• Title/Summary/Keyword: data modelling

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The Bidirectional Relationship between Objective and Subjective Knowledge: Applying the Heuristic-systematic Model in Vietnamese Mobile Banking

  • Hai Nguyen Thi Thanh;Tommi Tapanainen;Yen Nguyen Thi Hoang
    • Journal of Information Technology Applications and Management
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    • v.31 no.3
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    • pp.71-92
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    • 2024
  • This study investigates the contribution of customer knowledge to customer intention to adopt mobile banking by evaluating the interaction among knowledge, perceived risk and trust, and behavioral intentions. Analysis is conducted through structural equation modelling using SPSS and AMOS and data from 783 customers representing the seven largest banks in Vietnam. Our study is the first one to find the existence of the bidirectional perspective between objective and subjective knowledge. The study further shows that the attenuation effect in the heuristic-systematic model could be used to explain the stronger influence of objective knowledge on intention compered to subjective knowledge. Our findings suggest that customer knowledge, perceived risk and trust impact the intention of mobile banking users in different manners and to different degrees. Particularly, objective customer knowledge is the most influential predictor of mobile banking adoption. Having a greater understanding of these relationships can help firms in deciding the kind of intervention that is most likely to convince customers to adopt a service.

A Study on the Initial Stability Calculation of Small Vessels Using Deep Learning Based on the Form Parameter Method (Form Parameter 기법을 활용한 딥러닝 기반의 소형선박 초기복원성 계산에 관한 연구)

  • Dongkeun Lee;Sang-jin Oh;Chaeog Lim;Jin-uk Kim;Sung-chul Shin
    • Journal of the Korean Society of Industry Convergence
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    • v.27 no.1
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    • pp.161-172
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    • 2024
  • Approximately 89% of all capsizing accidents involve small vessels, and despite their relatively high accident rates, small vessels are not subject to ship stability regulations. Small vessels, where the provision of essential basic design documents for stability calculations is omitted, face challenges in directly calculating their stability. In this study, considering that the majority of domestic coastal small vessels are of the Chine-type design, the goal is to establish the major hull form characteristic data of vessels, which can be identified from design documents such as the general arrangement drawing, as input data. Through the application of a deep learning approach, specifically a multilayer neural network structure, we aim to infer hydrostatic curves, operational draft ranges, and more. The ultimate goal is to confirm the possibility of directly calculating the initial stability of small vessels.

Research Trends on PBM (Performance-based Management) in Korea

  • Ho Taek KIM;Jin Won KIM;Hyun Sung PARK
    • Journal of Research and Publication Ethics
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    • v.5 no.2
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    • pp.1-6
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    • 2024
  • Purpose: PBM is emerging as a major management system for securing corporate productivity and enhancing competitiveness, and various studies are being conducted. The purpose of this study is to analyze research trends published in KCI-listed journals and papers since 1999 to understand the current status of research and provide basic data for more extensive research and development of performance management in the future. Research design, data and methodology: A detailed examination of research trends was conducted through the analysis of abstracts from 154 research papers on PBM. To facilitate a comprehensive analysis of these trends, LDA topic modelling was employed. Results: First, it should be noted that research on PBM is not limited to the area of HRM. Instead, PBM research is expanding to encompass comprehensive personnel systems. Second, the results of topic modeling analysis show that although the initial focus of research was on human resource management, there is now a growing interest in fairness and organizational culture in the entire organization. Conclusions: PBM is becoming a dominant paradigm as it shifts from HR systems to organizational fairness and culture. This suggests that future research should consider both quantitative and qualitative aspects of PBM to improve corporate performance while prioritizing organizational fairness and culture.

