• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.383-384
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• 2015

This paper presents a new space vector modulation (SVM) method for indirect matrix converters (IMCs) to reduce commonmode voltage as well as minimize output current ripple in a high voltage transfer ratio. In the proposed SVM, the three-vector modulation scheme is used in the rectifier stage, while the nonzero state modulation technique, where the three nearest active vectors are selected to synthesize the desired output voltage, is applied to inverter stage to reduce the CMV. The proposed SVM method can significantly reduce the output current ripple and common-mode voltage of the IMC without any extra hardware. Simulated results are provided to demonstrate the effectiveness of the proposed SVM method.

• Journal of the Korean Mathematical Society
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• v.58 no.1
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• pp.109-122
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• 2021

In this paper, we consider the problem of finding the hypersurface Mn in the Euclidean (n + 1)-space ℝn+1 that satisfies an equation of mean curvature type, called singular minimal hypersurface equation. Such an equation physically characterizes the surfaces in the upper half-space ℝ+3 (u) with lowest gravity center, for a fixed unit vector u ∈ ℝ3. We first state that a singular minimal cylinder Mn in ℝn+1 is either a hyperplane or a α-catenary cylinder. It is also shown that this result remains true when Mn is a translation hypersurface and u is a horizantal vector. As a further application, we prove that a singular minimal translation graph in ℝ3 of the form z = f(x) + g(y + cx), c ∈ ℝ - {0}, with respect to a certain horizantal vector u is either a plane or a α-catenary cylinder.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2976-2990
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• 2022

The adaptive adjustment of reference or weight vectors in decomposition-based methods has been a hot research topic in the evolutionary community over the past few years. Although various methods have been proposed regarding this issue, most of them aim to diversify solutions in the objective space to cover the true Pareto fronts as much as possible. Different from them, this paper proposes a knee point-based reference vector adaptive adjustment strategy to concurrently balance the convergence and diversity. To be specific, the knee point-based reference vector adaptive adjustment strategy firstly utilizes knee points to construct the adaptive reference vectors. After that, a new fitness function is defined mathematically. Then, this paper further designs a many-objective evolutionary algorithm with knee point-based reference vector adaptive adjustment strategy, where the mating operation and environmental selection are designed accordingly. The proposed method is extensively tested on the WFG test suite with 8, 10 and 12 objectives and MPDMP with state-of-the-art optimizers. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art optimizers and the practicability of the proposed method in tackling practical many-objective optimization problems.

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

• Journal of Power Electronics
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• v.10 no.4
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• pp.357-364
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• 2010

In this paper, a novel analysis of a carrier based PWM method for multilevel inverters is presented. The space vector PWM and carrier based PWM correlations in multilevel inverters are investigated in a nominal two-level switching diagram. The obtained results can be applied to design various carrier PWM techniques. In this paper, a carrier based single-state PWM technique, which reduces the switching number and optimizes the active voltage errors, is presented. This PWM technique can be advantageous if there are a large number of levels. The proposed method is mathematically formulated and demonstrated by simulations and experimental results.

• Korean Journal of Computational Design and Engineering
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• v.14 no.4
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• pp.217-224
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• 2009

Most divide-and-conquer implementations for Delaunay triangulation utilize quad-edge or winged-edge data structure since triangles are frequently deleted and created during the merge process. How-ever, the proposed divide-and-conquer algorithm utilizes the array based data structure that is much simpler than the quad-edge data structure and requires less memory allocation. The proposed algorithm has two important features. Firstly, the information of space partitioning is represented as a permutation vector sequence in a vertices array, thus no additional data is required for the space partitioning. The permutation vector represents adaptively divided regions in two dimensions. The two-dimensional partitioning of the space is more efficient than one-dimensional partitioning in the merge process. Secondly, there is no deletion of edge in merge process and thus no bookkeeping of complex intermediate state for topology change is necessary. The algorithm is described in a compact manner with the proposed data structures and operators so that it can be easily implemented with computational efficiency.

• Journal of Power Electronics
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• v.19 no.1
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• pp.168-178
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• 2019

Discrete space vector modulation (DSVM) is an effective method to improve the steady-state performance of the finite control set predictive control for permanent magnet synchronous motor drive systems. However, it requires complex computations due to the presence of numerous virtual voltage vectors. This paper proposes an improved finite control set model-free predictive control using DSVM to reduce the computational burden. First, model-free deadbeat current control is used to generate the reference voltage vector. Then, based on the principle that the voltage vector closest to the reference voltage vector minimizes the cost function, the optimal voltage vector is obtained in an effective way which avoids evaluation of the cost function. Additionally, in order to implement double-objective control, a two-level decisional cost function is designed to sequentially reduce the stator currents tracking error and the inverter switching frequency. The effectiveness of the proposed control is validated based on experimental tests.

• Journal of Astronomy and Space Sciences
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• v.25 no.3
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• pp.283-290
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• 2008

Satellite formation flying can provide the platform for interferometric observation to acquire the precise data and ensure the flexibility for space mission. This paper presents development and verification of an algorithm to estimate the baseline between formation flying satellites. To estimate a baseline(relative navigation) in real time, EKF(Extended Kalman Filter) and UKF(Unscented Kalman Filter) are used. Measurements for updating a state-vector in Kalman Filter are GPS single difference data. In results, The position errors in estimated baseline are converged to less than ${\pm}1m$ in both EKF and UKF. And as using the two types of Kalman filter, it is clear that the unscented Kalman filter shows a relatively better performance than the extended Kalman filter by comparing an efficiency to the model which has a non-linearity.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.223-233
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• 2023

Gauss Sieve is an algorithm for solving SVP and requires exponential time and space complexity. The terminationcondition of the Sieve is determined by the size of the constructed list and the number of collisions related to space complexity. The term 'collision' refers to the state in which the sampled vector is reduced to the vector that is already inthe list. if collisions occur more than a certain number of times, the algorithm terminates. When executing previous algorithms, we noticed that unnecessary operations continued even after the shortest vector was found. This means that the existing termination condition is set larger than necessary. In this paper, after identifying the point where unnecessary operations are repeated, optimization is performed on the number of operations required. The tests are conducted by adjusting the threshold of the collision that becomes the termination condition and the distribution in whichthe sample vector is generated. According to the experiments, the operation that occupies the largest proportion decreased by62.6%. The space and time complexity also decreased by 4.3 and 1.6%, respectively.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.312-317
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• 2015

The dynamic system approach in time series has been used in many real problems. Based on Taken's embedding theorem, we can build the predictive function where input is the time delay coordinates vector which consists of the lagged values of the observed series and output is the future values of the observed series. Although the time delay coordinates vector from multivariate time series brings more information than the one from univariate time series, it can exhibit statistical redundancy which disturbs the performance of the prediction function. We apply dimension reduction techniques to solve this problem and analyze the effect of this approach for prediction. Our experiment uses delayed Lorenz series; least squares support vector regression approximates the predictive function. The result shows that linearly preserving projection improves the prediction performance.