• Journal of the Korea Institute of Military Science and Technology
• /
• v.27 no.2
• /
• pp.147-155
• /
• 2024

Based on the recently developed deep learning technology, many studies have been conducted on deep learning networks that simultaneously detect and classify targets of interest in synthetic aperture radar(SAR) images. Although numerous research results have been derived mainly with the open SAR ship datasets, there is a lack of work carried out on the deep learning network aimed at detecting and classifying SAR ground targets and trained with the synthetic dataset generated from electromagnetic scattering simulations. In this respect, this paper presents the deep learning network trained with the synthetic dataset and applies it to detecting and classifying real SAR ground targets. With experiment results, this paper also analyzes the network performance according to the composition ratio between the real measured data and the synthetic data involved in network training. Finally, the summary and limitations are discussed to give information on the future research direction.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.1
• /
• pp.23-31
• /
• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• International Journal of Knowledge Content Development & Technology
• /
• v.7 no.1
• /
• pp.101-120
• /
• 2017

Healthcare professionals have edited many health questions (HQs) and their answers for healthcare consumers on the Internet. The HQs provide both readable and reliable health information, and hence retrieval of those HQs that are relevant to a given question is essential for health education and promotion through the Internet. However, retrieval of relevant HQs needs to be based on the recognition of the intention of each HQ, which is difficult to be done by predefining syntactic and semantic rules. We thus model the intention recognition problem as a text classification problem, and develop two techniques to improve a learning-based text classifier for the problem. The two techniques improve the classifier by location-based and area-based feature weightings, respectively. Experimental results show that, the two techniques can work together to significantly improve a Support Vector Machine classifier in both the recognition of HQ intentions and the retrieval of relevant HQs.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1998.10a
• /
• pp.141-144
• /
• 1998

This paper complements the work of Courtney et al. in viewing learning organizations as inquiring systems. Hegelian inquiring systems are based on the dialectic. Dialectic can not exist without dialogue. The guarantor of this system is conflict. Hegelian inquiring systems would facilitate multiple and contradictory interpretations of reality. Hegelian synthesis of two opposing models-thesis and antithesis-is the epitome of open systems and double-loop learning. Knowledge gained from the Hegelian inquiring systems may result in an entirely new strategic direction to organizations. This paper reviews some guidelines and principles of Hegelian learning organizations and IT support of it. Also it proposes the immediate deployment of Hegelian learning organizations in the wicked business environments and finally suggests the development of new, flexible information technologies and systems for Hegelian organizations.

• Nuclear Engineering and Technology
• /
• v.55 no.1
• /
• pp.285-294
• /
• 2023

Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environments. In this work, the detection task is decomposed into two subtasks: exploration and localization. A hierarchical control policy (HC) is proposed to perform the subtasks at different stages. The low-level controller learns how to execute the individual subtasks by deep reinforcement learning, and the high-level controller determines which subtasks should be executed at the current stage. In experimental tests under different geometrical conditions, HC achieves the best performance among the autonomous decision policies. The robustness and generalized ability of the hierarchy have been demonstrated.

• Journal of KIISE:Software and Applications
• /
• v.31 no.3
• /
• pp.369-378
• /
• 2004

In partially free word order languages like Korean and Japanese, the rule-based method is effective for text chunking, and shows the performance as high as machine learning methods even with a few rules due to the well-developed overt Postpositions and endings. However, it has no ability to handle the exceptions of the rules. Exception handling is an important work in natural language processing, and the exceptions can be efficiently processed in memory-based teaming. In this paper, we propose a hybrid of rule-based method and memory-based learning for Korean text chunking. The proposed method is primarily based on the rules, and then the chunks estimated by the rules are verified by memory-based classifier. An evaluation of the proposed method on Korean STEP 2000 corpus yields the improvement in F-score over the rules or various machine teaming methods alone. The final F-score is 94.19, while those of the rules and SVMs, the best machine learning method for this task, are just 91.87 and 92.54 respectively.

• Journal of Engineering Education Research
• /
• v.8 no.1
• /
• pp.20-30
• /
• 2005

Recently, a new education paradigm 'Self-directed Learning' has attracted considerable attention. Problem-Based Learning (PBL) has been recognized as methodology to help students expand scientific thinking and knowledge. improve applicability, develope critical knowledge, and creatively solve problems. There have been significant efforts to develope PBL-based courses in mechanical engineering. A new PBL-based, multi-disciplinary course 'Product Dissection and Design Reasoning' has been developed in this paper. The course examines the way in which products and machines work and is intended to show freshman or sophomore level students how fundamental physical principles relate to engineering practice through hands-on dissection experience : thus, the course emphasizes the importance of knowledge of the fundamental physics for design reasoning. The primary role of this course is to develop creative design manpower. This paper describes the philosophy and content of this course and presents results from one year of development.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.2_2
• /
• pp.263-270
• /
• 2020

In this paper, In this paper, we describe the UAV system using image processing for autonomous quadcopters, where they can apply logistics, rescue work etc. we propose high-speed hovering height and posture control method based on state feedback control with CNN from camera because we can get image of the information only every 30ms. Finally, we show the advantages of proposed method by simulations and experiments.

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.302-307
• /
• 1989

A pressure tracking control of hydroforming process is considered in this paper. To account for nonlinearities and uncertainties of the process, an iterative learning control scheme is proposed using Cerebellar Model Arithmatic Computer (CMAC). The experimental result shows that the proposed learning control is superior to any fixed gain controller in the sense that it enables the system to do the same work more effectively as the number of operation increases.

• Spatial Information Research
• /
• v.23 no.3
• /
• pp.123-132
• /
• 2015

Recently both the importance and the interests of field-experience learning have kept increasing since the Education of Ministry encourages students to take a field-experience learning with the self-driven and creative education as the main issue in the 7th revised education curriculum in 2009. As the importance of field-experience learning goes up continuously, not only the release of its related mobile applications is increasing, but also a variety of researches for supporting the field-experience learning are ongoing. In this research we perform two things. First, we define the basic concept of authoring tool for which support the field-experience learning based on its characteristics and user requirements analysis. We confirmed that the authoring tool for the field-experience learning has to 1) support all activities that happen in pre-field, field-experience and post-field phases, 2) be possible to write the location-based field work, 3) have the authoring function which reflects the characteristics of curriculum such as history, science and geography, etc. that the field learning can be realized, 4) be designed as the structure that the results of inquiring activity after the field experience activity can be reused. Second, we create a conceptual design after confirming the authoring tool.