ANALYSIS OF SPATIAL FACTORS AFFECTING DENGUE EPIDEMICS USING GIS IN THAILAND

  • Nakhapakorn Kanchana;Tripatht Nitin;Nualchawee Kaew;Kusanagt Michiro;Pakpien Preeda
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.774-777
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    • 2005
  • Dengue Fever(DF) and Dengue haemorrhagic fever(DHF) has become a major international public health concern. Dengue Fever(DF) and Dengue haemorrhagic Fever (DHF) is also still the major health problem of Thailand, although many campaigns against it have been conducted throughout the country. GIS and Remotely Sensed data are used to evaluate the relationships between socio-spatial, environmental factors/indicators and the incidences of viral diseases. The aim of the study is to identify the spatial risk factors in Dengue and Dengue Haemorrhagic Fever in Sukhothai province, Thailand using statistical, spatial and GIS Modelling. Preliminary results demonstrated that physical factors derived from remotely sensed data could indicate variation in physical risk factors affecting DF and DHF. The present study emphasizes the potential of remotely sensed data and GIS in spatial factors affecting Dengue Risk Zone analysis. The relationship between land cover and the cases of incidence of DF and DHF by information value method revaluated that highest information value is obtained for Built-up area. A negative relationship was observed for the forest area. The relations between climate data and cases of incidence have shown high correlation with rainfall factors in rainy season but poor correlation with temperature and relative humidity. The present study explores the potential of remotely sensed data and GIS in spatial analysis of factors affecting Dengue epidemic, strong spatial analysis tools of GIS. The capabilities of GIS for analyst spatial factors influencing risk zone has made it possible to apply spatial statistical analysis in Disease risk zone.

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Stochastic Modelling of Monthly flows for Somjin river (섬진강 월유출량의 추계학적 모형)

  • 이종남;이홍근
    • Water for future
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    • v.17 no.4
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    • pp.281-291
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    • 1984
  • In our Koreans river basins there are many of monthly rainfall data, but unfortrnately streamflow data needed are rare. Analysing monthly rainfall data of Somjin river basin, the stochastic theory model for calculation of monthly streamflow series of that region is determined. The model is composed of Box & Jenkins stansfer function plus ARIMA residual models. This linear stochastic differenced time series equation models can adapt themselves to the structure and variety of rainfall, streamflow data on the assumption of the stationary covarience. The fiexibility of Box-Jenkins method consists mainly in the iterative technique of building an AIRMA model from observations and by the use of autocorrelation functions. The best models for Somjin river basin belong to the general calss: $Y_t=($\omega$o-$\omega$_1B) C_iX_t+$\varepsilon$t$ $Y_t$ monthly streamflow, $X_t$ : monthly rainfall, $C_i$ :monthly run-off, $$\omega$o-$\omega$_1$ : transfer parameter, $$\varepsilon$_t$ : residual The streamflow series resulted from the proposed model is satisfactory comparing with the exsting streamflow data of Somjin gauging station site.

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Modelling Missing Traffic Volume Data using Circular Probability Distribution (순환확률분포를 이용한 교통량 결측자료 보정 모형)

  • Kim, Hyeon-Seok;Im, Gang-Won;Lee, Yeong-In;Nam, Du-Hui
    • Journal of Korean Society of Transportation
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    • v.25 no.4
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    • pp.109-121
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    • 2007
  • In this study, an imputation model using circular probability distribution was developed in order to overcome problems of missing data from a traffic survey. The existing ad-hoc or heuristic, model-based and algorithm-based imputation techniques were reviewed through previous studies, and then their limitations for imputing missing traffic volume data were revealed. The statistical computing language 'R' was employed for model construction, and a mixture of von Mises probability distribution, which is classified as symmetric, and unimodal circular probability were finally fitted on the basis of traffic volume data at survey stations in urban and rural areas, respectively. The circular probability distribution model largely proved to outperform a dummy variable regression model in regards to various evaluation conditions. It turned out that circular probability distribution models depict circularity of hourly volumes well and are very cost-effective and robust to changes in missing mechanisms.

