• The Korean Journal of Health Service Management
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• v.13 no.4
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• pp.145-161
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• 2019

Objectives: Work-Family Balance (WFB) is a significant social issue in Korea. We examined the effects of employees' burnout on organizational performances by determining the moderating effect of family-friendly organizational culture in firefighter's organization. Methods: To test the hypothesis, data were collected from firefighters who are working at five fire stations in Deajeon and Chungnam province. Based on quantitative survey from 489 respondents, hierarchical regression analyses were performed. Results: The analysis revealed that job burnout had a negative effect on both organizational commitment (OC) and organizational citizen behavior (OCB). Family-Supportive Organization Perception (FSOP) negatively moderated the relationship between burnout and OCB. On the other hand, there was no significant moderating effect of FSOP on the relationship between burnout and OC. Conclusions: This study raises the importance of creating an organizational culture that gives its members a belief that the organization guarantees and supports the work-family balance system.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.951-969
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• 2010

A Study on the Management Performance of the Distribution channel's CRM : Balanced Scorecard Approach as to CRM application field of the Distribution Company About Korea Airport Service In this paper, the performances of Distribution channel's estimation schemes for CRM system are investigated. We anticipate these moves will have a positive effect on the CRM Scorecard infra, process, elevate royalty environments. As is study of used SPSSWN 15.0 correlation analysis, regression analysis & AMOS 7.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.229-234
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• 2021

The explosive growth of video-based services is considered as the dominant contributor to Internet traffic. Hence it is very important for video service providers to meet the quality expectations of end-users. In the past, the Quality of Service (QoS) was the key performance of networks but it considers only the network performances (e.g., bandwidth, delay, packet loss rate) which fail to give an indication of the satisfaction of users. Therefore, Quality of Experience (QoE) may allow content servers to be smarter and more efficient. This work is motivated by the inherent relationship between the QoE and the QoS. We present a no-reference (NR) prediction model based on Deep Neural Network (DNN) to predict video QoE. The DNN-based model shows a high correlation between the objective QoE measurement and QoE prediction. The performance of the proposed model was also evaluated and compared with other types of neural network architectures, and three known machine learning methodologies, the performance comparison shows that the proposed model appears as a promising way to solve the problems.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.17-23
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• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.289-300
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• 2022

Purpose - This study is a study on China's national flower, the Peony Festival', with a focus on the Heze International Peony Festival, which is called the "City of Peonies, to investigate the difference in the importance and achievement of festival items". Design/methodology/approach - IPA is used to identify items that need to be improved for festival satisfaction attributes, and to suggest the direction of the annual peony festival in the future. Findings - The difference in importance and achievement for each of the 17 items of the Heze International Peony Festival, there was a statistically significant difference between importance and satisfaction, except for the peony admission ticket. It was found that satisfaction was relatively low in these items as it was higher than the average value. Second, the first quadrant showed information on traffic communication, food taste, convenient facilities (parking lot, restroom, shelter), service delivery speed, service provider attitude, natural scenery at the festival site, road signs, and information boards. Third, the diversity of cultural performances appeared in the fourth quadrant. Finally, the second quadrant is an area of high importance but low achievement. Two items were derived: the environment of the entertainment venue and the diversity of the entertainment venue. Research implications or Originality - These are the items that led to the dissatisfaction of visitors to the festival, and seem to be the items that need to be improved the most urgently.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.121-127
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• 2023

A 500 km/h linear motor high speed terrestrial transportation service is planned to launch 2027 in Japan. In order to support 5G service in the train, the Sub-carrier spacing frequency of 30 kHz is planned to be used instead of common 15 kHz sub-carrier spacing to mitigate Doppler effect in such high-speed transportation. In addition, to increase the cell size of 5G mobile system, plural Base Station antenna will transmit the identical Down Link (DL) signal to form the expanded cell size along the train rail. In this situation, forward and backward antenna signals will be Doppler shifted by reverse direction respectively and the receiver in the train might suffer to estimate accurate Channel Transfer Function (CTF) for its demodulation. In this paper, Delay and Doppler Profiler (DDP) based Channel Estimator is proposed and it is successfully implemented in signal processing simulation system. Then the simulated performances are compared with the conventional Time domain linear interpolated estimator. According to the simulation results, QPSK modulation can be used even under severe channel condition such as 500 km/h, 2 path reverse Doppler Shift condition, although QPSK modulation can be used less than 200 km/h with conventional Channel estimator.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.1
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• pp.127-136
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• 2013

In 2013, the small retailer organized support service, which commenced in 2009, has been expanding its place in the policy by transforming into a small retailer cooperating support service. This support service supports two main sides. One of them is strengthening the financial aspects of the scale by organizing the small retailers in order to make the distribution efficient. The second is improving the management and enhancing the efficiency through visitation education. This research focuses on studying the effects of the visitation education which is one of the two aspects of small retailers support service. Empirical analysis on the effects of the visitation education's quality on the loyalty of support service and business performance was performed in detail by dividing the structure of the companies into chain form and combined form of small retailers. According to the results of the analysis, the visitation education's quality positively affected the support service's loyalty without any restrictions on the structures of the retailers. However in the business performances, the visitation education's quality only showed positive effects on the combined form of small retailers. Therefore, it is implied that in order to continuously receive support program by increasing the loyalty of the retailers, the quality of the visitation education must be increased and the retailer support service need to be considering the structure of the companies in order to improve small retailer's business performance.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.2
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• pp.276-290
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• 2023

The purpose of this study is to analyze the characteristics and specific elements of successful science classes that pre-service elementary school teachers think. For the study, 61 pre-service elementary school teachers (47 females, 14 males) were recruited as research participants. The data used in the study are mutual evaluation papers prepared during class performances by group and individual. The amount of data was a vast amount of qualitative data with a total of 150 pages, and the research results were derived by inductively categorizing this data through qualitative analysis. The summary of the research results is as follows. First, the factors constituting a successful science class were analyzed into 7 categories (14 sub-categories, 33 sub-elements). The elements that make up a successful science class in detail were analyzed as science subject contents, class management, selection of teaching and learning methods and organization of class contents, teaching and learning materials, understanding of students, understanding of teaching situations, and class-related efforts. Second, it was possible to describe the practical classes of pre-service teachers by collecting the details of the elements that make up a successful science class recognized by pre-service teachers. As seen in the above research results, the characteristics and specific elements of successful science classes recognized by pre-service teachers were identified, and based on this, pre-service teachers will be able to develop support for effective science class operation, and continuous analysis should be conducted.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.