• The Journal of the Korea Contents Association
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• v.11 no.8
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• pp.475-487
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• 2011

In this paper, we present a systematic method to derive spectrum of high-order pulse and a novel design of e-Learning system that deals with deriving the spectrum using concept-based branching method. Spectrum of high-order pulse can be derived using conventional methods including 'Consecutive Differentiations' or 'Convolutions', however, their complexity of calculation should be too high to be used as the order of the pulse increase. We develop a recursive algorithm according to the order of pulse, and then derive the formula of spectrum connected to the order with a newly designed look-up table. Moving along, we design an e-Learning content for studying the procedure of deriving high-order pulse spectrum described above. In this authoring, we use the concept-based object branching method including conventional page or title-type branching in sequential playing. We design all four Content-pages divided into 'Modeling', 'Impulse Response and Transfer Function', 'Parameters' and 'Look-up Table' by these conceptual objects. And modules and sub-modules are constructed hierarchically as conceptual elements from the Content-pages. Students can easily approach to the core concepts of the analysis because of the effects of our new teaching method. We offer step-by-step processes of the e-Learning content through unit-based branching scheme for difficult modules and sub-modules in our system. In addition we can offer repetitive learning processes for necessary block of given learning objects. Moreover, this method of constructing content will be considered as an advanced effectiveness of content itself.

• Physical Therapy Korea
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• v.11 no.3
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• pp.11-18
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• 2004

This study is aimed at investigating the influence of different quantitative knowledge of results on the measurement error during lumbar proprioceptive sensation training. Twenty-eight healthy adult men participated and subjects were randomly assigned into four different feedback groups(100% relative frequency with an angle feedback, 50% relative frequency with an angle feedback, 100% relative frequency with a length feedback, 50% relative frequency with a length feedback). An electrogoniometer was used to determine performance error in an angle, and the Schober test with measurement tape was used to determine performance error in a length. Each subject was asked to maintain an upright position with both eyes closed and both upper limbs stabilized on their pelvis. Lumbar vertebrae flexion was maintained at $30^{\circ}$ for three seconds. Different verbal knowledge of results was provided in four groups. After lumbar flexion was performed, knowledge of results was offered immediately. The resting period between the sessions per block was five seconds. Training consisted of 6 blocks, 10 sessions per one block, with a resting period of one minute. A resting period of five minutes was provided between 3 blocks and 4 blocks. A retention test was performed between 10 minutes and 24 hours later following the training block without providing knowledge of results. To determine the training effects, a two-way analysis of variance and a one-way analysis of variance were used with SPSS Ver. 10.0. A level of significance was set at .05. A significant block effect was shown for the acquisition phase (p<.05), and a significant feedback effect was shown in the immediate retention phase (p>.05). There was a significant feedback effect in the delayed retention phase (p<.05), and a significant block effect in the first acquisition phase and the last retention phase (p<.05). In conclusion, it is determined that a 50% relative frequency with a length feedback is the most efficient feedback among different feedback types.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.113-120
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• 2019

Recently, the artificial intelligence(AI) is used in various environments in life, and research on this is being actively conducted in education. In this paper, we designed a Design-Based Research(DBR)-based AI programming education program and analyzed the application of the program for the improvement of understanding of AI in elementary school. In the artificial intelligence education program in elementary school, we should considerthat itshould be used in conjunction with software education through programming activities, rather than creating interest through simple AI experiences. The designed education program reflects the collaborative problem-solving procedures following the DBR process of analysis - design - execution - redesign, allowing the real-world problem-solving activities using AI experiences and block-type programming language. This paper also examined the examples of education programs to improve understanding of AI by using Machine Learning for Kids and to draw implications for developing and operating such a program.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1411-1418
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• 1999

In this paper, we propose a fast motion estimation algorithm using motion prediction and neural network. Considering that the motion vectors have high spatial correlation, the motion vector of current block is predicted by those of neighboring blocks. The codebook of motion vector is designed by Kohonen self-organizing feature map(KSFM) learning algorithm which has a fast learning speed and 2-D adaptive chararteristics. Since the similar codevectors are closely located in the 2-D codebook the motion is progressively estimated from the predicted codevector in the codebook. Computer simulation results show that the proposed method has a good performance with reduced computational complexity.

