• Journal of IKEEE
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• v.23 no.4
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• pp.1373-1380
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• 2019

In order to increase energy efficiency, developments of the advanced metering infrastructure (AMI) in the smart grid technology have recently been actively conducted. An essential part of AMI is analyzing power consumption and forecasting consumption patterns. In this paper, we analyze the power consumption and summarized the data errors. Monthly power consumption patterns are also analyzed using the k-means clustering algorithm. Forecasting the consumption pattern by each household is difficult. Therefore, we first classify the data into 100 clusters and then predict the average of the next day as the daily average of the clusters based on the deep neural network. Using practically collected AMI data, we analyzed the data errors and could successfully conducted power forecasting based on a clustering technique.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1795-1803
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• 2011

Advanced Metering Infrastructure (AMI) is one of the important components to form a smart-gird, which is an advanced power system by combining the power system with the communication systems. This AMI makes it possible to exchange information between operators and consumers for the efficient and reliable operation of the power system through a smart meter or a In-Home Display. However, according to the increase of the demanded information such as the power quality, the accurate load-profile, and the billing data to help customers manage their power consumption, it is necessary to gather more accurate analytical data from each house appliances and transfer it to the smart meter for synthesizing the information and controlling each loads. In this paper, the development of the wireless data acquisition device for the individual load data metering, which is connected with the smart meter for advanced functions, is proposed. AVR, a kind of microcontroller, and Bluetooth are used and integrated into the proposed the wireless data acquisition device to transmit the detailed power data (voltage and current) to the smart meter. To verify the effectiveness of the proposed system, a hardware experiment is carried out including the confirmation of the possibility for providing the more various information by applying analysis algorithms to the obtained data. Also, the application structure of the wireless data acquisition device to gather the data from the various house appliances is presented.

• Journal of Korean Neurosurgical Society
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• v.65 no.4
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• pp.507-513
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• 2022

Objective : The goal of the following statewide age and gender-coordinated cohort study in Korea is to find out if there is a link between acute myocardial infarction (AMI) and Parkinson's disease (PD). Methods : Utilizing the National Health Insurance Sharing Service cohort, patient data were collected. Six thousand four hundred seventy-five individuals with PD were distinguished by utilizing the International Classification of Diseases 10 code G20 and have enrolled in the PD group. The number of participants decreased to 5259 after excluding 1039 patients who were hospitalized less than one time or who visited an outpatient clinic less than twice. Then, 26295 individuals were selected as part of the control group after case control matching was conducted through 1 : 5 age- and gender-coordinated matching. The Cox proportional hazard regression analysis and Kaplan-Meier method were utilized to analyze the likelihood of AMI in PD. Results : After controlling for age and gender, the hazard ratio of AMI in the PD group was 3.603 (95% confidence interval [CI], 2.837-4.577). After that, the following hazard ratio of AMI in the PD group was modified against for co-morbid medical disorders, resulting in 3.551 (95% CI, 2.795-4.511). According to a subgroup analysis, in males and females aged <65 and aged ≥65 and in the non-diabetes and diabetes, hypertension and non-hypertension, dyslipidemia and non-dyslipidemia subgroups, the AMI incidence rates were dramatically higher in the PD group compared to that of the control. Conclusion : Individuals with PD have a greater chance of AMI, according to this cross-national study.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.359-365
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• 2021

GEO-KOMPSAT-2A (GK2A) AMI (Advanced Meteorological Imager) Best Detector Select (BDS) map is pre-determined and uploaded before the satellite launch. After the launch, there is some possibility of a detector performance change driven by an abrupt temperature variation and thus the status of BDS map needs to be evaluated and updated if necessary. To investigate performance of entire elements of the detectors, AMI BDS analyses were conducted based on a technical note provided from the AMI vendor (L3HARRIS). The concept of the BDS analysis is to investigate the stability of signals from detectors while they are staring at targets (deep space and internal calibration target). For this purpose, Long Time Series (LTS) and Output Voltage vs. Bias Voltage (V-V) methods are used. The LTS for 30 secs and the V-V for two secs are spanned respectively for looking at the targets to compute noise components of detectors. To get the necessary data sets, these activities were conducted during the In-Orbit Test (IOT) period since a normal operation of AMI is stopped and special mission plans are commanded. With collected data sets during the GK2A IOT, AMI BDS map was intensively examined. It was found that about 1% of entire detector elements, which were evaluated at the ground test, showed characteristic changes and those degraded elements are replaced by alternative best ones. The stripping effects on AMI raw images due to the BDS problem were clearly removed when the new BDS map was applied.

• Korean Journal of Adult Nursing
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• v.26 no.2
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• pp.203-213
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• 2014

Purpose: The purpose of this study was to understand and describe the every day life experience of patients with acute myocardial infarction (AMI) during the recovery period after Percutaneous Coronary Intervention (PCI) using a qualitative approach. Methods: Twelve patients with AMI participated in this study. Their age ranged from 42 to 75. The data were collected by individual in-depth interviews and all interviews were audio-taped and transcribed verbatim. The transcribed data were analyzed using traditional qualitative content analysis. Results: Six sub-themes emerged from the data as follows: Getting to know about illness, getting motivated for health behavior, putting an effort into health behavioral change, having difficulties maintaining health behavior, setting up coping strategies for health behavior and having a need for a tailored education. The results of this study showed how the health behaviors of patients with AMI are related to their every day life experiences. Conclusion: The results of this study could help health professionals to better understand patients with AMI and design effective educational interventions to improve their health behaviors.

