• Journal of Korean Society of Rural Planning
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• v.20 no.4
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• pp.263-276
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• 2014

The purpose of this study is to investigate the entire processes of rural amenity resources survey from the beginning to the end, to discuss the results of the survey and resources information establishment, and to comprehensively analyze the status of resources information application. Rural amenity resources survey, which is aimed at finding rural amenity resources to respond to the demands of the resources and support rural development, was first conducted by National Academy of Agricultural Science under Rural Development Administration in 2005. The first survey subjects were 149 eups and myeons in Korea, expanding to the nationwide rural villages. In 2012, the rural amenity resources survey was completed. Next year, the information establishment was completely made. It is expected that the rural amenity resources information established by the survey will be more applied and used, and that with the constant addition of new analyses in line with the changing environment demands, rural amenity resources will contribute to dynamic rural development.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.10
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• pp.3010-3039
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• 2024

This research delves into the realm of Maritime Cybersecurity, focusing on the application of Artificial Intelligence (AI) mechanisms, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Networks (ANN). The maritime industry faces evolving cyber threats, necessitating innovative approaches for robust defense. The maritime sector is increasingly reliant on digital technologies, making it susceptible to cyber threats. Traditional security measures are insufficient against sophisticated attacks, necessitating the integration of AI mechanisms. This research aims to evaluate the effectiveness of KNN, RF, and ANN in fortifying maritime cybersecurity, providing a proactive defense against emerging threats. Investigate the application of KNN, RF, and ANN in the maritime cybersecurity landscape. Assess the performance of these AI mechanisms in detecting and mitigating cyber threats. Explore the adaptability of KNN, RF, and ANN to the dynamic maritime environment. Provide insights into the strengths and limitations of each algorithm for maritime cybersecurity. The study employs these AI algorithms to analyze historical maritime cybersecurity data, evaluating their accuracy, precision, and recall in threat detection. Results demonstrate the effectiveness of KNN in identifying localized anomalies, RF in handling complex threat landscapes, and ANN in learning intricate patterns within maritime cybersecurity data. Comparative analyses reveal the strengths and weaknesses of each algorithm, offering valuable insights for implementation. In conclusion, the integration of KNN, RF, and ANN mechanisms presents a promising avenue for enhancing maritime cybersecurity. The study underscores the importance of adopting AI solutions to the maritime domain's unique challenges. While each algorithm demonstrates efficacy in specific scenarios, a hybrid approach may offer a comprehensive defense strategy. As the maritime industry continues to evolve, leveraging AI mechanisms becomes imperative for staying ahead of cyber threats and safeguarding critical assets. This research contributes to the ongoing discourse on maritime cybersecurity, providing a foundation for future developments in the field.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.561-568
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• 2006

WPS(Wearable Personal Station) for next generation PC can define as a core terminal of 'Ubiquitous Computing' that include information processing and network function and overcome spatial limitation in acquisition of new information. As a way to acquire significant dynamic gesture data of user from haptic devices, traditional gesture recognizer based on desktop-PC using wire communication module has several restrictions such as conditionality on space, complexity between transmission mediums(cable elements), limitation of motion and incommodiousness on use. Accordingly, in this paper, in order to overcome these problems, we implement hand gesture recognition system using fuzzy algorithm and neural network for Post PC(the embedded-ubiquitous environment using blue-tooth module and WPS). Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data 2) Relational Database Management System(hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition modulo: fuzzy max-min and neural network recognition module to recognize significant gesture of continuous / dynamic gestures. Experimental result shows the average recognition rate of 98.8% in fuzzy min-nin module and 96.7% in neural network recognition module about significantly dynamic gestures.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.91-121
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• 2008

Electronic commerce, commonly known as e-commerce or eCommerce, has become a major business trend in these days. The amount of trade conducted electronically has grown extraordinarily by developing the Internet technology. Most electronic commerce has being conducted between businesses to customers; therefore, the researches with respect to e-commerce are to find customer's needs, behaviors through statistical methods. However, the statistical researches, mostly based on a questionnaire, are the static researches, They can tell us the dynamic relationships between initial purchasing and repurchasing. Therefore, this study proposes dynamic research model for analyzing the cause of initial purchasing and repurchasing. This paper is based on the System-Dynamic theory, using the powerful simulation model with some restriction, The restrictions are based on the theory TAM(Technology Acceptance Model), PAM, and TPB(Theory of Planned Behavior). This article investigates not only the customer's purchasing and repurchasing behavior by passing of time but also the interactive effects to one another. This research model has six scenarios and three steps for analyzing customer behaviors. The first step is the research of purchasing situations. The second step is the research of repurchasing situations. Finally, the third step is to study the relationship between initial purchasing and repurchasing. The purpose of six scenarios is to find the customer's purchasing patterns according to the environmental changes. We set six variables in these scenarios by (1) changing the number of products; (2) changing the number of contents in on-line shopping malls; (3) having multimedia files or not in the shopping mall web sites; (4) grading on-line communities; (5) changing the qualities of products; (6) changing the customer's degree of confidence on products. First three variables are applied to study customer's purchasing behavior, and the other variables are applied to repurchasing behavior study. Through the simulation study, this paper presents some inter-relational result about customer purchasing behaviors, For example, Active community actions are not the increasing factor of purchasing but the increasing factor of word of mouth effect, Additionally. The higher products' quality, the more word of mouth effects increase. The number of products and contents on the web sites have same influence on people's buying behaviors. All simulation methods in this paper is not only display the result of each scenario but also find how to affect each other. Hence, electronic commerce firm can make more realistic marketing strategy about consumer behavior through this dynamic simulation research. Moreover, dynamic analysis method can predict the results which help the decision of marketing strategy by using the time-line graph. Consequently, this dynamic simulation analysis could be a useful research model to make firm's competitive advantage. However, this simulation model needs more further study. With respect to reality, this simulation model has some limitations. There are some missing factors which affect customer's buying behaviors in this model. The first missing factor is the customer's degree of recognition of brands. The second factor is the degree of customer satisfaction. The third factor is the power of word of mouth in the specific region. Generally, word of mouth affects significantly on a region's culture, even people's buying behaviors. The last missing factor is the user interface environment in the internet or other on-line shopping tools. In order to get more realistic result, these factors might be essential matters to make better research in the future studies.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.67-79
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• 2017

