• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.759-768
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• 2021

This work introduces a new approach that classifies individual household water usage by examining the characteristics of smart meter end-user demand data. Here, one of the most well-known unsupervised machine learning, K-means algorithm, is applied to classify water consumptions by each household. The intensity and duration of end-user demands are used as main features to determine the households with similar water consumption pattern. The results showed that 21 households are classified into 13 clusters with each cluster having one, two, three, or five houses. The reasoning why multiple households are classified into the same cluster is described in this paper with respect to the collected data and end-user water consumption behavior.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.47-64
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• 2021

In this study, the annual and monthly groundwater recharge for the Sapgyo-cheon upstream basin in Chungnam Province was evaluated by water balance analysis utilizing WetSpass-M model. The modeling input data such as topography, climate parameters, LAI (Leaf Area Index), land use, and soil characteristics were established using ArcGIS, QGIS, and Python programs. The results showed that the annual average groundwater recharge in 2001 - 2020 was 251 mm, while the monthly groundwater recharge significantly varied over time, fluctuating between 1 and 47 mm. The variation was high in summer, and relatively low in winter. Variation in groundwater recharge was the largest in July in which precipitation was heavily concentrated, and the variation was closely associated with several factors including the total amount of precipitation, the number of days of the precipitation, and the daily average precipitation. This suggests the extent of groundwater recharge is greatly influenced not only by quantity of precipitation but also the precipitation pattern. Since climate condition has a profound effect on the monthly groundwater recharge, evaluation of monthly groundwater recharge need to be carried out by considering both seasonal and regional variability for better groundwater usage and management. In addition, the mathematical tools for groundwater recharge analysis need to be improved for more accurate prediction of groundwater recharge.

• Food Engineering Progress
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• v.14 no.2
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• pp.118-126
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• 2010

The fibrous material fraction as a by-product from the commercial aloe vera gel processing was obtained and freeze dried. The physicochemical characteristics such as the proximate composition, crystalline/surface structures and several physical functionalities including the water holding capacity (WHC), swelling capacity (SW), oil holding capacity (OHC), emulsion/foam properties and viscosity properties of this powdered sample (100 mesh) were investigated and analyzed by comparison with commercial $\alpha$-cellulose as a reference sample. The total dietary fiber content of powdered sample was very high as much as 87.5%, and the insoluble dietary and soluble dietary fiber content ratios were 77.6 and 22.4%, respectively. The FT-IR spectrum of powdered sample showed a typical polysaccharide property and exhibited a x-ray diffraction pattern for cellulose III and IV like structure. SW (8.24${\pm}$0.15 mL/g), WHC(6.40${\pm}$0.19 g water/g solid) and OHC(10.32${\pm}$0.29 g oil/g solid) of freeze dried aloe cellulose were about 3.3, 1.4 and 2 times higher than those of commercial $\alpha$-cellulose, respectively. Aloe cellulose (~2%, w/v) alone had no foam capacity while improved the foam stability of protein solution (1% albumin+0.5% $CaCl_{2}$) by factor of 300%. Emulsion capacity of 2%(w/v) aloe cellulose was about 70% level of 0.5%(w/v) xanthan gum, but its emulsion stability was about 1.2 times higher than that of xanthan gum. Also, aloe cellulose containing CMC (carboxyl methyl cellulose) of 0.3%(w/v) showed a very good dispersity. Aloe cellulose dispersion of above 1%(w/v) exhibited higher pseudoplasticity and concentration dependence than those of $\alpha$-cellulose dispersion, indicating the viscosity properties for new potential usage such as an excellent thickening agent.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.159-164
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• 2018

This paper deals with the comparative analysis the two surveys called term1, term2 by collecting 4-digit password data 1313 for 2006~2011 and 2519 for 2012~ 2017. Numbers lacking prudence were significantly reduced in the term2 survey and over time, the use of four digit PWs became increasingly prudent. There was a difference in the use of digit numbers between male and female. The top five types accounted over 60%, which imply that certain types of preferences are present. It was the outcome of this paper that we can indirectly deduce these facts. Studies such as reuse of four digit PWs in user's convenience will need to be supplemented in the near future.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.28-38
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• 2019

The smart card data stores the transit usage records of individual passengers. By using this, it is possible to analyze the traffic demand by station and time. However, since the purpose of the trip is not recorded in the smart card data, the demand for each purpose such as commuting, school, and leisure is estimated based on the survey data. Since survey data includes only some samples, it is difficult to predict public transport demand for each purpose close to the complete enumeration survey. In this study, we estimates the purposes of trip for individual passengers using the smart card data corresponding to the complete enumeration survey of public transportation. We estimated trip purposes such as commute, school(university) considering frequency of O-D, duration, and departure time of a passenger. Based on this, the passengers are classified as workers and university students. In order to verify our methodology, we compared the estimation results of our study with the patterns of the survey data.

