• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.4
• /
• pp.58-63
• /
• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.6
• /
• pp.881-891
• /
• 2023

Recently, various studies on malicious URL detection using artificial intelligence have been conducted, and most of the research have shown great detection performance. However, not only does classical machine learning require a process of analyzing features, but the detection performance of a trained model also depends on the data analyst's ability. In this paper, we propose a DL-ML Fusion Hybrid Model for malicious web site URL detection based on URL lexical features. the propose model combines the automatic feature extraction layer of deep learning and classical machine learning to improve the feature engineering issue. 60,000 malicious and normal URLs were collected for the experiment and the results showed 23.98%p performance improvement in maximum. In addition, it was possible to train a model in an efficient way with the automation of feature engineering.

• Archives of design research
• /
• v.18 no.3 s.61
• /
• pp.149-160
• /
• 2005

The Purpose of this study is to embody self-directed learning management system and contents which are considered of learner's interactivity and usability in e-learning environment; It is based on self directed teaching and learning strategies. It divides type of interaction centering on the learners into four categories: (1) learner and instructor (2) learner and learner (3) learner and contents (4) learner and learning management system. The specific elements of learning management system is set up and embodied to present the interaction strategies according to the above-mentioned patterns, to improve the self-directed learning ability and to facilitate an online communication. The learning contents based on the self directed learning strategies, design the interface in due consideration of the learners' usability based on six strategies such as simple navigation, consistency, intuitive interface, linkage, user supports and immediate feedback. This research makes up for the weak points in the self-directed functions of learning management system and links up with learning contents, therefore it has a value to improve learner's interactivity and usability. It is expected that the research results can be helpful in quality improvement of e-learning environment.

• Journal of The Korean Association of Information Education
• /
• v.25 no.3
• /
• pp.557-569
• /
• 2021

In preparation for the future society, the educational curriculum is changing according to the trend of the times, and with the advent of the era of the 4th Industrial Revolution, the purpose of the new 2015 revised curriculum was suggested to foster the convergence creativity of students. The purpose of software education is to promote creativity and further develop problem-solving skills in connection with real life. In addition, flow in learning leads to outstanding educational achievement. However, in elementary school computer education, there is still a lack of development of a convergence class model for students to easily immerse themselves and promote creative problem-solving skills. Therefore, in this study, we designed convergence computer education using Novel Engineering, which is a convergence class model suitable for these educational conditions and applied it to classes. Further, to measure the effect on the improvement of learning flow and creative problem-solving ability. the Novel Engineering-based computer class was applied to the experimental group for 6th graders, and the general computer class was applied to the control group. As a result of the pre-post test between groups, it was found that computer classes using Novel Engineering had a positive effect on learning flow and creative problem-solving ability.

• The Korean Journal of Coaching Psychology
• /
• v.5 no.1
• /
• pp.33-65
• /
• 2021

This study is an exploratory study to investigate the concept of student success recognized by college students. In order to listen to students' free thoughts, an open questionnaire was conducted using an online questionnaire and 99 responses were analyzed. The response results were analyzed using CQR-M (Consensual Qualitative Research-Modified). As a result of the analysis, 25 categories were derived from three domains. The frist domain, 'meaning of student success' recognized by college students is with 9 categories: 'academic achievement', 'job competency improvement', 'gaining diverse experiences', 'satisfactory employment', 'setting of desired career path', 'relationship-building ability improvement', 'setting and achievement of own goals', 'self-development', and 'satisfactory college life'. The second domain, 'college environmental factors for student success' is with 7 categories: 'career development support', 'job competency improvement system', 'support for participation in college life', 'opportunities to form human networks', 'learning capability enhancement system', 'expansion of economic support', and 'environment for student convenience'. The third domain, 'psychological factors necessary for student success' is with 9 categories: 'learning ability', 'self-efficacy', 'interpersonal competence', 'self-awareness and improvement', 'job competency', 'self-regulation ability', 'rich experience', 'career awareness', and 'self-esteem'. The frequency and results of each domain were analyzed and presented, the significance and limitations of the study were discussed, and suggestions for subsequent studies were made.

