• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.797-803
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• 2014

In this paper, 3D face recognition system is designed by using polynomial based on RBFNNs. In case of 2D face recognition, the recognition performance reduced by the external environmental factors such as illumination and facial pose. In order to compensate for these shortcomings of 2D face recognition, 3D face recognition. In the preprocessing part, according to the change of each position angle the obtained 3D face image shapes are changed into front image shapes through pose compensation. the depth data of face image shape by using Multiple Point Signature is extracted. Overall face depth information is obtained by using two or more reference points. The direct use of the extracted data an high-dimensional data leads to the deterioration of learning speed as well as recognition performance. We exploit principle component analysis(PCA) algorithm to conduct the dimension reduction of high-dimensional data. Parameter optimization is carried out with the aid of PSO for effective training and recognition. The proposed pattern classifier is experimented with and evaluated by using dataset obtained in IC & CI Lab.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1373-1374
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• 2015

본 논문은 복잡한 비선형 모델링 방법인 다항식 기반 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)와 벡터공간에서 임의의 비선형 경계를 찾아 두 개의 집합을 분류하는 방법으로 주어진 조건하에서 수학적으로 최적의 해를 찾는 SVM(Support Vector Machine)를 사용하여 3차원 얼굴인식 모델을 설계하고 두 모델의 3차원 얼굴 인식률을 비교한다. 3D스캐너를 통해 3차원 얼굴형상을 획득하고 획득한 영상을 전처리 과정에서 포인트 클라우드 정합과 포즈보상을 수행한다. 포즈보상 통해 정면으로 재배치한 영상을 Multiple Point Signature기법을 이용하여 얼굴의 깊이 데이터를 추출한다. 추출된 깊이 데이터를 RBFNN과 SVM의 입력패턴과 출력으로 선정하여 모델을 설계한다. 각 모델의 효율적인 학습을 위해 PCA 알고리즘을 이용하여 고차원의 패턴을 축소하여 모델을 설계하고 인식 성능을 비교 및 확인한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4534-4555
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• 2015

We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8A
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• pp.898-908
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• 2004

In this paper, we propose a high speed preamble searcher feasible for RACH(Random Access Channel) preamble structure in WCDMA reverse link receiver. Unlike IS-95, WCDMA system uses AISMA(Acquisition Indication Sense Multiple Access) process. Because of the time limit between RACH preamble transmission and AI(Acquisition Indicators), and the restriction on the number of RACH signatures assigned to RACH preamble, fast acquisition indication is required for efficient operation. The preamble searcher proposed in this paper is based on 2-antenna system and has adopted FHT algorithm that has the radix-2 16 point FFT structure. The acquisition speed using FHT is 64 times faster than the conventional method that correlates each signature. Based on their fast aquisition scheme, we improved the acquisition performance by calculating the correlation up to the 4096 chips of the total preamble length. The performance is analyzed by using Neyman-pearson method. The proposed algorithm has been applied for the implementation of WCDMA reverse link receiver modem successfully.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.647-669
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• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.

• Journal of Yeungnam Medical Science
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• v.39 no.2
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• pp.98-107
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• 2022

Background: Stroke patients usually experience damage to multiple functions and a long rehabilitation period. Hence, there is a large volume of patient clinical information. It thus takes a long time for clinicians to identify the patient's information and essential pieces of information may be overlooked. To solve this, we stored the essential clinical information of stroke patients in a blockchain and implemented the blockchain technology using the Java programming language. Methods: We created a mini blockchain to store the medical information of patients using the Java programming language. Results: After generating a unique pair of public/private keys for identity verification, a patient's identity is verified by applying the Elliptic Curve Digital Signature Algorithm based on the generated keys. When the identity verification is complete, new medical data are stored in the transaction list and the generated transaction is verified. When verification is completed normally, the block hash value is derived using the transaction value and the hash value of the previous block. The hash value of the previous block is then stored in the generated block to interconnect the blocks. Conclusion: We demonstrated that blockchain can be used to store and deliver the patient information of stroke patients. It may be difficult to directly implement the code that we developed in the medical field, but it can serve as a starting point for the creation of a blockchain system to be used in the field.

• Journal of Life Science
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• v.28 no.6
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• pp.639-647
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• 2018

A new heat shock protein 70 was identified in red-spotted grouper (Epinephelus akaara) based on an expression analysis. The cDNA of red-spotted grouper Hsp70 (designated RgHsp70) was cloned by the rapid amplification of cDNA ends (RACE) techniques. The full-length of RgHsp70 cDNA was 2,152 bp, consisting of a 5'-terminal untranslated region (UTR) of 105 bp, a 3'-terminal UTR of 274 bp, and an open reading frame (ORF) of 1,773 bp that encode a polypeptide of 590 amino acids with a theoretical molecular weight of 64.9 kDa and an estimated isoelectric point of 5.2. Multiple alignment and phylogenetic analyses revealed that the RgHsp70 gene shares a high similarity with other Hsp70 fish genes. RgHsp70 contained all three classical Hsp70 family signatures. The results indicated the RgHsp70 is a member of the heat shock protein 70 family. RgHsp70 mRNA was predominately expressed in the liver, with reduced expression noted in the head-kidney tissues. The expression analysis of different water temperatures (21, 18, 15 and $12^{\circ}C$) for sampled livers revealed that expression gradually increased at $12^{\circ}C$ compared to $21^{\circ}C$. In this study, the effects of water temperature lowering on the physiological conditions were investigated, and the results revealed that novel RgHsp70 may be an important molecule involved in stress responses.