• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.612-615
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• 2007

Water quality modelling is an ideal tool of simulating physical, chemical and biological changes occurring in water systems. It has been utilized in a number of GIS-based water quality management and analysis applications. However, there is a need of a decision making process to translate the modelling result into an understandable form thereby implement the modelling results to the real world. This paper outlines a new water quality index called the QUAL2E's water quality index (QWQI) based on the water quality modelling using QUAL2E. The development mainly includes four steps: variable selection, sub-index development, weight assignment and sub-index aggregation. An experiment of applying the index and GIS to the Sapgyo River in Korea was implemented. Different from other water quality indices for general water uses, the index is specifically used for the simulated water quality indicators. The index can provide a simple and easy-to-understand decision support. Furthermore, interfacing with GIS, the decision analysis can be performed within a spatial environment. However, more study needs to be made in the future including the improvement of aggregation function.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.31-43
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• 2004

Voice over Internet Protocol (VoIP) is currently a popular research topic as a real time voice packet transmission method. But current Internet environment do not guarantee the quality of voice when we take a side view of delay, jitter and loss. Up to now, many voice based evaluation algorithms have been used to measure speech quality of VoIP systems. However, these algorithms have the defects that their results are different according to voice samples and some algorithms can not take network environment for speech transmission path. The E-model can be used to solve the problems of these algorithms. In this paper. we introduce VoIP planning guidelines through the various analysis of E-model which can model impairments of network quality as well as VoIP equipment quality systematically, We, also, show the evaluation method and results of speech transmission quality.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.77-88
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• 2022

In response to the significant COVID-19 outbreak, countries have enforced the use of E-learning systems as an alternative to traditional learning; to contain the virus and minimize the infection rate while maintaining the continuity of the learning experience. However, the effective adoption of E-learning systems requires a well-understanding of critical factors, especially in times of crisis. In this regard, this study intends to assess the success of the E-learning system adoption by Higher Education Institutions (HEIs) during the crisis of COVID-19 by utilizing the Information Systems Success (ISS) model. This study's adopted model consists of nine interdependent dimensions, namely: Technical System Quality, Information Quality, Service Quality, Learner Quality, Perceived Satisfaction, Perceived Usefulness, System Use, Intention to Use, and System Success. An electronic survey was distributed among higher education students from different universities in Saudi Arabia to explore each model's dimension. Structural Equation Modeling (SEM) has been applied via SmartPLS software to test the causal relationships between dimensions. This study's main results revealed that students' Service Quality, Learner Quality, and the Intention to Use by students are essential drives for E-learning System Use during the Covid-19 pandemic. Meanwhile, the Intention to Use the system is significantly influenced by Perceived Satisfaction and Perceived Usefulness dimensions. Further, Perceived Satisfaction, Perceived Usefulness, and System Use are interdependent, and all three have a significant positive impact on E-learning System Success.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.4
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• pp.491-498
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• 2014

As internet services are being provided through a wireless access, the importance of quality of experience (QoE) is stressed that is defined as the quality that indicates user's actual feeling when a service is provided. Unlike quality of service (QoS) that can be expressed as a numerical value, it is difficult to represent QoE in an objective way. If an internet service is serviced over a wireless channel, its QoE can be affected by a number of factors such as fading, mobility and so on. This paper, therefore, attempts to specify the relationship between QoE and QoS by conducting practical measurements for the voice service through 3G high speed packet access (HSPA) access network. Analysing the measured results, it has been shown that received signal strength indicator (RSSI) has a great influence on mean opinion score (MOS) through transmission delay.

• Journal of Korean Society on Water Environment
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• v.20 no.3
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• pp.223-230
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• 2004

The QUAL2E and Box-Jenkins time series model were applied to the Miho river, a main tributary of the Geum river, to predict water quality. The models are widely used to predict water quality in rivers and watersheds because of its accuracy. As results of the study, we concluded as follows: Pollutant loadings in upper stream of Miho river were determined to 57,811 kgBOD/d, 19,350 kgTN/d, and 5,013 kgTP/d. The loading of TN in Mushim river was 19,450 kgTN/d, respectively. As the mass loadings were compared with pollutant sources, it concluded that the farming livestock contributed highly to mass emissions of BOD and TP and the population contributed to TN mass loading. The observed water quality values were applied to the models to verify and the models were used to predict the water quality. The QUAL2E Model predicted the concentrations of DO, BOD, TN and TP with high accuracy, but not for E-Coli. The Box-Jenkins time series model also showed high prediction for DO, BOD and TN. However, the concentrations of TP and E-Coli were poorly predicted. The result shows that the QUAL2E model is more applicable in Miho basin for prediction of water quality compared to Box-Jenkins time series model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2513-2530
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• 2021

