• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.845-850
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• 2023

In this paper, we propose a method for diagnosing ball bearing vibration using transfer learning. STFT, which can analyze vibration signals in time-frequency, was used as input to CNN to diagnose failures. In order to rapidly learn CNN-based deep artificial neural networks and improve diagnostic performance, we proposed a transfer learning-based deep learning learning technique. For transfer learning, the feature extractor and classifier were selectively learned using a VGG-based image classification model, the data set for learning was publicly available ball bearing vibration data provided by Case Western Reserve University, and performance was evaluated by comparing the proposed method with the existing CNN model. Experimental results not only prove that transfer learning is useful for condition diagnosis in ball bearing vibration data, but also allow other industries to use transfer learning to improve condition diagnosis.

• The Journal of the Acoustical Society of Korea
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• v.14 no.4
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• pp.21-28
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• 1995

In this paper an intonation conversion method is presented which provides the basic study on converting the original speech into the artificially intoned one. This method employs the other speaker's excitation signals as intonation information and the original vocal tract spectra, which are warped with the other speaker's ones by using DTW. as vocal features, and intonation converted speech signals are synthesized through short-time inverse Fourier transform(STIFT) of their product. To evaluate the intonation converted speech by this method, we collect Korean single vowels and sentences spoken by 30 males and compare fundamental frequency contours spectrograms, distortion measures and MOS test between the original speech and the converted one. The result shows that this method can convert and speech into the intoned one of the other speaker's.

• Smart Media Journal
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• v.6 no.4
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• pp.9-16
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• 2017

The railway point machine is an especially important component that changes the traveling direction of a train. Failure of the point machine may cause a serious railway accident. Therefore, early detection of failures is important for the management of railway condition monitoring systems. In this paper, we propose a noise-robust anomaly detection method in railway condition monitoring systems using sound data. First, we extract feature vectors from the spectrogram image of sound signals and convert it into modulation feature to ensure robust performance, and lastly, use the support vector machine (SVM) as an early anomaly detector of railway point machines. By the experimental results, we confirmed that the proposed method could detect the anomaly conditions of railway point machines with acceptable accuracy even under noisy conditions.

• Journal of Environmental Science International
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• v.22 no.10
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• pp.1263-1271
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• 2013

Graphene oxide (GO)-titania composites have emerged as an attractive heterogeneous photocatalyst that can enhance the photocatalytic activity of $TiO_2$ nanoparticles owing to their potential interaction of electronic and adsorption natures. Accordingly, $TiO_2$-GO mixtures were synthesized in this study using a simple chemical mixing process, and their heterogeneous photocatalytic activities were investigated to determine the degradation of airborne organic pollutants (benzene, ethyl benzene, and o-xylene (BEX)) under different operational conditions. The Fourier transform infrared spectroscopy results demonstrated the presence of GO for the $TiO_2$-GO composites. The average efficiencies of the $TiO_2$-GO mixtures for the decomposition of each component of BEX determined during the 3-h photocatalytic processes were 26%, 92%, and 96%, respectively, whereas the average efficiencies of the unmodified $TiO_2$ powder were 3%, 8%, and 10%, respectively. Furthermore, the degradation efficiency of the unmodified $TiO_2$ powder for all target compounds decreased during the 3-h photocatalytic processes, suggesting a potential deactivation even during such a short time period. Two operational conditions (air flow entering into the air-cleaning devices and the indoor pollution levels) were found to be important factors for the photocatalytic decomposition of BEX molecules. Taken together, these results show that a $TiO_2$-GO mixture can be applied effectively for the purification of airborne organic pollutants when the operating conditions are optimized.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.5
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• pp.638-647
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• 2011

Various approaches directly using vibrations of speakers have been suggested to effectively display the aural information such as the music to the hearing-impaired or the deaf. However, in these approaches, the human can't sense the frequency information over the maximum perceivable vibro-tactile frequency (around 1kHz). Therefore, in this study, an approach via spectral modulation of compressing the high frequency audio information into perceivable vibro-tactile frequency domain and outputting the modulated signals through the designated speakers is proposed. Then it is shown, through simulations of using Short-Time Fourier Transform (STFT) with Hanning windows and through preliminary experiments of using the vibro-tactile display testbed which is built and interfaced with a notebook PC, that the modulated signal of a natural sound composing sounds of a frog, a bird, and a water stream could produce the noise-free signal suitable enough for vibro-tactile speakers without causing Significant interfering disturbances, Lastly, for three different combinations of information provided to the subject, that is, i) with only video image, ii) with video image along with the modulated vibro-tactile stimuli as proposed in this study to the forearm of the subject, and iii) with video image along with full audio information, the effects to the human sense of reality and his emotion to given audio-video clips including various sounds and images are investigated and compared. It is shown from results of those experiments that the proposed method of providing modulated vibro-tactile stimuli along with the video images to the human has very high feasibility to transmit pseudo-aural sense to the human.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.88-94
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• 2023

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2511-2517
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• 2009

Recently, a FMCW radar is used for the various purposes in the short range detection and tracking of targets. The main advantages of a FMCWradar are the comparative simplicity of implementation and the low peak power transmission characterizing the very low probability of signal interception. Since it uses the frequency modulated continuous wave for transmission and demodulation, the received beat frequency represents the range and Doppler information of targets. Detection and extraction of useful information from targets are performed in this beat frequency domain. Therefore, the resolution and accuracy in the estimation of a beat spectrum are very important. However, using the conventional FFT estimation method, the high resolution spectrum estimation with a low sidelobe level is not possible if the acquisition time is very short in receiving target echoes. This kind of problems deteriorates the detection performance of adjacent targets having the large magnitude differences in return echoes and also degrades the reliability of the extracted information. Therefore, in this paper, the model parameter estimation methods such as autoregressive and eigenvector spectrum estimation are applied to mitigate these problems. Also, simulation results are compared and analyzed for further improvement.

