• Journal of Food Hygiene and Safety
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• v.33 no.2
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• pp.146-150
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• 2018

Red pepper is one of the most important spices popularly utilized in Korea. Because of the differences in tariff rates between red pepper powder and seasoned red-pepper sauce, seasoned red-pepper sauce is often therefore imported by consumers, then dried, ground, and added to red pepper powder for cost effective purposed to use the product the most effectively. In this study, we combined species-specific polymerase chain reaction (PCR) assays (for red pepper, garlic, onion, spring onion, and ginger) with whole-genome amplification (WGA). Thirty-nine red pepper powders were well in accordance with their labels. However, six red pepper powder and five seasoned red-pepper sauce products failed to meet their compliance requirements. As a consequence, our monitoring results revealed that the overall mislabeling rate detected in this study was identified at 22%. Thus, our findings showed that the species-specific PCR in conjunction with WGA was an ideal method to identify raw materials that are used in the manufacturing of red pepper powder and seasoned red-pepper sauce.

• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.25-34
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• 2015

Recently, in distribution market, demand for electronic shelf label system is increasing gradually to provide the accurate price immediately and detailed product information to consumers and reduce operation costs. Most of electronic shelf label system companies develop the full-graphic display device to display a wide variety of product information as well as the exact price. Our system had introduced Go-Back-N retransmission method in the early. However, we encountered performance problems that it delayed updating of the electronic shelf label system and exhausted the battery life time. Proposed adaptive image retransmission technique based on the selective scheme is that tags of electronic shelf label system recognize idle time of transmission cycle and require partial image retransmission to sever by itself. As a result, it can acquire much more opportunities of partial image retransmission within the same period and increase reception rate of full image for each tags. The experimental result shows that adaptive image retransmission technique's reception rate of full image for each tags is approximately 4% higher than existing previous works. And total battery life time increases 30 hours because tag reduce wake-up time as it receive only lost data instead of whole data.

• Annual Conference of KIPS
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• 2019.05a
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• pp.375-378
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• 2019

최근 딥러닝과 머신러닝 기법이 소프트웨어의 성능 향상에 도움이 되는 것이 입증됨에 따라, 의료 영상 진단 보조 소프트웨어를 개발하기 위한 시도가 활발해 지고 있다. 그 중 캡슐내시경은 소장 소화기관을 관찰할 수 있는 초소형 의료기기로, 기존의 내시경 검사와 다르게 이물감이 느껴지지 않고 의료보험 적용으로 최근 들어 널리 이용되고 있다. 일반적으로 캡슐 내시경은 8 시간 동안 소화기간을 촬영하며, 한 번의 검사 결과로 생성된 동영상 데이터 셋은 수 만장의 이미지를 포함하기 때문에, 방대한 양의 이미지들을 효율적으로 관리하기 위한 체계가 필요하다. 특히, 방대한 양의 캡슐내시경 이미지를 학습하는 경우, 수 만장의 이미지 속에서 유의미한 특징(촬영정보, 의사소견, 환자정보, 병변의 위치 및 크기 등)을 추출해내야 하므로 학습 데이터 레이블링을 위한 정보를 정확히 추출해야 하는 작업이 요구된다. 따라서 본 논문에서는 캡슐내시경 영상을 학습할 때, 학습 데이터 레이블 정보를 체계적으로 구축할 수 있게 하는 레이블 정보 추출 기법을 제안하고자 한다. 제안하는 기법은 병원에서 14년간 수집된 총 340명의 캡슐내시경 데이터(약 1,700 만장의 이미지)를 토대로 영상데이터를 구조적으로 분석하여 유의미한 정보를 추출하고 노이즈 데이터를 제거한 뒤, 빅데이터 저장소에 적재할 수 있음을 보였다.

• Journal of Radiation Industry
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• v.7 no.2_3
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• pp.191-200
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• 2013

In this study, a novel bombesin (BBN) analogues, DOTA-Ala($SO_3H$)-4 aminobenzoyl-Gln-Trp-Ala-Val-Gly-His-Leu-Met-$NH_2$ (DOTA-sBBN) and DOTA-Lys(glucose)-4 aminobenzoyl-Gln-Trp-Ala-Val-Gly-His-Leu-Met-$NH_2$ (DOTA-gluBBN), were synthesized and radiolabeled, and their binding affinities were evaluated. Peptides were prepared by a solid phase synthesis method and their purities were over 98%. DOTA is the chelating agent for $^{177}Lu$-labeling, and the DOTA-conjugated peptides were radiolabeled with $^{177}Lu$ by a high radiolabeling yield (>98%). The Log P values of DOTA-sBBN and DOTA-gluBBN were -2.20 and -2.79, respectively. 50.41% of $^{177}Lu$-DOTA-sBBN and 72.97% of $^{177}Lu$-DOTA-gluBBN were left undegraded by the serum incubation at $37^{\circ}C$ for 48 hr. A competitive displacement of $^{125}I-[Tyr^4]$-BBN on the PC-3 human prostate carcinoma cells revealed that 50% inhibitory concentration ($IC_{50}$) were 1.46 nM of DOTA-sBBN and 4.67 nM of DOTA-gluBBN indicating a highly nanomolar binding affinity for GRPR. Therefore, it is concluded that $^{177}Lu$-DOTA-sBBN and $^{177}Lu$-DOTA-gluBBN can be potential candidates as a targeting modality for the Gastrin-releasing peptide receptor (GRPR)-over-expressing tumors, and further studies to evaluate their biological and pharmacological characteristics are needed.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.466-472
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• 2021

