• Proceedings of the KSPS conference
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• 1997.07a
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• pp.228-229
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• 1997

1 Intonation is important. It cannot be ignored. To convince students of the importance of intonation, we can use sentences with two very different interpretations according to intonation. Example: "I thought it would rain" with a fallon "rain" means it did not rain, but with a fall on "thought" and a rise on "rain" it means that it did rain. 2 Although complex, intonation is structured. For both teacher and student, the big job of tackling intonation is made simpler by remembering that intonation can be analysed into systems and units. There are three main systems in English intonation: Tonality (division into phrases) Tonicity (selection of accented syllables) Tone (the choice of pitch movements) Examples: Tonality: My brother who lives in London is a doctor. Tonicity: Hello. How ARE you. Hello. How are YOU. Tone: Ways to say "Thank you" 3 In deciding what to teach, we must distinguish what is universal from what is specifically English. This is where contrastive studies of intonation are very valuable. Usually, for instance, division into phrases (tonality) works in broadly similar ways across languages. Some uses of pitch are also similar across languages - for example, very high pitch may signal excitement or urgency. 4 Although most people think that intonation is mainly about pitch (the tone system), actually accent placement (tonicity) is probably the single most important aspect of English intonation. This is because it is connected with information focus, and the effects on interpretation are very clear-cut. Example: They asked for coffee, so I made them coffee. (The second occurrence of "coffee" must not be accented). 5 Ear-training is the beginning of intonation training in the VeL approach. First, students learn to identify fall vs rise vs fall-rise. To begin with, single words are used, then phrases and sentences. When learning tones, the fIrst words used should have unstressed syllables after the stressed syllable (Saturday) to make the pitch movement clearer. 6 In production drills, the fIrst thing is to establish simple neutral patterns. There should be no drama or really special meanings. Simple drills can be used to teach important patterns: Example: A: Peter likes football B: Yes JOHN likes football TOO A: Mary rides a bike B: Yes JENny rides a bike TOO 7 The teacher must be systematic and let learners KNOW what they are learning. It is no good using new patterns and hoping that students will "pick them up" without noticing. 8 Visual feedback of fundamental frequency with a computer display can help students learn correct patterns. The teacher can use the display to demonstrate patterns, or students can practise by themselves, imitating recorded models.

• Journal of the Korean Society of Radiology
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• v.14 no.3
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• pp.211-220
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• 2020

High-quality computed tomography (CT) images enable precise lesion detection and accurate diagnosis. A lot of studies have been performed to improve CT image quality while reducing radiation dose. Recently, deep learning-based techniques for improving CT image quality have been developed and show superior performance compared to conventional techniques. In this study, a super-resolution convolutional neural network (SRCNN) model was used to improve the spatial resolution of CT images, and image quality according to the hyperparameters, which determine the performance of the SRCNN model, was evaluated in order to verify the effect of hyperparameters on the SRCNN model. Profile, structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and full-width at half-maximum (FWHM) were measured to evaluate the performance of the SRCNN model. The results showed that the performance of the SRCNN model was improved with an increase of the numbers of epochs and training sets, and the learning rate needed to be optimized for obtaining acceptable image quality. Therefore, the SRCNN model with optimal hyperparameters is able to improve CT image quality.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.102-109
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• 2008

In this paper, the robust hybrid modulation type classifier which use both HOS and WT key features and can recognize 10 digitally modulated signals without a priori information in multipath fading channel conditions is proposed. The proposed classifier developed using data taken field measurements in various propagation model (i,e., rural area, small town and urban area) for real world scenarios. The 9 channel data are used for supervised training and the 6 channel data are used for testing among total 15 channel data(i.e., holdout-like method). The Proposed classifier is based on HOS key features because they are relatively robust to signal distortion in AWGN and multipath environments, and combined WT key features for classifying MQAM(M=16, 64, 256) signals which are difficult to classify without equalization scheme such as AMA(Alphabet Matched Algorithm) or MMA(Multi-modulus Algorithm. To investigate the performance of proposed classifier, these selected key features are applied in SVM(Support Vector Machine) which is known to having good capability of classifying because of mapping input space to hyperspace for margin maximization. The Pcc(Probability of correct classification) of the proposed classifier shows higher than those of classifiers using only HOS or WT key features in both training channels and testing channels. Especially, the Pccs of MQAM 3re almost perfect in various SNR levels.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.1
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• pp.39-48
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• 2022

