• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.10
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• pp.985-993
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• 2012

In this paper, a new method is proposed to estimate the sound pressure generated from gasoline direct injection (GDI) engine. There are many noise sources as much as components in GDI engine. Among these components, fuel pump, fuel injector, fuel rail, pressure pump and intake/exhaust manifolds are major components generated from top of the engine. In order to estimate the contribution of these components to engine noise, the total sound pressure at the front of the engine is estimated by using airborne source quantification (ASQ) method. Airborne source quantification method requires the acoustic source volume velocity of each component. The volume velocity has been calculated by using the inverse method. The inverse method requires many tests and has ill-condition problem. This paper suggested a method to obtain volume velocity directly based on the direct measurement of sound intensity and particle velocity. The method is validated by using two known monopole sources installed at the anechoic chamber. Finally the proposed method is applied to the identification and contribution of noise sources caused by the GDI components of the test engine.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.407-412
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• 2019

In this paper, we propose a technique for processing of speech recognition in korean words. Speech recognition is a technology that converts acoustic signals from sensors such as microphones into words or sentences. Most foreign languages have less difficulty in speech recognition. On the other hand, korean consists of vowels and bottom consonants, so it is inappropriate to use the letters obtained from the voice synthesis system. That improving the conventional structure speech recognition can the correct words recognition. In order to solve this problem, a new algorithm was added to the existing speech recognition structure to increase the speech recognition rate. Perform the preprocessing process of the word and then token the results. After combining the result processed in the Levenshtein distance algorithm and the hashing algorithm, the normalized words is output through the consonant comparison algorithm. The final result word is compared with the standardized table and output if it exists, registered in the table dose not exists. The experimental environment was developed by using a smartphone application. The proposed structure shows that the recognition rate is improved by 2% in standard language and 7% in dialect.

• Journal of Platform Technology
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• v.9 no.3
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• pp.44-51
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• 2021

In general, golden time refers to the most important time in the initial response to accidents such as saving lives or extinguishing fires. The golden time varies from disaster to disaster, but is aimed at five minutes in terms of fire and first aid. However, for the actual site, the average dispatch time for ambulances is 9 minutes and the average transfer time is 17.6 minutes, which is quite large compared to the golden time. There are various causes for this delay, but the main cause is traffic jams. In order to solve the problem, the government has established emergency car concession obligations and secured golden time to prioritize ambulances in places with the highest accident rate, but it is not a solution in rush hour when traffic is increasing rapidly. Therefore, this paper proposed a deep learning-based emergency vehicle priority signal system using collected sound data by installing sound sensors on traffic lights and conducted an experiment to classify frequency signals that differ depending on the distance of the emergency vehicle.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.845-853
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• 2020

In this paper, we propose a method for classifying environmental sound for selective noise cancellation in industrial sites. Noise in industrial sites causes hearing loss in workers, and researches on noise cancellation have been widely conducted. However, the conventional methods have a problem of blocking all sounds and cannot provide the optimal operation per noise type because of common cancellation method for all types of noise. In order to perform selective noise cancellation, therefore, we propose a method for environmental sound classification based on deep learning. The proposed method uses new sets of acoustic features consisting of temporal and statistical properties of Mel-spectrogram, which can overcome the limitation of Mel-spectrogram features, and uses convolutional neural network as a classifier. We apply the proposed method to five-class sound classification with three noise classes and two non-noise classes. We confirm that the proposed method provides improved classification accuracy by 6.6% point, compared with that using conventional Mel-spectrogram features.

• Journal of the Korean Society of Safety
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• v.35 no.6
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• pp.32-45
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• 2020