• Publications of The Korean Astronomical Society
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• v.27 no.5
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• pp.399-409
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• 2012

As a part of the short-period variability survey (SPVS) at Bohyunsan Optical Astronomy Observatory, we obtained time-series BV CCD images in the region of Cyg OB3 association centered on the open cluster NGC 6871. The observations were performed for 18 nights from September 5, 2008 to September 1, 2009. We found 15 short-period variable stars in the region. They are ${\delta}$ Scuti type stars belonging to the local spiral arm, Orion spur. Among them, only two stars were previously known, and the rest are newly discovered ones. In this paper, we have performed a multiple-frequency analysis to determine frequencies of the 15 ${\delta}$ Scuti type stars, using the discrete Fourier transform and linear least-square fitting methods. One of the newly discovered variable stars is a double-mode ${\delta}$ Scuti type star with the fundamental and the first overtone modes, and two are high amplitude ${\delta}$ Scuti stars.

• Proceedings of the Optical Society of Korea Conference
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• 2003.07a
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• pp.60-61
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• 2003

A new approach to reduce the computation time of genetic algorithm (GA) for making binary phase holograms is described. Synthesized holograms having diffraction efficiency of 75.8% and uniformity of 5.8% are proven in computer simulation and experimentally demonstrated. Recently, computer-generated holograms (CGHs) having high diffraction efficiency and flexibility of design have been widely developed in many applications such as optical information processing, optical computing, optical interconnection, etc. Among proposed optimization methods, GA has become popular due to its capability of reaching nearly global. However, there exits a drawback to consider when we use the genetic algorithm. It is the large amount of computation time to construct desired holograms. One of the major reasons that the GA' s operation may be time intensive results from the expense of computing the cost function that must Fourier transform the parameters encoded on the hologram into the fitness value. In trying to remedy this drawback, Artificial Neural Network (ANN) has been put forward, allowing CGHs to be created easily and quickly (1), but the quality of reconstructed images is not high enough to use in applications of high preciseness. For that, we are in attempt to find a new approach of combiningthe good properties and performance of both the GA and ANN to make CGHs of high diffraction efficiency in a short time. The optimization of CGH using the genetic algorithm is merely a process of iteration, including selection, crossover, and mutation operators [2]. It is worth noting that the evaluation of the cost function with the aim of selecting better holograms plays an important role in the implementation of the GA. However, this evaluation process wastes much time for Fourier transforming the encoded parameters on the hologram into the value to be solved. Depending on the speed of computer, this process can even last up to ten minutes. It will be more effective if instead of merely generating random holograms in the initial process, a set of approximately desired holograms is employed. By doing so, the initial population will contain less trial holograms equivalent to the reduction of the computation time of GA's. Accordingly, a hybrid algorithm that utilizes a trained neural network to initiate the GA's procedure is proposed. Consequently, the initial population contains less random holograms and is compensated by approximately desired holograms. Figure 1 is the flowchart of the hybrid algorithm in comparison with the classical GA. The procedure of synthesizing a hologram on computer is divided into two steps. First the simulation of holograms based on ANN method [1] to acquire approximately desired holograms is carried. With a teaching data set of 9 characters obtained from the classical GA, the number of layer is 3, the number of hidden node is 100, learning rate is 0.3, and momentum is 0.5, the artificial neural network trained enables us to attain the approximately desired holograms, which are fairly good agreement with what we suggested in the theory. The second step, effect of several parameters on the operation of the hybrid algorithm is investigated. In principle, the operation of the hybrid algorithm and GA are the same except the modification of the initial step. Hence, the verified results in Ref [2] of the parameters such as the probability of crossover and mutation, the tournament size, and the crossover block size are remained unchanged, beside of the reduced population size. The reconstructed image of 76.4% diffraction efficiency and 5.4% uniformity is achieved when the population size is 30, the iteration number is 2000, the probability of crossover is 0.75, and the probability of mutation is 0.001. A comparison between the hybrid algorithm and GA in term of diffraction efficiency and computation time is also evaluated as shown in Fig. 2. With a 66.7% reduction in computation time and a 2% increase in diffraction efficiency compared to the GA method, the hybrid algorithm demonstrates its efficient performance. In the optical experiment, the phase holograms were displayed on a programmable phase modulator (model XGA). Figures 3 are pictures of diffracted patterns of the letter "0" from the holograms generated using the hybrid algorithm. Diffraction efficiency of 75.8% and uniformity of 5.8% are measured. We see that the simulation and experiment results are fairly good agreement with each other. In this paper, Genetic Algorithm and Neural Network have been successfully combined in designing CGHs. This method gives a significant reduction in computation time compared to the GA method while still allowing holograms of high diffraction efficiency and uniformity to be achieved. This work was supported by No.mOl-2001-000-00324-0 (2002)) from the Korea Science & Engineering Foundation.