• Journal of Advanced Navigation Technology
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• v.10 no.4
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• pp.385-393
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• 2006

In this paper, we proposed a location estimation algorithm considering an extension of searching area in 2.45GHz band RTLS and analyzed its performance in terms of an average estimation error distance. The extendable searching area was assumed to be square of $300m{\times}300m$ and 2 dimensions. The arrangement shape of available readers was considered circle, rectangle, and shrinkage rectangle for extendable searching area. Also, we assumed that propagation path was LOS (Line-Of-Sight) environment, and analyzed the estimation error performance as a function of the number of received sub-blink considering an arrangement shape of available readers in searching area. From the results, compared with rectangle shape, circle shape showed the higher estimation accuracy. Also, we confirmed that the proposed location estimation algorithm provided high estimation accuracy in the shrinkage rectangle shape that was suitable for extension of searching area.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.87-95
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• 2002

In this paper, we proposed a fast multi-resolution motion estimation(MRME) algorithm. This algorithm exploits the half-pixel accuracy motion estimation(HPAME) for exact motion vectors in the baseband and block classification for the reduction of bit amounts and computational loads. Generally, as the motion vector in the baseband are used as initial motion vector in the high frequency subbands, it has crucial effect on quality of the motion compensated image. For this reason, we exploit HPAME in the motion estimation for the baseband. But HPAME requires additional bit and computational loads so that we use block classification for the selective motion estimation in the high frequency subbands to compensate these problems. In result, we could reduce the bit rate and computational load at the similar image quality with conventional MRME. The superiority of the proposed algorithm was confirmed by the computer simulation.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.71-76
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• 2023

In this paper, we propose a smoothed received signal strength indicator (RSSI)-based distance estimation using deep neural network (DNN) for accurate distance estimation in an environment where a single receiver is used. The proposed scheme performs a data preprocessing consisting of data splitting, missing value imputation, and smoothing steps to improve distance estimation accuracy, thereby deriving the smoothed RSSI values. The derived smoothed RSSI values are used as input data of the Multi-Input Single-Output (MISO) DNN model, and are finally returned as an estimated distance in the output layer through input layer and hidden layer. To verify the superiority of the proposed scheme, we compared the performance of the proposed scheme with that of the linear regression-based distance estimation scheme. As a result, the proposed scheme showed 29.09% higher distance estimation accuracy than the linear regression-based distance estimation scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2879-2890
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• 2020

In this paper, we study the estimation of angle, range and polarization parameters of a bistatic polarization sensitive frequency diverse array multiple-input multiple-output (PSFDA-MIMO) radar system. The application of polarization sensitive array in receiver is explored. A signal model of bistatic PSFDA-MIMO radar system is established. In order to utilize the multi-dimensional structure of array signals, the matched filtering radar data can be represented by a third-order tensor model. A joint estimation of the direction-of-departure (DOD), direction-of-arrival (DOA), range and polarization parameters based on parallel factor (PARAFAC) algorithm is proposed. The proposed algorithm does not need to search spectral peaks and singular value decomposition, and can obtain automatic pairing estimation. The method was compared with the existing methods, and the results show that the performance of the method is better. Therefore, the accuracy of the parameter estimation is further improved.

• IE interfaces
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• v.10 no.3
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• pp.125-132
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• 1997

We proposed a 2-step neural network approach for estimating machining parameters of electric discharge wire cut. The first step net, which is described as a backward neuro-estimation, is designed for estimating coarse cutting parameters while the second phase net, as a polishing forward neuro-estimation, is utilized for determining fine parameters. Sequential estimation procedure, based on backward and forward net, is performed using the net's approximation capability which is M to 1 and 1 to M mapping property. Experimental results an given to evaluate the accuracy of the proposed 2-step neuro-estimation.

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.9-9
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• 2018

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.73-77
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• 1986

Optimal input design problem for linear regression model with constrained output variance has been considered. It is shown that the optimal input signal for the linear regression model can also be realized as an ARMA process. Monte-Carlo simulation results show that the optimal stochastic input leads to comparatively better estimation accuracy than white input signal.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.323-324
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• 2010

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.17-21
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• 2017

In this paper, the service execution accuracy was compared by ontology based rule inference method and machine learning method, and the amount of data at the point when the service execution accuracy of the machine learning method becomes equal to the service execution accuracy of the rule inference was found. The rule inference, which measures service execution accuracy and service execution accuracy using accumulated data and pattern matching on service results. And then machine learning method measures service execution accuracy using cross validation data. After creating a confusion matrix and measuring the accuracy of each service execution, the inference algorithm can be selected from the results.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.815-822
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• 2006

It is very important that they accurately predict the software development cost in the early stage of a software development. Because cost estimations are required when bidding for a contract or determining whether a project is feasible in terms of a cost-benefit analysis. The criterions of the software cost estimation was set up to calculate software development cost more exactly, which is applied to made up a budget of the software business or to calculate a suitable cost to start the business in our country. However, as the software technology and environment are changing very rapidly, it need to enhance the criterion of the cost estimation continuously. Therefore, we tried to apply technology of software and a variety of factors of environment changes in present. Most of all, we proposed an introduction and readjustment of the adjustment factor applying 14 general system characteristics to improve the accuracy of the cost estimation and the schedule adjustment factor that is required by practicians. For evaluating the accuracy in terms of the real data, we have used MMRE & PRED. In result, we proved that the accuracy was clearly improved by applying the scale factor and readjusted VAF with 14 general system characteristics. Moreover, we evaluated the accuracy of the schedule adjustment factor.