• Smart Media Journal
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• v.10 no.2
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• pp.9-15
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• 2021

Due to widespread construction of AMI(Advanced Metering Infrastructure), various service tends to increase, which are not only remote metering service collection measuring data but also demand management and energy saving using measuring data. In order to support a stable management of such services, it is necessary for measuring data to be processed efficiently. In this paper, we analyze a performance of measured data processing efficiency using duplicated data removal according to AMI construction purpose on real environments.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.259-268
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• 2016

KEPCO installed AMI (Advanced Metering Infrastructure) metering system for low-voltage customers from 2008. AMI metering system of KEPCO has operated 2.55 million customers and will plan to operate 22 million customers until 2020. KEPCO developed AMI NMS (Network Management System) to operate the meter reading network efficiently. NMS monitors the network status of DCUs (Data Concentration Unit) and modems. NMS provides functionalities of data collection and analysis. It collects property data of network device, network topology information, communication performance information, fault information, and etc. It analyzes collected data and controls network devices by remote access. AMI NMS collects about 370 MIBs (Mangement Information Bases) using SNMP (Simple Network Management Protocol). This paper introduces main functionalities, designed context, and implemented service screen.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.129-148
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• 2016

Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi-modal medical images with flat and unstructured data. It has a lack of semantic information between multi-modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.617-634
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• 2022

Purpose: This paper introduces Pohang University of Science Technology (POSTECH) advanced metering infrastructure (AMI) and Open Innovation Big Data Center (OIBC) platform and analysis results of electricity consumption data collected via the AMI in POSTECH campus. Methods: We installed 248 sensors in seven buildings at POSTECH for the AMI and collected electricity consumption data from the buildings. To identify the amounts and trends of electricity consumption of the seven buildings, electricity consumption data collected from March to June 2019 were analyzed. In addition, this study compared the differences between the amounts and trends of electricity consumption of the seven buildings before and after the COVID-19 outbreak by using electricity consumption data collected from March to June 2019 and 2020. Results: Users can monitor, visualize, and download electricity consumption data collected via the AMI on the OIBC platform. The analysis results show that the seven buildings consume different amounts of electricity and have different consumption trends. In addition, the amounts of most buildings were significantly reduced after the COVID-19 outbreak. Conclusion: POSTECH AMI and OIBC platform can be a good reference for other universities that prepare their own microgrid. The analysis results provides a proof that POSTECH needs to establish customized strategies on reducing electricity for each building. Such results would be useful for energy-efficient operation and preparation of unusual energy consumptions due to unexpected situations like the COVID-19 pandemic.

• The Korean Journal of Emergency Medical Services
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• v.17 no.1
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• pp.21-39
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• 2013

Purpose : The purpose of this study is to provide the basic data to improve pre-hospital phase emergency care for acute myocardial infarction (AMI) patients by analyzing AMI patients' clinical characteristics and emergency care situations. Methods : Data were collected through medical records of 385 AMI patients including ambulance records of 107 AMI patients transferred to the emergency medical center for three and a half years. Results : Regarding emergency care for AMI patients in pre-hospital phase, 47% of the care revealed moderate level or higher, and appropriateness of pre-hospital phase emergency care for cardiopulmonary complaints practiced by paramedics showed statistically significant improvement in recent years (p<.001). The time from onset of symptom to ballooning intervention by 119 emergency services was shorter than that in other cases. However, emergency care by paramedic was mainly basic life support. Conclusion : Since prognosis of AMI shows vast differences depending on prompt detection and medical intervention, cooperation between pre-hospital and in-hospital phase is highly required. 119 paramedics should be trained focusing on the accurate assessment and emergency care, and medical direction should be activated. In addition, regulation on 12-lead EKG, cardiac enzyme analysis, use of analgesics and thrombolytic agents should be legally implemented.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1767-1772
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• 2021

Global satellite observation surface albedo data over a long period of time are actively used to monitor changes in the global climate and environment, and their utilization and importance are great. Through the generational shift of geostationary satellites COMS (Communication, Ocean and Meteorological Satellite)/MI (Meteorological Imager sensor) and GK-2A (GEO-KOMPSAT-2A)/AMI (Advanced Meteorological Imager sensor), it is possible to continuously secure surface albedo outputs. However, the surface albedo outputs of COMS/MI and GK-2A/AMI differ between outputs due to Differences in retrieval algorithms. Therefore, in order to expand the retrieval period of the surface albedo of COMS/MI and GK-2A/AMI to secure continuous climate change monitoring linkage, the analysis of the two satellite outputs and errors should be preceded. In this study, error characteristics were analyzed by performing comparative analysis with ground observation data AERONET (Aerosol Robotic Network) and other satellite data GLASS (Global Land Surface Satellite) for the overlapping period of COMS/MI and GK-2A/AMI surface albedo data. As a result of error analysis, it was confirmed that the RMSE of COMS/MI was 0.043, higher than the RMSE of GK-2A/AMI, 0.015. In addition, compared to other satellite (GLASS) data, the RMSE of COMS/MI was 0.029, slightly lower than that of GK-2A/AMI 0.038. When understanding these error characteristics and using COMS/MI and GK-2A/AMI's surface albedo data, it will be possible to actively utilize them for long-term climate change monitoring.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.225-238
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• 2022

