• Journal of the Korean School Mathematics Society
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• v.10 no.1
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• pp.27-43
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• 2007

The aim of this paper is to develop a web-based learning system in order to motivate college students in the area of science and engineering to study college calculus. We designed and developed web-based contents, named MathBooster, using Mathematica, webMathematica and phpMath taking advantages of rapid computation and symbolic computation. The features of MathBooster consists of four parts: graphical representation of calculus concepts, textual illustrations of conceptual understanding, example-based step-by-step learning with phpMath, and quizzes with diagnostic feedback. After the MathBooster was practiced with engineering students, the formative evaluation was conducted with survey items composed in four categories: user responses, screen layout, practicing examples and diagnostic feedback in solving quizzes. The overall level of user satisfaction was statistically measured using SPSS. Those results indicate which parts of MathBooster are needed for future enhancement.

• Journal of The Korean Association For Science Education
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• v.30 no.2
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• pp.206-217
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• 2010

This study aims to explore preservice teachers' overall views of nature of science and compare their views by gender and certificate area (biology vs. non-biology teachers). In addition, a comparison with American preservice teachers' views was also implemented. The Views of Nature of Science (Abd-El-Khalick, 1998), an openended questionnaire, is utilized to explore participants' views of nature of science. Along with 'definition of science', six aspects of the nature of science, (1) theories and laws, (2) tentativeness, (3) social and cultural embeddedness, (4) creativity, (5) theory-laden, and (6) inference, were investigated. The qualitative result indicates that the participants' first image of science was about the method of science such as observations and experiments. Furthermore, these preservice science teachers particularly have a difficulty in differentiating between scientific theories and laws, and understanding the importance of creation and imagination in developing scientific knowledge. In addition, compared to American preservice teachers' view of the nature of science, Korean preservice teachers possess naive views in creative and imaginative aspect. Interestingly, biology teachers demonstrated lower understanding in theories and laws as well as tentativeness of scientific knowledge. However, there is no difference by gender.

• Cartoon and Animation Studies
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• s.35
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• pp.155-180
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• 2014

The roles of the characters of 3D animation Toystory3, which was released in 2010 and achieved worldwide success, can be classified into typical, simple and easy-to-understand roles such as hero, villain, princess, and assistant. However, the process, in which each character's role is finally recognized by the audience, is embodied in a very colorful and exquisite manner and makes the curiosity of the audience continue effectively. The stream of the diverse role changes of the characters of Toystory3 is represented through "visual rhythm of the lighting design" and such rhythm can be confirmed most clearly in the digital colorscript stage. This researcher analyzed the characters' role changes in the work based on Propp's folktale character analysis theory, and extracted the core scenes that lead to inference, doubt, performance, reinforcement by character in order to grasp how the audience gasps major characters' role changes. The visual differences of the lighting design, which the four core scenes of each character show, were represented in graph and analyzed, and the results showed that the changes that one character has constituted rhythmical, visual contrast gradually and the rhythms of each independent character achieve visual contrast and harmony each other like the voice part of polyphony. This researcher calls this "the visual counterpoint of character's changing" and derives the conclusion that a dual visual counterpoint is hidden in the character production of the full-length animation Toystory3. Along with this, this researcher proposes the production of full-length animation that actively utilizes constructive aesthetics.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.93-100
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• 2005

This study is to desist and implement an efficient microbial medical image retrieval system based on knowledge and content of them which can make use of more accurate decision on colony as doll as efficient education for new techicians. For this. re first address overall inference to set up flexible search path using rule-base in order U redure time required original microbial identification by searching the fastest path of microbial identification phase based on heuristics knowledge. Next, we propose a color ffature gfraction mtU, which is able to extract color feature vectors of visual contents from a inn microbial image based on especially bacteria image using HSV color model. In addition, for better retrieval performance based on large microbial databases, we present an integrated indexing technique that combines with B+-tree for indexing simple attributes, inverted file structure for text medical keywords list, and scan-based filtering method for high dimensional color feature vectors. Finally. the implemented system shows the possibility to manage and retrieve the complex microbial images using knowledge and visual contents itself effectively. We expect to decrease rapidly Loaming time for elementary technicians by tell organizing knowledge of clinical fields through proposed system.

