• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.82-89
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• 2015

The researches of image recognition have been processed traditionally. Especially, face recognition technology has been received attractions with advance and applied to various areas according as camera sensor embedded into many devices such as smart phone. In this study, we design and develop a feature vector generation technique of face for making animation caricatures using methods for face detection which are previous stage of face recognition. At first, we detect both face region and detailed eye region of component element by Viola&Johns's realtime detection method which are called as ROI(Region Of Interest). And then, we generate feature vectors of eye region by utilizing factors as opposed to the periphery and by using appearance information of eye. At this point, we focus on the embedded information in many color spaces to overcome the problems which can be occurred by using one color space. We propose a feature vector generation method using information from many color spaces. Finally, we experiment the test of feature vector generation by the proposed method with enough quantity of sample picture data and evaluate the proposed method for factors of estimating performance such as error rate, accuracy and generation time.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1358-1363
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• 1995

This paper presents a new learning method for a noise tolerant Fuzzy ART. In the conventional Fuzzy ART, the top-down and bottom-up weight vectors have the same value. They are updated by a fuzzy AND operation between the input vector and the current value of the top-down or bottom- up weight vectors. However, it can not prevent the abrupt change of the weight vector and can not achieve good performance for a noisy input vector. To solve the problems, we updated using the weighted sum of the input vector and the current value of the top-down vector. To achieve stability, the bottom-up weight vector is updated using the fuzzy AND operation between the newly learned top-down vector and the current value of the bottom-up vector. Computer simulations show that the proposed method prominently resolves the category proliferation problem without increasing the training epoch for stabilization in noisy environments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1039-1045
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• 2014

Acoustic noise from a transformer, recently, has drawing more and more attentions. One of the main source of the noise is thought to be magnetostriction of the electrical steel sheets which compose transformer core. This paper deals with the magnetostriction of a highly grain-oriented electrical steel sheet measured by using a vector single sheet tester and a three-axial strain gauge. The results show that direction and axis ratio as well as the magnitude of the applied magnetic flux density contribute much to magnetostriction.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.1 no.2
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• pp.19-29
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• 2002

This paper deals with heterogeneous video transcoding, which is one of key technologies for the MPEG-21 digital item adaptation. It is noted that motion vector reuse Is necessarily required for computationally efficient implementation of the transcoder. But conventional transcoder employs the motion vector reuse methods only for P-pictures. In this paper, we propose two new motion vector reuse method for B-pictures. By using the proposed methods, we can produce the MPEG bitstream, which is encoded in a I/B/P picture mode. Computer simulation results show that the proposed methods can reduce the computational burden of the transcoder significantly, while allowing only a small amount of performance degradation.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.195-203
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• 2007

In this paper we propose support vector quantile regression (SVQR) for randomly right censored data. The proposed procedure basically utilizes iterative method based on the empirical distribution functions of the censored times and the sample quantiles of the observed variables, and applies support vector regression for the estimation of the quantile function. Experimental results we then presented to indicate the performance of the proposed procedure.

• The KIPS Transactions:PartA
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• v.12A no.3 s.93
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• pp.263-272
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• 2005

The addressing mode for data is performed by the software in yon Neumann-concept(SISD) computer a priori without hardware design of an address counter for operands. Therefore, in the addressing mode for the vector the corresponding variables as much as the number of the elements should be specified and used also in the software method. This is because not for operand but only for an instructions, quasi PC(program counter) is designed in hardware physically. A vector has a characteristic of a structural dimension. In this paper we propose to design a hardware unit physically external to the CPU for addressing only the elements of a vector unit with the structure and dimension. Because of the high speed performance for a vector processing it should be designed in the SIMD pipeline mechanics. The proposed mechanics is evaluated through a simulation. Our result shows $12\%$ to $30\%$ performance enhancement over CRAY architecture under the same hardware consideration(processing unit).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.336-342
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• 2015

Fall-related injuries are the most common cause of accidental death for the elderly and the most frequent work-related injuries in construction sites. Due to the growing popularity of smartphones, there has been a number of research work related to the use of sensors embedded in the smartphone for fall detection. Falls can be detected easily by measuring the magnitude and direction of acceleration vectors. In general, the direction of the acceleration vector does not show the object movement, but the velocity vector directly indicates the tangential direction in which the object is moving. In this paper, we proposed a new method for computing the fall direction based on the characteristics of the velocity vector extracted from the accelerometer.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.55-56
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• 2018

화력발전소의 중요 구성품인 보일러 튜브의 예기치 못한 누설 사고로 인해 수억원에 해당하는 손실이 발생하고 있다. 본 논문에서는 보일러 튜브의 누설 감지를 위해 유전 알고리즘을 이용하여 추출 가능한 특징들 중 누수 감지에 유용한 특징들을 선택하고, 선택된 특징으로 서포트 벡터 머신을 이용하여 보일러 튜브의 누설 감지하는 방법을 제안한다. 이는 뛰어난 성능을 보였으며, 향후 본 기술을 이용하면 발전소의 손실 예방에 크게 도움이 될 것으로 기대된다.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.298-301
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• 2000

We discuss the iterative methods for linear systems on a single processing node of the HITACHI SR8000. Each processing node of the SR8000 is a shared memory parallel computer which is composed of eight RISC processors with a pseudo-vector facility. We implement highly optimized codes for basic linear operations including a matrix-vector product and apply them to the conjugate gradient (CG) and the conjugate residual (CR) methods for linear systems. Our tuned codes for both method score nearly 50% of the theoretical peak performance, which is the best in the sense that it corresponds to an asymptotic performance of the inner product.