• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.6
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• pp.146-154
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• 2023

In this study, the peak wave period T_p and mean wave period T₀₂ and T_{m-1, 0}, which are major parameters for classifying ocean characteristics, were calculated using water surface elevation data observed from the second west coast oceanographic and meteorological observation tower. In addition, the ratio of abnormal data, correlation analysis, and optimal probability density function were estimated. In the case of T_p among the calculated representative periods, the proportion of abnormal data was 5.73% and 0.67% at each point, and T₀₂ was 4.35% and 0.01%. T_{m-1, 0} was found to be 2.82% and 0.03%. Meanwhile, as a result of analyzing the relationship between T₀₂ and T_p, the relationship was calculated to be 0.53 and 0.63 for each point. The relationship between T_{m-1, 0} and T_p was 1.15 and 1.32, respectively, and T₀₂, T_{m-1, 0} was 1.18 and 1.22. As a result of estimating the optimal probability density function of the calculated representative period, T_p followed the 'Log-normal' and 'Normal' distributions at each point, and T₀₂ was 'Gamma', 'Normal' distribution and T_{m-1, 0} showed that 'Log-normal' and 'Normal' distribution were dominant, respectively. It is decided that these results can be used as basic data for wave analysis conducted on the west coast.

• Knowledge Management Research
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• v.21 no.3
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• pp.249-269
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• 2020

Recently, as the online market has matured, it is facing many problems to prevent the growth. The most common problem is the homogenization of online products, which fails to increase the number of customers any more. Moreover, although the portion of the online market has increased significantly, it now becomes essential to expand offline for further development. In response, many online firms have recently sought to expand their businesses and marketing channels by securing offline spaces that can complement the limitations of online platforms, on top of their existing advantages of online channels. Based on their competitive advantage in terms of analyzing large volumes of customer data utilizing information technologies (e.g., big data and artificial intelligence), they are reinforcing their offline influence as well through this online for offline (O4O) business model. On the other hand, most of the existing research has primarily focused on online to offline (O2O) business model, and there is still a lack of research on O4O business models, which have been actively attempted in various industrial fields in recent years. Since a few of O4O-related studies have been conducted only in an experience marketing setting following a case study method, it is critical to conduct an empirical study on O4O selection attributes and their impact on customer satisfaction and loyalty. Therefore, focusing on China's representative O4O business model, 'Fresh Hema,' this study attempts to identify some key selection attributes specialized for O4O services from the customers' viewpoint and examine the impact of these attributes on customer satisfaction and loyalty. The results of the structural equation modeling (SEM) with 300 O4O (Fresh Hema) experienced customers, reveal that, out of seven O4O selection attributes, four (mobile app quality, mobile payment, product quality, and store facilities) have an impact on customer satisfaction, which also leads to customer loyalty (reuse intention, recommendation intention, and brand attachment). This study would help managers in an O4O area well adapt to rapidly changing customer needs and provide them with some guidelines for enhancing both customer satisfaction and loyalty by allocating more resources to more significant selection attributes, rather than less significant ones.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.6
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• pp.27-36
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• 2021

At a time when the equitable use of urban parks is gradually emerging as a social issue, this study was initiated to expand the influence of urban parks by improving the quality of park services, thereby resolving areas not covered by urban park services. This study targeted the Hangang Park in Seoul, where the qualitative service of parks shows the greatest difference. The influence relationship between the qualitative services of the park and the user's sphere of influence, which indicates the distribution of park users, was proposed to assess the influence of improvements in the quality of service. As a research method, the top three districts and the bottom three districts were selected through the Han River Park user satisfaction survey conducted from 2017 to 2019, and a qualitative service evaluation was carried out. It was derived using the data acquired in September. Afterward, by performing a spatial autocorrelation analysis on the user's sphere of influence, additional verification of the user's sphere of influence was performed numerically and visually. As a result of the study, the user influence in the top three districts, with high-quality service, was stronger and wider than that of the lower three districts. It was confirmed that the quality of service of the park affects the user influence. This shows that to realize park equity, it is necessary to improve the quality of services through continuous management and improvement of individual parks and the creation of new parks. This study has significance in that it recognizes the limitations of research on park services from a supplier's point of view and evaluates the qualitative services of parks from the perspective of actual park users. We propose an alternative to deal with the lower the park deprivation index.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.1-14
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• 2016

In the recent years, the internet has become accessible without limitation of time and location to anyone with smartphones. It resulted in more convenient travel information access both on the pre-trip and en-route phase. The main objective of this study is to conduct a stationary test for traffic information web/mobile application access indexes from TCS (Toll Collection System); and analyzing the relationship between the web/mobile application access indexes and actual traffic volume on expressways, in order to analyze searching behavior of expressway related travel information. The key findings of this study are as follows: first, the results of ADF-test and PP-test confirm that the web/mobile application access indexes by time periods satisfy stationary conditions even without log or differential transformation. Second, the Pearson correlation test showed that there is a strong and positive correlation between the web/mobile application access indexes and expressway entry and exit traffic volume. In contrast, truck entry traffic volume from TCS has no significant correlation with the web/mobile application access indexes. Third, the time gap relationship between time-series variables (i.e., concurrent, leading and lagging) was analyzed by cross-correlation tests. The results indicated that the mobile application access leads web access, and the number of mobile application execution is concurrent with all web access indexes. Lastly, there was no web/mobile application access indexes leading expressway entry traffic volumes on expressways, and the highest correlation was observed between webpage view/visitor/new visitor/repeat visitor/application execution counts and expressway entry volume with a lag of one hour. It is expected that specific individual travel behavior can be predicted such as route conversion time and ratio if the data are subdivided by time periods and areas and utilizing traffic information users' location.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.137-152
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• 2017

The decline in the price of IT devices and the development of the Internet have created a new field called Internet of Things (IoT). IoT, which creates new services by connecting all the objects that are in everyday life to the Internet, is pioneering new forms of business that have not been seen before in combination with Big Data. The prospect of IoT can be said to be unlimited in its utilization. In addition, studies of standardization organizations for smooth connection of these IoT devices are also active. However, there is a part of this study that we overlook. In order to control IoT equipment or acquire information, it is necessary to separately develop interworking issues (IP address, Wi-Fi, Bluetooth, NFC, etc.) and related application software or apps. In order to solve these problems, existing research methods have been conducted on augmented reality using GPS or markers. However, there is a disadvantage in that a separate marker is required and the marker is recognized only in the vicinity. In addition, in the case of a study using a GPS address using a 2D-based camera, it was difficult to implement an active interface because the distance to the target device could not be recognized. In this study, we use 3D Depth recognition camera to be installed on smartphone and calculate the space coordinates automatically by linking the distance measurement and the sensor information of the mobile phone without a separate marker. Coordination inquiry finds equipment of IoT and enables information acquisition and control of corresponding IoT equipment. Therefore, from the user's point of view, it is possible to reduce the burden on the problem of interworking of the IoT equipment and the installation of the app. Furthermore, if this technology is used in the field of public services and smart glasses, it will reduce duplication of investment in software development and increase in public services.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.