• ETRI Journal
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• v.45 no.5
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• pp.735-745
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• 2023

This paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness.

• Journal of Educational Research in Mathematics
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• v.15 no.2
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• pp.215-232
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• 2005

Computer algebra system (CAS) calculators are becoming increasingly common in schools and universities. While Hey offer quite sophisticated mathematical capability to teachers and students, it is not clear at present how they may best be employed. In particular their integration into students' learning and problem-solving remains an issue. In this paper we address this issue through the lens of a study that considered the introduction of the TI-89 CAS calculator to students about to enter university. We describe a number of different aspects of the partnership they formed with the calculator as they began the process of instrumentation of the CAS in their learning.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.155-174
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• 2005

Parametric life-cycle cost(LCC) models have been integrated with traditional design tools, and used in prior work to demonstrate the rapid solution of holistic, analytical tradeoffs between detailed design variations. During early designs stages there may be competing concepts with dramatic differences. Additionally, detailed information is scarce, and decisions must be models. for a diverse range of concepts, and the lack of detailed information make the integration make the integration of traditional LCC models impractical. This paper explores an approximate method for providing preliminary life-cycle cost. Learning algorithms trained using the known characteristics of existing products be approximated quickly during conceptual design without the overhead of defining new models. Artificial neural networks are trained to generalize on product attributes and life cycle cost date from pre-existing LCC studies. The Product attribute data to quickly obtain and LCC for a new and then an application is provided. In additions, the statistical method, called regression analysis, is suggested to predict the LCC. Tests have shown it is possible to predict the life cycle cost, and the comparison results between a learning LCC model and a regression analysis is also shown

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.546-557
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• 2022

Recently, the demand to interpret image data with artificial intelligence in various fields is rapidly increasing. Object recognition and detection techniques using deep learning are mainly used, and video integration analysis to determine unstructured object recognition is a particularly important problem. In the case of natural disasters or social disasters, there is a limit to the object recognition structure alone because it has an unstructured shape. In this paper, we propose intelligent video integration analysis system that can recognize unstructured objects based on video turning point and object detection. We also introduce a method to apply and evaluate object recognition using virtual augmented images from 2D to 3D through GAN.

• Korean Journal of Artificial Intelligence
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• v.12 no.2
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• pp.17-23
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• 2024

Various methods have been developed to predict the flight path of an air-launched weapon to intercept a fast-moving target in the air. However, it is also getting more challenging to predict the optimal firing zone and provide it to a pilot in real-time during engagements for advanced weapons having new complicated guidance and thrust control. In this study, a method is proposed to develop an optimized weapon engagement zone by the SCG (Scaled Conjugate Gradient) algorithm to achieve both accurate and fast estimates and provide an optimized launch display to a pilot during combat engagement. SCG algorithm is fully automated, includes no critical user-dependent parameters, and avoids an exhaustive search used repeatedly to determine the appropriate stage and size of machine learning. Compared with real data, this study showed that the development of a machine learning-based weapon aiming algorithm can provide proper output for optimum weapon launch zones that can be used for operational fighters. This study also established a process to develop one of the critical aircraft-weapon integration software, which can be commonly used for aircraft integration of air-launched weapons.

• International Journal of Contents
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• v.15 no.4
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• pp.89-96
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• 2019

This study examines the impact of online collaborative English language learning to enhance learner motivation and classroom engagement in university English instruction. The role of learner motivation and classroom engagement has gained much attention under the premises of current constructivist framework of English as a foreign language education. To promote learner motivation and classroom interaction in English instruction, participants in this study engaged in integrative English learning activities through online group collaboration and peer-tutoring. They exchanged productive peer response and shared their learning experiences throughout the integrative English learning activities. Digital technology played an integral role in motivating the learning process of the participants. Data for this study were gathered through an online questionnaire survey and semi-structured interviews. The data were analyzed based on the ARCS motivational model of instructional design to identify the motivational aspects of integrative English learning activities. This study reveals that participants of this study regarded online collaborative English learning activities as the positive and motivating learning experience. The online collaborative English reading instruction had positive effect on improving EFL university students' learning performance. Participants of this study also identified affective and metacognitive benefits of online collaborative EFL learning activities for learner motivation and classroom engagement. This study reveals that the social networking platform in online group collaboration played a crucial role for the participants in understanding the integration of online group collaboration as the positive and effective language learning strategy. This study may have implications in suggesting the effective instructional design for promoting learner motivation and classroom interaction in EFL education.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1679-1686
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• 2007