Correction of Radiometric Distortion Caused by Geometric Property in SAR image using SAR Simulation (SAR영상의 모의제작에 의한 기하학적 복사왜곡의 보정)

  • Jeong, Soo;Yeu, Bock-Mo
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.16 no.1
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    • pp.1-7
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    • 1998
  • SAR data can be achieved independently of weather conditions or sun illumination which is main limitation of electro-optical sensor to get image. The information from imagery can be more enlarged using Shh data be-cause SAR data offers different information from electro-optical sensor. SAR data contains various distortions caused by the radar specification and geometric properties of data acquisition. These distortions should be removed to get the information with acceptable accuracy. In this study, we aimed to correct the radiometric distortion in Shh image caused by the geometric property of the object. For this purpose, we simulated the SAR image by modelling of the power of return beam which is variable according to the geometric configuration between SAR antenna and ground object. Dividing the SAR image by the simulation image, then, we can get the radiometrically corrected image. As a result of this study, we could minimize the effect of radiometric distortion in achieving some qualitative information from SAR image for the related field, such as Geospatial Information System.

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A Comparative Analysis on the Efficiency of Monitoring between EWMA and Shewhart Chart in Instrumental Process with Autocorrelation (자기상관이 있는 장치 공정에서 EWMA와 Shewhart 관리도와의 모니터링 효율성 비교 분석)

  • Cho, Jin-Hyung;Oh, Hyun-Seung;Lee, Sae-Jae;Jung, Su-Il;Lim, Taek;Baek, Seong-Seon;Kim, Byung-Keug
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.35 no.4
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    • pp.118-125
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    • 2012
  • When monitoring an instrumental process, one often collects a host of data such as characteristic signals sent by a sensor in short time intervals. Characteristic data of short time intervals tend to be autocorrelated. In the instrumental processes often the practice of adjusting the setting value simply based on the previous one, so-called 'adjacent point operation', becomes more critical, since in the short run the deviations are harder to detect and in the long run they have amplified consequences. Stochastic modelling using ARIMA or AR models are not readily usable here. Due to the difficulty of dealing with autocorrelated data conventional practice is resorting to choosing the time interval where autocorrelation is weak enough then to using I-MR control chart to judge the process stability. In the autocorrelated instrumental processes it appears that using the Shewhart chart and the time interval data where autocorrelation is relatively not existent turns out to be a rather convenient and very useful practice to determine the process stability. However in the autocorrelated instrumental processes we intend to show that one would presumably do better using the EWMA control chart rather than just using the Shewhart chart along with some arbitrarily intervalled data, since the former is more sensitive to shifts given appropriate weights.

Search Trend's Effects On Forecasting the Number of Outbound Passengers of the Incheon Airport (포탈의 검색 트렌드를 활용한 인천공항 출국자 수 예측 연구)

  • Shin, Euiseob;Yang, Dong-Heon;Sohn, Sei Chang;Huh, Moonhaeng;Baek, Seokchul
    • Journal of IKEEE
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    • v.21 no.1
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    • pp.13-23
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    • 2017
  • Short-term prediction of the number of passengers at the airport is very essential for the efficient and stable operation of the airport. Here, to forecast the immigration of Incheon International Airport, we perform the predictive modeling of Korean and Chinese outbound travelers comprising most of immigration. We conduct the Granger Causality test between the number of outbound travelers and related search trend data to confirm the correlation. It is found that the forecasting with both "outbound travelers" and "search term trends" data outperforms the one only with "outbound travelers" data. This is because search activities are done before doing something and this study confirms that search trend data inherently possess the potential for prediction.

Survival analysis for contract maintenance period using life insurance data (생명보험자료를 이용한 계약유지기간에 대한 생존분석)

  • Yang, Dae Geon;Ha, Il Do;Cho, Geon Ho
    • The Korean Journal of Applied Statistics
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    • v.31 no.6
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    • pp.771-783
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    • 2018
  • The life insurance industry is interested in various factors that influence the long-term extensions of insurance contracts such as the necessity for the advisors' long-term management of consumers, product consulting, and improvement of the investment aspects. This paper investigates important factors leading to a long-term contract that forms an important part of the life insurance industry in Korea. For this purpose we used the data of contents (i.e., data from Jan 1, 2011 to Dec 31, 2016) of the contracts of xxx insurance company. In this paper, we present how to select important variables to influence the duration of the contract maintenance via a penalized Cox's proportional hazards (PH) modelling approach using insurance life data. As the result of analysis, we found that the selected important factors were the advisor's status, the reward type 2 (annuity insurance) and tendency 4 (safety-pursuing type).