• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.100-110
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• 2020

Recently, the frequency of typhoon and torrential rain due to climate change is increasing. In addition, the upsurge in the complexity of urban sewer network and impervious surfaces area aggravates the inland flooding damage. In response to these worsening situations, the central and local governments are conducting R&D tasks related to predict and mitigate the flood risk. Researches on the analysis of inundation in urban areas have been implemented through various ways, and the common features were to evaluate the accuracy and justification of the model by comparing the model results with the actual inundation data. However, the evaluation procesure using available urban flooding data are not consistent, and if there are no quantitative urban inundation data, verification has to be performed by using press releases, public complaints, or photos of inundation occurring through 'CCTV'. Because theses materials are not quantitative, there is a problem of low reliability. Therefore, this study intends to develop a comparative analysis procedure on the quantitative degree and applicability of the verifiable inundation data, and a systematic framework for the performance assessment of urban flood analysis model was proposed. This would contribute to the standardization of the evaluation and verification procedure for urban flooding modelling.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Korean journal of applied entomology
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• v.61 no.2
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• pp.335-347
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• 2022

We investigated the emergence patterns of Chilo suppressalis (Lepidoptera: Crambidae) adults using sex pheromone traps in the three northeastern areas, Dandong (40°07'N 124°23'E) (Liaoning province), and Gongzhuling (43°30'N 124°49') and Longjing (42°46'N 129°26'E) (Jilin province), China, in 2020 and 2021. Two times of adult flight seasons were isolated clearly during the rice growing periods in the all areas, in which the first season from mid May to late July, and the second season from mid July to mid September were observed. The adult emergence seasons in the areas at higher latitude were later than that at lower latitude. Using the adult emergence data during the first flight seasons, the second flight seasons were estimated through insect phenology modelling, and compared with the observed data. Temperature-dependent life history models (developmental rate, development completion, survival rate, adult aging rate, total fecundity, oviposition completion, and adult survival completion) were collected or constructed for each life stage of C. suppressalis, in which the data from the four previous studies were used. Those models were combined in an insect phenology estimation software, PopModel, and operated for the observed areas. In the results, the phenology modelling operated with the models based on the data of shorter larval periods in the previous studies estimated more accurately the second flight seasons. In 2021, we investigated the change of damaged hill ratios of rice with observing the adult emergence at Dandong and Longjing, 2021. The increase periods of damaged hill ratios of rice were observed two times during the total rice cultivation season, which may be caused by different generations of C. suppressalis larvae.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.113-120
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• 2008

This study analyzed the spatial correlograms of minutely rainfall data with respect to various accumulation times. A bivariate mixed lognormal distribution was applied for rainfall modelling. A total of 26 minutely rainfall data sets from rain gauge stations in the central part of Korean peninsula were analyzed, also repeated for several storm types like Jang-Ma, typhoon and convective storms for their comparison. The accumulation times 1, 2, 3, 5, 10, 30 and 60 minutes were considered in this study. As results, it was found that the minutely rainfall data available was not good enough for estimating minutely rainfall intensity at ungaged locations. It seems more practical to use the hourly rainfall data with much higher rain gauge density, if proper methods for interpolation and data dis-aggregation are provided.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4349-4358
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• 2012

ASRP(Ammunition Stockpile Reliability Program) Data is stored and operated in the field of evaluating the ammunition is not only the only field data but also the ammunition performance-oriented data can determine objectively the power of the artillery. However, ASRP has been used as a yardstick to judge the status of ammunitions stockpiled in the field. On the other hand re-evaluation of the accumulated data and in-depth research have not been carried out. A Study on the Effective Management of Artillery Ammunition using ASRP data suggests how to utilize the ASRP data to analyze and manage existing artillery forces whose focus is centered on increasing the performance of artillery ammunitions through setting the test intervals of deployed stockpiled ammunitions, forecasting the shelf-life of ammunitions, and analyzing the effectiveness of the military strength through modelling and simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4B
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• pp.285-290
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• 2006

Various aspects should be taken into account while developing a distributed architecture based on a multi-tier model or an enterprise architecture. Among those, the separation of role between page designer and page developer, defining entity which is used for database connection and transaction processing are very much important. In this paper, we presented DONSL(Data Server of Non SQL query) architecture to solve these problems applying web tier object modelling. This architecture solves the above problems by simplifying tiers coupling and removing DAO(Data Access Object) and entity from programming logic. We concentrate upon these three parts. One is about how to develop the DAO not concerning the entity modification, another is automatic transaction processing technique including SQL generation and the other is how to use the AET/MET(Automated/Manual Execute d Transaction) effectively.

• The Journal of Bigdata
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• v.1 no.1
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• pp.9-17
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• 2016

This study identifies major features in water supply and introduces important factors in water services based on the information from data mining analysis of water quantity and water pressure measured from sensors. It also suggests more accurate methods using multiple regression analysis and neural network in predicting short term prediction of water demand in water service. A small block of a county is selected for the data collection and tests. There isa water demand on business such as public offices and hospitalstoo in this area. Real stream data from sensors in this area is collected. Among 2,728 data sets collected, 2,632 sets are used for modelling and 96 sets are used for testing. The shows that neural network is better than multiple regression analysis in their prediction performance.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.153-161
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• 2004

The systematic material and labor management planning should be established on accomplished EVM data. The matrix method of integrated cost and schedule was used with common category concept according to the construction project control system. The construction project control system was suggested through analyzing process and data modeling based on integrated cost, schedule and material. Information of construction project can be developed the relationship between the field data and the integrated cost, schedule database. Process and data modelling is provide a standard data format which are related to the material, labor management based on integrated cost, schedule database.

• Journal of the korean society of oceanography
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• v.32 no.2
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• pp.75-84
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• 1997

Relation between the length scale and the wall proximity function in the Mellor-Yamada level 2.5 turbulence closure model has been investigated through various experiments using a range of wall proximity functions. The model performance has been evaluated quantitatively by comparing with laboratory data for wind-driven flow (Baines and Knapp, 1965) and for open-channel flows without and with adverse wind action (Tsuruya, 1985). Comparison shows that a symmetric wall proximity function used by Blumberg and Mellor(1987) gives rise to current profiles with better accuracy than asymmetric wall proximity functions considered. It is noted that in modelling homogeneous flows the length scale 1= 0.31${\|}$z${\|}$(1+z/h) can be used with tolerable accuracy.