited for line cards in gigabit/terabit routers, and optical cross-connect switches, and SONET/SDH-based transmission systems.