• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.614-621
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• 2006

We have implemented a multiclassifier learning approach in a GA-based inductive learning environment that learns classification rules that are similar to rules used in PROSPECTOR. In the multiclassifier learning approach, a classification system is constructed with several classifiers that are obtained by running a GA-based learning system several times to improve the overall performance of a classification system. To implement the multiclassifier learning approach, we need a decision-making scheme that can draw a decision using multiple classifiers. In this paper, we introduce two decision-making schemes: one is based on combining posterior odds given by classifiers to each class and the other one is a voting scheme based on ranking assigned to each class by classifiers. We also present empirical results that evaluate the effect of the multiclassifier learning approach on the GA-based inductive teaming environment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.54-60
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• 2010

The local appearance-based method is one of the face recognition methods that divides face image into small areas and extracts features from each area of face image using statistical analysis. It collects classification results of each area and decides identity of a face image using a voting scheme by integrating classification results of each area of a face image. The conventional local appearance-based method divides face images into small pieces and uses all the pieces in recognition process. In this paper, we propose a local appearance-based method that makes use of only the relatively important facial components. The proposed method detects the facial components such as eyes, nose and mouth that differs much from person to person. In doing so, the proposed method detects exact locations of facial components using support vector machines (SVM). Based on the detected facial components, a number of small images that contain the facial parts are constructed. Then it extracts features from each facial component image using principal components analysis (PCA). We compared the performance of the proposed method to those of the conventional methods. The results show that the proposed method outperforms the conventional local appearance-based method while preserving the advantages of the conventional local appearance-based method.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.33-43
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• 2002

Various kinds of estimation methods have been developed for imputation of categorical missing data. They include category method, logistic regression, and association rule. In this study, we propose two fusions algorithms based on both neural network and voting scheme that combine the results of individual imputation methods. A Mont-Carlo simulation is used to compare the performance of these methods. Five factors used to simulate the missing data pattern are (1) input-output function, (2) data size, (3) noise of input-output function (4) proportion of missing data, and (5) pattern of missing data. Experimental study results indicate the following: when the data size is small and missing data proportion is large, modal category method, association rule, and neural network based fusion have better performances than the other methods. However, when the data size is small and correlation between input and missing output is strong, logistic regression and neural network barred fusion algorithm appear better than the others. When data size is large with low missing data proportion, a large noise, and strong correlation between input and missing output, neural networks based fusion algorithm turns out to be the best choice.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1946-1956
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• 2001

In this paper, we propose a novel 2-D partial matching algorithm based on model-based stochastic analysis of feature correspondences in a relation vector space, which is quite robust to shape variations as well as invariant to geometric transformations. We represent an object using the ARG (Attributed Relational Graph) model with features of a set of relation vectors. In addition, we statistically model the partial occlusion or noise as the distortion of the relation vector distribution in the relation vector space. Our partial matching algorithm consists of two-phases. First, a finite number of candidate sets areselected by using logical constraint embedding local and structural consistency Second, the feature loss detection is done iteratively by error detection and voting scheme thorough the error analysis of relation vector space. Experimental results on real images demonstrate that the proposed algorithm is quite robust to noise and localize target objects correctly even inseverely noisy and occluded scenes.

• Journal of the Korea Society for Simulation
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• v.23 no.2
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• pp.65-72
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• 2014

Sensor nodes are easily compromised by attacker when which are divided in open environment. The attacker may inject false report and false vote attack through compromised sensor node. These attacks interrupt to transmission legitimate report or the energy of sensor node is exhausted. PVFS are proposed by Li and Wu for countermeasure in two attacks. The scheme use inefficiency to energy of sensor node as fixed report threshold and verification node. In this paper, our propose the next neighbor node selection scheme based on fuzzy logic system for energy improvement of PVFS. The parameter of fuzzy logic system are energy, hops, verification success count, CH select high the next neighbor node among neighbor nodes of two as deduction based on fuzzy logic system. In the experimental, our proposed scheme was improvement to energy of about 9% compare to PVFS.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2002.11a
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• pp.369-372
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• 2002

