• The Journal of Korean Academic Society of Nursing Education
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• v.3 no.2
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• pp.150-162
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• 1997

The purpose of this study is to examine hospital noise level and discomfort due to noise. The subjects were 156 patients from University hospital in Taegu. The data was collected from April 10 to May 14, 1997. The collected data were analyzed by SPSS program using percentage, paired t-test, ANOVA, and Pearson Correlation Coefficient. The results were as follows ; The mean score of noise level was 1.62. There was no statistically significant difference in noise level between day and night. Patients perceived higher noise in the categories of conversation of visitors, conversation of care providers, noise of air conditioners, and the conversation of nearby patients than others during the day. Patients perceived higher noise in the categories of noise of air conditioners, conversation of visitors, conversation of care providers, and telephone ringing than others during the night. There were no statistically significant differences in noise level among the 4 wards during the day or night. Discomfort was due to the forementioned noise, categories of high scores were sleep disturbed, irritated, not so bad or not noisy, and noisy. To avoid noise, the subjects coped by putting on a quilt, going out, sleeping, opening or closing the window or door, and plugging ears. These results indicated that hospital noise have a negative influence on patients' health. So noise levels should be reduced in hospitals.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.109-112
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• 1999

Image segmentation is the critical first step in image information extraction for computer vision systems. Clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are divided from the fuzzy c-means(FCM) algorithm. The FCM algorithm uses fie probabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belonging or compatibility. Moreover, the FCM algorithm has considerable trouble under noisy environments in the feature space. Recently, a possibilistic approach to clustering(PCM) for solving above problems was proposed. In this paper, we used the PCM for color image segmentation. This approach differs from existing fuzzy clustering methods for color image segmentation in that the resulting partition of the data can be interpreted as a possibilistic partition. So, the problems in the FCM can be solved by the PCM. But, the clustering results by the PCM are not smoothly bounded, and they often have holes. The region growing was used as a postprocessing after smoothing the noise points in the pixel seeds. In our experiments, we illustrate that the PCM us reasonable than the FCM in noisy environments.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.4
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• pp.335-346
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• 2009

As the economy has grown and the main industry in Korea has been changed from secondary industry to tertiary industry, the importance of indoor environment has been a matter of common concern, in which one of the main concerns is to improve the indoor acoustic conditions. However, even though this is required more than before, there are no measures to protect the human being from the noise of electric home appliances. This is owing to the absence of the data about sound power level of electric home appliances. So, we investigate the sound power level of them and analyze the acoustical characteristics of each one. First, we tried to investigate the sound power measurement method of each electric home appliance. After it we test the sound power level of them. From the survey, we can know that the vacuum cleaner is the most noisy electric home appliance, and the refrigerator is the least noisy one. This results will help us predict the indoor noise level using the basic data of sound power level.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.959-967
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• 2008

The purpose of this paper is to suggest a discrete feature vector, robust in various levels of noisy environment and inexpensive in computation, for detection of speech segments and is to show such properties of the feature with real speech data. The suggested feature is one dimensional vector which represents slope of short term energies and is discretized into three values to reduce computational burden of computations in HMM. In experiments with speech data, the method with the suggested feature vector showed good performance even in noisy environments.

• Smart Structures and Systems
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• v.5 no.6
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• pp.681-697
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• 2009

Limited, noisy data in vibration testing is a hindrance to the development of structural damage detection. This paper presents a method for optimizing sensor placement and performing damage detection using finite element model updating. Sensitivity analysis of the modal flexibility matrix determines the optimal sensor locations for collecting information on structural damage. The optimal sensor locations require the instrumentation of only a limited number of degrees of freedom. Using noisy modal data from only these limited sensor locations, a method based on model updating and changes in the flexibility matrix successfully determines the location and severity of the imposed damage in numerical simulations. In addition, a steel cantilever beam experiment performed in the laboratory that considered the effects of model error and noise tested the validity of the method. The results show that the proposed approach effectively and robustly detects structural damage using limited, optimal sensor information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.670-684
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• 2024

