• Applied Biological Chemistry
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• v.38 no.1
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• pp.49-54
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• 1995

To understand the molecular structure and pathogenesis mechanism of Korean garlic viruses, we have isolated cDNA clones for garlic viruses. The partial nucleotide sequences of 24 cDNA clones were determined and those of five clones containing poly(A) tail were compared with sequences of other plant viruses. One of these clones, V9, has a primary structure similar to the carlavirus group, suggesting that the clone V9 derived from a part of garlic latent virus (GLV). Northern blot analysis with the clone V9 as a probe demonstrated that GLV genome is 8.5 knt long and has a poly(A) tail. The clone V9 encodes coat protein (CP) of 33 kDa and nucleic acid binding protein of 10 kDa in different reading frame. The hexanucleotide motif, 5'-ACCUAA, which is conserved in the 3' noncoding region arid was proposed to be a cis-acting element involved in the production of negative strand genomic RNA was noticed. Complementary sequence to the hexanucleotide motif, 5'-TTAGGT, is also found in the positive strand of V9 RNA. The putative CP gene was cloned into the pRSET-A expression vector and expressed in E. coli BL21. The expressed recombinant V9CP protein was purified by $Ni^{2+}$ NTA affinity chromatography. The anti-V9CP antibody recognizes 34 kDa polypeptide which could be CP of GLV in infected garlic leaf extract. Immunoblot and Northern blot analysis of various cultivars shows wide occurrence of GLV in Korean garlic plants.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.51-59
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• 2024

Collecting a dataset with a corresponding labeled gaze vector requires a high cost in the gaze estimation field. In this paper, we suggest a data augmentation of manipulating the gaze of an original image, which improves the accuracy of the gaze estimation model when the number of given gaze labels is restricted. By conducting multi-class gaze bin classification as an auxiliary task and adjusting the latent variable of the diffusion model, the model semantically edits the gaze from the original image. We manipulate a non-binary attribute, pitch and yaw of gaze vector to a desired range and uses the edited image as an augmented train data. The improved gaze accuracy of the gaze estimation network in the semi-supervised learning validates the effectiveness of our data augmentation, especially when the number of gaze labels is 50k or less.

• Journal of Korea Port Economic Association
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• v.40 no.2
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• pp.1-20
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• 2024

The purpose of this study is to clarify a safety efficiency of ports in South Korea considering port casualties. The paper conducted a vector auto regression to analyze a cause-and-effect relationship between latent factors and port casualties. Subsequently, the paper evaluated safety efficiency for the ports using an undesirable outputs model. The results implied that the number of workers in the shipping union had a statistical effect on the port casualties. In contrast, the number of workers in the operators and working hours are unrelated to the port casualties. In addition, it was found that Yeosu Gwangyang Port is the most safety efficiency port in South Korea. Based on these results, this paper may provide various policy implications for port operators, developers, and managers.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.197-204
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• 2020

In this paper, we present a learning platform for robotic grasping in real world, in which actor-critic deep reinforcement learning is employed to directly learn the grasping skill from raw image pixels and rarely observed rewards. This is a challenging task because existing algorithms based on deep reinforcement learning require an extensive number of training data or massive computational cost so that they cannot be affordable in real world settings. To address this problems, the proposed learning platform basically consists of two training phases; a learning phase in simulator and subsequent learning in real world. Here, main processing blocks in the platform are extraction of latent vector based on state representation learning and disentanglement of a raw image, generation of adapted synthetic image using generative adversarial networks, and object detection and arm segmentation for the disentanglement. We demonstrate the effectiveness of this approach in a real environment.

• Korean journal of applied entomology
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• v.16 no.2 s.31
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• pp.87-92
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• 1977

A barley virus disease has been severe in central Korea since 1963. To investigate the causal virus, examination of host ranges, transmission by insect vectors and · electron microscopy were conducted. In electron microscopy, particles identical with northern cereal mosaic virus were observed. The size of baciliform particles ranged from 300nm to 370nm in length and 57-60nm in diameter. The virus was transmitted by the small brown planthopper Laodelphax striatellus (Fallen). The latent period in the vector was seven to nineteen days, with 10 days the most prevalent. Barley, corn, wheat, rye, and oats were susceptible to the virus when inoculated by the insect vectors. It was concluded that the disease agent of the barley disease in Korea is northern cereal mosaic virus. This is the first known report of this disease in Korea.

