• Journal of Power Electronics
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• v.16 no.3
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• pp.1004-1011
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• 2016

The inaccurate model parameters in the predictive current control of surface-mounted permanent magnet synchronous motor (SMPMSM) affect the current dynamic response and steady-state error. This paper presents a model parameter correction algorithm based on the relationship between the errors of model parameters and the static errors of dq-axis current. In this correction algorithm, the errors of inductance and flux are corrected in two steps. Resistance is ignored. First, the proportional relations between inductance and d-axis static current errors are utilized to correct the error of model inductance. Second, the flux is corrected by utilizing the proportional relations between flux and q-axis static current errors under the condition that inductance is corrected. An experimental study with a 100 W SMPMSM is performed to validate the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.816-839
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• 2023

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2659-2665
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• 2024

This study proposes an algorithm that combines a Kalman Filter method with effective decay constant correction to improve the accuracy of predicting radiation dose rate distribution during emergencies. The algorithm addresses the limitations of relying solely on measurement data by incorporating calculation data and refining the estimations. The effectiveness of algorithm was assessed using hypothetical test scenarios, which demonstrated a significant improvement in the accuracy of dose rate prediction compared to the model predictions. The estimates generated by the algorithm showed a good agreement with the measured data, and the discrepancies tend to decrease over time. Furthermore, the application of the effective decay constant correction accelerated the reduction of prediction errors. In conclusion, it was confirmed that the combined use of the Kalman filter method and effective decay constant correction is an effective approach to improve the accuracy of dose rate prediction.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.475-484
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• 2021

The optimal design for water distribution system (WDS) is not only satisfying the minimum required water pressure of the nodes, but also minimizing pipe cost, etc. The number of designs of WDS increases exponentially due to the arrangement of various pipes. Various optimization algorithms were applied to propose an optimized design of WDS. In this study, Modified Hybrid Vision Correction Algorithm (MHVCA) with improved self-adapting parameter was applied to optimal design of WDS. The performance was improved by changing the Hybrid Rate (HR) of the existing Hybrid Vision Correction Algorithm (HVCA) to nonlinear HR. To verify the performance of the proposed MHVCA, it applied to mathematical problems consisting of 2 and 30 decision variables and constrained mathematical problems. In order to review the application results of MHVCA, it was compared with Harmony Search (HS), Improved Harmony Search (IHS), Vision Correction Algorithm (VCA) and HVCA. Finally, MHVCA was applied to the optimal design problem of WDS and the results were compared with other algorithms. MHVCA showed better results than other algorithms in mathematical problems and WDS problem. MHVCA will be able to show good results by applying to various water resource engineering problems as well as problems applied in this study.

• Journal of IKEEE
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• v.26 no.2
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• pp.186-195
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• 2022

In this paper, we propose a new algorithm to improve the accuracy of indoor positioning techniques using Wi-Fi access points as beacon nodes. The proposed algorithm is based on the Weighted Centroid algorithm, a popular method widely used for indoor positioning, however, it improves some disadvantages of the Weighted Centroid method and also for other kinds of indoor positioning methods, by using the received signal strength correction method and genetic algorithm to prevent the signal strength fluctuation phenomenon, which is caused by the complex propagation environment. To validate the performance of the proposed algorithm, we conducted experiments in a complex indoor environment, and collect a list of Wi-Fi signal strength data from several access points around the standing user location. By utilizing this kind of algorithm, we can obtain a high accuracy positioning system, which can be used in any building environment with an available Wi-Fi access point setup as a beacon node.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.196-196
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• 2021

상수관망은 많은 관을 통해 물의 수요가 있는 곳으로 물을 공급해주는 역할을 하는 사회기반 시설물이다. 상수관망 설계의 요점은 두 가지로 구분할 수 있다. 첫 번째 요점은 다양한 종류의 관배치로 인한 상수관망 설계안의 많은 경우의 수이다. 두 번째 요점은 상수관망 내 절점의 최저 요구수압 등의 제약조건이다. 두 가지 요점이 있는 상황에서 상수관망 설계비용의 최소화를 위한 상수관망 최적설계는 많은 계산이 요구된다. 많은 계산이 요구되기 때문에 상수관망 최적설계에 최적화 기법을 적용할 수 있다. 본 연구에서 상수관망 최적설계를 위해 적용된 최적화 기법은 Hybrid Rate(HR)를 개선한 Hybrid Vision Correction Algorithm(HVCA)이다. HVCA는 Vision Correction Algorithm(VCA)을 기반으로 추가적인 전역탐색을 실행하는 Centralized Global Search(CGS)의 적용 및 자가적응형 매개변수인 Hybrid Rate(HR)를 적용하여 사용성과 성능을 개량한 알고리즘이다. HVCA의 기존 HR은 선형적으로 증가하는 형태이다. 선형적으로 증가하는 HR로 인해 HVCA는 최적해 탐색과정에서 지역해에 빠지는 문제가 발생하였다. HVCA의 문제를 해결하기 위해 HR을 비선형적으로 증가하는 형태로 개량하였다. HR이 개량된 HVCA를 수자원공학 문제인 상수관망 최적설계 문제에 적용하여 결과를 비교하였다. 적용결과 HR이 개량된 HVCA가 기존의 HVCA보다 낮은 설계 비용을 나타내었다. 상수관망 최적설계 적용결과를 바탕으로 HR이 개량된 HVCA는 상수관망 최적설계 이외의 수자원공학 문제에도 적용가능할 것이다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.5
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• pp.1023-1029
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• 2011

In the industrial vision systems, image correction has great influence on the overall performance of measurement or inspection. The overall area of distorted image is usually splitted into small control areas, and each area is corrected by its control points. The performance of correction methods using control points can be improved by reduction of control areas because the computational time and memory highly depend on the number of control areas. We develop a merging algorithm that reduces control areas and preserves the correction accuracy. The algorithm merges the splitted control areas by use of quad tree method. Experimental results are presented to verify the usefulness of the proposed method.

• Proceedings of the KSRS Conference
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• v.2
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• pp.619-622
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• 2006

Atmospheric correction is one of critical procedures to extract quantitative information related to biophysical variables from hyperspectral data. In this study, we attempted to generate the water vapor contents image from hyperspectral data itself and developed the atmospheric correction algorithm for EO-1 Hyperion data using pre-calculated atmospheric look-up-table (LUT) for fast processing. To apply the new atmospheric correction algorithm, Hyperion data acquired June 3, 2001 over Seoul area is used. Reflectance spectrums of various targets on atmospheric corrected Hyperion reflectance images showed the general spectral pattern although there must be further development to reduce the spectral noise.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1289-1297
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• 2006

Before other statistical data analysis the preprocessing steps should be performed adequately to have meaningful results. These steps include processes such as baseline correction, normalization, denoising, and multiple alignment. In this paper an algorithm for baseline correction is proposed with using the piecewise cubic Hermite interpolation with block-selected points and local minima after denoising for SELDI or MALDI mass spectrometry data.