• Korean Journal of Poultry Science
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• v.47 no.1
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• pp.9-19
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• 2020

Avian influenza (AI) viruses are highly contagious viruses that infect many bird species and are zoonotic. Ducks are resistant to the deadly and highly pathogenic avian influenza virus (HPAIV) and remain asymptomatic to the low pathogenic avian influenza virus (LPAIV). In this study, we identified common differentially expressed genes (DEGs) after a reanalysis of previous transcriptomic data for the HPAIV and LPAIV infected duck lung cells. Microarray datasets from a previous study were reanalyzed to identify common target genes from DEGs and their biological functions. A total of 731 and 439 DEGs were identified in HPAIV- and LPAIV-infected duck lung cells, respectively. Of these, 227 genes were common to cells infected with both viruses, in which 193 genes were upregulated and 34 genes were downregulated. Functional annotation of common DEGs revealed that translation related gene ontology (GO) terms were enriched, including ribosome, protein metabolism, and gene expression. REACTOME analyses also identified pathways for protein and RNA metabolism as well as for tissue repair, including collagen biosynthesis and modification, suggesting that AIVs may evade the host defense system by suppressing host translation machinery or may be suppressed before being exported to the cytosol for translation. AIV infection also increased collagen synthesis, showing that tissue lesions by virus infection may be mediated by this pathway. Further studies should focus on these genes to clarify their roles in AIV pathogenesis and their possible use in AIV therapeutics.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.179-188
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• 2012

A kiwifruit cultivar 'Hayward' has been grown in Jeju Island where the current climate is suitable for growth and development of this crop. Prediction of the geographical shift in the phenology can help the kiwifruits growers to adapt to the local climate change in the future. Two phenology models (i.e., chill-day and DVS) were parameterized to estimate flowering date of kiwifruits 'Hayward' based on the data collected from field plots and chamber experiments in the southern coastal and island locations in South Korea. Spatio-temporally independent datasets were used to evaluate performance of the two models in predicting flowering date of 'Hayward'. Chill-day model showed better performance than DVS model (2.5 vs. 4.0 days in RMSE). Daily temperature data interpolated at a higher spatial resolution over Jeju Island were used to predict flowering dates of 'Hayward' in 2021-2100 under the A1B scenario. According to the model calculation under the future climate condition, the flowering of kiwifruits shall accelerate and the area with poor flowering might increase due to the warmer winter induced insufficient chilling. Optimal land area for growing 'Hayward' could increase for a while in the near future (2021-2030), whereas such areas could decrease to one half of the current areas by 2100. The geographic locations suitable for 'Hayward' cultivation would migrate from the current coastal area to the elevated mountain area by 250 m.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.3
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• pp.119-123
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• 2012

The duration of low temperature in winter season is one of the important agrometeorological characteristics in crop growing fields. This study was conducted to develop a method to estimate the duration of low-temperature with monthly meteorological data. Using daily meteorological data from 61 observation sites from 1981 to 2010, we analyzed the relationships between the averages of monthly temperature minima and the durations of low-temperature ranging from -15 to $5^{\circ}C$, The monthly mean of the January minimum air temperature was appropriate for theestimation of the durations of lowtemperature below $0^{\circ}C$. We tested a simple second order equation to predict durations of low-temperature. To apply the equation to various temperature ranges, we suggested two different equations for the estimation of coefficients a and b, which are dependent on the base temperatures from -15 to $0^{\circ}C$. Thevalidation of the equations using other daily meteorological datasets from 1971 to 2000 showed that they were appropriate for the range from -10 to $0^{\circ}C$, but underestimated at $-15^{\circ}C$.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.169-176
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• 2020

