• Annual Conference of KIPS
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• 2005.11a
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• pp.1323-1326
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• 2005

무선 센서 네트워크의 설계에 있어서 전력 소모는 중요 요소이다. 본 논문은 AODV (Ad-hoc On-demand Distance Vector) 라우팅 프로토콜을 센서 네트워크에 적용하여 망 전체의 전력 소모를 최소화하고 수명 시간을 연장하려 한다. 이를 위해 전송 경로 탐색시 필요한 경로 요구 메시지(RREQ:Route Request)에 불응답 필드(No Reply Field)와 데이터 필드(Data Field)를 추가, 확장하여 전송 경로 탐색과 데이터 전송이 동시에 수행되어져 각종 제어 메시지(경로 응답, 경로 에러, 헬로 메시지)를 감소시키는 것을 제안한다. 이는 싱크 노드에 최종 데이터가 전송되는 시간이 기존 대비 20% 연장되고 노드 이동성에 따른 망의 토폴로지의 변화에도 강한 경향을 나타낸다.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.22-38
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• 2015

While Modern Standard Arabic is the formal spoken and written language of the Arab world; dialects are the major communication mode for everyday life. Therefore, identifying a speaker's dialect is critical in the Arabic-speaking world for speech processing tasks, such as automatic speech recognition or identification. In this paper, we examine two approaches that reduce the Universal Background Model (UBM) in the automatic dialect identification system across the five following Arabic Maghreb dialects: Moroccan, Tunisian, and 3 dialects of the western (Oranian), central (Algiersian), and eastern (Constantinian) regions of Algeria. We applied our approaches to the Maghreb dialect detection domain that contains a collection of 10-second utterances and we compared the performance precision gained against the dialect samples from a baseline GMM-UBM system and the ones from our own improved GMM-UBM system that uses a Reduced UBM algorithm. Our experiments show that our approaches significantly improve identification performance over purely acoustic features with an identification rate of 80.49%.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.381-391
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• 2016

The Joint Bayesian (JB) method has been used in most state-of-the-art methods for face verification. However, since the publication of the original JB method in 2012, no improved verification method has been proposed. A lot of studies on face verification have been focused on extracting good features to improve the performance in the challenging Labeled Faces in the Wild (LFW) database. In this paper, we propose an improved version of the JB method, called the two-dimensional Joint Bayesian (2D-JB) method. It is very simple but effective in both the training and test phases. We separated two symmetric terms from the three terms of the JB log likelihood ratio function. Using the two terms as a two-dimensional vector, we learned a decision line to classify same and not-same cases. Our experimental results show that the proposed 2D-JB method significantly outperforms the original JB method by more than 1% in the LFW database.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.37-39
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• 2006

지지 벡터 기계(Support Vector Machine; SVM)는 기본적으로 이진분류를 위해 고안되었지만, 최근 다양한 분류기 생성전략과 결합전략이 고안되어 다중부류 분류에도 적용되고 있다. 본 논문에서는 OVR(One-Vs-Rest) 전략으로 생성된 SVM을 NB(Naive Bayes) 분류기를 이용하여 동적으로 구성함으로써, OVR SVM을 이용한 다중부류 분류 시스템에서 자주 발생하는 동점을 효과적으로 해결하는 방법은 제안한다. 이 방법을 유전발현 데이터를 이용한 다중부류 암 분류에 적용하였는데, 고차원의 데이터로부터 NB 분류기 구축에 유용한 유전자를 선택하기 위해 Pearson 상관계수를 사용하였다. 14개의 암 유형과 16,063개의 유전발현 수준을 가지는 대표적인 다중부류 암 분류 데이터인 GCM 암 데이터에 적용하여 제안하는 방법의 유용성을 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.19-21
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• 2006

