• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.510-518
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• 2014

In this study, in order to improve and optimize the performance of the turning mechanism for a fish robot in the fluid, we propose the tail joint trajectories using neural networks to mimic the CST(C-shape Sharp Turn) patterns of a real fish which is optimized in the natural environment. In order to mimic the CST patterns of a fish, we convert the sequential recording CST patterns into the coordinate data, and change the numerical coordinate data into a functions. We change the motion functions to the relative joint angles which is adapted to suit robot's shape and data. However, these relative joint trajectories obtained by the sequential recording of the carp have low-precision. It is difficult to apply to the control of a fish robot. Therefore, the relative joint trajectories are interpolated using neural networks with superior generalization ability and applied to the fish robot. we have found that the proposed method using neural networks is superior to ones using high-order polynomial equation through the computer simulations.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.57-61
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• 1999

In the field of image compression, Classified Vector Quantization(CVQ) reveals attractive characteristics for preserving perceptual features, such as edges. However, the classification scheme is not generalized to effectively reconstruct different kinds of edge patterns in the original CVQ that predefines several linear-type edge classes: vortical edge horizontal edge diagonal edge classes. In this paper, we propose a new classification scheme, especially for edge blocks based on the similarity measure for edge patterns. An edge block is transformed to a feature vector that describes the detailed shape of the edge pattern The classes for edges are formulated automatically from the training images to result in the generalization of various shapes of edge patterns. The experimental results show the generated linear/nonlinear types of edge classes. The integrity of all the edges is faithfully preserved in the reconstructed image based on the various type of edge codebooks generated at 0.6875bpp.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.113-122
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• 2009

Since the traditional pattern mining methods only probe unspecified moving patterns that seem to satisfy users' requests among diverse patterns within the limited scopes of time and space, they are not applicable to problems involving the mining of optimal moving patterns, which contain complex time and space constraints, such as 1) searching the optimal path between two specific points, and 2) scheduling a path within the specified time. Therefore, in this paper, we illustrate some problems on mining the optimal moving patterns with complex time and space constraints from a vast set of historical data of numerous moving objects, and suggest a new moving pattern mining method that can be used to search patterns of an optimal moving path as a location-based service. The proposed method, which determines the optimal path(most frequently used path) using pattern frequency retrieved from historical data of moving objects between two specific points, can efficiently carry out pattern mining tasks using by space generalization at the minimum level on the moving object's location attribute in consideration of topological relationship between the object's location and spatial scope. Testing the efficiency of this algorithm was done by comparing the operation processing time with Dijkstra algorithm and $A^*$ algorithm which are generally used for searching the optimal path. As a result, although there were some differences according to heuristic weight on $A^*$ algorithm, it showed that the proposed method is more efficient than the other methods mentioned.

• Journal of the Korea Concrete Institute
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• v.17 no.3 s.87
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• pp.369-374
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• 2005

This study proposes a technique for the recognition of crack patterns, which includes horizontal, vertical, diagonal($-45^{\circ}$), diagonal($+45^{\circ}$), and random cracks, based on image processing technique and artificial neural network. A MATLAB code was developed for the proposed image processing algorithm and artificial neural network. Features were determined using total projection technique, and the structure(no. of layers and hidden neurons) and weight of artificial neural network were determined by learning from artificial crack images. In this process, we adopted Bayesian regularization technique as a generalization method to eliminate overfitting Problem. Numerical tests were performed on thirty-eight crack images to examine validity of the algorithm. Within the limited tests in the present study, the proposed algorithm was revealed as accurately recognizing the crack patterns when compared to those classified by a human expert.

