• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5132-5148
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• 2017

Cyber attacks are evolving commensurate with recent developments in information security technology. Intrusion detection systems collect various types of data from computers and networks to detect security threats and analyze the attack information. The large amount of data examined make the large number of computations and low detection rates problematic. Feature selection is expected to improve the classification performance and provide faster and more cost-effective results. Despite the various feature selection studies conducted for intrusion detection systems, it is difficult to automate feature selection because it is based on the knowledge of security experts. This paper proposes a feature selection technique to overcome the performance problems of intrusion detection systems. Focusing on feature selection, the first phase of the proposed system aims at constructing a feature subset using a sequential forward floating search (SFFS) to downsize the dimension of the variables. The second phase constructs a classification model with the selected feature subset using a random forest classifier (RFC) and evaluates the classification accuracy. Experiments were conducted with the NSL-KDD dataset using SFFS-RF, and the results indicated that feature selection techniques are a necessary preprocessing step to improve the overall system performance in systems that handle large datasets. They also verified that SFFS-RF could be used for data classification. In conclusion, SFFS-RF could be the key to improving the classification model performance in machine learning.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.3
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• pp.67-84
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• 2004

Information literacy is usually described as the ability to locate, manage and use information effectively for a range of purposes. It Is acknowledged the most people are likely to change careers throughout their lives. Therefore, people must learn Information literacy. Those who are information literate must be able to recognize when information is needed and have the ability to locate, evaluate, use effectively the needed information, ultimately have how to team. Since a basic objective of education ts for student to learn how to learn, Information literacy become increasingly more important for all students. This paper presents an overview of definitions, standards and models of information literacy and information literate. Based on these, we suggested learning method and curriculum of information literacy for the lower grades of elementary school.

• Journal of the Korean Dietetic Association
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• v.17 no.2
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• pp.130-141
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• 2011

This study was conducted to develop a nutrition education workbook to improve picky eating in children and to evaluate the effect of nutrition education using this developed workbook. The developed workbook focused on 2nd and 3rd grade students and consisted of five units. The contents included: multi-grain, vegetables (2 units), fish, and milk. The activities, including observation, and learning the roles and names of the foods, were developed mainly to increase motivation for eating a balanced diet. This workbook was developed from April to December 2008, and was applied at 15 elementary schools containing 1,674 students from April to September 2009 in Changwon City, Korea. We evaluated changes in knowledge before and after education on nutrition, eating behavior, dietary habits, and educational activities using self-administered questionnaires. The children demonstrated significant improvements in nutritional knowledge (P<0.001), eating behavior (P<0.001), and dietary habits (P<0.001). Most of the children answered that the education program was helpful and exciting. Based on these results, we believe that the developed workbook is suitable for children picky eaters, and hope it will be used in the field of child nutrition education.

• Journal of Engineering Education Research
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• v.18 no.6
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• pp.70-79
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• 2015

This paper proposes a convergence education method that combines architecture and communication through a documentary film entitled the Talking Architecture. The purpose of this program is to propose a new teaching and learning method for architecture education and investigates what would be an effective method of communication for architects. As mentioned above, this paper proposes a teaching method and a model applicable to actual classes based on the Talking Architect. It is proved that the method can be used for various types of classes and fields such as architectural expression, architectural planning and designing, housing theory, building structure, building materials, and other subject matters. In addition, this paper explores how architects communicate as described in the film. The findings show the potential of integrity, negotiating capability, and the convergent method of thinking and communication between the humanities and architecture as a positive communication model for architects. This paper opens up the possibilities for convergence education in the field of engineering education through three key words: film, architecture, and communication. And this paper is worth in that it is a useful method for developing convergence courses and team-teaching courses.

• Journal of Korea Game Society
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• v.1 no.1
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• pp.61-67
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• 2001

This paper describes a program which learns good strategies for two-poison, deterministic, zero-sum board games of perfect information. The program learns by simply playing the game against either a human or computer opponent. The results of the program's teaming of a lot of games are reported. The program consists of search kernel and a move generator module. Only the move generator is modified to reflect the rules of the game to be played. The kernel uses a temporal difference procedure combined with a backpropagation neural network to team good evaluation functions for the game being played. Central to the performance of the program is the search procedure. This is a the capture tree search used in most successful janggi playing programs. It is based on the idea of using search to correct errors in evaluations of positions. This procedure is described, analyzed, tested, and implemented in the game-teaming program. Both the test results and the performance of the program confirm the results of the analysis which indicate that search improves game playing performance for sufficiently accurate evaluation functions.

• Journal of Korean Elementary Science Education
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• v.17 no.1
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• pp.11-21
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• 1998

Portfolio assessment provides many opportunities to foster children's creativity and to increase their responsibility for learning. few research study has been conducted in this area, and this assessment method has scarcely been administered in primary science class, Proper and effective use of portfolio assessment in our primary science class requires basic research on how to design and administer the method. Based on the earlier study on typical structures and components of portfolio assessment, the assessment instrument was developed on various primary science topics, The development team was consisted of one science education specialist and nine pre-service elementary school teachers. It takes ten months to develop instruments for 27 class hours. The development process was reciprocal in that development and revision cycle was repeated more than 7 times. The portfolio assessment instruments consist of instructional objectives, developers' evidence for the objectives, and assessment criteria. Adopting a new way of assessment into science class inevitably causes lots of confusions to teachers and children. The absence of basic research studies must be a critical barrier for successful administration of a new assessment method such as portfolio assessment. further research is required in the preparation and administration of portfolio assessment in our primary science classroom.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.283-288
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• 2019

In order to overcome the students' lack of information and experience, we developed a content planning tree that utilizes a decision tree. The content planning tree consists of a tree trunk creation step in which students select a theme and a story to develop, a parent branch generation step for selecting a category that can be developed based on the story, a child branch generation step for selecting the interesting "effect" method of producing the content effectively, a leaf generation step for selecting a multimedia expression 'element' to be visualized. The educational model was applied to game planning design and information visualization lectures, and provides examples of the categories, effects, and elements used in each lecture. The model was used for 145 team projects and the efficiency was confirmed by a step-by-step learning process.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.147-158
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• 2021

Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET). In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization. In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time. The problem can be defined as classifying the split mode of each CU. To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced. For boosting classification performance, we utilize additional information including inter-picture information while training the CNN. The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0. The CUs of size 128×128 can be the inputs of the CNN. The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}. The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR). Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.37-44
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• 2022

With the outbreak of COVID-19, non-face-to-face activities such as remote learning and telecommuting have increased rapidly. As a result, the number of people staying at home and the number of hours spent inside the house have also increased since the pandemic. Our team had previously worked on methods for detecting abnormal conditions in a person's health in various circumstances within the house by converging single sensor-based algorithms. In our previous research, we installed IoT sensors indoors to detect people emergency situations requiring aids, the scope of detection was limited to indoor space due to the limitation in sensors. In this study, we have come up with a system that integrates our previous study with a new method for detecting abnormal conditions in outdoor environments using outdoor security cameras and wearable devices. The proposed system enables users to be notified of emergency situations in both indoor and outdoor areas and respond to them as quickly as possible.