• 한국축산식품학회지
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• 제33권4호
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• pp.463-473
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• 2013

Through the development of efficient data collecting technologies like RFID, and inter-enterprise collaboration platforms such as web services, companies which participate in supply chains can acquire visibility over the whole supply chain, and can make decisions to optimize the overall supply chain networks and processes, based on the extracted knowledge from historical data collected by the visibility system. Although not currently active, the MeatWatch system has been developed, and is used in part for this purpose, in the imported beef distribution network in Korea. However, the imported beef distribution network is too complicated to analyze its various aspects using ordinary process analysis approaches. In this paper, we suggest a novel approach, called RFID-based supply chain process mining, to automatically discover and analyze the overall supply chain processes from the distributed RFID event data, without any prior knowledge. The proposed approach was implemented and validated, by using a case study of the imported beef distribution network in Korea. Specifically we demonstrated that the proposed approach can be successfully applied to discover supply chain networks from the distributed event data, to simplify the supply chain networks, and to analyze anomaly of the distribution networks. Such novel process mining functionalities can reinforce the capability of traceability services like MeatWatch in the future.

• 한국식생활문화학회지
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• 제38권4호
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• pp.185-190
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• 2023

This paper elucidates the novel direction of food research in the era of the 4^th Industrial Revolution characterized by personalized approaches. Since conventional approaches for identifying novel food materials for health benefits are expensive and time-consuming, there is a need to shift towards AI-based approaches which offer more efficient and cost-effective methods, thus accelerating progress in the field of food science. However, relevant research papers in this field present several challenges such as regional and ethnic differences and lack of standardized data. To tackle this problem, our study proposes to address the issues by acquiring and normalizing food and biological big data. In addition, the paper demonstrates the association between heath status and biological big data such as metabolome, epigenome, and microbiome for personalized healthcare. Through the integration of food-health-bio data with AI technologies, we propose solutions for personalized healthcare that are both effective and validated.

• International Journal of Advanced Culture Technology
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• 제11권4호
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• 대한임상검사과학회지
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• 제41권4호
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• pp.145-152
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• 2009

Novel influenza A virus, subtype H1N1 of swine-lineage, has been transmitted rapidly to many regions of the world. Rapid detection of the virus is essential to instigate appropriate patient care and public health management and for disease surveillance. The aim of this study is to determine the prevalence of novel influenza A (H1N1) virus in Korea using reverse-transcription real time polymerase chain reaction (rRT-PCR). Novel H1N1 virus was detected in a total of 8,948 nasopharyngeal samples from patients with influenza-like illness throughout Korea from August to September 2009. RNA was extracted from $300{\mu}l$of sample using an RNA extraction kit (Zymo Research, CA, USA). In the present study, Genekam kit (Genekam, Duisburg, Germany) was used to detect novel H1N1 virus. Novel H1N1 virus was found in 1,130 samples from a total of 8,948 samples (12.6%). The highest frequency was found in 10- to 19-year-olds (M: 29.3% vs. F: 16.4%), followed by 20- to 29-year-olds (M: 17.9% vs. F: 15.4%), 40- to 49-year-olds (M: 6.5% vs. F: 8.1%), 50- to 59-year-olds (M: 6.0% vs. F: 5.5%), and 30- to 39-year-olds (M: 4.6% vs. F: 3.8%). The mean positive rate was higher in men than in women (M: 14.7% vs. F: 7.4%). Novel H1N1 virus showed the lowest prevalence in patients over 60 years old. The positive rate increased daily and showed a significant high peak in mid-September 2009. In 19 provinces of Korea, Cheonan (41.1%), Busan (37.3%), Gangneung (33.3%), Jinju (32.1%), Ulsan (24.6%), Deajeon (23.7%) areas showed high frequencies and other provinces were found less than 10% of novel H1N1 virus. Since reverse-transcription real time PCR assay is rapid, accurate, and convenient, it may assist public health laboratories in detecting novel H1N1 virus. Moreover, these data could be useful for the management of patients with influenza-like illness.

• International Journal of Industrial Entomology and Biomaterials
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• 제3권1호
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• pp.75-81
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• 2001

We have constructed a novel recombinant Bombyx mori nuclear polyhedrosis virus (BmNPV) producing the green fluorescent polyhedra. For the production of the fluorescent polyhedra, partial polyhedrin gene containing KRKK as nuclear localization site from the BmNPV polyhedrin gene and the green fluorescent protein (gfp) gene were introduced under the control of p10 promoter of BmNPV. The recombinant BmNPV was stably produced fluorescent polyhedra in the infected Bm5 cells and the morphology of the fluorescent polyhedra was similar to that of wild-type BmNPV. The fluorescent polyhedra had 32 kDa native polyhedrin and 41 kDa fusion protein. From these data, we have further developed a novel BmNPV p10-based transfer vector producing recombinant polyhedra with foreign gene Product. The novel BmNPV P10-based transfer vector is composed of partial polyhedrin gene, factor Xa, and multiple cloning sites.