Recently, deep neural networks (DNNs) have brought revolutions to many mobile and embedded devices by providing human-level machine intelligence for various applications. However, high inference accuracy of such DNNs comes at high computational costs, and, hence, there have been significant efforts to reduce computational overheads of DNNs either by compressing off-the-shelf models or by designing a new small footprint DNN architecture tailored to resource constrained devices. One notable recent paradigm in designing small footprint DNN models is sharing parameters in several layers. However, in previous approaches, the parameter-sharing techniques have been applied to large deep networks, such as ResNet, that are known to have high redundancy. In this paper, we propose a parameter-sharing method for already parameter-efficient small networks such as ShuffleNetV2. In our approach, small templates are combined with small layer-specific parameters to generate weights. Our experiment results on ImageNet and CIFAR100 datasets show that our approach can reduce the size of parameters by 15%-35% of ShuffleNetV2 while achieving smaller drops in accuracies compared to previous parameter-sharing and pruning approaches. We further show that the proposed approach is efficient in terms of latency and energy consumption on modern embedded devices.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.15-23
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• 2017

The person Re-identification is the most challenging part of computer vision due to the significant changes in human pose and background clutter with occlusions. The picture from non-overlapping cameras enhance the difficulty to distinguish some person from the other. To reach a better performance match, most methods use feature selection and distance metrics separately to get discriminative representations and proper distance to describe the similarity between person and kind of ignoring some significant features. This situation has encouraged us to consider a novel method to deal with this problem. In this paper, we proposed an enhanced recurrent neural network with three-tier hierarchical network for person re-identification. Specifically, the proposed recurrent neural network (RNN) model contain an iterative expectation maximum (EM) algorithm and three-tier Hierarchical network to jointly learn both the discriminative features and metrics distance. The iterative EM algorithm can fully use of the feature extraction ability of convolutional neural network (CNN) which is in series before the RNN. By unsupervised learning, the EM framework can change the labels of the patches and train larger datasets. Through the three-tier hierarchical network, the convolutional neural network, recurrent network and pooling layer can jointly be a feature extractor to better train the network. The experimental result shows that comparing with other researchers' approaches in this field, this method also can get a competitive accuracy. The influence of different component of this method will be analyzed and evaluated in the future research.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.87-98
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• 2012

A river boundary is defined as the intersection between a main stream of a river and the land. Mapping of the river boundary is important for the protection of the properties in river areas, the prevention of flooding and the monitoring of the topographic changes in river areas. However, the utilization of the ground surveying technologies is not efficient for the mapping of the river boundary due to the irregular surfaces of river zones and the dynamic changes of water level of a river stream. Recently, the spatial information data sets such as the airborne LiDAR and aerial images are widely used for coastal mapping due to the acquisition of the topographic information without human accessibility. Due to these advantages, this research proposes a semi-automatic method for mapping of the river boundary using the spatial information data set such as the airborne LiDAR and the aerial photographs. Multiple image processing technologies such as the image segmentation algorithm and the edge detection algorithm are applied for the generation of the 3D river boundary using the aerial photographs and airborne topographic LiDAR data. Check points determined by the experienced expert are used for the measurement of the horizontal and vertical accuracy of the generated 3D river boundary. Statistical results show that the generated river boundary has a high accuracy in horizontal and vertical direction.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.76-86
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• 2012

The purpose of this study is to investigate the environmental equity of the accessibility to public transportation services in the city of Daegu. The 2005 census data as well as bus stop and subway station datasets were integrated for building the GIS database. Public transportation service areas were then identified by a coverage method. Mann Whitney U test was used for statistically comparing the socioeconomic characteristics over different levels of access to the public transportation services. Both Dong-gu, Suseong-gu, Dalseo-gu, and Buk-gu located outside of the city had worse accessibility than others while Jung-gu, Seo-gu, and Nam-gu had better accessibility than others. There appeared no environmental inequity pattern in terms of the percentages of men, women, and teenagers over the city of Daegu whereas there existed some environmental inequity pattern in terms of the percentages of people above the age of 65 and people below poverty line. This environmental inequity pattern would be caused by some factors. Firstly, the lower income class has tended to reside in the declined or blighted areas far away from public transportation facilities since this class can not afford to pay expensive rents and land prices around the main roads with higher accessibility. Many old people belonging to the lower income class also reside in the declined or blighted areas. Secondly, there has been no law to locate bus stops and subway stations considering residents' socioeconomic characteristics and the spatial distribution of public transportation facilities has been not managed systematically by the city government. This research would shed insight on building the public transportation policy to locate bus stops and subway stations and to select the routes of buses and subways considering the spatial distribution of residents' socioeconomic characteristics.