3D Cross-Modal Retrieval (3DCMR) is a task that retrieves 3D objects regardless of modalities, such as images, meshes, and point clouds. One of the most prominent methods used for 3DCMR is the Cross-Modal Center Loss Function (CLF) which applies the conventional center loss strategy for 3D cross-modal search and retrieval. Since CLF is based on center loss, the center features in CLF are also susceptible to subtle changes in hyperparameters and external inferences. For instance, performance degradation is observed when the batch size is too small. Furthermore, the Mean Squared Error (MSE) used in CLF is unable to adapt to changes in batch size and is vulnerable to data variations that occur during actual inference due to the use of simple Euclidean distance between multi-modal features. To address the problems that arise from small batch training, we propose a Noisy Center Loss (NCL) method to estimate the optimal center features. In addition, we apply the simple Siamese representation learning method (SimSiam) during optimal center feature estimation to compare projected features, making the proposed method robust to changes in batch size and variations in data. As a result, the proposed approach demonstrates improved performance in ModelNet40 dataset compared to the conventional methods.

• Geophysics and Geophysical Exploration
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• v.10 no.1
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• pp.77-88
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• 2007

Following a successful bathymetric mapping demonstration in a previous study, the potential of airborne EM for seafloor characterisation has been investigated. The sediment thickness inferred from 1D inversion of helicopter-borne time-domain electromagnetic (TEM) data has been compared with estimates based on marine seismic studies. Generally, the two estimates of sediment thickness, and hence depth to resistive bedrock, were in reasonable agreement when the seawater was ${\sim}20\;m$ deep and the sediment was less than ${\sim}40\;m$ thick. Inversion of noisy synthetic data showed that recovered models closely resemble the true models, even when the starting model is dissimilar to the true model, in keeping with the uniqueness theorem for EM soundings. The standard deviations associated with shallow seawater depths inferred from noisy synthetic data are about ${\pm}5\;%$ of depth, comparable with the errors of approximately ${\pm}1\;m$ arising during inversion of real data. The corresponding uncertainty in depth-to-bedrock estimates, based on synthetic data inversion, is of order of ${\pm}10\;%$. The mean inverted depths of both seawater and sediment inferred from noisy synthetic data are accurate to ${\sim}1\;m$, illustrating the improvement in accuracy resulting from stacking. It is concluded that a carefully calibrated airborne TEM system has potential for surveying sediment thickness and bedrock topography, and for characterising seafloor resistivity in shallow coastal waters.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.2
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• pp.42-60
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• 1992

An adaptive fuzzy system can efficiently classify subimages into four categories according to image activity level for image data compression. The system estimates fuzzy rules by clustering input-output data generated from a given adaptive transform image coding process. The system encodes different images without modification and reduces side information when encoding multiple images. In the second part, a fuzzy system estimates optimal bit maps for the four subimage classes in noisy channels assuming a Gauss-Markov image model. The fuzzy systems respectively estimate the sampled subimage classification and the bit-allocation processes without a mathematical model of how outputs depend on inputs and without rules articulated by experts.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.817-823
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• 2004

This paper describes a three dimensional motion recognition algorithm and a system which adopts the algorithm for non-contact human-computer interaction. From sequence of stereos images, five feature regions are extracted with simple color segmentation algorithm and then those are used for three dimensional locus calculation precess. However, the result is not so stable, noisy, that we introduce principal component analysis method to get more robust motion recognition results. This method can overcome the weakness of conventional algorithms since it directly uses three dimensional information motion recognition.

• Journal of applied mathematics & informatics
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• v.29 no.1_2
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• pp.23-37
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• 2011

In this paper, we use a modified Tikhonov regularization method to solve the Fredholm integral equation of the first kind. Under the assumption that measured data are contaminated with deterministic errors, we give two error estimates. The convergence rates can be obtained under the suitable choices of regularization parameters and the number of measured points. Some numerical experiments show that the proposed method is effective and stable.