• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.16-22
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• 2012

In this paper, the forensic marking algorithm is proposed using psychoacoustic model II and MDCT for high-quality audio. The proposed forensic marking method, that inserts the user fingerprinting code of the audio content into the selected sub-band, in which audio signal energy is lower than the spectrum masking level. In the range of the one frame which has 2,048 samples for FFT of original audio signal, the audio forensic marking is processed in 3 sub-bands. According to the average attack of the fingerprinting codes, one frame's SNR is measured on 100% trace ratio of the collusion codes. When the lower strength 0.1 of the inserted fingerprinting code, SNR is 38.44dB. And in case, the added strength 0.5 of white gaussian noise, SNR is 19.09dB. As a result, it confirms that the proposed audio forensic marking algorithm is maintained the marking robustness of the fingerprinting code and the audio high-quality.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.27-34
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• 2011

In this paper, the novel approach for image contrast enhancement is introduced. The method is based on the unsharp mask and directional information of images. Since the unsharp mask techniques give better visual quality than the conventional sharpening mask, there are much works on image enhancement using unsharp masks. The proposed algorithm decomposes the image to several blocks and extracts directional information using DCT. From the geometric properties of the block, each block is labeled as appropriate type and processed by adaptive unsharp mask. The masking process is skipped at the flat area to reduce the noise artifact, but at the texture and edge area, the adaptive unsharp mask is applied to enhance the image contrast based on the edge direction. Experiments show that the proposed algorithm produces the contrast enhanced images with superior visual quality, suppressing the noise effects and enhancing edge at the same time.

• Journal of Broadcast Engineering
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• v.16 no.5
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• pp.846-860
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• 2011

The binaural auditory system of human has ability to differentiate the direction and the distance of the sound sources by using the information which are inter-aural intensity difference(IID), inter-aural time difference(ITD) and/or the spectral shape difference(SSD). These information is generated from the acoustical transfer of a sound source to pinna, the outer ears. We can create a virtual sound system using the information which is called Head related transfer function(HRTF). However the performance of 3D sound is not always satisfactory because of non-individual characteristics of the HRTF. In this paper, we propose the algorithm that uses human's auditory characteristics for accurate perception. To achieve this, excitation energy of HRTF, global masking threshold and loudness are applied to the proposed algorithm. Informal listening test shows that the proposed method improves the sound localization characteristics much better than conventional methods.

• Biomedical Science Letters
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• v.25 no.1
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• pp.99-106
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• 2019

Amyloid positron emission tomography (PET) allows early and accurate diagnosis in suspected cases of Alzheimer's disease (AD) and contributes to future treatment plans. In the present study, a method of implementing a diagnostic system to distinguish ${\beta}$-Amyloid ($A{\beta}$) positive from $A{\beta}$ negative with objectiveness and accuracy was proposed using a machine learning approach, such as the Principal Component Analysis (PCA) and Support Vector Machine (SVM). $^{18}F$-Florbetaben (FBB) brain PET images were arranged in control and patients (total n = 176) with mild cognitive impairment and AD. An SVM was used to classify the slices of registered PET image using PET template, and a system was created to diagnose patients comprehensively from the output of the trained model. To compare the per-slice classification, the PCA-SVM model observing the whole brain (WB) region showed the highest performance (accuracy 92.38, specificity 92.87, sensitivity 92.87), followed by SVM with gray matter masking (GMM) (accuracy 92.22, specificity 92.13, sensitivity 92.28) for $A{\beta}$ positivity. To compare according to per-subject classification, the PCA-SVM with WB also showed the highest performance (accuracy 89.21, specificity 71.67, sensitivity 98.28), followed by PCA-SVM with GMM (accuracy 85.80, specificity 61.67, sensitivity 98.28) for $A{\beta}$ positivity. When comparing the area under curve (AUC), PCA-SVM with WB was the highest for per-slice classifiers (0.992), and the models except for SVM with WM were highest for the per-subject classifier (1.000). We can classify $^{18}F$-Florbetaben amyloid brain PET image for $A{\beta}$ positivity using PCA-SVM model, with no additional effects on GMM.

• Journal of the Korean Vacuum Society
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• v.5 no.2
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• pp.139-146
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• 1996

Transparent and conducting Sb-doped $SnO_2$ thin films were prepared by rf magnetron sputtering technology. But it showed a serious damage phenomenon on the surface of as-deposited films. In order to avoid a damage caused in the substrate center and location facing to target erosion, a ring plate of masking glass was installed at 1.5 cm above target surface. The uniformity and electrical characteristic of $SnO_2$ thin films were evaluated by the control of optimal conditions in the magnetron sputtering operation such as rf power, sputtering gas pressure, and substrate temperature. In the experimental results using the operating conditions, the optimum temperature, which produced uniform and damageless films, shifted with the change of gas pressure. The rate was about $100^{\circ}C$/5 mTorr at rf power of 50 W Similarly, the optimum temperature in compensation for an increase of rf power shifted down to a proper rate.