Increasing transportation dependence on the metro system has lead to the convenience of passengers becoming as important as the transportation capacity. In this study, a pedestrian simulator has been developed that can quantitatively assess the pedestrian environment in terms of attributes such as speed and distance. The simulator consists of modules designed for 3D indoor map authoring and algorithmic pedestrian modeling. Module functions for 3D indoor map authoring include 3D spatial modeling, network generation, and evaluation of obtained results. The pedestrian modeling algorithm executes functions such as conducting a path search, allocation of users, and evaluation of level of service (LOS). The primary objective behind developing the said functions is to apply and analyze various scenarios repeatedly, such as before and after the improvement of the pedestrian environment, and to integrate the spatial information database with the dynamic information database. Furthermore, to demonstrate the practical applicability of the proposed simulator in the future, a test-bed was constructed for a currently operational metro station and the quantitative index of the proposed improvement effect was calculated by analyzing the walking speed of pedestrians before and after the improvement of the passage. The possibility of database extension for further analysis has also been discussed in this study.

• Trans-
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• v.15
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• pp.25-56
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• 2023

VR is a dynamic image simulation technology with very high information density. Among them, spatial depth, temporality, and realism bring an unprecedented sense of immersion to the experience. However, due to its high information density, the information contained in it is very easy to be manipulated, creating an illusion of objectivity. Users need guidance to help them interpret the high density of dynamic image information. Just like setting up navigation interfaces and interactivity in games, interactivity in virtual reality is a way to interpret virtual content. At present, domestic research on VR content is mainly focused on technology exploration and visual aesthetic experience. However, there is still a lack of research on interactive storytelling design, which is an important part of VR content creation. In order to explore a better interactive storytelling model in virtual reality content, this paper analyzes the interactive storytelling features of the VR animated version of <Wolves in the walls> through the methods of literature review and case study. We find that the following rules can be followed when creating VR content: 1. the VR environment should fully utilize the advantages of free movement for users, and users should not be viewed as mere observers. The user's sense of presence should be fully considered when designing interaction modules. Break down the "fourth wall" to encourage audience interaction in the virtual reality environment, and make the hot media of VR "cool". 2.Provide developer-driven narrative in the early stages of the work so that users are not confused about the ambiguous world situation when they first enter a virtual environment with a high degree of freedom. 1.Unlike some games that guide users through text, you can guide them through a more natural interactive approach that adds natural dialog between the user and story characters (NPC). Also, since gaze guidance is an important part of story progression, you should set up spatial scene user gaze guidance elements within it. For example, you can provide eye-following cues, motion cues, language cues, and more. By analyzing the interactive storytelling features and innovations of the VR animation <Wolves in the walls>, I hope to summarize the main elements of interactive storytelling from its content. Based on this, I hope to explore how to better showcase interactive storytelling in virtual reality content and provide thoughts on future VR content creation.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.2
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• pp.41-53
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• 2010

Traffic safety assessment is often accomplished by analyzing the number of crashes occurring in some geographic space over certain specific time duration. In this paper, we introduce a procedure that can efficiently analyze spatial and temporal changes in traffic crashes before-and-after implementation of a certain traffic controlling measure. For the analysis, crash frequency data before-and-after closing a major highway around St. Louis in Missouri was collected through Transportation Management System(TMS) database that is maintained by Missouri Department of Transportation (MoDOT). In order to identify any spatial and temporal pattern in crashes, each crash is pinpointed on a map using the dynamic segmentation in GIS. Then, the identified pattern is statistically confirmed using an analysis of variance table. The advantage of this approach is to easily assess spatial and temporal trend of crashes that are not readily attainable otherwise. The results from this study can possibly be applied in enhancing the highway safety assessment procedure. This paper also makes several suggestions for future development of a comprehensive transportation data system in Korea which is similar to MoDOT's TMS database.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.627-634
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• 2006

Overlay network eliminates the need to change the application-layer tree when the underlying network changes and enables the overlay network to survive in environments where nonmember nodes do not support multicast functionality. An overlay protocol monitors group dynamics, while underlying unicast protocols track network dynamics, resulting in more stable protocol operation and low control overhead even in a highly dynamic environment. But, if overlay multicast protocols does not know the location information of node, this makes it very difficult to build an efficient multicasting tree. So, we propose a Hierarchical Overlay Multicast Architecture (HOMA) with the location information. Because proposed architecture makes static region-based dynamic group by multicast members, it is 2-tired overlay multicasts of application layer that higher layer forms overlay multicast network between members that represent group, and support multicast between multicast members belonging to region at lower layer. This use GPS, take advantage of geographical region, and realizes a region-sensitive higher layer overlay multicast tree which is impervious to the movements of nodes. The simulation results show that our approach solves the efficiency problem effectively.