With the fast development of information and communication technologies, video streaming services and applications are increasing rapidly. However, the network condition is volatile. In order to provide users with better quality of service, it is necessary to develop an accurate and low-complexity model for Quality of Experience (QoE) prediction of time-varying video. Memory effects refer to the psychological influence factor of historical experience, which can be taken into account to improve the accuracy of QoE evaluation. In this paper, we design subjective experiments to explore the impact of Short-Term Memory (STM) on QoE. The experimental results show that the user's real-time QoE is influenced by the duration of previous viewing experience and the expectations generated by STM. Furthermore, we propose analytical models to determine the relationship between intrinsic video quality, expectation and real-time QoE. The proposed models have better performance for real-time QoE prediction when the video is transmitted in a fluctuate network. The models are capable of providing more accurate guidance for improving the quality of video streaming services.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.291-300
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• 2006

In this study a statistical water quality monitoring network design of Kyung-An stream is proposed. Water quality data for the design is obtained by QUAL2E model simulation. The observed monthly average water quality data from March to November in Kyung-An stream has been applied to this study. HEC-RAS model is also used for QUAL2E hydrauric parameter estimation. Before QUAL2E water quality parameter estimation, FORA is performed to reduce the number of parameters to be estimated, and then water quality parameters are calibrated with a observed monthly average data. Using these simulated water quality data, the number of gage station and its location are estimated by kriging theory and branch & boundary method. Such a network design is based on two case; average flow and low flow case, respectively. Next, proportional sampling method is applied to estimate the sampling frequency.

• Korean Management Science Review
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• v.20 no.2
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• pp.113-133
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• 2003

This study developed the ISM (Electronic Store Success Model) by basing upon the revised D&M IS Success Model and reflecting the characteristics of marketing and e business. The theoretical rationale of this model was as following : 1) Though the revised D&M IS Success Model (2002) must be more logical and complete model than the original D&M IS Success Model (1992), it did not reflect characteristics of marketing and e business. 2) In the marketing studios, Service Quality has been suggested as one of basic strategies for success. 3) In e business studies, Trust has been emerging as a potentially important antecedent of e loyalty. 4) There has been a paradigm shift from satisfaction to loyalty in marketing emphasis. 5) Nowadays, ERP (Enterprise Resource Planning) has been pervaded in many organizations and has supported PCA (Profit Center Accounting) or Channel based Accounting, so it has not been difficult to evaluate financial performance of Electronic Store. The ESM that has six dimensions (System Quality, information Quality. Service Quality, Trust, Loyalty, Financial Performance) and expresses the causal relationship among them was proposed and was compared with BSC (Balanced Scorecard), WebQual, and EWAM (Extended Web Assessment Method). The results showed that the ESM coincides with BSC and contains dimensions of WebQual, and EWAM.

• Journal of Distribution Science
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• v.15 no.2
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• pp.37-46
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• 2017

Purpose - This research investigates whether e-service quality has the influence on long-term orientation, consumer commitment, and satisfaction. Research design, data, and methodology - Data collection took place for 30 days from October 1, 2015 to October 31, 2015. 315 copies had been analyzed. Covariance structure analysis with credibility and factor analysis was conducted to verify this research hypotheses. Results - First, the lower dimension concepts of e-service quality, reliability, and tangibility had a significant influence on customer commitment, and responsiveness had a significant negative influence. However, aesthetics did not have a significant influence on that. Secondly, aesthetics had a significant influence on long-term orientation, but reliability, tangibility, and responsiveness did not have a significant influence. Thirdly, reliability, tangibility, and aesthetics had a positive influence on customer satisfaction. However, responsiveness had a negative influence. Fourthly, customer commitment had a significant and positive influence on customer satisfaction, but customer commitment and customer satisfaction had a negative influence on long-term orientation. Conclusions - This research verifies the influencing relationship between e-service quality of a franchise coffee shop application with customer commitment, customer satisfaction, and long term orientation. By the results of this research, possibility of application and expansion of theory has been proven.

• The Journal of Industrial Distribution & Business
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• v.9 no.4
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• pp.43-52
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• 2018

Purpose - The purpose of this article is to examine the effects of perceived value, satisfaction, and continued use intention of e-service Quality of taxi-booking apps. Research design, data, and methodology - The questionnaire was created to examine every relevant variables to practical and theoretical implications. The pilot survey was conducted for 15 days from April 7^th to April 21^st in 2016. Their total numbers were 354 surveys. But 330 copies were used for the analysis except 24 of them. To make a more effective analysis, several analysis tools and analysis programmes were used, such as IBM SPSS and AMOS. Results - The results are as follows. First, all variables of e-service quality except safety make influences on perceived value. Second, e-service quality except safety makes influences on satisfaction. Third, perceived value makes influence on satisfaction and on intention of continued use. Fourth, satisfaction makes influence on intention of continued use. Conclusions - First, 5 variables are very limited. more valuable variables are need to test with this model. Second, this study was conducted only in China. So there may be possible to the representatives. To use this model for better analysis and application abroad, more characteristics and strategic factors should be considered like the local culture of a particular country, attributes for different environment etc.