• 제목/요약/키워드: Daily Output

검색결과 194건 처리시간 0.022초

KZ 필터를 이용한 부산지역 PM10의 장기 추세 분석 (Analysis of the Long-term Trend of PM10 Using KZ Filter in Busan, Korea)

  • 도우곤;정우식
    • 한국환경과학회지
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    • 제26권2호
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    • pp.221-230
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    • 2017
  • To determine the effect of air pollution reduction policies, the long-term trend of air pollutants should be analyzed. Kolmogorov-Zurbenko (KZ) filter is a low-pass filter, produced through repeated iterations of a moving average to separate each variable into its temporal components. The moving average for a KZ(m, p) filter is calculated by a filter with window length m and p iterations. The output of the first pass subsequently becomes the input for the next pass. Adjusting the window length and the number of iterations makes it possible to control the filtering of different scales of motion. To break down the daily mean $PM_{10}$ into individual time components, we assume that the original time series comprises of a long-term trend, seasonal variation, and a short-term component. The short-term component is attributable to weather and short-term fluctuations in precursor emissions, while the seasonal component is a result of changes in the solar angle. The long-term trend results from changes in overall emissions, pollutant transport, climate, policy and/or economics. The long-term trend of the daily mean $PM_{10}$ decreased sharply from $59.6ug/m^3$ in 2002 to $44.6ug/m^3$ in 2015. This suggests that there was a long-term downward trend since 2005. The difference between the unadjusted and meteorologically adjusted long-term $PM_{10}$ is small. Therefore, we can conclude that $PM_{10}$ is unaffected by the meteorological variables (total insolation, daily mean temperature, daily mean relative humidity, daily mean wind speed, and daily mean local atmospheric pressure) in Busan.

인공 신경망 모형을 활용한 저수지 군의 연계운영 기준 수립 (Development of Operating Guidelines of a Multi-reservoir System Using an Artificial Neural Network Model)

  • 나미숙;김재희;김승권
    • 산업공학
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    • 제23권4호
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    • pp.311-318
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    • 2010
  • In the daily multi-reservoir operating problem, monthly storage targets can be used as principal operational guidelines. In this study, we tested the use of a simple back-propagation Artificial Neural Network (ANN) model to derive monthly storage guideline for daily Coordinated Multi-reservoir Operating Model (CoMOM) of the Han-River basin. This approach is based on the belief that the optimum solution of the daily CoMOM has a good performance, and the ANN model trained with the results of daily CoMOM would produce effective monthly operating guidelines. The optimum results of daily CoMOM is used as the training set for the back-propagation ANN model, which is designed to derive monthly reservoir storage targets in the basin. For the input patterns of the ANN model, we adopted the ratios of initial storage of each dam to the storage of Paldang dam, ratios of monthly expected inflow of each dam to the total inflow of the whole basin, ratios of monthly demand at each dam to the total demand of the whole basin, ratio of total storage of the whole basin to the active storage of Paldang dam, and the ratio of total inflow of the whole basin to the active storage of the whole basin. And the output pattern of ANN model is the optimal final storages that are generated by the daily CoMOM. Then, we analyzed the performance of the ANN model by using a real-time simulation procedure for the multi-reservoir system of the Han-river basin, assuming that historical inflows from October 1st, 2004 to June 30th, 2007 (except July, August, September) were occurred. The simulation results showed that by utilizing the monthly storage target provided by the ANN model, we could reduce the spillages, increase hydropower generation, and secure more water at the end of the planning horizon compared to the historical records.

Sensor Module for Detecting Postural Change and Falls

  • Jeon, G.R.;Ahn, S.J.;Shin, B.J.;Kang, S.C.;Kim, J.H.
    • 센서학회지
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    • 제23권6호
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    • pp.362-367
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    • 2014
  • In this study, a postural change detection sensor module (PCDSM) was developed to detect postural changes in activities of daily living (ADL) and falls. The PCDSM consists of eight mercury sensors that measure angle variations in $360^{\circ}$ rotation and $90^{\circ}$ tilting. From the preliminary study, the output characteristics of the PCDSM were confirmed with the angle variations of rotational motion and a tilting table. Three experiments were conducted to test rotational motion, postural changes, and falling and lying. The results confirmed that the PCDSM could effectively detect postural changes, movement patterns, and falls or non-falls.