• 한국광학회지
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• 제27권3호
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• pp.101-105
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• 2016

본 논문에서는 푸리에 변환의 위상천이 특성을 이용하여 기존의 계산방법보다 처리 속도가 빠르고, 이상적인 CGH의 재생이미지와의 차이가 크지 않은 Phase-only CGH의 새로운 최적화 계산방법을 제시한다. 기존의 수치적인 해를 얻는 접근과는 다르게 푸리에 변환 자체의 위상천이 특성을 이용하여 노이즈를 선택적으로 필터링하는 방법으로 Phase-only CGH를 얻기 때문에 계산속도를 현저하게 줄일 수 있다. 이상적인 CGH와 기존의 방법, 그리고 새로운 계산방법을 통한 CGH를 시뮬레이션을 통하여 각각 SLM에 저장하여 수렴렌즈를 이용한 푸리에 홀로그램 방식으로 이미지를 재생하였다. 그리고 시뮬레이션 재생 이미지를 비교하여 본 연구의 타당성을 살펴보았다. 기존의 방법과 비교하였을 때, 이미지 물체의 질감과 예리도가 이상적인 CGH와 비슷한 정도의 수준으로 향상되었고, 계산속도 또한 크게 줄었음을 알 수 있다.

• Genomics & Informatics
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• 제21권1호
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• pp.6.1-6.8
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• 2023

Glaucoma is the second leading cause of irreversible blindness, and primary open-angle glaucoma (POAG) is the most common type. Due to inadequate diagnosis, treatment is often not administered until symptoms occur. Hence, approaches enabling earlier prediction or diagnosis of POAG are necessary. We aimed to identify novel drugs for glaucoma through bioinformatics and network analysis. Data from 36 samples, obtained from the trabecular meshwork of healthy individuals and patients with POAG, were acquired from a dataset. Next, differentially expressed genes (DEGs) were identified to construct a protein-protein interaction (PPI) network. In both stages, the genes were enriched by studying the critical biological processes and pathways related to POAG. Finally, a drug-gene network was constructed, and novel drugs for POAG treatment were proposed. Genes with p < 0.01 and |log fold change| > 0.3 (1,350 genes) were considered DEGs and utilized to construct a PPI network. Enrichment analysis yielded several key pathways that were upregulated or downregulated. For example, extracellular matrix organization, the immune system, neutrophil degranulation, and cytokine signaling were upregulated among immune pathways, while signal transduction, the immune system, extracellular matrix organization, and receptor tyrosine kinase signaling were downregulated. Finally, novel drugs including metformin hydrochloride, ixazomib citrate, and cisplatin warrant further analysis of their potential roles in POAG treatment. The candidate drugs identified in this computational analysis require in vitro and in vivo validation to confirm their effectiveness in POAG treatment. This may pave the way for understanding life-threatening disorders such as cancer.

• 대한화학회지
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• 제56권1호
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• pp.68-73
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• 2012

In the present study, we have synthesized novel Isopropyl 2-(4-substitutedbenzylidene)-5-methyl-3-oxo-7-phenyl-3,7-dihydro-2H-thiazolo[3,2-a]-pyrimidine-6-carboxylate derivatives (6a-j). Elemental analysis, IR, $^1H$ NMR and mass spectral data elucidated structure of newly synthesized compounds. The newly synthesized compounds were screened for antiinflammatory and anti microbial studies. Their biological activity data of the 10 compounds indicates that two compounds posses potent anti-inflammatory and five have antimicrobial activities.

• ETRI Journal
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• 제32권5호
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• pp.676-686
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• 2010

Mining sequential patterns is an important research issue in data mining and knowledge discovery with broad applications. However, the existing sequential pattern mining approaches consider only binary frequency values of items in sequences and equal importance/significance values of distinct items. Therefore, they are not applicable to actually represent many real-world scenarios. In this paper, we propose a novel framework for mining high-utility sequential patterns for more real-life applicable information extraction from sequence databases with non-binary frequency values of items in sequences and different importance/significance values for distinct items. Moreover, for mining high-utility sequential patterns, we propose two new algorithms: UtilityLevel is a high-utility sequential pattern mining with a level-wise candidate generation approach, and UtilitySpan is a high-utility sequential pattern mining with a pattern growth approach. Extensive performance analyses show that our algorithms are very efficient and scalable for mining high-utility sequential patterns.

• Journal of Computing Science and Engineering
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• 제5권3호
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• pp.223-235
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• 2011

Performing approximate data matching has always been an intriguing problem for both industry and academia. This task becomes even more challenging when the requirement of data privacy rises. In this paper, we propose a novel technique to address the problem of efficient privacy-preserving approximate record linkage. The secure framework we propose consists of two basic components. First, we utilize a secure blocking component based on phonetic algorithms statistically enhanced to improve security. Second, we use a secure matching component where actual approximate matching is performed using a novel private approach of the Levenshtein Distance algorithm. Our goal is to combine the speed of private blocking with the increased accuracy of approximate secure matching.