• Nuclear Engineering and Technology
• /
• v.40 no.5
• /
• pp.327-348
• /
• 2008

A risk-informed regulatory approach implies that risk insights be used as supplement of deterministic information for safety decision-making purposes. In this view, the use of risk assessment techniques is expected to lead to improved safety and a more rational allocation of the limited resources available. On the other hand, it is recognized that uncertainties affect both the deterministic safety analyses and the risk assessments. In order for the risk-informed decision making process to be effective, the adequate representation and treatment of such uncertainties is mandatory. In this paper, the risk-informed regulatory framework is considered under the focus of the uncertainty issue. Traditionally, probability theory has provided the language and mathematics for the representation and treatment of uncertainty. More recently, other mathematical structures have been introduced. In particular, the Dempster-Shafer theory of evidence is here illustrated as a generalized framework encompassing probability theory and possibility theory. The special case of probability theory is only addressed as term of comparison, given that it is a well known subject. On the other hand, the special case of possibility theory is amply illustrated. An example of the combination of probability and possibility for treating the uncertainty in the parameters of an event tree is illustrated.

• The KIPS Transactions:PartB
• /
• v.12B no.4 s.100
• /
• pp.413-420
• /
• 2005

In this paper, we apply software agents, which use fuzzy logic and make autonomous decisions according to state transitions, to car driving environment. We carry out an experiment on artificial intelligent car driving in terms of real-time reactive agents. Inference techniques for constructing real-time reactive agents consider the settings with max-product inference, n-fuzzy rules, and n-associatives ($A_l,\;B_l),\;{\ldots}(A_n,\;B_n$). Then we perform defuzzification processes, extract a central value, and work out inference processes.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.21 no.3
• /
• pp.55-64
• /
• 2013

As the price competition between airlines became increasingly intensified, due to increased participation of low-cost carriers in the air transport industry and the continued deregulation of international air transport, each airline has introduced various management techniques for securing international competitiveness and operational efficiency in order to cope with the uncertainty in air transport industry. The world leading LCCs, such as Ryan air, easyJet and Southwest, have changed their traditional business models by increasing operation to primary airports, diversifying operating routes, making strategic alliances with FSCs or other LCCs, increasing the operations on the mid or long distance routes, expanding ancillary revenues, etc. As Korea's air transport industry is confronting with intense competition, our LCCs are requested to adjust to this new challenging situation. As the world leading LCCs did, Korean LCCs are recommended to adopt new business models such as restructuring of the air transport industry through M&A, operating more flexibly in terms of frequency or route, launching of services to primary airports, making strategic alliances with foreign FSCs, developing of 4th or 6th traffic demands, etc.

• Journal of Korean Society for Geospatial Information Science
• /
• v.12 no.1 s.28
• /
• pp.31-37
• /
• 2004

As social demands on forest are diversified, we need some economic and pro-environmental methods of forest management. In this study, developing an FMS(Forest Management System) using GIS techniques, we aimed at making workers handle quickly and exactly their works related to forest, offering customers the exact information and making clients modify or renew the variously changing information about forest. Therefore, we can efficiently handle the works related to forest management. For this, we designed and developed the system to be in accord with working-level and we can handle various information efficiently and conveniently. Accordingly we can process and print out data based on each subject. In addition to that, we can preserve the consistency of data by connecting work zones and providing database through network.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.5
• /
• pp.293-302
• /
• 2019

In mountain accident events, it is important for the search team commander to determine the search area in order to secure the Golden Time. Within this period, assistance and treatment to the concerned individual will most likely prevent further injuries and harm. This paper proposes a method to determine the search priority area based on missing persons behavior and missing persons incidents statistics. GIS (Geographic Information System) and MCDM (Multi Criteria Decision Making) are integrated by applying WLC (Weighted Linear Combination) techniques. Missing persons were classified into five types, and their behavioral characteristics were analyzed to extract seven geographic analysis factors. Next, index values were set up for each missing person and element according to the behavioral characteristics, and the raster data generated by multiplying the weight of each element are superimposed to define models to select search priority areas, where each weight is calculated from the AHP (Analytical Hierarchy Process) through a pairwise comparison method obtained from search operation experts. Finally, the model generated in this study was applied to a missing person case through a virtual missing scenario, the priority area was selected, and the behavioral characteristics and topographical characteristics of the missing persons were compared with the selected area. The resulting analysis results were verified by mountain rescue experts as 'appropriate' in terms of the behavior analysis, analysis factor extraction, experimental process, and results for the missing persons.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.271-281
• /
• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.

