• Journal of The Korean Association of Information Education
• /
• v.22 no.4
• /
• pp.419-425
• /
• 2018

The national curriculum includes the problem solving process, algorithms, and programming of SW education. The education using robots is one of attractive alternatives for students who have no interest of SW or are poor at programming. We have developed a courseware using robots for SW education based on paper art activities with narrative storytelling to enhance students' creative thinking and problem solving within limitation of class time in schools. We apply the courseware and obtained the result of pre and post-test on the creative problem solving ability of third graders in the elementary school The four factors of creative problem solving have shown significantly increase. In addition, it had an significant effects for understanding robot technology and for learning attitude using robots of SW or programming.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.21 no.1
• /
• pp.47-60
• /
• 2018

In this paper, we proposed a method for eliminating aiming error of unguided anti-tank rocket using improved target tracking. Since predicted fire is necessary to hit moving targets with unguided rockets, a method was proposed to estimate the position and velocity of target using fire control system. However, such a method has a problem that the hit rate may be lowered due to the aiming error of the shooter. In order to solve this problem, we used an image-based target tracking method to correct error caused by the shooter. We also proposed a robust tracking method based on TLD(Tracking Learning Detection) considering characteristics of the FCS(Fire Control System) devices. To verify the performance of our proposed algorithm, we measured the target velocity using GPS and compared it with our estimation. It is proved that our method is robust to shooter's aiming error.

• Journal of Digital Contents Society
• /
• v.15 no.5
• /
• pp.651-661
• /
• 2014

The complexity to search a specific content over TV platform is drastically increasing. Based on previous studies from computer data management system, we propose a new method that helps users to search and select content effectively. In general, contents over computers are represented by spatial metaphor, which replicates our physical environment and value systems about space. We suggest 3D lattice structure to construct information space for TV platform. Users can infer relevance between contents via special clue in information space, so as to select content more easily. Also, they can search contents through its temporal property that also represented in space. We make full use of our natural capability that can reduce additional overload to learning new interface. The results of this study can be significant and heuristic contributions, as they can be applied to diverse service areas utilizing video contents.

• Journal of Digital Convergence
• /
• v.13 no.9
• /
• pp.169-175
• /
• 2015

The significant changes of convergence study in the 21st century is the shift from theoretical-based instruction to computer-based instruction. This study investigated the student's cognitive engagement in TPB(Theory of Planned Behavior) of accounting information classes in the Digital Convergence Era. Hypothesis test was conducted from 99 accounting information learners from four year course university in the Gyeonggi-do. A questionnaire was given and analyzed. The results showed. First, the TPB variables han a positive effect on intrinsic curiosity of students. Second, the subjective norm of TPB variables could develop student's enjoyment. Subject norm could have a positive effect on enjoyment. The results of this study may contribute to propose desirable ways of improving the psychological states in the relationship between TPB and cognitive engagement. In the future, variables of cognitive engagement will be compliment to analyze.

• The KIPS Transactions:PartD
• /
• v.14D no.5
• /
• pp.501-508
• /
• 2007

Simulation system was introduced and used a lot in the fields of aviation, vessel, and medical treatment. 3D Simulation system has been used quite insufficiently as it requires a lot of system resource and huge amount of computer calculation. As the graphic card performance and simulation function developed, however, PC based simulation has been activated and is verified of its possibility as an educational software. However, educational institutions need to invest huge amount of budget and manpower to purchase and maintain CT Equipment. For such a reason, educational institutions entrust their students to hospitals for indirect experience of operation or for mere observation. This study, therefore, developed a CT Virtual reality education system with which medical CT Equipment can be directly operated in PC based 3D Virtual environment.

• KIPS Transactions on Computer and Communication Systems
• /
• v.8 no.12
• /
• pp.297-304
• /
• 2019

The technological advances such as communication, sensor, and artificial intelligence lead smart factory construction. Smart factory aims at efficient process control by utilizing data from the existing automation process and intelligence technology such as machine learning. As a result of constructing smart factory, productivity increases, but costs increase. Therefore, small companies try to make a step-by-step transition from existing process to smart factory. In this paper, we have proposed a remote control system that support data collection, monitoring, and control for manufacturing equipment to support the construction of CNC cutting machine based small-scale smart factory. We have proposed the structure and design of the proposed system and efficient sensing data transmission scheme. To check the feasibility, the system was implemented for CNC cutting machine and functionality verification was performed. For performance evaluation, the web page access time was measured. The results means that the implemented system is available level.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.3
• /
• pp.388-393
• /
• 2010

Cultural Content Technology(CT, Culture Technology) for the development of cultural industry and the commercialization of technology, cultural contents, media, mount, pass the value chain process and increase the added value of cultural products that are good for all forms of intangible technology. In the field of Culture Technology, Music by analyzing the characteristics of the development of a variety of applications has been studied. Associated with EEG measures and the results of their research in response to musical stimuli are used to detect and study is getting attention. In this paper, the musical stimuli in EEG signals by amplifying the corresponding reaction to the averaging method, ERP (Event-Related Potentials) experiments based on the process of extracting sound methods for removing noise from the ICA algorithm to extract the tone and noise removal according to the results are applied to analyze the characteristics of EEG.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.3
• /
• pp.237-246
• /
• 2019

In order to improve the accuracy of the rating prediction of the recommendation model, not only user-item rating data are used but also consider auxiliary information of item such as comments, tags, or descriptions. The traditional approaches use a word-level model of the bag-of-words for the auxiliary information. This model, however, cannot utilize the auxiliary information effectively, which leads to shallow understanding of auxiliary information. Convolution neural network (CNN) can capture and extract feature vector from auxiliary information effectively. Thus, this paper proposes character-level deep-Convolution Neural Network based matrix factorization (Char-DCNN-MF) that integrates deep CNN into matrix factorization for a novel recommendation model. Char-DCNN-MF can deeper understand auxiliary information and further enhance recommendation performance. Experiments are performed on three different real data sets, and the results show that Char-DCNN-MF performs significantly better than other comparative models.

• Journal of the Korea Convergence Society
• /
• v.10 no.5
• /
• pp.35-42
• /
• 2019

Recently, as the Internet and various wearable devices have appeared, Internet technology has contributed to obtaining more convenient information and doing business. However, as the internet is used in various parts, the attack surface points that are exposed to attacks are increasing, Attempts to invade networks aimed at taking unfair advantage, such as cyber terrorism, are also increasing. In this paper, we propose a feature selection method to improve the classification performance of the class to classify the abnormal behavior in the network traffic. The UNSW-NB15 dataset has a rare class imbalance problem with relatively few instances compared to other classes, and an undersampling method is used to eliminate it. We use the SVM, k-NN, and decision tree algorithms and extract a subset of combinations with superior detection accuracy and RMSE through training and verification. The subset has recall values of more than 98% through the wrapper based experiments and the DT_PSO showed the best performance.

• Phonetics and Speech Sciences
• /
• v.13 no.3
• /
• pp.71-78
• /
• 2021

Most neural-network-based speech synthesis models utilize neural vocoders to convert mel-scaled spectrograms into high-quality, human-like voices. However, neural vocoders combined with mel-scaled spectrogram prediction models demand considerable computer memory and time during the training phase and are subject to slow inference speeds in an environment where GPU is not used. This problem does not arise in linear spectrogram prediction models, as they do not use neural vocoders, but these models suffer from low voice quality. As a solution, this paper proposes a Tacotron 2 and Transformer-based linear spectrogram prediction model that produces high-quality speech and does not use neural vocoders. Experiments suggest that this model can serve as the foundation of a high-quality text-to-speech model with fast inference speed.