The problem of determining the discharge rates of gases from pressurized vessels through pressure relief devices was dealt with comprehensively. First, starting from basic fluid flow equations, detailed modeling procedures were presented for isentropic nozzle flows and frictional flows in a pipe, respectively. Meanwhile, physical explanations were given to choking phenomena in terms of the acoustic velocity, elucidating the widespread use of Mach numbers in gas flow models. Frictional flows in a pipe were classified into adiabatic, isothermal, and general flows according to the heat transfer situation around the pipe, but the adiabatic flow model was recommended suitable for gas discharge through pressure relief devices. Next, for the isentropic nozzle flow followed by adiabatic frictional flow in the pipe, two equations were established for two unknowns that consist of the Mach numbers at the inlet and outlet of the pipe, respectively. The relationship among the ratio of downstream reservoir pressure to upstream pressure, mass flux, and total frictional loss coefficient was shown in various forms of MATLAB 2-D plot, 3-D surface plot and contour plot. Then, the profiles of gas properties and velocity in the pipe section were traced. A method to quantify the relationship among the pressure head, velocity head, and total friction loss was presented, and was used in inferring that the rapid increase in gas velocity in the region approaching the choked flow at the pipe outlet is attributed to the conversion of internal energy to kinetic energy. Finally, the Levenspiel chart reproduced in this work was compared with the Lapple chart used in API 521 Standatd.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.175-182
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• 2022

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.

• The Journal of the Acoustical Society of Korea
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• v.42 no.1
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• pp.1-6
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• 2023

Sound radiated from a structure in vibration is an important physical characteristic to evaluate vibro-acoustic problem. Although sound radiation power can be typically obtained by intensity measurement, long measuring time and strict measuring condition remain difficult. As an alternative method, simulation-based estimation can be taken into account and its accuracy is known to be acceptable. However, difficulty still lies in that specialized softwares may be necessary to obtain sound radiation power and radiation efficiency. In this context, this study suggests two methods using an ordinary FE method to calculate sound radiation power. They are well-known reciprocity technique and p-p method, which are basically test methods. It is shown that either method can practically estimate sound radiation in the frame of conventional Finite Element Method (FEM). The methods and their corresponding limit are discussed with some results.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.161-167
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• 2023

Recently, floor impact sound has become a serious social problem in Korea. There is an increasing need to improve floor impact sound performance using floor covering installed on the floor of apartment houses. KS F ISO 717-2 and KS F 2863 require measurement under conditions in which the resilient material is not installed. But most apartment houses in Korea install resilient materials to reduce floor imapct sound. The performance evaluation method of floor covering should provide reduced performance for use by residents of apartment houses with resilient materials. Therefore, this paper proposes a reduction performance evaluation under the conditions in which a resilient material is installed to verify the performance of floor covering.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.252-265
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• 2022

Subsurface velocity is critical for the accurate resolution geological structures. The estimation of acoustic impedance is also critical, as it provides key information regarding the reservoir properties. Therefore, researchers have developed various inversion approaches for the estimation of reservoir properties. The Markov chain Monte Carlo method, which is a stochastic method, has advantages over the deterministic method, as the stochastic method enables us to attenuate the local minima problem and quantify the uncertainty of inversion results. Therefore, the Markov chain Monte Carlo inversion method has been applied to various kinds of geophysical inversion problems. However, studies on the Markov chain Monte Carlo inversion are still very few compared with deterministic approaches. In this study, we reviewed various types of reversible jump Markov chain Monte Carlo applications and explained the key concept of each application. Furthermore, we discussed future applications of the stochastic method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.1-12
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• 2011

Large-sized multi-touch screen is usually made using infrared rays. That is because it has technical constraints or cost problems to make the screen with the other ways using such as existing resistive overlays, capacitive overlay, or acoustic wave. Using infrared rays to make multi-touch screen is easy, but is likely to have technical limits to be implemented. To make up for these technical problems, two other methods were suggested through Surface project, which is a next generation user-interface concept of Microsoft. One is Frustrated Total Internal Reflection (FTIR) which uses infrared cameras, the other is Diffuse Illumination (DI). FTIR and DI are easy to be implemented in large screens and are not influenced by the number of touch points. Although FTIR method has an advantage in detecting touch-points, it also has lots of disadvantages such as screen size limit, quality of the materials, the module for infrared LED arrays, and high consuming power. On the other hand, DI method has difficulty in detecting touch-points because of it's structural problems but makes it possible to solve the problem of FTIR. In this thesis, we study the algorithms for effectively correcting the distort phenomenon of optical lens, and image processing algorithms in order to solve the touch detecting problem of the original DI method. Moreover, we suggest calibration algorithms for improving the accuracy of multi-touch, and a new tracking technique for accurate movement and gesture of the touch device. To verify our approaches, we implemented a table-based multi touch screen.