• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2158-2167
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• 2018

This paper analyzed the magnetic field produced by the cylindrical stator coils of permanent magnet spherical motor (PMSM). The elliptic equations about the vector magnetic potential were given. Given that the eddy current effects are neglected, the magnet field of the PMSM is regarded as irrotational field, which can be calculated by scalar magnetic potential. The current density of cylindrical stator coil was proposed based on the definition of current density. The expression of current density of stator coil was obtained according to the double Fourier series decomposition and spherical harmonic functions. Then the magnetic flux density for scalar magnetic potential was derived. Further, the influence of different parameters on radial flux density was also analyzed. Finally, the results by the analytical method in this paper were validated by finite element analysis (FEA).

• Journal of Mechanical Science and Technology
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• v.16 no.3
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• pp.292-301
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• 2002

Ultrasound waves interact strongly with the orientation and sequence of the plies in a layup when propagating in the thickness direction of composite laminates. Also the layup orientation greatly influences its properties in a composite laminate. If the layup orientation of a ply is misaligned, it could result in the part being rejected and discarded. Now, most researchers cut a small coupon from the waste edge and use a microscope to optically verify the ply sequences on important parts. This may add a substantial cost to the production since the test is both labor intensive and performed after the part is cured. A nondestructive technique would be very beneficial, which could be used to test the part after curing and requires less time than the optical test. Therefore we have developed, reduced, and implemented a novel ply-by-ply vector decomposition model for composite laminates fabricated from unidirectional plies. This model decomposes the transmission of a linearly polarized ultrasound wave into orthogonal components through each ply of a laminate. High probability is found, by comparisons between the model and tests, in characterizing cured layups of the laminates by using the proposed method.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.401-406
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• 2001

When propagating the thickness direction of composite laminates ultrasound waves interacts strongly with the orientation and sequence of the plies in a layup. Also the layup orientation greatly influences its properties in a composite laminate. If one ply of the layup orientation is misaligned, it could result in the part being rejected and discarded. Now, most researchers cut a small coupon from the waste edge and use a microscope to optically verify the ply sequences on important parts. Those may add a substantial cost to the product since the test is both labor hard and performed after the part is cured. A nondestructive technique would be very beneficial, which could be used to test the part after curing and require less time than the optical test. Therefore we have developed, reduced, and implemented a novel ply-by-ply vector decomposition model for composite lam mates fabricated from unidirectional plies. This model decomposes the transmission of a linearly polarized ultrasound wave into orthogonal components through each ply of a laminate. It is found that a high probability shows between the model and tests developed in characterizing cured layups of the laminates.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.3
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• pp.1-7
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• 2008

This paper proposes a new QRD-LS adaptive algorithm with computational complexity of O(N). The main idea of proposed algorithm(D-QR-RLS) is based on the fact that the computation for the unit vector of is made from the process during Givens Rotation. The performance of the algorithm is evaluated through computer simulation of FIR system identification problem. As verified by simulation results, this algorithm exhibits a good performance. And, we can see the proposed algorithm converges to optimal coefficient vector theoretically.

• International Journal of Advanced Culture Technology
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• v.6 no.3
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• pp.142-150
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• 2018

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the edge orientation histogram and principle component analysis. By using the detected object region as a recognition input image, in this paper the object recognition method combined with principle component analysis and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input object image, this method computes the eigenspace through principle component analysis and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the object recognition is performed by inputting the multi-layer neural network.

• 한국전산유체공학회:학술대회논문집
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• 1997.10a
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• pp.17-22
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• 1997

The performances of the CRAYT3E and CRAYC90 were compared in the point of aerodynamics. The CRAYC90 with and without the highest vector option was run, respectively. The CRAYT3E was run with various processors (from 1pe to 32pes). The communication utilities of MPI and SHMEM were used to inform the boundary data to the other processors. The DADI Euler solver, which is implicit scheme and use central difference method, was used. The domain decomposition method was also used. As the result, the CRAYC90 with the highest vector option is 5.7 times faster than the CRAYT3E with 1 processor. However, because of the scalability of the CRAYT3E, the CRAYT3E with more than 6 processors is faster than CRAYC90. In case that 32 processors used, the CRAYT3E is 6 times faster than CRAYC90 with the highest vector option.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.523-533
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• 2020

This paper suggests three available methods for finding nonnegative estimates of variance components of the random effects in mixed models. The three proposed methods based on the concepts of projections are called projection method I, II, and III. Each method derives sums of squares uniquely based on its own method of projections. All the sums of squares in quadratic forms are calculated as the squared lengths of projections of an observation vector; therefore, there is discussion on the decomposition of the observation vector into the sum of orthogonal projections for establishing a projection model. The projection model in matrix form is constructed by ascertaining the orthogonal projections defined on vector subspaces. Nonnegative estimates are then obtained by the projection model where all the coefficient matrices of the effects in the model are orthogonal to each other. Each method provides its own system of linear equations in a different way for the estimation of variance components; however, the estimates are given as the same regardless of the methods, whichever is used. Hartley's synthesis is used as a method for finding the coefficients of variance components.

• Journal of Power Electronics
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• v.11 no.4
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• pp.401-407
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• 2011

In order to enhance torque density of five-phase permanent magnetic synchronous motor with third harmonic injection for electric vehicles (EVs) applications, optimum seeking method for injection ratio of third harmonic was proposed adopting theoretical derivation and finite element analysis method, under the constraint of same amplitude for current and air-gap flux. By five-dimension space vector decomposition, the mathematic model in two orthogonal space plane, $d_1-q_1$ and $d_3-q_3$, was deduced. And the corresponding dual-plane vector control method was accomplished to independently control fundamental and third harmonic currents in each vector plane. A five-phase PMSM prototype with quasi-trapezoidal flux pattern and its fivephase voltage source inverter were designed. Also, the dual-plane vector control was digitized in a single XC3S1200E FPGA. Simulation and experimental results prove that using the proposed optimum seeking method, the torque density of five-phase PMSM is enhanced by 20%, without any increase of power converter capacity, machine size and iron core saturation.

• 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.