• Journal of the Korea Society for Simulation
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• v.23 no.2
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• pp.73-84
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• 2014

Recently, researches on the application of IT technology to various fields including traditional industries are becoming more popular. One challenge in the field of education is to understand the way how technology may support learning, and research on self-directed learning has been accelerated by integrating education and IT technology. The process of self-directed learning in e-learning applications such as Car Maintenance Training is very difficult and complicated. Previous studies on car maintenance training applications provided simple training scenarios with predetermined action sequences. To incorporate self-directed learning in car maintenance training, however, trainees must be able to perform various maintenance operations himself and experience various situations. To provide such functionality, it is necessary to obtain an accurate response for various operations of trainees, but it requires complicated calculations with respect to varieties in the electrical and mechanical processes of a car. In this paper, we develop a logic simulation agent using JESS inference engine in which self-directed learning is achieved by capturing the behavior of trainees and simulating car operations without complicated physical simulations in car maintenance training.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.169-176
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• 2020

Recently, deep neural networks (DNNs) have brought revolutions to many mobile and embedded devices by providing human-level machine intelligence for various applications. However, high inference accuracy of such DNNs comes at high computational costs, and, hence, there have been significant efforts to reduce computational overheads of DNNs either by compressing off-the-shelf models or by designing a new small footprint DNN architecture tailored to resource constrained devices. One notable recent paradigm in designing small footprint DNN models is sharing parameters in several layers. However, in previous approaches, the parameter-sharing techniques have been applied to large deep networks, such as ResNet, that are known to have high redundancy. In this paper, we propose a parameter-sharing method for already parameter-efficient small networks such as ShuffleNetV2. In our approach, small templates are combined with small layer-specific parameters to generate weights. Our experiment results on ImageNet and CIFAR100 datasets show that our approach can reduce the size of parameters by 15%-35% of ShuffleNetV2 while achieving smaller drops in accuracies compared to previous parameter-sharing and pruning approaches. We further show that the proposed approach is efficient in terms of latency and energy consumption on modern embedded devices.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.135-140
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• 2012

The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer's range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.749-754
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• 2011

In this study, we propose the design of optimized pRBFNNs-based face recognition system using two-dimensional Image and ASM algorithm. usually the existing 2 dimensional face recognition methods have the effects of the scale change of the image, position variation or the backgrounds of an image. In this paper, the face region information obtained from the detected face region is used for the compensation of these defects. In this paper, we use a CCD camera to obtain a picture frame directly. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. We can butt up personal profile by extracting the both face contour and shape using ASM(Active Shape Model) and then reduce dimension of image data using PCA. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to real-time face image database and then demonstrated from viewpoint of the output performance and recognition rate.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.655-666
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• 2007

Reasoners using typical Tableaux algorithm such as RacerPro, Pellet have a problem in Tableaux algorithm large ABox reasoning. Researches to solve these Problems are dealt with Instance Store of University of Manchester which uses Tableaux algorithm based reasoner and DBMS and KAON2 of University of Karlsruhe using Disjunctive Datalog approach. An evaluation experiment for present reasoners is the experiment of TBox reasoning in most of Tableaux algorithm based one. The most of benchmarking tests in reasoning systems haven't done with ABox reasoning based Tableaux Algorithm but done with TBox reasoning based Tableaux Algorithm. Especially, rarely reported benchmarking tests in reasoners have been issued nowadays. Therefore, this thesis evaluates systems with theory of each reasoners for large ABox reasoning that becomes issues recently with typical reasoners. The large AoBx reasoning engine will be analyzed using Instance Store and KAON2 of Manchester University for large ABox processing. At the analysing method, LUBM(Lehigh University BenchMark), benchmarking test method, and it's test system will be introduced. In conclusion, I recommend appropriate reasoner in various environment with experiment result and characteristic of algorithm used for each reasoner.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.921-932
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• 2015

This study analyzed military personnel survivability in regards to offensive operations according to the scientific military training data of a reinforced infantry battalion. Scientific battle training was conducted at the Korea Combat Training Center (KCTC) training facility and utilized scientific military training equipment that included MILES and the main exercise control system. The training audience freely engaged an OPFOR who is an expert at tactics and weapon systems. It provides a statistical analysis of data in regards to state-of-the-art military training because the scientific battle training system saves and utilizes all training zone data for analysis and after action review as well as offers training control during the training period. The methodologies used the Cox PH modeling (which does not require parametric distribution assumptions) and decision tree modeling for survival data such as CART, GUIDE, and CTREE for richer and easier interpretation. The variables that violate the PH assumption were stratified and analyzed. Since the Cox PH model result was not easy to interpret the period of service, additional interpretation was attempted through univariate local regression. CART, GUIDE, and CTREE formed different tree models which allow for various interpretations.