• Quality Improvement in Health Care
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• v.17 no.1
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• pp.69-78
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• 2011

Objectives : The purpose of this study was to determine whether the published AMI report card could reduce in-patient mortality, 7-day after discharge mortality, and length of stay (LOS). Methods : Interrupted time-series intervention analysis was used to evaluate the impact of the report card for AMI care quality in November 2005 in terms of risk-adjusted in-patient mortality, risk-adjusted 7-day after discharge mortality, and DRGs case-mix LOS using the claim data of Health Insurance Review and Assessment Service. Results : Public disclosure of AMI care quality decreased risk-adjusted in-patient mortality and DRGs case-mix LOS by 0.00050% per month and 0.042 days per month respectively, however there was no effect on risk-adjusted 7-day after discharge mortality. Patterns of effect of public disclosure on AMI outcomes were a fluctuating pattern on risk-adjusted mortalities and a pulse impact for 1 month on DRGs case-mix LOS. Conclusions : We found the public disclosure of AMI care quality had decreasing effects on risk-adjusted in-patient mortality and DRGs case-mix LOS, but the size of the effect was marginal.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.791-804
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• 2020

Residential electricity consumption can be predicted more accurately by utilizing the realtime household electricity consumption reference that can be collected by the AMI as the ICT developed under the smart grid circumstance. This paper studied the model that predicts residential power load using the ARIMA, TBATS, NNAR model based on the data of hour unit amount of household electricity consumption, and unlike forecasting the consumption of the whole households at once, it computed the anticipated amount of the electricity consumption by aggregating the predictive value of each established model of cluster that was collected by the households which show the similiar load profile. Especially, as the typical time series data, the electricity consumption data chose the clustering analysis method that is appropriate to the time series data. Therefore, Dynamic Time Warping and Periodogram based method is used in this paper. By the result, forecasting the residential elecrtricity consumption by clustering the similiar household showed better performance than forecasting at once and in summertime, NNAR model performed best, and in wintertime, it was TBATS model. Lastly, clustering method showed most improvements in forecasting capability when the DTW method that was manifested the difference between the patterns of each cluster was used.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.76-81
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• 2019

This paper is a review of the certification system required for various energy prosumer business models, including P2P energy trading and participation in small demand response programs, which are based on reliable production and consumption certification. One of the most important parameter in energy trading is ensuring the reliability of trading account balancing. Therefore, we studied to use big data pattern analysis based blockchain smart contract between trading partners to make its tradings are more reliable. For this purpose big data analysis system collected from the IoT AMI and a production authentication system using a private blockchain network linked with the AMI is discussed, using the blockchain smart contract are also suggested. Futhermore, energy trading system concept and business models are introduced.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.10
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• pp.4910-4918
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• 2013

This study aims to design a Severity-Adjusted LOS(Length of Stay) Model in order to efficiently manage LOS of AMI(Acute Myocardial Infarction) patients. We designed a Severity-Adjusted LOS Model with using data-mining methods(multiple regression analysis, decision trees, and neural network) which covered 6,074 AMI patients who showed the diagnosis of I21 from 2004-2009 Korean National Hospital Discharge in-depth Injury Survey. A decision tree model was chosen for the final model that produced superior results. This study discovered that the execution of CABG, status at discharge(alive or dead), comorbidity index, etc. were major factors affecting a Sevirity-Adjustment of LOS of AMI patients. The difference between real LOS and adjusted LOS resulted from hospital location and bed size. The efficient management of LOS of AMI patients requires that we need to perform various activities after identifying differentiating factors. These factors can be specified by applying each hospital's data into this newly designed Severity-Adjusted LOS Model.

• Journal of Preventive Medicine and Public Health
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• v.46 no.4
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• pp.201-209
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• 2013

Objectives: The goal of this study was to perform an economic analysis of a primary stenting with drug-eluting stents (DES) compared with bare-metal stents (BMS) in patients with acute myocardial infarction (AMI) admitted through an emergency room (ER) visit in Korea using population-based data. Methods: We employed a cost-minimization method using a decision analytic model with a two-year time period. Model probabilities and costs were obtained from a published systematic review and population-based data from which a retrospective database analysis of the national reimbursement database of Health Insurance Review and Assessment covering 2006 through 2010 was performed. Uncertainty was evaluated using one-way sensitivity analyses and probabilistic sensitivity analyses. Results: Among 513 979 cases with AMI during 2007 and 2008, 24 742 cases underwent stenting procedures and 20 320 patients admitted through an ER visit with primary stenting were identified in the base model. The transition probabilities of DES-to-DES, DES-to-BMS, DES-to-coronary artery bypass graft, and DES-to-balloon were 59.7%, 0.6%, 4.3%, and 35.3%, respectively, among these patients. The average two-year costs of DES and BMS in 2011 Korean won were 11 065 528 won/person and 9 647 647 won/person, respectively. DES resulted in higher costs than BMS by 1 417 882 won/person. The model was highly sensitive to the probability and costs of having no revascularization. Conclusions: Primary stenting with BMS for AMI with an ER visit was shown to be a cost-saving procedure compared with DES in Korea. Caution is needed when applying this finding to patients with a higher level of severity in health status.