• JSTS:Journal of Semiconductor Technology and Science
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• v.2 no.2
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• pp.93-101
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• 2002

We have proposed and fabricated two lateral type field emission diodes, poly-Si emitter by utilizing the local oxidation of silicon (LOCOS) and GaN emitter using metal organic chemical vapor deposition (MOCVD) process. The fabricated poly-Si diode exhibited excellent electrical characteristics such as a very low turn-on voltage of 2 V and a high emission current of $300{\;}\bu\textrm{A}/tip$ at the anode-to-cathode voltage of 25 V. These superior field emission characteristics was speculated as a result of strong surface modification inducing a quasi-negative electron affinity and the increase of emitting sites due to local sharp protrusions by an appropriate activation treatment. In respect, two kinds of procedures were proposed for the fabrication of the lateral type GaN emitter: a selective etching method with electron cyclotron resonance-reactive ion etching (ECR-RIE) or a simple selective growth by utilizing $Si_3N_4$ film as a masking layer. The fabricated device using the ECR-RIE exhibited electrical characteristics such as a turn-on voltage of 35 V for $7\bu\textrm{m}$ gap and an emission current of~580 nA/l0tips at anode-to-cathode voltage of 100 V. These new field emission characteristics of GaN tips are believed to be due to a low electron affinity as well as the shorter inter-electrode distance. Compared to lateral type GaN field emission diode using ECR-RIE, re-grown GaN emitters shows sharper shape tips and shorter inter-electrode distance.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.311-318
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• 2013

This paper deals with a noise reduction algorithm which uses the binary masking in the time-frequency domain. To improve speech intelligibility in noise, noise-masked speech is decomposed into time-frequency units and mask "0" is assigned to masker-dominant region removing time-frequency units where noise is dominant compared to speech. In the previous research, Gaussian mixture models were used to classify the speech-dominant region and noise-dominant region which correspond to mask "1" and mask "0", respectively. In each frequency band, data were collected and trained to build the Gaussian mixture models and detection procedure is performed to the test data where each time-frequency unit belongs to speech-dominant region or noise-dominant region. In this paper, we consider the correlation of masks in the frequency domain and propose a post-processing method which exploits the Viterbi algorithm.

• Smart Media Journal
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• v.12 no.5
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• pp.17-27
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• 2023

Recently, automatic text summarization, which automatically summarizes only meaningful information for users, is being studied steadily. Especially, research on text summarization using Transformer, an artificial neural network model, has been mainly conducted. Among various studies, the GSG method, which trains a model through sentence-by-sentence masking, has received the most attention. However, the traditional GSG has limitations in selecting a sentence to be masked based on the degree of overlap of tokens, not the meaning of a sentence. Therefore, in this study, in order to improve the quality of text summarization, we propose SbGSG (Semantic-based GSG) methodology that selects sentences to be masked by GSG considering the meaning of sentences. As a result of conducting an experiment using 370,000 news articles and 21,600 summaries and reports, it was confirmed that the proposed methodology, SbGSG, showed superior performance compared to the traditional GSG in terms of ROUGE and BERT Score.

• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.35-43
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• 2004

In this paper, we proposed a vehicle recognition and electronic toll collection system with detection and classification of vehicle identification mark and emblem as well as recognition of vehicle license plate to unman toll fee collection system or incoming/outcoming vehicles to an institution. In the proposed algorithm, we first process pre-processing step such as noise reduction and thinning from the rear side input image of vehicle and detect vehicle mark, emblem and license plate region using intensity variation informations, template masking and labeling operation. And then, we classify the detected vehicle features regions into vehicle mark and emblem as well as recognize characters and numbers of vehicle license plate using hybrid and seven segment pattern vector. To show the efficiency of the proposed algorithm, we tested it on real vehicle images of implemented vehicle recognition system in highway toll gate and found that the proposed method shows good feature detection/classification performance regardless of irregular environment conditions as well as noise, size, and location of vehicles. And also, the proposed algorithm may be utilized for catching criminal vehicles, unmanned toll collection system, and unmanned checking incoming/outcoming vehicles to an institution.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.373-380
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• 2018

We present the peridynamic dynamic fracture analysis to solve impact fracturing of multilayered glass impacted by a high-velocity object. In the most practical multilayered glass structures, main layers are glued by thin elastic masking films. Thus, it is difficult and expensive to construct the numerical model for such a multilayered structure. In this paper, we employ efficient numerical modeling of multilayered structures with a nonlocal ghost interlayer model in which ghost particles are distributed between main layers and they are interacting with each other in peridynamic way. We also consider a simple nonlocal contact condition in peridynamic frameworks to solve impact and penetration of the high-velocity impactor to the multilayered structure. Finally we can confirm the fracture capabilities of the method using a multilayered glass model in which 7 glass layers and a single elastic backing layer are affixed by polyvinyl butyral films.