Using environmental DNA (eDNA) in the fisheries and oceanography fields, research on the diversity of biological species, the presence or absence of specific species and quantitative evaluation of species has considerably been performed. Up to date, no study on eDNA has been tried in the area of fisheries acoustics in Korea. In this study, the biomass of a dominant species in the northwestern waters of Jeju Island was examined using 1) the catch ratio of the species from trawl survey results and 2) the ranking ratio of the species from the eDNA results. The dominant species was Zoarces gillii, and its trawl catch ratio was 68.2% and its eDNA ratio was 81.3%. The Zoarces gillii biomass from the two methods was 7199.4 tons (trawl) and 8584.6 tons (eDNA), respectively. The mean and standard deviation of the acoustic backscattering strength values (120 kHz) from the entire survey area were 135.5 and 157.7 m²/nm², respectively. The strongest echo signal occurred at latitude 34° and longitude 126°15' (northwest of Jeju Island). High echo signals were observed in a specific oceanographic feature (salinity range of 32-33 psu and the water temperature range of 19-20℃). This study was a pilot study on evaluating quantitatively aquatic resources by applying the eDNA technique into acoustic-trawl survey method. Points to be considered for high-quality quantitative estimation using the eDNA to fisheries acosutics were discussed.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.315-323
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• 2023

Post-mortem computed tomography (PMCT) is commonly employed in the field of forensic pathology. PMCT was mainly performed using a whole-body scan with a wide field of view (FOV), which lead to a decrease in spatial resolution due to the increased pixel size. This study aims to evaluate the potential for developing a super-resolution model based on conditional generative adversarial networks (CGAN) to enhance the image quality of CT. 1761 low-resolution images were obtained using a whole-body scan with a wide FOV of the head phantom, and 341 high-resolution images were obtained using the appropriate FOV for the head phantom. Of the 150 paired images in the total dataset, which were divided into training set (96 paired images) and validation set (54 paired images). Data augmentation was perform to improve the effectiveness of training by implementing rotations and flips. To evaluate the performance of the proposed model, we used the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Deep Image Structure and Texture Similarity (DISTS). Obtained the PSNR, SSIM, and DISTS values of the entire image and the Medial orbital wall, the zygomatic arch, and the temporal bone, where fractures often occur during head trauma. The proposed method demonstrated improvements in values of PSNR by 13.14%, SSIM by 13.10% and DISTS by 45.45% when compared to low-resolution images. The image quality of the three areas where fractures commonly occur during head trauma has also improved compared to low-resolution images.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.30-39
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• 2003

In this paper, we carried out the performance evaluation of HM-Net(Hidden Markov Network) speech recognition system for Korean speech databases. We adopted to construct acoustic models using the HM-Nets modified by HMMs(Hidden Markov Models), which are widely used as the statistical modeling methods. HM-Nets are carried out the state splitting for contextual and temporal domain by PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) algorithm, which is modified the original SSS algorithm. Especially it adopted the phonetic decision tree to effectively express the context information not appear in training speech data on contextual domain state splitting. In case of temporal domain state splitting, to effectively represent information of each phoneme maintenance in the state splitting is carried out, and then the optimal model network of triphone types are constructed by in the parameter. Speech recognition was performed using the one-pass Viterbi beam search algorithm with phone-pair/word-pair grammar for phoneme/word recognition, respectively and using the multi-pass search algorithm with n-gram language models for sentence recognition. The tree-structured lexicon was used in order to decrease the number of nodes by sharing the same prefixes among words. In this paper, the performance evaluation of HM-Net speech recognition system is carried out for various recognition conditions. Through the experiments, we verified that it has very superior recognition performance compared with the previous introduced recognition system.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.4 no.4
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• pp.400-404
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• 1999

The conventional mercury delay method to measure compressional wave velocity of unconsolidated sediment is inconvenient because the signal must be analyzed on the oscilloscope and the mercury column has to be calibrated between measurements. We developed an automated compressional wave velocity measurement technique by connecting an oscilloscope and a PC with a GPIB (General Purpose Interface Bus) card. The GPIB card buses signals from the oscilloscope to the PC where the signal from a sample is analyzed and compared to the input pulse thereby the compressional wave velocity of the sample is computed and recorded automatically. Differences between the mercury delay method and the automated measurement technique are negligible except the slightly greater velocity in the automated measurement technique. We concluded that the new technique can be used to measure the velocity for unconsolidated marine sediment. It also has an advantage to calculate sediment attenuation through the processing of waveform using the spectral ratio technique.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.139-147
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• 2015

Steady state visual evoked potential (SSVEP) has been actively studied because of its short training time, relatively higher signal-to-noise ratio, and higher information transfer rate. There are two popular analysis methods for SSVEP signals: power spectral density analysis (PSDA) and canonical correlation analysis (CCA). However, the PSDA is known to be vulnerable to noise due to the use of a single channel. Although conventional CCA is more accurate than PSDA, it may not be appropriate for the real-time SSVEP-based BCI system when it has short time window length because it uses sinusoidal signals as references. Therefore, the two methods are not efficient for the real-time BCI system that requires a short TW and a high recognition accuracy. To overcome this limitation of the conventional methods, this paper proposes a frequency recognition method with a combination of CCA and PSDA using the difference between powers of canonical variables obtained from the results of CCA. Experimental results show that the performance of the combination of CCA and PSDA is better than that of CCA for the case of a short TW.