• Phonetics and Speech Sciences
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• v.12 no.2
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• pp.29-37
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• 2020

Recently, the neural network-based deep learning algorithm has dramatically improved performance compared to the classical Gaussian mixture model based hidden Markov model (GMM-HMM) automatic speech recognition (ASR) system. In addition, researches on end-to-end (E2E) speech recognition systems integrating language modeling and decoding processes have been actively conducted to better utilize the advantages of deep learning techniques. In general, E2E ASR systems consist of multiple layers of encoder-decoder structure with attention. Therefore, E2E ASR systems require data with a large amount of speech-text paired data in order to achieve good performance. Obtaining speech-text paired data requires a lot of human labor and time, and is a high barrier to building E2E ASR system. Therefore, there are previous studies that improve the performance of E2E ASR system using relatively small amount of speech-text paired data, but most studies have been conducted by using only speech-only data or text-only data. In this study, we proposed a semi-supervised training method that enables E2E ASR system to perform well in corpus in different domains by using both speech or text only data. The proposed method works effectively by adapting to different domains, showing good performance in the target domain and not degrading much in the source domain.

• Korean Journal of Food Science and Technology
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• v.41 no.1
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• pp.111-115
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• 2009

In this study we analysed for peanuts in processed foods using an enzyme-linked fluorescent immunoassay (ELFA), and compared the results with labeled ingredients. Crude peanut protein (CPP) was immunized into rabbits to produce specific antibodies(Ab). A sandwich ELFA was established using anti-CPP Ab and Ab-horseradish peroxidase (HRP) conjugate. The cross-reactivities of the Ab toward CPP, peanuts, almonds, soybeans, and walnuts were 100, 9.8, $1.1{\times}10^{-2},\;4.4{\times}10^{-3}$, and 0%, respectively. The samples included 19 items consisting of biscuits, snacks, chocolates, and so on. The results from the sandwich ELFA showed that peanuts were contained in 7 of the processed food items, among which, 5 items were labeled as having peanuts present but 2 items were not. One of the 2 items that was peanut-detected but unlabeled was a biscuit labeled to contain almonds and assayed to contain $2.1{\times}10^{-3}%$ peanuts, which might have been due to the weak cross-reactivity of the Ab toward almonds. The other item was a snack labeled to contain soybeans and assayed to contain 0.098% peanuts, which might have been due to peanut cross-contamination during processing, since the crossreactivity of the Ab toward soybeans was very weak. These results suggest that ELFA is a good tool to detect peanuts in processed foods, and allergens in certain processed foods should be labeled correctly.

• Archives of Pharmacal Research
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• v.18 no.2
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• pp.90-99
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• 1995

The effect of protein kinase C inhibitors, sturosporine and 1-(5-isoquinolinyl sulfonyl)-2-methyl piperazine(H7) on in vitro differentiation of erythroid progenitor cells which were isolated from spleens of mice infected with the anemia-inducing strain of Friend virus were examined. Erythropoietin-mediated differentitation of erythroid progenitor cells, as determined by the incorporation of $^{59}Fe$ into protoporphyrin, was inhibited by staurosporine and H7 in a concentration -dependent manner. Scatchard analysis of the $^3H-phorbol-12$, 13-dibutyrate binding to erythroid progenitor cells revealed that at the high affinity sites the dissociation constant was 22nM and the maximum number of $^3H-phorbol-12$, 13-dibutyrate binding to erythroid progenitor cells revealed that at the high affinity sites the dissociation constant was 22nM and the maximum number of $^3H-phorbol-12$, 13-dibutyrate binding sites per cell was approximately $3.7\times10^5$. Cytosonic protein kinase C was isolated from erthroid progenitor cells and then purified by sequential column chromatogrphy. Two isoforms of protein kinase C were found. Photoaffinity labeling of the purified protein kinase C samples with $^3H-phorbol-12$12-myristate 13-acetate followed by analysis of SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and autofluorography showed radiolabeled 82-KDa pepticles. Rediolabeling of the 82-KDa peptides with $^3H-phorbol-12$myristate 13-acete was almost completely blocked by excess unlabeled phorbol 12-myristate 13-acetate was almost 12-muristate 13-acetate-promoted phosphorylation with the puyrified protein kinase C samples showed that the phosphorylation of 82-KDa peptides was increased as the concentration of phorbol 12-myristate 13-acetate was increased from $10^{-8}M{\;}to{\;}10^{-4}$M. In light of the findings that erythroid progenitor cells possessed an abundance of protein kinase C and that stauroporine and H7 inhibited erythroid differentiation, it seemed likely that protein kinase C would play a role in the erythroid progenitor cell development.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.479-487
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• 2021

In this paper, we propose an Sound Event Detection (SED) model using self-training based on a noisy student model. The proposed SED model consists of two stages. In the first stage, a mean-teacher model based on an Residual Convolutional Recurrent Neural Network (RCRNN) is constructed to provide target labels regarding weakly labeled or unlabeled data. In the second stage, a self-training-based noisy student model is constructed by applying different noise types. That is, feature noises, such as time-frequency shift, mixup, SpecAugment, and dropout-based model noise are used here. In addition, a semi-supervised loss function is applied to train the noisy student model, which acts as label noise injection. The performance of the proposed SED model is evaluated on the validation set of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4. The experiments show that the single model and ensemble model of the proposed SED based on the noisy student model improve F1-score by 4.6 % and 3.4 % compared to the top-ranked model in DCASE 2020 challenge Task 4, respectively.

