• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.41-48
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• 2012

In this paper, we propose a method of edge detection for noisy image. The proposed method uses a progressive filter for noise reduction and a Sobel operator for edge detection. The progressive filter combines a median filter and a modified rational filter. The proposed method for noise reduction adjusts rational filter direction according to an edge in the image which is obtained by median filtering. Our method effectively attenuates the noise while preserving the image details. Edge detection is performed by a Sobel operator. This operator can be implemented by integer operation and is therefore relatively fast. Our proposed method not only preserves edge, but also reduces noise in uniform region. Thus, edge detection is well performed. Our proposed method could improve results using further developed Sobel operator. Experimental results show that our proposed method has better edge detection with correct positions than those by existing median and rational filtering methods for noisy image.

• Journal of Preventive Medicine and Public Health
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• v.22 no.3 s.27
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• pp.337-354
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• 1989

In order to evaluate the noisy environment and hearing loss of workers served in noisy working environment, the author investigated 212 manufacturing industries located in Ulsan Industrial District that could be observed for 3 successive years from 1986 to 1988. The obtained results were as follows: 1. There was increased tendency in the number of workers served in noisy working environment and that of examined of hearing loss for three years. 2. In the noise level of working environment, the number of industries less than 89dB(A) was increased every year, while more than 90dB(A) was in decreasing tendency. 3. Mean hearing loss by frequency was the most prominent in 4,000Hz, the level of hearing loss was in increasing tendency yearly, and that of left eat was higher than right ear in almost all type of industry. 4. In 1986, the level of hearing loss by type of industry was highest in manufacture of electric and electronic, and followed by paper and plywood, and metal products in right ear: that was in the order of manufacture of electric and electronic, metal products and textile products in left ear. In 1987, that was in the order of manufacture of metal products, machinery and others in right ear, and metal products, machinery and food stuff in left ear in 1988, manufacture of others, food stuff and machinery in both ear. 5. In hearing loss by service duration, right ear of 5-9 years group was higher than that of less than 5 years in 1987, whereas in 1988, the longer in service duration, the higher in the level of hearing loss in both ear. 6. In 1986, 1987 and 1988, the prevalence rate of noise-induced hearing loss were showed increasing tendency as 0.4% ,0.8% and 1.5% , respectively, and manufacture of textile products was highest(1.0%) in 1986, machinery(1.2%) in 1987 and others(2.8%) in 1988. 7. The proportion of grade E in early loss index were 76.1% (1986), 78.2% (1987) and 80.5% (1988) in left ear, 75.9% (1986), 76.4% (1987) and 75.9% (1988) in right ear.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.332-339
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• 2007

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. The noise of a range hood is distributed over a wide range frequency. Lastly, we intented to propose the proper method measuring sound power level on each electric home appliance.

• 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.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.2
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• pp.42-53
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• 2014

Robust voice recognition (RVR) is essential for a robot to communicate with people. One of the main problems with RVR for robots is that robots inevitably real environment noises. The noise is captured with strong power by the microphones, because the noise sources are closed to the microphones. The signal-to-noise ratio of input voice becomes quite low. However, it is possible to estimate the noise by using information on the robot's own motions and postures, because a type of motion/gesture produces almost the same pattern of noise every time it is performed. In this paper, we propose an RVR system which can robustly recognize voice by adults and children in noisy environments. We evaluate the RVR system in a communication robot placed in a real noisy environment. Voice is captured using a wireless microphone. Navigation Strategy is shown Obstacle detection and local map, Design of Goal-seeking Behavior and Avoidance Behavior, Fuzzy Decision Maker and Lower level controller. The final hypothesis is selected based on posterior probability. We then select the task in the motion task library. In the motion control, we also integrate the obstacle avoidance control using ultrasonic sensors. Those are powerful for detecting obstacle with simple algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.2
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• pp.137-146
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• 2019

This study proposed a new technique to control of mobile robot based on voice command for (Human-Robot Cooperative operation in manufacturing precess). High performance voice recognition and control system was designed In this paper for smart factory. robust voice recognition is essential for a robot to communicate with people. One of the main problems with voice recognition robots is that robots inevitably effects real environment including with noises. The noise is captured with strong power by the microphones, because the noise sources are closed to the microphones. The signal-to-noise ratio of input voice becomes quite low. However, it is possible to estimate the noise by using information on the robot's own motions and postures, because a type of motion/gesture produces almost the same pattern of noise every time it is performed. In this paper, we describe an robust voice recognition system which can robustly recognize voice by adults and students in noisy environments. It is illustrated by experiments the voice recognition performance of mobile robot placed in a real noisy environment.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.35-41
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• 2018

In this paper, we propose a swarm-based possibilistic c-means(PCM) algorithm in order to overcome the problems of PCM, which are sensitiveness of clustering performance due to initial cluster center's values and producing coincident or close clusters. To settle the former problem of PCM, we adopt a swam-based global optimization method which can be provided the optimal initial cluster centers. Furthermore, to settle the latter problem of PCM, we design an adaptive thresholding model based on the optimized cluster centers that yields preliminary clustered and un-clustered dataset. The preliminary clustered dataset plays a role of preventing coincident or close clusters and the un-clustered dataset is lastly clustered by PCM. From the experiment, the proposed method obtains a better performance than other PCM algorithms on a simulated magnetic resonance(MR) brain image dataset which is corrupted by various noises and bias-fields.

• Journal of Preventive Medicine and Public Health
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• v.30 no.4 s.59
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• pp.815-829
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• 1997

This study was performed to assist the employer to establish the effective program for hearing conservation of noisy industry. The study subjects were health care managers of an industry and the study industries were devided into two groups(Group I, 37 industries; have the workers diagnosed as noise-induced hearing loss, Group II, 41 industries; not have the workers diagnosed as noise-induced hearing loss) and the question method carried out through the face to face interview. The contents of questionnaire for OSHA's hearing conservation program(HCP) consisted of seven components: 5 questions of monitoring of employee noise exposures(component 1), 6 questions of the institution of engineering, work practice, and administrative controls for excessive noise(component 2), 8 questions of the provision of each overexposed employee with an individually fitted hearing protector with an adequate noise reduction rating(component 3), 14 questions of employee training and education regarding noise hazards and protection measures(component 4), 9 questions of baseline and annual audiometry(component 5), 3 questions of procedures for preventing further occupational hearning loss by an employee whenever such an event has been identified(component 6), and 1 question of recording keeping(component 7), thus total numbers of questions was 46. The numbers of statistially significant difference(p<0.05) between two groups were 2(25.0%) among 8 questions of component 3, 10(71.4%) among 14 questions of component 4, 3(33.3%) among 8 questions of component 5, 2(6.7%) among 3 questions of component 6, and 17(37.0%) among total 46 questions of questionnaire. Above results showed that the level of HCP acceptance in group I was lower than in group II. Thus employer's understanding about HCP should be precede for the effective hearing conservation program of employee and the adequate hearing protector, training and education, baseline and annual audiometry, and procedures for preventing further occupational hearning loss for hearing conservation would be more emphasized.

• Korean Chemical Engineering Research
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• v.46 no.5
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• pp.949-957
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• 2008

Estimating the first order plus time delay model on the basis of the step responses has been widely used in industry for the tuning of PID controllers. Even though various model identification methods from simple graphical approaches to complicated approaches based on least squares method have been proposed, simple approaches to incorporate noisy step responses are rarely available. In this research, we will compare and analyze recent approaches using the integrals of the step responses and develop an improved identification method to incorporate real situations more effectively.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.402-411
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• 2020

Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.