In this paper, we propose a clustering method for advanced metering infrastructure (AMI) data in Korea. As AMI data presents non-stationarity, we consider time-dependent frequency domain principal components analysis, which is a proper method for locally stationary time series data. We develop a new clustering method based on time-varying eigenvectors, and our method provides a meaningful result that is different from the clustering results obtained by employing conventional methods, such as K-means and K-centres functional clustering. Simulation study demonstrates the superiority of the proposed approach. We further apply the clustering results to the evaluation of the electricity price system in South Korea, and validate the reform of the progressive electricity tariff system.

• Journal of Convergence for Information Technology
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• v.10 no.7
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• pp.41-48
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• 2020

Recently, the spreading business of AMI (Advanced Metering Infrastructure) remote metering systems in various regions of the country has been activated, and it provides various metering functions such as two-way communication and security plan functions for power demand management. Current AMI system is difficult to analyze based on the existing RDB(Relational Database) due to the increase in the size of new internal IoT devices and networks. This study proposes a new GDB(Graph Database) based failure analysis method that utilizes existing RDB data. It analyzes the correlation of new failure patterns through accumulated data such as internal thresholds and status values. As a result of GDB-based simulation, it was confirmed that RDB can predict to a new obstacle pattern that was difficult to analyze.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.223-223
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• 2018

취수원에서 정수장과 배수지를 거쳐 수용가에 이르기까지 공급되는 급수량을 결정하는데 있어 각 수용가별 물 사용 패턴은 수요량을 예측하여 취수량을 결정하는데 있어 매우 중요한 지표이다. 생활용수 추정은 용도별(가정용, 상업용, 공업용 등)로 분류하여 경향성이 나타날 수 있도록 과거 사용실적을 바탕으로 장래 용도별 사용량을 추정한다. 이는 경험을 바탕으로 한 것으로 일반적으로 시계열 모형을 이용하는데 수요예측의 실패 가능성이 높으며 효율적인 방법이라 할 수 없다. 이에 본 연구에서는 최근 통신기술의 발달로 양방향 통신이 가능한 AMI(Advanced Metering Infrastructure, 원격검침인프라)센서를 영종도 112블록의 528개의 수용가에 설치하였다. AMI는 스마트 미터에서 측정한 데이터를 원격 검침기를 통해 물 사용량을 자동으로 계측할 수 있다. AMI 데이터를 이용하여 영종도 112블록의 운북동과 운서동의 각 용도별, 요일별, 그리고 도심지와 농가의 실시간 물 사용 패턴을 분석하였다. 분석 결과 운북동과 운서동의 물 사용 패턴은 비슷한 경향을 보이는 것으로 보이나 도시화된 운서동에 비해 운북동의 물사용량이 상대적으로 적고 첨두사용량의 발생시간 또한 빠른 것으로 나타났다. 또한 가정용과 공공용의 경우 시간별 물 사용량이 요일에 따라 일정한 경향이 있으나 상업용과 공업용은 일정한 사용량을 보였다. 향후 112블록의 관망해석에 실시간 물사용 패턴을 적용하여 효율적으로 급수량 결정을 할 수 있을 것으로 사료된다.

• Journal of Korean Academy of Nursing
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• v.45 no.3
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• pp.378-387
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• 2015

Purpose: Acute myocardial infarction (AMI) leads to death if the patient does not receive emergency treatment. Thus it is very important to recognize the symptoms in the early stage. The purpose of this study was to identify clusters of symptoms that represent AMI in Koreans. Methods: The study used a retrospective, descriptive design with secondary data analysis. Data were abstracted from 725 medical records of AMI patients admitted from June 1, 2006 to August 15, 2014 at a university hospital. Results: Analysis of the AMI symptoms revealed five symptom clusters; Cluster 1 (n=140): middle chest pain (100%), shortness of breath, and cold sweating, Cluster 2 (n=256): substernal pain (100%), cold sweating, and shortness of breath, Cluster 3 (n=47): substernal pain (95.7%), left arm pain, shortness of breath, cold sweating, left shoulder pain, right arm pain, and the lower neck pain, Cluster 4 (n=212): shortness of breath (28.3%), left chest pain, and upper abdominal pain, and Cluster 5 (n=70): cold sweating (100%), left chest pain, shortness of breath, left shoulder pain, and upper abdominal pain. Length of hospital stay and mortality rate were significantly different according to symptom clusters (F=2.52, p =.040; F=3.62, p =.006, respectively). Conclusion: Symptom clusters of AMI from this study can be used for AMI patients in order to recognize their symptoms at an early stage. The study findings should be considered when developing educational prevention programs for Koreans with AMI.