An anonymous authentication scheme allows a user to identify himself as a member of a group of users in a secure and anonymous way. It seems to be crucial and indispensable components in English auction, electronic voting and open procurement, which are getting very popular business areas in E-commerce. First, we briefly describe the previous anonymous authentication protocols how to work and what cryptographic techniques adopted to increase performance and achieve anonymity. Second, we compare those protocols from the viewpoint of the communication and computation complexity and the specific cryptographic techniques used in their protocols.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2002.11a
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• pp.395-400
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• 2002

전자투표 시스템은 유권자들이 온라인상에서 안전하게 투표할 수 있도록 하기 위한 프로토콜이다. 현재까지 대부분의 전자투표 시스템은 몇몇 신뢰할 수 있는 서버로 하여금 투표를 모아서 선거의 결과를 공정하게 계산할 수 있도록 하고 있다. 이러한 전자투표 시스템은 전자 서명[7,8,13,14,19], mix-net[20,21], homorphic encryption schemes[23,24]등을 이용하여 제안되었다. 또한 그룹 멤버가 그룹을 대표하여 서명을 하는 그룹 서명의 개념을 적용[15]할 수 있다. 본 논문에서는 그룹 서명을 전자투표 시스템에 그대로 적용할 수 없기 때문에 변형된 그룹 서명을 제안하고, 그것을 이용하여 전자투표 시스템에 적용하고자 한다. 우리는 Camenisch 와 Michels가 제안한 그룹 서명[1]을 기초로 한다.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2002.11a
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• pp.95-98
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• 2002

공정하고 투명한 선거를 이루기 위해서는 비밀성(privacy), 선거권(eligibiligy) 등과 더불어 전체검증(universally verifiability)과 매표방지(receipt-freeness) 속성이 반드시 제공되어야 한다. 그러나 전체검증은 누구나 투표내용을 확인할 수 있는 방법을 제공하는 것이고 매표방지는 투표내용과 투표자를 연결할 수 있는 방법을 차단하는 것으로 두가지 특성은 상호표리적 관계에 있어서 두가지 특성을 동시에 만족시키기가 어렵다. 이 논문에서는 신뢰할 수 있는 제3자인 정직한 랜덤마이저(Honest Randomizer, HR)와 최소한의 물리적 가정인 HR에서 유권자로 가는 도청 불가능한 채널(untappable channel)을 이용하여 전체검증과 매표방지를 제공하는 효율적인 전자선거 기법을 제안한다.

• Journal of Korea Multimedia Society
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• v.18 no.8
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• pp.901-908
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• 2015

Methods of detecting, describing, matching image features, like corners and blobs, have been actively studied as a fundamental step for image processing and computer vision applications. As one of feature description/matching methods, LLAH(Locally Likely Arrangement Hashing) describes image features based on the geometric relationship between their neighbors, and thus is suitable for scenes with poor texture. This paper presents a modified LLAH algorithm, which includes the image features themselves for robustly describing the geometric relationship unlike the original LLAH, and employes a voting-based feature matching scheme that makes feature description much simpler. Then, this paper quantitatively analyzes its performance with synthetic images in the presence of Gaussian noise.

• Journal of information and communication convergence engineering
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• v.14 no.1
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• pp.45-50
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• 2016

Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques to mitigate the natural variations in gait among different subjects. We incorporated several machine learning algorithms into this study using the data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA's convenient interface enabled us to apply various sets of machine learning algorithms to understand whether each algorithm can capture certain distinctive gait features. First, we defined 24 gait features by analyzing three-axis acceleration data, and then selectively used them for distinguishing subjects 10 years of age or younger from those aged 20 to 40. We also applied a machine learning voting scheme to improve the accuracy of the classification. The classification accuracy of the proposed system was about 81% on average.