• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.350-353
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• 2001

In general, the Least Square Error method is used for signal classification to measure distance in the $l^2$ metric or the $L^2$ metric space. A defect of the Least Square Error method is that it does not classify properly some waveforms, which is due to the property of the Least Square Error method: the global analysis. This paper proposes a new linear operator, the Integra-Normalizer, that removes the problem. The Integra-Normalizer possesses excellent property that measures the degree of relative similarity between signals by expanding the functional space with removing the restriction on the functional space inherited by the Least Square Error method. The Integra-Normalizer shows superiority to the Least Square Error method in measuring the relative similarity among one dimensional waveforms.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.208-210
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• 1998

A high speed digital distance relaying algorithm based on a modified least square error method is proposed. To obtain stable phasor values very Quickly, first, a lowpass filter which has very short transient period and no overshoot is used. Secondly, the conventional least square error method is modified to the one having the data window of 3 samples by applying a FIR filter which removes the DC-offset component in current relaying signals.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.855-862
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• 1999

A high speed digital distance relaying algorithm based on a least square error method is proposed. To obtain stable phasor values very quickly, first, a lowpass filter which has very short transient period and no overshoot is presented. Secondly, the least square error method having the data window of 3 samples is used by applying a FIR filter which removes the DC-offset component in current relaying signals. Test results show that the proposed distance relaying algorithm detects most of internal faults within a half cycle after faults in a 154[kV] overhead transmission line system.

• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.217-224
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• 2012

Location-Based Services(LBS) is a service that provide location information by using communication network or satellite signal. In order to provide LBS precisely and efficiently, we studied how we can reduce the error on location determination of objects such people and things. We focus on using the least square method and triangulation positioning method to improves the accuracy of the existing location determination method. Above two methods is useful if the distance between the AP and the tags can be find. Though there are a variety of ways to find the distance between the AP and tags, least squares and triangulation positioning method are wildely used. In this thesis, positioning method is composed of preprocessing and calculation of location coordinate and detail of methodology in each stage is explained. The distance between tag and AP is adjusted in the preprocessing stage then we utilize least square method and triangulation positioning method to calculate tag coordinate. In order to confirm the performance of suggested method, we developed the test program for location determination with Labview2010. According to test result, triangulation positioning method showed up loss error than least square method by 38% and also error reduction was obtained through adjustment process and filtering process. It is necessary to study how to reduce error by using additional filtering method and sensor addition in the future and also how to improve the accuracy of location determination at the boundary location between indoor and outdoor and mobile tag.

• Journal of KSNVE
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• v.6 no.5
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• pp.679-684
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• 1996

The Instrumental Variable Method(IVM), modified from least square algorithm, is applied to parameter identification of a noise contaminated bearing test rig. The signal to noise ratio included in Frequency Response Function(FRF) can cause significant errors in parameter identification. Therefore, among several candidates of parameter identification method, results of the applied IVM were compared with noise-contaminated least square method. This study shows that the noise-contaminated least square method can have indonsistent accuracy depending on the degree of noise level, while the IVM has robuster performance to signal to noise ratio than least square method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.4
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• pp.907-914
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• 1999

This paper studies on the model identification of electro-hydraulic servo systems, which are composed of servo valves, double-rod cylinder and load mass. The identified plant is described as a discrete-time ARX or ARMAX model which is respectively obtained from the identification algorithms of least square error method, instrumental variable method and prediction error method. where a nominal model and the variation of model parameters are quantitatively evaluated.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.172-177
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• 2024

This paper presents a method to overcome the problem of increasing elevation estimation error when estimating elevation in a multipath situation with radar. A multipath situation means that radar reception signals reflected from the same target come from multiple paths. In non-multipath, the monopulse method is accurate. For the opposite case, the least square error method is accurate. In multipath situation and when the elevation angle is very low, a singular occurs where the least square error estimate diverges. This singular was identified based on the propagation factor, and monopulse and least square error estimation methods were selectively used. As a result, we succeeded in increasing the accuracy of elevation estimation. MATLAB simulations were performed to verify the method proposed in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.683-687
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• 2007

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and Closed-Form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-Form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a Self-Tuning Weighted Least Square AOA algorithm that is a modified version of the conventional Closed-Form solution. In order to estimate the error covariance matrix as a weight, a two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

• Journal of Power Electronics
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• v.17 no.1
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• pp.149-160
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• 2017

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.175-185
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• 1998

In this paper, we studied some design methodologies of typical FIR filters based on the peak-error constrained least square criterion which was first introducedd by Adams in 1991. This method is a mixed type of the classical least squared error method(LSM) and the so-called min-max error method (MMM). And by considering both the least squared error as well as the maximum error, the solution, i.e. the impulse response of the filter, can be found only when the restrictions on maximum gain, transition bandwidth, and the squared error are satisfied simultaneously under some trade-off conditions. We used the multiple exchange algorithms for optimization procedure and applied the design methodology to the cases of the multiband filter, the differentiator, and the Hilbert transformer by taking the balance of two design criteria into account. The results show that the peak-error constrained least weighted square error design method(PLEM) is superior in performance to the existing LSM and MMM from both the squared error and the maximum error standpoints. And it is verified that PLEM can be applied to not only the case of simple low pass filter, but also to various types of FIR filters.