• Journal of KSNVE
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• v.7 no.6
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• pp.919-929
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• 1997

Induction motors are used in many areas to transform electrical energy to mechanical energy. In the design of an induction motor, not only energy efficiency but also noise becomes an important factor. To effectively address the noise problem, it will be convenient if one can see where and how noise is generated and propagated. In this study sound radiation by an induction motor is visualized using cylindrical acoustic holography. To minimize the bias error by window effect Minimum Error Window(MEW) is used. Its performance is verified by numerical simulations. Based on these theoretical understanding, sound pressure measurement with an induction motor are performed. Not only sound radiation are visualized but sound pressure level and sound power level are also estimated. Results show that the main source is located at nearly bottom part of the motor and the total sound pressure level is 49dB, which satisfies the guideline value suggested by the KS C 4202.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.146-151
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• 2019

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.43-46
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• 2000

In this paper, we propose a fast adaptive diamond search algorithm(FADS) for block matching motion estimation. Fast motion estimation algorithms reduce the computational complexity by using the UESA (Unimodal Error Search Assumption) that the matching error monotonically increases as the search moves away from the global minimum error. Recently many fast BMAs(Block Matching Algorithms) make use of the fact that the global minimum points in real world video sequences are centered at the position of zero motion. But these BMAs, especially in large motion, are easily trapped into the local minima and result in poor matching accuracy. So, we propose a new motion estimation algorithm using the spatial correlation among the adjacent blocks. We change the origin of search window according to the spatially adjacent motion vectors and their MAE(Mean Absolute Error). The computer simulation shows that the proposed algorithm has almost the same computational complexity with UCBDS(Unrestricted Center-Biased Diamond Search)〔1〕, but enhance PSNR. Moreover, the proposed algorithm gives almost the same PSNR as that of FS(Full Search), even for the large motion case, with half the computational load.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.105-112
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• 2006

In this study, we propose a simple preprocessing method for classification of basal cell carcinoma (BCC), which is one of the most common skin cancer. The preprocessing step consists of data clipping with a half Hanning window and dimension reduction with principal components analysis (PCA). The application of the half Hanning window deemphasizes the peak near $1650cm^{-1}$ and improves classification performance by lowering the false negative ratio. Classification results with various classifiers are presented to show the effectiveness of the proposed method. The classifiers include maximum a posteriori probability (MAP), k-nearest neighbor (KNN), probabilistic neural network (PNN), multilayer perceptron(MLP), support vector machine (SVM) and minimum squared error (MSE) classification. Classification results with KNN involving 216 spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic BCC detection.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.128-132
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• 1996

In [1], it is shown that a subclass of ETBFs, which are self-dual ETBFs, can be expressed as a weighted average of median subfiltered outputs. In this paper, we extend this result to general ETBFs. In particular, we show that any ETBF can be represented as a weighted average of minimum (or maximum) subfiltered outputs. These representations naturally lead to a subclass of ETBF, called the K-th order ETBF (K-ETBF) that employs only those subfilters whose window sizes are less than or equal to K. By designing K-ETBFs under the mean square error criterion for various values of K and applying them to restore noisy signals, the tradeoff between the performance and the complexity of this class of filters is examined.

• The Journal of Korean Society for Radiation Therapy
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• v.14 no.1
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• pp.35-39
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• 2002

Introduction : It is essential to input patients external contour in 3D treatment plan. We would like to see changes in depth and dose when 3D RTP is operating auto contouring when windows value (Width/Level) differs in this process. Material & Methode : We have analyzed the results with 3D RTP after CT Scanning with round CT Phantom. We have compared and analyzed MU values according to depth changes to Isocenter changing external contour and inputting random Window value. We have watched change values according to dose optimization in 4 directions(LAO, LPO, RAO, RPO), We plan 100 case for exact analyzation. We have results changing window value random to each beam in 100 cans. Result : It showed change between minimum and maximum value in 4 beam is Depth 0.26mm, MU $1.2\%$ in LAO. It showed LPO-Depth 0.13mm, MU $0.9\%$, RAO-Depth 0.2mm MU $0.8\%$, RPO-Depth 0.27mm, MU $1.1\%$ Conclusion : Maximum change in depth 0.27 mm, MU error rate is $0.12\%$ according to Window change. As we can see in these results, it seems Window value change doesn't effect in treatment. However, it seems there needs to select appropriate Window value in precise treatment.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.2
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• pp.15-21
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• 2007

In this paper, we propose a technique of a practical symbol acquisition and tracking using a low complex ADC and simple digital circuits for noncoherent asynchronous impulse-radio-based Ultra Wideband (IR-UWB) receiver based on energy detection. Compared to previous approaches of detecting an exact acquisition time that require much hardware resource, the proposed technique is to detect the target symbol by finding the symbol acquisition interval per symbol with a target symbo, thus the complexity of the complete signal processing and power consumption by ADC are reduced. To do this, we define the bit decision window (BDW) and analyze the relation between SNR, hardware resource, size of BDW and BER(Bit Error Rate). Using the results, the optimum BDW size for the minimum BER with limited hardware resource is selected. The proposed synchronization technique is verified with an aid of a simulator programmed by considering practical impulse channels.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.551-561
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• 2023

In this study, we investigated the effects of the spectral fitting window and absorption cross-section on the retrieval of the formaldehyde (HCHO) slant column density (SCD) from the direct-sun measurement of pandora spectrometer system using differential optical absorption spectroscopy (DOAS). Pandora Level 1 data observed at Yonsei University in Seoul from October 12 to 31, 2022 were used. The HCHO column density was retrieved under eight ranges including the spectral fitting window used in the Second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) and seven types of absorption cross-section composition. The spectral fitting window was selected from 336.5 to 359.0 nm with minimum residual and HCHO SCD error. When the nitrogen dioxide (NO2) absorption cross-section at 220 K was added to the cross-section composition used in the CINDI-2 campaign among seven types, the residual and HCHO SCD error were the smallest and the HCHO column density wasstably retrieved. The average HCHO SCD with the highest retrieval accuracy and the values retrieved under other conditions differed from a minimum of 4% to a maximum of 40%.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.483-492
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• 2007

Time series of conventional prediction techniques uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.4
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• pp.363-370
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• 2017

In this paper, we proposed a low-complexity speech enhancement algorithm based on a improved minima controlled recursive averaging (IMCRA) and log minimum mean square error (logMMSE). The IMCRA algorithm track the minima value of input power within buffers in local window and identify the speech presence using ratio between input power and its minima value. In this process, many number of operations are required. To reduce the number of operations of IMCRA algorithm, minima value is tracked using time-varying frequency-dependent smoothing based on speech presence probability. The proposed algorithm enhanced speech quality by 2.778%, 3.481%, 2.980% and 2.162% in 0, 5, 10 and 15dB SNR respectively and reduced computational complexity by average 9.570%.