Single Nucleotide Polymorphism(SNP)는 인간 유전자 서열의 0.1%에 해당하는 부분으로 이는 각 개인의 체질 및 각종 유전질환과 밀접한 관련이 있다고 알려져 있다. 최근 이 SNP정보의 패턴을 이용 질병의 진단 및 치료에 연관지으려는 노력이 시도되고 있다. 그러나 아직 SNP를 이용한 효율적인 분석방법에 대한 전산학적 연구는 많지 않다. 본 논문에서는 대표적인 패턴인식기 중 하나인 Support Vector Machine(SVM)을 이용 한국인의 대표적인 유전질환으로 알려진 만성간염에 대해서 관련된 SNP에 대한 패턴 인식율 측정을 실험하였다. 실험 데이터는 간 및 소화기 질환 유전체 센터에서 얻어진 만성간염 환자와 관련 SNP정보를 사용하였으며, 실험 결과 전체 SNP 정보를 모두 가지는 환자그룹에 대한 학습인식율이 66.46%로 나타났으며, 부분그룹에서는 72.91%로 높은 인식율을 보였다. 이 결과는 SNP 정보를 이용한 만성간염의 초기진단예측에 SVM을 효율적으로 사용할 수 있음을 보인다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.157-163
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• 2005

In this paper, we present the multimodal system based on the fusion of two user-friendly biometric modalities: Iris and Face. In order to reach robust identification and verification we are going to combine two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. In addition, the Synergetic Neural Network(SNN) is used to obtain matching score of the preprocessed data. This system can operate in two modes: to identify a particular person or to verify a person's claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.1-12
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• 2016

In this paper, we propose an approach that efficiently builds regional hazardous weather prediction models based on past weather data. Doing so requires finding the proper weather attributes that strongly affect hazardous weather for each region, and that requires a large number of experiments to build and test models with different attribute combinations for each kind of hazardous weather in each region. Using our proposed method, we reduce the number of experiments needed to find the correct weather attributes. Compared to the traditional method, our method decreases the number of experiments by about 45%, and the average prediction accuracy for all hazardous weather conditions and regions is 79.61%, which can help forecasters predict hazardous weather. The Korea Meteorological Administration currently uses the prediction models given in this paper.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.1
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• pp.71-76
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• 2007

This paper deals with implementing a steganography based multi-modal biometric system. For this purpose, we construct a multi-biometrics system based on the face and iris recognition. Here, the feature vector of iris pattern is hidden in the face image. The recognition system is designed by the fuzzy-based Linear Discriminant Analysis(LDA), which is an expanded approach of the LDA method combined by the theory of fuzzy sets. Furthermore, we present a watermarking method that can embed iris information into face images. Finally, we show the advantages of the proposed watermarking scheme by computing the ROC curves and make some comparisons recognition rates of watermarked face images with those of original ones. From various experiments, we found that our proposed scheme could be used for establishing efficient and secure multi-modal biometric systems.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.5
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• pp.163-169
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• 2006

In order to use parallel computers in specific applications, algorithms need to be developed and mapped onto parallel computer architectures. Main memory access for shared memory system or global communication in message passing system deteriorate the computation speed. In this paper, it is found that the m-step generalization of the block Lanczos method enhances parallel properties by forming in simultaneous search direction vector blocks. QR factorization, which lowers the speed on parallel computers, is not necessary in the m-step block Lanczos method. The m-step method has the minimized synchronization points, which resulted in the minimized global communications and main memory access compared to the standard methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2424-2441
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• 2012

In this paper, we propose a fake iris detection method that combines the optical and textural features of the human eye. To extract the optical features, we used dual Purkinje images that were generated on the anterior cornea and the posterior lens surfaces based on an analytic model of the human eye's optical structure. To extract the textural features, we measured the amount of change in a given iris pattern (based on wavelet decomposition) with regard to the direction of illumination. This method performs the following two procedures over previous researches. First, in order to obtain the optical and textural features simultaneously, we used five illuminators. Second, in order to improve fake iris detection performance, we used a SVM (Support Vector Machine) to combine the optical and textural features. Through combining the features, problems of single feature based previous works could be solved. Experimental results showed that the EER (Equal Error Rate) was 0.133%.