In this paper, we introduce the sharable and reusable learning objects which are suitable for XML Web services in e-learning systems. These objects are extracted from the principles of pedagogical paradigms for reusable learning units. We call them LIO (Learning Item Object) objects. Existing models, such as Web-hosted and ASP-oriented service model, are difficult to cooperate and integrate among the different kinds of e-learning systems. So we developed the LIO objects that are suitable for XML Web services. The reusable units that are extracted from pedagogical paradigms are tutorial item, resource, case example, simulation, problems, test, discovery and discussion. And these units correspond to the LIO objects in our learning object model. As a result, the proposed model is that learner and instruction designer should increase the power of understanding about learning contents that are based on pedagogical paradigms. By using XML Web services, this guarantees the integration and interoperation of the different kinds of e-learning systems in distributed environments and so educational organizations can expect the cost reduction in constructing e-learning systems.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.872-884
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• 2022

With the development of the IoT industry, different types of time series data are being generated in various industries, and it is evolving into research that reproduces and utilizes it through re-integration. In addition, due to data processing speed and issues of the utilization system in the actual industry, there is a growing tendency to compress the size of data when using time series data and integrate it. However, since the guidelines for integrating time series data are not clear and each characteristic such as data description time interval and time section is different, it is difficult to use it after batch integration. In this paper, two integration methods are proposed based on the integration criteria setting method and the problems that arise during integration of time series data. Based on this, integration framework of a heterogeneous time series data was constructed that is considered the characteristics of time series data, and it was confirmed that different heterogeneous time series data compressed can be used for integration and various machine learning.

• Journal of The Korean Association of Information Education
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• v.7 no.2
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• pp.199-206
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• 2003

XML is becoming a new developing tool of web technology because of its ability of data management and flexibility in data presentation. So it's well researched that the reusability and integration with learning objects such as text, image, sound, video and plug-in programs of web contents in computer education. But the research for storing, extracting and integrating metadata about learning object was needed prior to implementing online learning system to integrate and manage it. Therefore this study propose a new method of using web service of DBMS for extracting learning object's metadata in database server which located in 3-tier system. To evaluate the efficiency of proposed method, The test server and two DBMSs(MS SQL Server 2000 and Oracle 9i) which have 30 metadata was implemented and the response time of it was measured. The response time of it was short, but in order to using this method the additional programming with SAX/DOM was necessary.

• Journal of the Korean Geographical Society
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• v.43 no.5
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• pp.761-774
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• 2008

Integration of GIS data and human expert knowledge into digital image processing has long been acknowledged as a necessity to improve remote sensing image analysis. We propose inductive machine learning algorithm for GIS data integration and rule-based classification method for land cover classification. Proposed method is tested with a land cover classification of a Landsat ETM+ multispectral image and GIS data layers including elevation, aspect, slope, distance to water bodies, distance to road network, and population density. Decision trees and production rules for land cover classification are generated by C5.0 inductive machine learning algorithm with 350 stratified random point samples. Production rules are used for land cover classification integrated with unsupervised ISODATA classification. Result shows that GIS data layers such as elevation, distance to water bodies and population density can be effectively integrated for rule-based image classification. Intuitive production rules generated by inductive machine learning are easy to understand. Proposed method demonstrates how various GIS data layers can be integrated with remotely sensed imagery in a framework of knowledge base construction to improve land cover classification.