• Database Research
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• v.34 no.3
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• pp.137-147
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• 2018

A key to a well-performing human activity recognition (HAR) system through machine learning technique is the availability of a substantial amount of labeled data. Collecting sufficient labeled data is an expensive and time-consuming task. To build a HAR system in a new environment (i.e., the target domain) with very limited labeled data, it is unfavorable to naively exploit the data or trained classifier model from the existing environment (i.e., the source domain) as it is due to the domain difference. While traditional machine learning approaches are unable to address such distribution mismatch, transfer learning approach leverages the utilization of knowledge from existing well-established source domains that help to build an accurate classifier in the target domain. In this work, we propose a transfer learning approach to create an accurate HAR classifier with very limited data through the multitask neural network. The classifier loss function minimization for source and target domain are treated as two different tasks. The knowledge transfer is performed by simultaneously minimizing the loss function of both tasks using a single neural network model. Furthermore, we utilize the unlabeled data in an unsupervised manner to help the model training. The experiment result shows that the proposed work consistently outperforms existing approaches.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.139-147
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• 2021

This paper introduces and implements a Sound Event Detection (SED) system based on weakly-supervised learning where only part of the data is labeled, and analyzes the effect of parameters. The SED system estimates the classes and onset/offset times of events in the acoustic signal. In order to train the model, all information on the event class and onset/offset times must be provided. Unfortunately, the onset/offset times are hard to be labeled exactly. Therefore, in the weakly-supervised task, the SED model is trained by "strongly labeled data" including the event class and activations, "weakly labeled data" including the event class, and "unlabeled data" without any label. Recently, the SED systems using the mean-teacher model are widely used for the task with several parameters. These parameters should be chosen carefully because they may affect the performance. In this paper, performance analysis was performed on parameters, such as the feature, moving average parameter, weight of the consistency cost function, ramp-up length, and maximum learning rate, using the data of DCASE 2020 Task 4. Effects and the optimal values of the parameters were discussed.

• Journal of Food Hygiene and Safety
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• v.36 no.3
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• pp.257-263
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• 2021

For this survey, PCR (polymerase chain reaction) testing was conducted using 14 species-specific primers to monitor the labeling of allergy-causing substances in various foods. Sixty samples from stationary stores near elementary schools and imported confectionery shops were tested, including snacks, candies, and chocolate. Allergens of milk, wheat, eggs, tomatoes, almonds and peanuts were detected in 30 cases (50.0%). In addition, many products were detected as either containing unlabeled substances or not showing allergen-related information and labeling in Korean. In order to ensure that consumers are able to purchase products safely and securely, a system for thorough guidance and monitoring of allergen-related labeling by domestic manufacturing and processing companies and import-related companies is required.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.8 no.2
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• pp.53-61
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• 2022

This study aimed to increase the targeting ability against PSMA in cell therapy using metabolic glycoengineering and biorthogonal chemistry and to visualize cell trafficking using PET imaging. Cellular membranes of THP-1 cells were decorated with azide(-N₃) using Ac₄ManNAz by metabolic glycoengineering. Engineered THP-1 cells were conjugated with DBCO-bearing fluorophore (ADIBO-Cy5.5) for 1 h at different concentrations and analyzed by confocal fluorescence microscopy and flow cytometry. For PSAM ligand conjugation to THP-1 cells, Ac₄ManNAz treated THP-1 cells were incubated with DBCO-PSMA ligand (ADIBO-GUL) at a final concentration with 100 µM for 1 h. To evaluate the effect on cell recognition, PSMA ligand conjugated THP-1 cells(as effectors) were co-cultured with PSMA positive 22RV1 (as target cells) at 3 : 1 a effector-to-target cell (E/T) ratio. The interaction between THP-1 and 22RV1 was monitored by confocal fluorescence microscopy. For preparing the radiolabeled THP-1, the cells were treated at the activity of ~ 740 kBq of [⁸⁹Zr]Zr(oxinate)₄/5 × 10⁶ cells. Radiolabeled cells were analyzed for determination of cell-associated radioactivity by gamma counting and viability using MTS assay. In the cytotoxicity assay, THP-1 cells did not have any cytotoxicity even when the Ac₄ManNAz concentration was 100 µM. In confocal microscopy and flow cytometry, THP-1 cells were efficiently labeled ADIBO-Cy5.5 in a dose-dependent manner, and the dose of 100 µM was the optimal concentration for the following experiments. The clusters of PSMA ligand-conjugated THP-1 cells and 22RV1 cells were identified, indicating cell-cell recognition over the cell surface between two types of cells. Cell radiolabeling efficiency was 54.5 ± 17.8%. THP-1 labeled with 0.09 ± 0.03 Bq/cell showed no significant cytotoxicity compared to unlabeled THP-1 up to 7 days. We successfully demonstrated that Ac₄ManNAz treated cells were efficiently conjugated with ADIBO-GUL for preparing the PSMA-targeting cells, and [⁸⁹Zr]Zr(oxinate)₄ could be used to label cells without toxicity. It suggested that PSMA-ligand conjugated cell therapy could be improved cell targeting and be monitored by PET imaging.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.21-30
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• 2023

Accurate segmentation of the kidney tumor is necessary to identify shape, location and safety margin of tumor in abdominal CT images for surgical planning before renal partial nephrectomy. However, kidney tumor segmentation is challenging task due to the various sizes and locations of the tumor for each patient and signal intensity similarity to surrounding organs such as intestine and spleen. In this paper, we propose a semi-supervised learning-based mean teacher network that utilizes both labeled and unlabeled data using a kidney local guided map including kidney local information to segment small-sized kidney tumors occurring at various locations in the kidney, and analyze the performance according to the kidney tumor size. As a result of the study, the proposed method showed an F1-score of 75.24% by considering local information of the kidney using a kidney local guide map to locate the tumor existing around the kidney. In particular, under-segmentation of small-sized tumors which are difficult to segment was improved, and showed a 13.9%p higher F1-score even though it used a smaller amount of labeled data than nnU-Net.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.113-119
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• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.