• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39A no.9
• /
• pp.519-527
• /
• 2014

Adaptive arrays can minimize contributions from interferences incident onto an sensor array while preserving a signal the direction vector of which corresponds to the array steering vector to within a scalar factor. If there exist errors in the steering vector, severe performance degradation can be caused since the desired signal is misunderstood as an interference by the array. This paper presents an adaptive beamforming method which is robust against steering vector errors, exploiting a range of the desired signal direction. In the presented method, an correlation matrix of array response vectors is obtained through integration over the direction range and a minimization problem is formulated using some eigenvectors of the correlation matrix such that a more accurate steering vector than initially given one can be found. The minimization problem is transformed into a relaxed SDP (semidefinite program) problem, which can be effectively solved since it is a sort of convex optimization. Simulation results show that the proposed method outperforms existing ones such as ORM (outside-range-based method) and USM (uncertainty-based method).

• Speech Sciences
• /
• v.13 no.4
• /
• pp.177-186
• /
• 2006

In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate of the speaker-independent speech recognition, we employ the Mahalanobis distance in the calculation of the similarity measure between feature vectors. It is assumed that the metric matrix of the Mahalanobis distance be diagonal for the sake of cost reduction in memory and time of calculation. We propose that the diagonal elements be given in terms of the variations of the feature vector components. Geometrically, this prescription tends to redistribute the set of data in the shape of a hypersphere in the feature vector space. The idea is applied to the speech recognition by hidden Markov model with fuzzy vector quantization. The result shows that the recognition is improved by an appropriate choice of the relevant adjustable parameter. The Viterbi score difference of the two winners in the recognition test shows that the general behavior is in accord with that of the recognition error rate.

• ETRI Journal
• /
• v.39 no.6
• /
• pp.841-850
• /
• 2017

We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends on the convolution effects of the spectrum input data. The larger convolution effect appearing at a relatively smaller nanoparticle size obstructs the network training. The trained single-layer network reveals the weighting matrix consisting of a basis vector in the form of Chebyshev polynomials of the second kind. The weighting matrix corresponds to an inverse system matrix, where an incoherency of basis vectors due to low convolution effects, as well as a nonlinear activation function, plays a key role in retrieving the matrix elements. Test images are well reconstructed through trained networks having an inverse kernel matrix. We also confirm that a multi-layer network with one hidden layer improves the performance. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.

• Journal of Power Electronics
• /
• v.18 no.2
• /
• pp.522-532
• /
• 2018

The cascaded H Bridge (CHB) multilevel inverter (MLI) is popular among the classical MLI topologies due to its modularity and reliability. Although space vector modulation (SVM) is the most suitable modulation scheme for MLIs, it has not been used widely in industry due to the higher complexity involved in its implementation. In this paper, a simple and novel generalized SVM algorithm is proposed, which has both reduced time and space complexity. The proposed SVM involves the generalization of both the duty cycle calculation and switching sequence generation for any n-level inverter. In order to generate the gate pulses for an inverter, a generalized switch matrix (SM) for the CHB inverter is also introduced, which further simplifies the algorithm. The algorithm is tested and verified for three-phase, three-level and five-level CHB inverters in simulations and hardware implementation. A comparison of the proposed method with existing SVM schemes shows the superiority of the proposed scheme.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.34 no.1
• /
• pp.81-88
• /
• 2006

In this paper, we analyze a noncommutativity error that is not able to be compensated with integrating gyro outputs in RLG-based INS. The system can suffer from some motion known as RLG dithering motion, coning motion, ISA motion derived by an AV mount and vehicle real dynamic motion. So these motions are a cause of the noncommutativity error, the system error derived by each motion has to be analyzed. For the analysis, a relation between rotation vector and gyro outputs is introduced and applied to define the coordinate transformation matrix and the angular vector.

• Proceedings of the KIPE Conference
• /
• 2005.07a
• /
• pp.638-642
• /
• 2005

This paper proposes a modified space-vector pulse width modulation (PWM) strategy which can restrict the common-mode voltage for three-phase to three-phase matrix converter and still keep sinusoidal input and output waveforms and unity power factor at the input side. The proposed control method has been developed based on contributing the appropriate space vectors instead of using zero space vectors. The advantages of this proposed method is to reduce the peak value of common-mode voltage to 42% beside the lower high harmonic components as compared to the conventional SVM method. Hence, the new table is also presented with the new space vector rearrangement. Furthermore, the voltage transfer ratio is unaffected by the proposed method. A simulation of the overall system has been carried out to validate the advantages of the proposed method.