• Communications of Mathematical Education
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• v.22 no.2
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• pp.137-164
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• 2008

Given the importance of early experience in algebraic thinking, we designed six consecutive lessons in which $4^{th}$ graders were encouraged to recognize patterns in the process of finding the relationships between two quantities and to represent a given problem with various mathematical models. The results showed that students were able to recognize patterns through concrete activities with manipulative materials and employ various mathematical models to represent a given problem situation. While students were able to represent a problem situation with algebraic expressions, they had difficulties in using the equal sign and letters for the unknown value while they attempted to generalize a pattern. This paper concludes with some implications on how to connect algebraic thinking with students' arithmetic or informal thinking in a meaningful way, and how to approach algebra at the elementary school level.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.35-40
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• 2019

Traditional Malware Detection is susceptible for detecting malware which is modified by polymorphism or obfuscation technology. By learning patterns that are embedded in malware code, machine learning algorithms can detect similar behaviors and replace the current detection methods. Data must collected continuously in order to learn malicious code patterns that change over time. However, the process of storing and processing a large amount of malware files is accompanied by high space and time complexity. In this paper, an HDFS-based distributed processing system is designed to reduce space complexity and accelerate feature extraction time. Using a distributed processing system, we extract two API features based on filtering basis, 2-gram feature and APICFG feature and the generalization performance of ensemble learning models is compared. In experiments, the time complexity of the feature extraction was improved about 3.75 times faster than the processing time of a single computer, and the space complexity was about 5 times more efficient. The 2-gram feature was the best when comparing the classification performance by feature, but the learning time was long due to high dimensionality.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.313-315
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• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.10
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• pp.940-945
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• 1998

In this paper, we obtained the data, which is required in training the neural network and diagnosing the degradation degree, by introducing the AE detection that is effective method in ordinary degradation diagnosis on activation. Aa the results of generalization tests by appling neural network to the unknown AE patterns obtained from two kinds of specimen, firstly as to evaluate an objective performance of neural network, the recognition ration for no-void specimen and 1[mm] -void specimen are appeared to be 98.9% and 92.5%, respectively. Also, in the evaluation of the adaptability of neural network with a new type of 0.2[mm] -void specimen, it is confirmed that the result appears to be 64% of recognition ratio at 94% of confidence interval coefficient in expectation output 0.2. On the other hand, the recognition capability of the neural network was confirmed by data from no-void and 1[mm] void specimen. The results prove the promising possibility of the application of ANN to discriminate specific void affecting as main degradation source at partial discharge condition in insulator containing multi-void by accummulated data base.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.623-632
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• 2012

In this paper, we analyzed the effect of wavelet decomposition levels in feature extraction for anomaly detection from hyperspectral imagery. After wavelet analysis, anomaly detection was experimentally performed using the RX detector algorithm to analyze the detecting capabilities. From the experiment for anomaly detection using CASI imagery, the characteristics of extracted features and the changes of their patterns showed that radiance curves were simplified as wavelet transform progresses and H bands did not show significant differences between target anomaly and background in the previous levels. The results of anomaly detection and their ROC curves showed the best performance when using the appropriate sub-band decided from the visual interpretation of wavelet analysis which was L band at the decomposition level where the overall shape of profile was preserved. The results of this study would be used as fundamental information or guidelines when applying wavelet transform to feature extraction and selection from hyperspectral imagery. However, further researches for various anomaly targets and the quantitative selection of optimal decomposition levels are needed for generalization.

• Journal of Ship and Ocean Technology
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• v.4 no.3
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• pp.1-12
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• 2000

A number of numerical methods like Computational Fluid Dynamics(CFD) have been developed to predict the flow fields of a vessel but the present study is developed to infer the wake fields on propeller plane by Statistical Fluid Dynamics(SFD) approach which is emerging as a new technique over a wide range of industrial fields nowadays. Neural network is well known as one prospective representative of the SFD tool and is widely applied even in the engineering fields. Further to its stable and effective system structure, generalization of input training patterns into different classification or categorization in training can offer more systematic treatments of input part and more reliable result. Because neural network has an ability to learn the knowledge through the external information, it is not necessary to use logical programming and it can flexibly handle the incomplete information which is not easy to make a definition clear. Three dimensional stern hull forms and nominal wake values from a model test are structured as processing elements of input and output layer respectively and a neural network is trained by the back-propagation method. The inferred results show similar figures to the experimental wake distribution.