• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.365-368
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• 2004

We present a family of elaborate numerical models for simulation and systematic optimization of complex LCDs for demanding applications (e.g. LCD-TV). These numerical models comprise modules for solving LCD-related problems in one, two and three dimensions. The three modules feature an intuitive graphical user surface for a jump-start into modeling, a common database for a range of materials and components as well as sophisticated and proven algorithms with more than 15 years of reliable performance in the LCD-industry. Methods for obtaining data required for the modeling of key components are presented.

• Electronics and Telecommunications Trends
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• v.17 no.6 s.78
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• pp.173-181
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• 2002

컬러 디스플레이의 색재현 특성을 좋게 하는 것은 전세계 소비자들의 공통된 바램이다. 이를 위해서, 컬러 재현 장치에서 장치 독립적인 컬러 이미징 기술의 개발이 필요하다. 이를 뒷받침하는 기술은 컬러 어피어런스 모델(color appearance models)을 이용한 컬러의 재현능력 향상인 것이다. 따라서, 본 고에서는 컬러 어피어런스 모델의 최신 기술 동향, 색순응 변환에 있어서의 최신 기술 및 컬러의 차이를 정확히 나타내는 색오차식의 최신 기술을 소개하고자 한다.

• ETRI Journal
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• v.43 no.1
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• pp.74-81
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• 2021

The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the efficient online variational Bayesian approach, which enhances the information exchange between the whole and the part to a certain extent and applies to scalable datasets. The experiments on the scene database indicate that the novel clustering framework, when combined with a convolutional neural network for feature extraction, has meaningful advantages over other models.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.335-336
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• 2009

In this paper, I propose an automatic approach to annotating images dynamically based on MBRM(Multiple Bernoulli Relevance Models) using GLCM(Grey Level Co-occurrence Matrix). MBRM is more appropriate to annotate images compare with multinomial distribution. The model is used in limited test set, MSRC-v2 (Microsoft Research Cambridge Image Database). The results show that this model is significantly outperforms previously reported results on the task of image annotation and retrieval.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.117-124
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• 2014

Cluttered background is a major obstacle in developing salient object detection and tracking system for mobile device captured natural scene video frames. In this paper we propose a context aware feature vector selection model to provide an efficient noise filtering by machine learning based classifiers. Since the context awareness for feature selection is achieved by searching nearest neighborhoods, known as NP hard problem, we apply a fast approximation method with complexity analysis in details. Separability enhancement in feature vector space by adding the context aware feature subsets is studied rigorously using principal component analysis (PCA). Overall performance enhancement is quantified by the statistical measures in terms of the various machine learning models including MLP, SVM, Naïve Bayesian, CART. Summary of computational costs and performance enhancement is also presented.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.31-39
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• 2023

In this paper, we propose an Internal/External Knowledge Distillation (IEKD), which utilizes both external correlations between feature maps of heterogeneous models and internal correlations between feature maps of the same model for transferring knowledge from a teacher model to a student model. To achieve this, we transform feature maps into a sequence format and extract new feature maps suitable for knowledge distillation by considering internal and external correlations through a transformer. We can learn both internal and external correlations by distilling the extracted feature maps and improve the accuracy of the student model by utilizing the extracted feature maps with feature matching. To demonstrate the effectiveness of our proposed knowledge distillation method, we achieved 76.23% Top-1 image classification accuracy on the CIFAR-100 dataset with the "ResNet-32×4/VGG-8" teacher and student combination and outperformed the state-of-the-art KD methods.

• Earthquakes and Structures
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• v.4 no.6
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• pp.607-627
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• 2013

The application of shape memory alloys (SMAs) to the seismic response reduction of civil engineering structures has attracted growing interest due to their self-centering feature and excellent fatigue performance. The loading rate dependence of SMAs raises a concern in the seismic analysis of SMA-based devices. However, the implementation of micromechanics-based strain-rate-dependent constitutive models in structural analysis software is rather complicated and computationally demanding. This paper investigates the feasibility of replacing complex rate-dependent models with rate-independent constitutive models for superelastic SMA elements in seismic time-history analysis. Three uniaxial constitutive models for superelastic SMAs, including one rate-dependent thermomechanical model and two rate-independent phenomenological models, are considered in this comparative study. The pros and cons of the three nonlinear constitutive models are also discussed. A parametric study of single-degree-of-freedom systems with different initial periods and strength reduction factors is conducted to examine the effect of the three constitutive models on seismic simulations. Additionally, nonlinear time-history analyses of a three-story prototype steel frame building with special SMA-based damping braces are performed. Two suites of seismic records that correspond to frequent and design basis earthquakes are used as base excitations in the seismic analyses of steel-braced frames. The results of this study show that the rate-independent constitutive models, with their parameters properly tuned to dynamic test data, are able to predict the seismic responses of structures with SMA-based seismic response modification devices.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.71-79
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• 1998

In this paper, we propose a new mART(modified ART) neural network by combining the winner neuron definition method of SOM(self-organizing map) and the real-time adaptive clustering function of ART(adaptive resonance theory) and construct it in a modular structure, for the purpose of organizing the feature maps of three dimensional targets. Being constructed in a modular structure, the proposed modular mART can effectively prevent the clusters from representing multiple classes and can be trained to organze two dimensional distortion invariant feature maps so as to recognize targets with three dimensional distortion. We also present the recognition result and self-organization perfdormance of the proposed modular mART neural network after carried out some experiments with 14 tank and fighter target models.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.10
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• pp.67-75
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• 1997

A design for a system to perform rapid recognition of three dimensional objects is presented, focusing on efficient indexing. In order to retrieve the best matched models without exploring all possible object matches, we have employed a bayesian framework. A decision-theoretic measure of the discriminatory power of a feature for a model object is defined in terms of posterior probability. Detectability of a featrue defined as a function of the feature itselt, viewpoint, sensor charcteristics, nd the feature detection algorithm(s) is also considered in the computation of discribminatory power. In order to speed up the indexing or selection of correct objects, we generate and verify the object hypotheses for rfeatures detected in a scene in the order of the discriminatory power of these features for model objects.