• Proceedings of the Korean Information Science Society Conference
• /
• 2005.07b
• /
• pp.715-717
• /
• 2005

Support vector machine(SVM)은 최근 각광받는 기계학습 방법 중 하나로서, kernel function 이라는 사상(mapping)을 이용하여 입력 공간의 벡터를 classification이 용이한 특징 (feature) 공간의 벡터로 변환하는 것을 근간으로 한다. SVM은 이러한 특징 공간에서 두 클래스를 구분 짓는 hyperplane을 일련의 최적화 방법론을 사용하여 찾아내며, 주어진 문제가 convex problem 인 경우 항상 global optimal solution 을 보장하는 등의 장점을 지닌다. 한편 bioinformatics 연구에서 주로 사용되는 데이터는 측정 오류 등 일련의 오류를 포함하고 있으며, 이러한 오류는 기계학습 방법론이 어떤 decision boundary를 찾아내는가에 영향을 끼치게 된다. 특히 SVM의 경우 이러한 오류는 특징 공간 벡터간의 관계를 나타내는 Gram matrix를 변화로 나타나게 된다. 본 연구에서는 입력 공간에 오류가 발생할 때 그것이 SVM 의 decision boundary를 어떻게 변화시키는가를 대표적인 두 가지 kernel function, 즉 linear kernel과 Gaussian kernel에 대해 분석하였다. Wisconsin대학의 유방암(breast cancer) 데이터에 대해 실험한 결과, 데이터의 오류에 따른 SVM 의 classification 성능 변화 양상을 관찰하여 커널의 종류에 따라 SVM이 어떠한 특성을 보이는가를 밝혀낼 수 있었다. 또 흥미롭게도 어떤 조건 하에서는 오류가 크더라도 오히려 SVM 의 성능이 향상되는 것을 발견했는데, 이것은 바꾸어 생각하면 Gram matrix 의 일부를 변경하여 SVM 의 성능 향상을 꾀할 수 있음을 나타낸다.

• Journal of KSNVE
• /
• v.4 no.1
• /
• pp.71-82
• /
• 1994

We propose a systematic method to select the master states, which are retained in the reduced model after the order reduction process. The proposed method is based on the fact that the range space of right eigenvector matrix is spanned by orthogonal base vectors, and tries to keep the orthogonality of the submatrix of the base vector matrix as much as possible during the reduction process. To quentify the skewness of that submatrix, we define "Absolute Singularity Factor(ASF)" based on its singular values. While the degree of observability is concerned with estimation error of state vector and up to n'th order derivatives, ASF is related only to the minimum state estimation error. We can use ASF to evaluate the estimation performance of specific partial measurements compared with the best case in which all the state variables are identified based on the full measurements. A heuristic procedure to find suboptimal master states with reduced computational burden is also proposed. proposed.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.7C
• /
• pp.635-639
• /
• 2009

In this paper, we show that any two Hadamard matrices of the same size are equivalent if they have the property that the rows of each Hadamard matrix are closed under binary vector addition. One of direct consequences of this result is that the equivalence between cyclic Hadamard matrices constructed by maximal length sequences and Walsh-Hadamard matrix of the same size generated by Kronecker product can be established.

• Journal of applied mathematics & informatics
• /
• v.28 no.1_2
• /
• pp.351-361
• /
• 2010

The solution of large sparse linear systems is one of the most important problems in large scale scientific computing. Among the many methods developed, the preconditioned Krylov subspace methods are considered the preferred methods. Selecting a suitable preconditioner with appropriate parameters for a specific sparse linear system presents a challenging task for many application scientists and engineers who have little knowledge of preconditioned iterative methods. The prediction of ILU type preconditioners was considered in [27] where support vector machine(SVM), as a data mining technique, is used to classify large sparse linear systems and predict best preconditioners. In this paper, we apply the data mining approach to the sparse approximate inverse(SAI) type preconditioners to find some parameters with which the preconditioned Krylov subspace method on the linear systems shows best performance.