Speaker Change Detection (SCD) refers to finding the moment when the main speaker changes from one person to the next in a speech conversation. In speaker change detection, difficulties arise due to overlapping speakers, inaccuracy in the information labeling, and data imbalance. To solve these problems, TIMIT corpus widely used in speech recognition have been concatenated artificially to obtain a sufficient amount of training data, and the detection of changing speaker has performed after identifying overlapping speakers. In this paper, we propose an speaker change detection system that considers the speaker overlapping. We evaluated and verified the performance using various approaches. As a result, a detection system similar to the X-Vector structure was proposed to remove the speaker overlapping region, while the Bi-LSTM method was selected to model the speaker change system. The experimental results show a relative performance improvement of 4.6 % and 13.8 % respectively, compared to the baseline system. Additionally, we determined that a robust speaker change detection system can be built by conducting related studies based on the experimental results, taking into consideration text and speaker information.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.19-26
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• 2019

In this paper, the classification performance of learning algorithms is compared for TAB digit recognition. The TAB digits that are segmented from TAB musical notes contain TAB lines and musical symbols. The labeling method and non-linear filter are designed and applied to extract fret digits only. The shift operation of the 4 directions is applied to generate more data. The selected models are Bayesian classifier, support vector machine, prototype based learning, multi-layer perceptron, and convolutional neural network. The result shows that the mean accuracy of the Bayesian classifier is about 85.0% while that of the others reaches more than 99.0%. In addition, the convolutional neural network outperforms the others in terms of generalization and the step of the data preprocessing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.367-373
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• 2022

As deep learning-based autonomous driving technology develops, artificial intelligence models for various purposes have been studied. Based on these studies, several models were used simultaneously to develop autonomous driving systems. It can occur by increasing hardware resource consumption. We propose a multi-tasks model using a shared backbone to solve this problem. This can solve the increase in the number of backbones for using AI models. As a result, in the proposed lightweight model, the model parameters could be reduced by more than 50% compared to the existing model, and the speed could be improved. In addition, each lane can be classified through lane detection using the instance segmentation method. However, further research is needed on the decrease in accuracy compared to the existing model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.185-190
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• 2022

Open data has a lot of economic value. Not only Korea, but many other countries are doing their best to make various policies and efforts to expand and utilize open data. However, although Korea has a large amount of data, the data is not utilized effectively. Thus, attempts to utilize those data should be made in various industries. In particular, in the fashion industry, exchange and refund problems are the most common due to unpredictable consumers. Better feedback is necessary for service providers to solve this problem. We want to solve it by showing improved images of dissatisfactions along with user reviews including consumer needs. In this paper, user reviews are analyzed on online shopping mall websites to identify consumer needs, and product attributes are defined by utilizing the attributes of K-fashion data. The users' request is defined as a dissatisfaction attribute, and labeling data with the corresponding attribute is searched. The users' request is provided to the service provider in forms of text data or attributes, as well as an image to help improve the product.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.1-9
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• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.

• The Korea Journal of Herbology
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• v.29 no.1
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• pp.19-26
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• 2014

Objectives : This study was performed in order to evaluate the effects of Salviae Miltiorrhizae Radix (SMR) water extract against the cerebral hemorrhage and the blood-brain barrier (BBB) impairment in the intracerebral hemorrhage (ICH). Method : ICH was induced by the stereotaxic intrastriatal injection of bacterial collagenase type IV in Sprague-Dawley rats. SMR was orally given three times every 20 hours during 3 days after the ICH induction. Hematoma volume, water content of brain tissue and volume of evans blue leakage were examined. Myeloperoxidase (MPO) positive neutrophils and tumor necrosis factor-${\alpha}$ (TNF-${\alpha}$) were observed with immunofluorescence labeling and confocal microscope. Results : SMR significantly reduced the hematoma volume of the ICH-induced rat brain. SMR significantly reduced the water content of brain tissue of the ICH-induced rat brain. SMR reduced the percentage of the evans blue leakage around the hematoma on the caudate putamen compared to the ICH group, especially on the cerebral cortex. SMR significantly reduced the volume of the evans blue leakage level in the peri-hematoma regions of the ICH-induced rat brain. SMR significantly reduced MPO positive neutrophils in the peri-hematoma regions of the ICH-induced rat brain. SMR reduced the TNF-${\alpha}$ expression in peri-hematoma regions of the ICH-induced rat brain. TNF-${\alpha}$ immuno-labeled cells were coincided with MPO immuno-labeled neutrophils in peri-hematoma regions of the ICH-induced rat brain. Conclusion : These results suggest that SMR plays a protective role against the blood-brain barrier impairment in the ICH through suppression of inflammation in the rat brain tissues.