• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.171-176
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• 1987

Wiener filtering method was applied to the dual energy imaging procedure in digital radiography(D.R.). A linear scanning photodiode arrays with 1024 elements(0.6mm H 1.3mm pixel size) were used to obtain chest images in 0.7 sec. For high energy image acquisition, X-ray tube was set at 140KVp, 100mA with a rare-earth phosphor screen. Low energy image was obtained with X-ray tube setting at 70KVp, 150mA. These measured dual energy images are represented in the vector matrix notation as a linear discrete model including the additive random noise. Then, the object images are restored in the minimum mean square error sense using Wiener filtering method in the transformed domain. These restored high and low energy images are used for computation of the basis image decomposition. Then the basis images are linearly combined to produce bone or tissue selective images. Using this process, we could improve the signal to noise ratio characteristics in the material selective images.

• Journal of Applied Reliability
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• v.17 no.2
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• pp.129-135
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• 2017

Purpose: This article provides step-stress accelerated degradation test (ADT) plans based on the Wiener process. Method: Step-stress levels and the stress change times are determined based on the D-optimality criteria to develop test plans. Further, a simple grid search method is provided for obtaining the optimal test plan. Results: Based on the solution procedure, ADT plans which include the stress levels and change times are developed for conducting the reliability test. Conclusion: Optimal step-stress ADT plans are provided for the case where the number of measurements is small.

• The Journal of the Acoustical Society of Korea
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• v.27 no.4
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• pp.191-199
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• 2008

In this paper, we propose a MWF-PMC noise processing method which enhances the input speech by using Mel-warped Wiener Filtering (MWF) at pre-processing stage and compensates the recognition model by using PMC (Parallel Model Combination) at post-processing stage for speech recognition in noisy environments. The PMC uses the residual noise extracted from the silence region of enhanced speech at pre-processing stage to compensate the clean speech model and thus this method is considered to improve the performance of speech recognition in noisy environments. For recognition experiments we dew.-sampled KLE PBW (Phoneme Balanced Words) 452 word speech data to 8kHz and made 5 different SNR levels of noisy speech, i.e., 0dB. 5dB, 10dB, 15dB and 20dB, by adding Subway, Car and Exhibition noise to clean speech. From the recognition results, we could confirm the effectiveness of the proposed MWF-PMC method by obtaining the improved recognition performances over all compared with the existing combined methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.261-269
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• 2013

In this paper, the evoked potential(EP) was represented by additive delay model to comply with the variational noisy response of stimulus-event synchronization. The hybrid method of delay compensated-Wiener filtered-ensemble averaging(DWEA) was proposed to enhance the EP signal distortion occurred during averaging procedure due to synchronization timing mismatch. The performance of DWEA has been tested by surrogated simulation, which is composed of synthesized arbitrary delay and arbitrary level of added noise. The performance of DWEA is better than those of Wiener filtered-ensemble averaging and of conventional ensemble averaging. DWEA is endurable up to added noise gain of 7 for 10 % mean square error limit. Throughout the experimentation observation, it has been demonstrated that DWEA can be applied to enhance the evoked potential having the synchronization mismatch with added noise.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.524-529
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• 2004

In this paper, We described the method which developed the optimal control system for air separation unit to change production rates frequently and rapidly. Control models of the process were developed from actual plant data using subspace identification method which is developed by many researchers in resent years. The model consist of a series connection of linear dynamic block and static nonlinear block (Wiener model). The model is controlled by model based predictive controller. In MPC the input is calculated by on-line optimization of a performance index based on predictions by the model, subject to possible constraints. To calculate the optimal the performance index, conditions are expressed by LMI(Linear Matrix Inequalities).In order to access at the Bailey DCS system, we applied the OPC server and developed the Client program. The OPC sever is a device which can access Bailey DCS system.The Client program is developed based on the Matlab language for easy calculation,data simulation and data logging. Using this program, we can apply the optimal input to the DCS system at real time.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1641-1648
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• 2017

The main purpose of this paper is to compare the performances of various filters with color images to remove the noise. Furthermore, we suggest a modified de-noising process by the transformation of color model from RGB to another color models, such as HSV and $YC_BC_R$, to improve the quality of de-noising methods encompassing Median, Wiener, and Mean filters. Neither the performance comparison of the de-noising filters with color images nor the converting the color model for better de-noise on the degraded images haven't been performed before. Inspired to make improvements, we conduct experiments with new de-noising process on color images. The result of the experiments is shown that it could assist on certain filters being more reliable techniques.

• Structural Engineering and Mechanics
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• v.45 no.1
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• pp.53-67
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• 2013

This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively large-dimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the Levenberg-Marquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.

• Journal of Advanced Navigation Technology
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• v.13 no.3
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• pp.372-377
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• 2009

This paper evaluates the performance of three methods for predicting resources requested by mobile's calls and a call admission algorithm based on these predicting methods respectively in wireless networks. The first method is based on Wiener prediction model and the second method is based on the MMOSPRED algorithm and the third method is based on the neural network. The proposed call admission algorithm is based on prioritized handoff call in resource allocation. The resources for future handoff calls are therefore predicted and reserved in advance, and then new calls are admitted as long as the remaining resources are sufficient. We compare their performances in terms of prediction error, new call blocking and handoff dropping probabilities by simulation. Results show that the CAC based on Wiener prediction model performs favorably due to exact resources prediction.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.264-270
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• 2016

In this paper, we proposed a method which can detect pedestrians from CCTV video and estimate the height of the detected objects. We separate the foreground using Gaussian mixture model and the pedestrian is detected using the conditions such as the width-height ratio and the size of the candidate objects. In order to obtain the optimal model for estimating the height of pedestrian, we get many training data from the pedestrian whose height is known. Using these training data, we designed optimal Wiener height estimator and used to estimate the height of pedestrians. The height of the pedestrian at various distance is estimated and the accuracy is evaluated. In the experimental results, proposed method shows that it can estimate the height of pedestrian for various positions effectively.

• The KIPS Transactions:PartC
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• v.12C no.1 s.97
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• pp.69-76
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• 2005

To effectively use limited resources in wireless cellular networks it is necessary to predict exactly the amount of resources required by handoff calls at a future time. In this paper we propose a method which predicts the amount of resources needed by handoff calls more accurately than the existing method based on Wiener processes. The existing method uses the current demands to predict future demands. Although this method is much simpler than using traffic information from neighbor cells, its prediction error increases as time elapses, leading to waste of wireless resources. By using an exponential parameter to decrease the magnitude of error over time, we show in simulation how to outperform the existing method in resource utilization as well as in prediction of resource demands.