• Fashion & Textile Research Journal
• /
• v.25 no.4
• /
• pp.429-438
• /
• 2023

In the 21st century fashion industry, the rise of digital environments has transformed it into a dynamic medium, expanding the horizons of media utilization. Consequently, digital fashion film has emerged as a pivotal tool for fashion communication. Functioning as a visual expression medium, fashion film animates fashion concepts into immersive moving images. Proficiency in digital fashion communication has become imperative, considering the attributes of fashion media. Notably, the role of creative directors in ensuring coherent communication across diverse fashion media platforms has gained prominence, underscoring the need for systematic fashion education to nurture specialized talent. This study, therefore, devised a comprehensive curriculum amalgamating fashion communication and practical digital media skills, implemented within fashion major courses. Through this approach, students gained experimental media proficiency and explored innovative approaches to crafting fashion films that eloquently convey fashion narratives. The participants were exposed to the entire spectrum of fashion media production, encompassing digital storytelling, fashion film conceptualization, filming techniques, meticulous editing, and adept utilization of special effects technology. The study's pedagogical strategy, characterized by a focused learning trajectory, garnered significant acclaim. In essence, this study holds significance by formulating a curriculum that nurtures the imaginative and pragmatic aptitudes of fashion majors, immersing them in the dynamic realm of rapidly evolving digital fashion films and their integration with fashion content.

• Journal of Information Technology Applications and Management
• /
• v.30 no.6
• /
• pp.1-15
• /
• 2023

With the advent of the Fourth Industrial Revolution, propelled by digital technology, we are transitioning into an era of hyperconnectivity, where everything and objects are becoming interconnected. A smart supply chain refers to a supply chain system where various sensors and RFID tags are attached to objects such as machinery and products used in the manufacturing and transportation of goods. These sensors and tags collect and analyze process data related to the products, providing meaningful information for operational use and decision-making in the supply chain. Before the spread of COVID-19, the fundamental principles of supply chain management were centered around 'cost minimization' and 'high efficiency.' A smart supply chain overcomes the linear delayed action-reaction processes of traditional supply chains by adopting real-time data for better decision-making based on information, providing greater transparency, and enabling enhanced collaboration across the entire supply chain. Therefore, in this study, a hierarchical model for building a smart supply chain was constructed to systematically derive the importance of key factors that should be strategically considered in the construction of a smart supply chain, based on the major factors identified in previous research. We applied AHP (Analytical Hierarchy Process) techniques to identify urgent improvement areas in smart SCM initiatives. The analysis results showed that the external supply chain integration is the most urgent area to be improved in smart SCM initiatives.

• Journal of Fashion Business
• /
• v.27 no.5
• /
• pp.108-120
• /
• 2023

Recently, interest in eco-friendliness and sustainability has been increasing due to the rapid progress of fast fashion and the crisis of sudden environmental changes after COVID-19. This study aims to develop upcycling textiles and express product design using digital 3D to realize a sustainable fashion industry and present environmental aspects, diversity, creativity, and new directions in fashion industry design. The research method is to develop and pattern upcycling textile designs by applying weaving techniques with waste materials. It uses the developed upcycling textile design in digital 3D to incorporate it into clothing fashion and shows the utility and practicality of upcycling textile design. As a result of the study, the appearance is realistic when outputting DTP of upcycling textile design. It endures without loosening or tearing, making it a durable and creatively expressive fashion item. Texpro 3D mapping reduces the time and cost of making actual sample fabric. Upcycling textile design and 3D CLO virtual clothing are combined to produce actual clothing samples, resulting in zero waste reduction due to cutting and sewing. This study anticipates actively and continuously advancing the development of upcycling textile design and digital 3D in terms of ethics and the environment.

• Journal of Korean Society for Quality Management
• /
• v.51 no.3
• /
• pp.363-379
• /
• 2023

Purpose: The main objective of this research is to construct an AI-based Composite Supplementary Index (ACSI) model to achieve accurate predictions of the Composite Index of Business Indicators. By incorporating various economic indicators as independent variables, the ACSI model enables the prediction and analysis of both the leading index (CLI) and coincident index (CCI). Methods: This study proposes an AI-based Composite Supplementary Index (ACSI) model that leverages diverse economic indicators as independent variables to forecast leading and coincident economic indicators. To evaluate the model's performance, advanced machine learning techniques including MLP, RNN, LSTM, and GRU were employed. Furthermore, the study explores the potential of employing deep learning models to train the weights associated with the independent variables that constitute the composite supplementary index. Results: The experimental results demonstrate the superior accuracy of the proposed composite supple- mentary index model in predicting leading and coincident economic indicators. Consequently, this model proves to be highly effective in forecasting economic cycles. Conclusion: In conclusion, the developed AI-based Composite Supplementary Index (ACSI) model successfully predicts the Composite Index of Business Indicators. Apart from its utility in management, economics, and investment domains, this model serves as a valuable indicator supporting policy-making and decision-making processes related to the economy.