• Journal of the Korea Computer Graphics Society
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• v.26 no.3
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• pp.31-37
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• 2020

Identification of the mandibular canal path in Computed Tomography (CT) scans is important in dental implantology. Typically, prior to the implant planning, dentists find a sagittal plane where the mandibular canal path is maximally observed, to manually identify the mandibular canal. However, this is time-consuming and requires extensive experience. In this paper, we propose a deep-learning-based framework to detect the desired sagittal plane automatically. This is accomplished by utilizing two main techniques: 1) a modified version of the iterative transformation network (ITN) method for obtaining initial planes, and 2) a fine searching method based on a convolutional neural network (CNN) classifier for detecting the desirable sagittal plane. This combination of techniques facilitates accurate plane detection, which is a limitation of the stand-alone ITN method. We have tested on a number of CT datasets to demonstrate that the proposed method can achieve more satisfactory results compared to the ITN method. This allows dentists to identify the mandibular canal path efficiently, providing a foundation for future research into more efficient, automatic mandibular canal detection methods.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.52-65
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• 2012

The net biomass accumulation (or net primary production, NPP) and gross primary production (GPP) have closely related with carbon accumulations(or carbon exchange) in vegetation. There are many approaches to estimate biomass using remote sensing techniques. The vegetation indices (VIs) can be a methodology to estimate biomass which assumes total chlorophyll contents. Various VIs were characterized with difference development conditions as vegetation species, input datasets. The hyperspectral data have also different spatial/spectral resolutions for aerial surveying. Additionally they need particular spectral bands selection difficulty to calculate the VIs. The objective of this study is to evaluate the correlations with airborne hyperspectral data (compact airborne spectrographic imager, CASI) and spectral unmixing model (or spectral mixture analysis, SMA) to characterize vegetation indices in forest area. The spectral mixture analysis was used to model the spectral purity of each pixel as an endmember. The endmembers are the fraction components derived from hyperspectral data through the SMA. In this study, we choose three endmembers represented vegetation pixels in the hyperspectral data. These endmembers were compared with 9 VIs by the Pearson's correlation coefficient. The results show MTVI1 and TVI have same correlation coefficient with 0.877. The MCARI, especially has very high relationship with vegetation endmembers as 0.9061 at less vegetation and soil distributed site. The MTVI1 and TVI have high correlations with the vegetation endmembers as 0.757 in whole test sites.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.487-498
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• 2005

Recently, It has focused on decision tree algorithm that can handle large dataset. However, because most of these algorithms for large datasets process data in a batch mode, if new data is added, they have to rebuild the tree from scratch. h more efficient approach to reducing the cost problem of rebuilding is an approach that builds a tree incrementally. Representative algorithms for incremental tree construction methods are BOAT and ITI and most of these algorithms use a local discretization method to handle the numeric data type. However, because a discretization requires sorted numeric data in situation of processing large data sets, a global discretization method that sorts all data only once is more suitable than a local discretization method that sorts in every node. This paper proposes an incremental tree construction method that efficiently rebuilds a tree using a global discretization method to handle the numeric data type. When new data is added, new categories influenced by the data should be recreated, and then the tree structure should be changed in accordance with category changes. This paper proposes a method that extracts sample points and performs discretiration from these sample points to recreate categories efficiently and uses confidence intervals and a tree restructuring method to adjust tree structure to category changes. In this study, an experiment using people database was made to compare the proposed method with the existing one that uses a local discretization.