3축 가속도 센서를 이용한 자세 및 활동 모니터링 (Posture and activity monitoring using a 3-axis accelerometer)

  • 정도운;정완영
    • 센서학회지
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    • 제16권6호
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    • pp.467-474
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    • 2007
  • The real-time monitoring about the activity of the human provides useful information about the activity quantity and ability. The present study implemented a small-size and low-power acceleration monitoring system for convenient monitoring of activity quantity and recognition of emergent situations such as falling during daily life. For the wireless transmission of acceleration sensor signal, we developed a wireless transmission system based on a wireless sensor network. In addition, we developed a program for storing and monitoring wirelessly transmitted signals on PC in real-time. The performance of the implemented system was evaluated by assessing the output characteristic of the system according to the change of posture, and parameters and acontext recognition algorithm were developed in order to monitor activity volume during daily life and to recognize emergent situations such as falling. In particular, recognition error in the sudden change of acceleration was minimized by the application of a falling correction algorithm

Automatic Detection of Anomalies in Blood Glucose Using a Machine Learning Approach

  • Zhu, Ying
    • Journal of Communications and Networks
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    • 제13권2호
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    • pp.125-131
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    • 2011
  • Rapid strides are being made to bring to reality the technology of wearable sensors for monitoring patients' physiological data.We study the problem of automatically detecting anomalies in themeasured blood glucose levels. The normal daily measurements of the patient are used to train a hidden Markov model (HMM). The structure of the HMM-its states and output symbols-are selected to accurately model the typical transitions in blood glucose levels throughout a 24-hour period. The learning of the HMM is done using historic data of normal measurements. The HMM can then be used to detect anomalies in blood glucose levels being measured, if the inferred likelihood of the observed data is low in the world described by the HMM. Our simulation results show that our technique is accurate in detecting anomalies in glucose levels and is robust (i.e., no false positives) in the presence of reasonable changes in the patient's daily routine.

Transfer Function 모형을 이용한 수도물 수요의 단기예측 (A Short-term Forecasting of Water Supply Demands by the Transfer Function Model)

  • 이재준
    • 상하수도학회지
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    • 제10권2호
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    • pp.88-103
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    • 1996
  • The objective of this study is to develop stochastic and deterministic models which could be used to synthesize water application time series. Adaptive models using mulitivariate ARIMA(Transfer Function Model) are developed for daily urban water use forecasting. The model considers several variables on which water demands is dependent. The dynamic response of water demands to several factors(e.g. weekday, average temperature, minimum temperature, maximum temperature, humidity, cloudiness, rainfall) are characterized in the model by transfer functions. Daily water use data of Kumi city in 1992 are employed for model parameter estimation. Meteorological data of Seonsan station are utilized to input variables because Kumi has no records about the meteorological factor data.To determine the main factors influencing water use, autocorrelogram and cross correlogram analysis are performed. Through the identification, parameter estimation, and diagnostic checking of tentative model, final transfer function models by each month are established. The simulation output by transfer function models are compared to a historical data and shows the good agreement.

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지방의 종류를 달리한 현미와 백미 식이를 섭취시켰을 때 흰쥐의 체내 지방 대사에 미치는 영향 (Effects of Feeding Polished or Brown Rice Diet with Different Kinds of Lipids on the Lipid Metabolism in Rats)

  • 김미경;원은주
    • Journal of Nutrition and Health
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    • 제17권2호
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    • pp.154-162
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    • 1984
  • This study was done to see effects of feeding a 77% polished or brown rice diet with corn oil, rice bran oil or butter on the lipid metabolism in weanling rats. The results are summarized as follows : 1) Food consumptions, body weight gains and tissue weights were not different among experimental groups. 2) Weights of daily fecal output and daily fecal excretions of total lipids, cholesterols, nitrogen and glucose were higher in brown rice groups than in polished rice groups. 3) Polished rice - rice bran oil group had the highest concentrations of total lipids and cholesterols in serum 4) Polished rice groups tended to have higher serum lipid and cholesterol concentrations than brown rice groups.