• Advances in concrete construction
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• v.8 no.1
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• pp.21-31
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• 2019

The rich recipe of ultra high performance concrete (UHPC) offers the higher mechanical, durability and dense microstructure property. The variable like cement/sand ratio, amount of supplementary cementitious material, water/binder ratio, amount of fiber etc. alters the UHPC hardened properties to any extent. Therefore, to understand the effects of these variables on the performance of UHPC, inevitably a stage-wise development is required. In the present experimental study, the effect of sand/cement ratio, the addition of finer material (fly ash and quartz powder) and, hybrid fiber on the fresh, compressive and microstructural property of UHPC is evaluated. The experiment is conducted in three phases; the first phase evaluates the flow value and strength attainment of ingredients, the second phase evaluates the efficiency of finer materials (fly ash and quartz powder) to develop the UHPC and the third phase evaluate the effect of hybrid fiber on the flow value and strength of ultra high performance hybrid fiber reinforced concrete (UHP-HFRC). It has been seen that the addition of fly ash improves the flow value and compressive strength of UHPC as compared to quartz powder. Further, the usage of hybrid fiber in fly ash contained matrix decreases the flow value and improves the strength of the UHP-HFRC matrix. The dense interface between matrix and fiber and, a higher amount of calcium silicate hydrate (CSH) in fly ash contained UHP-HFRC is revealed by SEM and XRD respectively. The dense interface (bond between the fiber and the UHPC matrix) and the higher CSH formation are the reason for the improvement in the compressive strength of fly ash based UHP-HFRC. The differential thermal analysis (DTA/TGA) shows the similar type of mass loss pattern, however, the amount of mass loss differs in fly ash and quartz powder contained UHP-HFRC.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• The Korean Journal of Health Service Management
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• v.13 no.3
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• pp.115-125
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• 2019

Objectives: Korea has been practicing the separation of dispensary from medical practice since 2000 as a national policy to prevent misuse or overuse of medicines. This study aimed to investigate prescription patterns from except pharmacies in order to determine the appropriateness of drug usage among those patients. Methods: Thirty-two pharmacies in the Yeongdong area of Gangwon Province were examined in this study. The same simulated patient complaining of cold symptoms for 3 days visited each pharmacy to obtain a prescription for medication. Results: At pharmacies prescribing medicine, steroids (53.1%) and antibiotics (50.0%) were used to treat the common cold. Duplicate prescriptions of drugs, such as antihistamines (47.0%) and decongestants (31.3%) were common. The average number of drug prescriptions was 6.59, and 53.2% of pharmacies had prescribed more than seven drugs. The average total cost of the prescriptions was 6,093 won, and the daily cost was 2,544 won. Conclusions: Steroids and antibiotics were frequently abused among patients whose medications had been prescribed by pharmacies. Also, there were a considerable number of drugs and duplicate prescriptions. The prices of the drugs were somewhat high.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.53-59
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• 2021

When using Medical Information Retrieval services, a typical retrieval method is to use the Anatomic Therapyutic Chemical Classification (ATC) code. Traditional ATC code-based medical information retrieval is very useful for single ingredient product retrieval with single ingredient. However, in the case of complex, retrieval errors often occur. The cause of this problem is that ATC code-based retrieval proceeds by pattern matching ATC code.In this work, we design the mapping scheme based on ATC code by analyzing the requirement scenarios for retrieval based on main ingredient in ATC code-based retrieval. the mapping scheme based on ATC is a schema that stores the ATC code of the complex and all the ATC code of the single agent included in the complex. ATC code-based retrieval using this schema retrieves a complex as ingredient of a single ingredient product, thus having higher accuracy than existing methods. the mapping scheme based on ATC is expected to increase the efficiency of doctors' prescription of patients and increase the accuracy of drug safety use services.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.270-281
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• 2021

The emergence of social media and technical development of smart phone allows media users to produce, share and spread a variety of contents, which results in a big change in their media usage pattern. Among those changes, the most prominent one is Youtube's powerful growth. Creators' enthusiastic content productions and users' active sharing actions are caused of the growth. Recently, creators give a powerful effect on users as social media influencer, and the practice of influencer marketing has been focused. This current study is examined the effects of parasocial interaction and relationship on brand attitude and purchase intention. Specially, parasical interaction is divided into identification, interest, and experience parasocial interaction, and the divisions of relationship are trustworthy, friendship, understanding, and commitment. As a result, interest and trustworthy are significant predictors of brand attitude. Also, experience parasocial interaction is negative and friendship is positive predictors of purchase intention. Theoretical and practical implication are discussed.