• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.665-675
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• 2022

The safety problems of giant hydraulic structures such as dams caused by terrorist attacks, earthquakes, and wars often have an important impact on a country's economy and people's livelihood. For the national defense department, timely and effective assessment of damage to or impending damage to dams and other structures is an important issue related to the safety of people's lives and property. In the field of damage assessment and vulnerability analysis, it is usually necessary to give the damage assessment results within a few minutes to determine the physical damage (crack length, crater size, etc.) and functional damage (decreased power generation capacity, dam stability descent, etc.), so that other defense and security departments can take corresponding measures to control potential other hazards. Although traditional numerical calculation methods can accurately calculate the crack length and crater size under certain combat conditions, it usually takes a long time and is not suitable for rapid damage assessment. In order to solve similar problems, this article combines simulation calculation methods with machine learning technology interdisciplinary. First, the common concrete gravity dam shape was selected as the simulation calculation object, and XFEM (Extended Finite Element Method) was used to simulate and calculate 19 cracks with different initial positions. Then, an LSTM (Long-Short Term Memory) machine learning model was established. 15 crack paths were selected as the training set and others were set for test. At last, the LSTM model was trained by the training set, and the prediction results on the crack path were compared with the test set. The results show that this method can be used to predict the crack propagation path rapidly and accurately. In general, this article explores the application of machine learning related technologies in the field of mechanics. It has broad application prospects in the fields of damage assessment and vulnerability analysis.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.85-93
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• 2016

Convergent education is essential to develop consilient thinking skills, ability to recreate information and knowledge, and problem-solving skills which are demanded in society of convergent knowledge. Accordingly, this study is going to develop a convergent class model of augmented reality and art based on consilient knowledge. Teaching model is designed based on the ADDIE model, which was operated by opening a real class in order to verify the validity. The result showed a high satisfaction of learners showed the ability to adapt to the major areas associated with the cultivation of learners. Characteristics of augmented reality medium were found to enable learners to analyze a new phenomenon and to fuse the necessary knowledge by inducing them to actively interact by the their intention in learning. Therefore, it is possible to cultivate creative and convergent persons of ability equipped with more flexible and creative thinking ability and discernment through deepened education for recognizing and understanding convergent cases between art and scientific technology. There is a study on the validation and the convergence of different subjects in terms of a variety of aspects, left behind of this study.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.79-87
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• 1996

A new method for recognizing and locating partially occluded or overlapped two-dimensional objects regardless of their size, translation, and rotation, is presented. Dominant points approximating occuluding contoures of objects are generated by finding local maxima of smoothed k-cosine function, and then used to guide the contour segment matching procedure. Primitives between the dominant points are produced by projecting the local contours onto the line between the dominant points. Robust classification of primitives. Which is crucial for reliable partial shape matching, is performed using adaptive resonance theory (ART2). The matched primitives having similar scale factors and rotation angles are detected in the hough space to identify the presence of the given model in the object scene. Finally the translation vector is estimated by minimizing the mean squred error of the matched contur segment pairs. This model-based matching algorithm may be used in diveerse factory automation applications since models can be added or changed simply by training ART2 adaptively without modifying the matching algorithm.

• The Journal of Art Theory & Practice
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• no.6
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• pp.123-145
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• 2008

Artist-in-residence which gets chances to create by artists' moving and encountering new culture is heightening its level in 21th century. Under the circumstance that issue of cultural diversity and the role of artists which is for city revitalization and sustainability are affect residency program in the midst of highly proceeded globalization that international exchange. Therefore, in the aspect of creative city, a new model is creating by reuse of abandoned industrial facilities and Asia or Eastern country become the subject in residency program management, the issue of cultural diversity is getting more important, programs based on project not just residence are managing. Furthermore, it has inter-country cooperating system in the rage of cultural management. It means that artists' space of creating activity has a new, social role in spontaneously we need to approach to following model in Korea, as well.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.457-458
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• 2007

Clustering plays an indispensable role for data analysis. Many clustering algorithms have been developed. However, most of them suffer poor performance of learning. To archive good clustering performance, we develop a hybrid neural network model. It is the combination of Multi-Layer Perceptron (MLP) and Adaptive Resonance Theory 2 (ART2). It inherits two distinct advantages of stability and plasticity from ART2. Meanwhile, by combining the merits of MLP, it improves the performance for clustering. Experiment results show that our model can be used for clustering with promising performance.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.127.1-127
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• 2001

In this paper we propose an input/output model based fault diagnosis method to detect and isolate single faults in the robot arm control system. The proposed algorithm is functionally composed of three main parts-parameter estimation, fault detection, and isolation, When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, and in this zone the estimated parameters are transferred to the fault classifier by ART2(adaptive resonance theory 2) neural network for fault isolation. Since ART2 neural network is an unsupervised neural network fault classifier does not require the knowledge of all possible faults to isolate the faults occurred in the system. Simulations are carried out to evaluate the performance of the proposed ...

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.2
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• pp.69-77
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• 1993

Automatic image recognition system is essential for a better man-to machine interaction. Because of the noise and deformation due to the sensor operation, it is not simple to build an image recognition system even for the fixed images. In this paper neural network which has been reported to be adequate for pattern recognition task is applied to the fixed and variational(rotation, size, position variation for the fixed image)recognition with a hope that the problems of conventional pattern recognition techniques are overcome. At fixed image recognition system. ART model is trained with face images obtained by camera. When recognizing an matching score. In the test when wigilance level 0.6 - 0.8 the system has achievel 100% correct face recognition rate. In the variational image recognition system, 65 invariant moment features sets are taken from thirteen persons. 39 data are taken to train multi-layer perceptron and other 26 data used for testing. The result shows 92.5% recognition rate.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.242-248
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• 2020

Most works of art are done through brokers, and transaction details are not disclosed to the public and are always at risk of tampering. To solve these problems, many Artworks Blockchain Platforms that apply blockchain technology to art transactions are being used. Several companies are currently operating these platforms, but since various blockchain platforms are operated according to the content, the operating methods of each platform are different, and a related model is needed to solve these problems due to compatibility issues between platforms. In this paper, we collect the latest Artworks Blockchain Platforms data, and based on this, we will create and analyze the Color Petri net model of Artworks Blockchain Platform.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.123-133
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• 2019

This research empirically investigated halo effect in evaluating culture and art performance program. We diagnosed halo effect by using correlation analysis, factor analysis, and regression model on results and scores of fifteen evaluation indicators within three categories for the 107 Local Representative Performance Art Festivals in 2014 and 2015. The results indicates strong possibility of halo effect in culture and art performance evaluation. The correlation coefficients between evaluation indicators is higher than 0.5 and factor structure does not match with evaluation categories in both years. Scores in categories and standard deviations also are also significantly correlated with each other. The results implies that more sophisticated standard, diversification of evaluator, education, and meta-anlysis are need to control halo effect.

• Journal of Korea Game Society
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• v.18 no.6
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• pp.49-58
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• 2018

This study analyzes semiotic aspects of game concept art which is developed considering individual 's color experience. The six stages of Frank H. Mahnke's color pyramid are roughly classified into three categories, and how the game concept art meets each stage. Using the Roland Barthes's mythological-symbolic model of meaning, The meaning of righteousness and the characteristics of newly derived symbols. The results showed that colors could make the background stage more recognizable or intended to have a particular impression. In this way, game concept art, in which what is intended to be implemented in game development, can identify various functions and possibilities of game concept art, such as presenting game convenience as well as impression through a combination of various colors.