• The Journal of Korean Association of Computer Education
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• v.18 no.5
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• pp.25-34
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• 2015

The purpose of this research is to suggest the method of code analysis for evaluating learners' projects in computational thinking(CT) education. Recently, block programming tools are applied to K-12 SW education, this study considered the assessment method for evaluating students' levels and learning about CT concepts through analyzing codes of the Scratch projects that students created. As a result from the analysis of 45 projects of novices, it showed the bad coding patterns of novices and verified that it is possible to evaluate students' learning about CT concepts through the analysis of their codes. The higher learner's level, the greater scores of logical thinking, synchronization, flow control, and data representation. This result is able to apply to student assessment of CT concepts in K-12 SW education.

• Journal of the Korea Fashion and Costume Design Association
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• v.21 no.1
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• pp.115-129
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• 2019

This study aimed to develop methods for technical design education that can be intimately connected to the industrial field. For this, technical design jobs performed in the fields of the domestic and foreign fashion industries and their required competences were examined, and educational methods based on constructivism were proposed. Korean fashion technical designers' works were identified, and then the fashion technical designer's responsibilities and qualifications were collected and analyzed from global employment sites. On the basis of the collection and analysis, hands-on staff members and education experts were interviewed about required competences for the actual business and possible suitable methods for education. The results of research showed that in the case of the US, job systems and relevant duties for technical designers were clearly defined by clothing brands, whereas in Korea, businesses were systematized around vendors, not brands, and as a result the businesses of technical package composition and specification proposals were not performed properly. This study organized the contents of technical design education into fit development and specification, the composition of technical design packages, the evaluation and approval of samples, fit schedule management and fitting, block pattern setting and pattern correction, sewing specifications appropriate for styles and materials, grading, technical terms, and production management. As for the technical design education models, the cognitive apprenticeship model, resource-based learning, the problem-based and anchored model, and the problem-based and resource-based models were proposed.

• Journal of The Korean Association of Information Education
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• v.23 no.4
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• pp.355-362
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• 2019

Elementary students are studying software training to teach coding education using text-based languages such as Python. In general, these higher-level languages support learning activities in combination with a kits for physical computing or various programming languages, in contrast to block-coding programming languages. In this study, we conducted a coding project based on computational thinking using the ARCS model to overcome the difficulties of text-based language. The results of the experiment show that students are generally confident and interested in programming. Especially, the understanding of repetition, function, and object was high in the change of computational thinking power, so this trend is believed to be due to the use of text-based languages and the Python module.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3654-3670
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• 2019

This paper proposes an Image Texture Median Filter (ITMF) to analyze and detect Android malware on Drebin datasets. We design a model of "ITMF" combined with Image Processing of Median Filter (MF) to reflect the similarity of the malware binary file block. At the same time, using the MAEVS (Malware Activity Embedding in Vector Space) to reflect the potential dynamic activity of malware. In order to ensure the improvement of the classification accuracy, the above-mentioned features(ITMF feature and MAEVS feature)are studied to train Restricted Boltzmann Machine (RBM) and Back Propagation (BP). The experimental results show that the model has an average accuracy rate of 95.43% with few false alarms. to Android malicious code, which is significantly higher than 95.2% of without ITMF, 93.8% of shallow machine learning model SVM, 94.8% of KNN, 94.6% of ANN.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.1
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• pp.1-9
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• 2019

In this paper, we propose a method of generating a 2D gray image from LiDAR 3D reflection intensity. The proposed method uses the Fully Convolutional Network (FCN) to generate the gray image from 2D reflection intensity which is projected from LiDAR 3D intensity. Both encoder and decoder of FCN are configured with several convolution blocks in the symmetric fashion. Each convolution block consists of a convolution layer with $3{\times}3$ filter, batch normalization layer and activation function. The performance of the proposed method architecture is empirically evaluated by varying depths of convolution blocks. The well-known KITTI data set for various scenarios is used for training and performance evaluation. The simulation results show that the proposed method produces the improvements of 8.56 dB in peak signal-to-noise ratio and 0.33 in structural similarity index measure compared with conventional interpolation methods such as inverse distance weighted and nearest neighbor. The proposed method can be possibly used as an assistance tool in the night-time driving system for autonomous vehicles.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.