• Molecules and Cells
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• v.37 no.7
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• pp.518-525
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• 2014

Gammaherpesvirus (${\gamma}HV$) infection of the central nervous system (CNS) has been implicated in diverse neurological diseases, and murine ${\gamma}HV$-68 (MHV-68) is known to persist in the brain after cerebral infection. The underlying molecular mechanisms of persistency of virus in the brain are poorly understood. Here, we characterized a unique pattern of MHV-68 persistent infection in neuroblastoma cells. On infection with MHV-68, both murine and human neuroblastoma cells expressed viral lytic proteins and produced virions. However, the infected cells survived productive infection and could be cultured for multiple passages without affecting their cellular growth. Latent infection as well as productive replication was established in these prolonged cultures, and lytic replication was further increased by treatment with lytic inducers. Our results provide a novel system to study persistent infection of ${\gamma}HVs$ in vitro following de novo infection and suggest application of MHV-68 as a potential gene transfer vector to the brain.

• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.1-11
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• 1991

It is of great interest to consider the homogeniety of covariance matrices in MANOVA of discriminant analysis. If we lock at the problem of testing hypothesis, H : $\Sigma_1 = \Sigma_2$ from an invariance point of view where $\Sigma_i$ are the covariance matrix of two independent p-variate distribution, the testing problem is invariant under the group of nonsingular transformations and the hypothesis becomes H : $\delta_1 = \delta_2 = \cdots = \delta_p = 1$ where $\delta = (\delta_1, \delta_2, \cdots, \delta_p)$ is a vector of latent roots of $\Sigma$. Bias-corrected estimators of eigenvalues and sampling distribution of the test statistics proposed are obtained. Pooled-bootstrap method also considered for Bartlett's modified likelihood ratio statistics.

• ETRI Journal
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• v.44 no.3
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• pp.413-425
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• 2022

The study of literary texts is one of the earliest disciplines practiced around the globe. Poetry is artistic writing in which words are carefully chosen and arranged for their meaning, sound, and rhythm. Poetry usually has a broad and profound sense that makes it difficult to be interpreted even by humans. The essence of poetry is Rasa, which signifies mood or emotion. In this paper, we propose a poetry classification-based approach to automatically extract similar poems from a repository. Specifically, we perform a novel Rasa-based classification of Hindi poetry. For the task, we primarily used lexical features in a bag-of-words model trained using the support vector machine classifier. In the model, we employed Hindi WordNet, Latent Semantic Indexing, and Word2Vec-based neural word embedding. To extract the rich feature vectors, we prepared a repository containing 37 717 poems collected from various sources. We evaluated the performance of the system on a manually constructed dataset containing 945 Hindi poems. Experimental results demonstrated that the proposed model attained satisfactory performance.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.192-198
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• 2023

Cyberbullying is a growing problem among adolescents and can have serious psychological and emotional consequences for the victims. In recent years, machine learning techniques have emerged as promising approach for detecting instances of cyberbullying in online communication. This research paper focuses on developing a machine learning models that are able to detect cyberbullying including support vector machines, naïve bayes, and random forests. The study uses a dataset of real-world examples of cyberbullying collected from Twitter and extracts features that represents the ideational metafunction, then evaluates the performance of each algorithm before and after considering the theory of systemic functional linguistics in terms of precision, recall, and F1-score. The result indicates that all three algorithms are effective at detecting cyberbullying with 92% for naïve bayes and an accuracy of 93% for both SVM and random forests. However, the study also highlights the challenges of accurately detecting cyberbullying, particularly given the nuanced and context-dependent nature of online communication. This paper concludes by discussing the implications of these findings for future research and the development of practical tool for cyberbullying prevention and intervention.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.