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Past and Future Temperature and Precipitation Changes over Korea using MM5 Model

  • Oh, Jai-Ho;Min, Young-Mi;Kim, Tae-Kook;Woo, Su-Min;Kwon, Won-Tae;Baek, Hee-Jeong
    • 한국제4기학회:학술대회논문집
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    • 한국제4기학회 2004년도 하계학술대회
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    • pp.29-29
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    • 2004
  • Long term observational analysis by climatologists has confirmedthat the global warming is no longer a topic of debate among scientists andpolicy makers. According to the report of IPCC-2001 (Intergovernmental Panelon Climate Change), the global mean surface air temperature is increasinggradually. The reported increase of mean temperature is by 0.6 degree in the end of twentieth century. This could represent severe threat for propertylosses especially due to increase in the number of extreme weather arising out of global warming. period of model integration from 2001 to 2100 using output of ECHAM4/HOPE-G of Max Planet Institute of Meteorology (MPI) for IPCC SRES (Special Report on Emission Scenarios). The main results of this study indicate increase of surface air temperature by 6.20C and precipitation by 2.6% over Korea in the end of 21st century. Simulation results also show that there is increase in daily maximum and minimum temperatures while decrease in diurnal temperature range (DTR). DTR changes are diminished mainly due to relatively rapid increase of daily minimum temperature than that of daily maximumtemperature. It has been observed that increase in precipitation amount anddecrease in the number of rainy days lead to increase of pre precipitationintensity.

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한국여자의 소금 및 질소대사에 관하여 (Sodium Chloride and Nitrogen Metabolism of Korean Females)

  • 김용근;양일석;정순동
    • The Korean Journal of Physiology
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    • 제9권1호
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    • pp.23-32
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    • 1975
  • In order to study the dally metabolism of sodium chloride and of nitrogen, 24-hour urine samples were collected from 1,593 normal Korean females whose ages varied from 2 to 80 years old. The volume, the concentration of chloride and the osmolality of the urine, add the total nitrogen were determined, along with the resting pulse rate and the blood pressure. The daily urine volume was maintained at $1,000{\sim}1,300\;ml/m^2/day$ in all age groups while the chloride concentration and osmolality of the urine samples were approximately 200 mEq/liter and 600 milliosmoles, respectively, in most of age groups. Hence the daily urinary output of sodium chloride was estimated to be approximately $15g/m^2$/day in adult groups. On the other hand, the daily excretion of total nitrogen amouted to approximately $5{\sim}6g/m^2/day$. These findings indicated that the average Korean females live on low-protein and high-salt diets throughout their life. Despite a known correlation between the incidence of hypertension and the high salt intake, none of the subjects employed in this work showed any sign of hypertension.

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Recognition of Falls and Activities of Daily Living using Tri-axial Accelerometer and Bi-axial Gyroscope

  • Park, Geun-chul;Kim, Soo-Hong;Kim, Jae-hyung;Shin, Beum-joo;Jeon, Gye-rok
    • 센서학회지
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    • 제25권2호
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    • pp.79-85
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    • 2016
  • This paper proposes a threshold-based fall recognition algorithm to discriminate between falls and activities of daily living (ADL) using a tri-axial accelerometer and a bi-axial gyroscope sensor mounted on the upper sternum. The experiment was executed ten times according to the proposed experimental protocol. The output signals of the tri-axial accelerometer and the bi-axial gyroscope were measured during eight falls and eleven ADL action sequences. The threshold values of the signal vector magnitude (SVM_Acc), angular velocity (${\omega}_{res}$), and angular variation (${\theta}_{res}$) parameter were calculated using MATLAB. From the preliminary study, three thresholds (TH1, TH2, and TH3) were set so that the falls could be distinguished from ADL. When the parameter SVM_Acc is greater than 2.5 g (TH1), ${\omega}_{res}$ is greater than 1.75 rad/s (TH2), and ${\theta}_{res}$ is greater than 0.385 rad (TH3), these action sequences are recognized as falls. If at least one or more of these conditions is not satisfied, the sequence is classified as ADL.