• Journal of Information Technology Services
• /
• v.14 no.1
• /
• pp.99-113
• /
• 2015

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user's preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user's preferences (i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.8
• /
• pp.4043-4060
• /
• 2017

Network Intrusion Detection (NID), an important topic in the field of information security, can be viewed as a pattern recognition problem. The existing pattern recognition methods can achieve a good performance when the number of training samples is large enough. However, modern network attacks are diverse and constantly updated, and the training samples have much smaller size. Furthermore, to improve the learning ability of SVM, the research of kernel functions mainly focus on the selection, construction and improvement of kernel functions. Nonetheless, in practice, there are no theories to solve the problem of the construction of kernel functions perfectly. In this paper, we effectively integrate the advantages of the radial basis function kernel and the polynomial kernel on the notion of the game theory and propose a novel kernel SVM algorithm with game theory for NID, called GTNID-SVM. The basic idea is to exploit the game theory in NID to get a SVM classifier with better learning ability and generalization performance. To the best of our knowledge, GTNID-SVM is the first algorithm that studies ensemble kernel function with game theory in NID. We conduct empirical studies on the DARPA dataset, and the results demonstrate that the proposed approach is feasible and more effective.

• Journal of The Korean Association of Information Education
• /
• v.23 no.5
• /
• pp.499-511
• /
• 2019

The preceding study of this study developed puzzle-based computer science education programs to enhance the computational thinking of elementary school students over 1 to 3 times. The preceding study then applied such programs into the field, categorized the effects of education into CT creativity and CT cognitive ability to improve the education programs. Based on the results of these preceding studies, the hierarchical Bayesian inference modeling was performed using age and CT thinking ability as parameters. From the results, this study predicted the effectiveness of puzzle-based computer science education programs in middle and high schools and proposed major improvement areas and directions for puzzle-based computer science education programs that are to be deployed in the future throughout middle and high schools.

• The Journal of Korean Association of Computer Education
• /
• v.23 no.2
• /
• pp.31-39
• /
• 2020

As the importance of software education is emphasized, various educations for improve the computational thinking of elementary school students are being provided. However, there are many limitations that apply to the school at preceding studies. because they were invented for specific environment or students. Therefore, in this study, software education was conducted by constructing teaching-learning activities in consideration of the characteristics of students such as teaching aids and level of computational thinking ability, which can influence students based on the contents of textbooks. Through the comparison about computational thinking improvements, this study tried to suggest the direction of effective software education method. As a result, there are differences in computational thinking improvements according to the student's level and used teaching aids. Specifically, visual programming and unplugged type teaching-learning activities were more effective in teaching aids, and according to the level of students, students with low level of computing thinking in pre-inspection showed relatively high improvement. This tendency suggests that it is necessary to consider various learning factors in designing teaching-learning activities in elementary software education.

• Journal of The Korean Association of Information Education
• /
• v.19 no.2
• /
• pp.197-206
• /
• 2015

The dissemination of smart appliances, along with the development of the latest information and communication technology has an effect on society. Their application can be seen in the smart learning environment, by use of digital textbooks with smart devices in the classroom. Hence in this study, the 4L-ACC activity using digital textbooks was applied in the third grade social studies class to improve critical thinking and problem solving skills of elementary school students. This study compared and analyzed the impact in critical thinking and problem-solving skills using digital textbooks versus standard social studies classes. As a result, in social studies classes that used the 4L-ACC activity compared to common social studies classes, elementary student's problem understanding, critical thinking, problem resolution, reflective thinking, and all lower areas of critical thinking and problem-solving were improved. In conclusion, using digital textbooks enhance student's ability to think critically and problem solve. This should provide learners a more comprehensive learning experience and therefore be integrated into more education environment.