• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.277-288
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• 2010

The multi-level $H_2O/CO_2$ profile system has been widely used to quantify the storage and advection effects on energy and mass fluxes measured by eddy covariance systems. In this study, we expanded the utility of the profile system by accommodating air sampling devices for isotope analyses of water vapor and $CO_2$. A pre-evacuated 2L glass flask was connected to the discharge of an Infrared Gas Analyzer (IRGA) of the profile system so that airs with known concentration of $H_2O$ and $CO_2$ can be sampled. To test the performance of this sampling system, we sampled airs from 8 levels (from 0.1 to 40 m) at the KoFlux tower of Gwangneung deciduous forest, Korea. Air samples in the 2L flask were separated into its component gases and pure $H_2O$ and $CO_2$ were extracted by using a vacuum extraction line. This novel technique successfully produced vertical profiles of ${\delta}D$ of $H_2O$ and ${\delta}^{13}C$ of $CO_2$ in a mature forest, and estimated ${\delta}D$ of evapotranspiration (${\delta}D_{ET}$) and ${\delta}^{13}C$ of $CO_2$ from ecosystem respiration (${\delta}^{13}C_{resp}$) by using Keeling plots. While technical improvement is still required in various aspects, our sampling system has two major advantages over other proposed techniques. First, it is cost effective since our system uses the existing structure of the profile system. Second, both $CO_2$ and $H_2O$ can be sampled simultaneously so that net ecosystem exchange of $H_2O$ and $CO_2$ can be partitioned at the same temporal resolution, which will improve our understanding of the coupling between water and carbon cycles in terrestrial ecosystems.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.384-398
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• 2015

In this paper, the high-resolution Weather Research and Forecasting/Noah-MultiParameterization (WRF/Noah-MP) modeling system is configured for the Cheongmicheon Farmland site in Korea (CFK), and its performance in land and atmospheric simulation is evaluated using the observed data at CFK during the 2014 special observation period (21 August-10 September). In order to explore the usefulness of turning on Noah-MP dynamic vegetation in midterm simulations of surface and atmospheric variables, two numerical experiments are conducted without dynamic vegetation and with dynamic vegetation (referred to as CTL and DVG experiments, respectively). The main results are as following. 1) CTL showed a tendency of overestimating daytime net shortwave radiation, thereby surface heat fluxes and Bowen ratio. The CTL experiment showed reasonable magnitudes and timing of air temperature at 2 m and 10 m; especially the small error in simulating minimum air temperature showed high potential for predicting frost and leaf wetness duration. The CTL experiment overestimated 10-m wind and precipitation, but the beginning and ending time of precipitation were well captured. 2) When the dynamic vegetation was turned on, the WRF/Noah-MP system showed more realistic values of leaf area index (LAI), net shortwave radiation, surface heat fluxes, Bowen ratio, air temperature, wind and precipitation. The DVG experiment, where LAI is a prognostic variable, produced larger LAI than CTL, and the larger LAI showed better agreement with the observed. The simulated Bowen ratio got closer to the observed ratio, indicating reasonable surface energy partition. The DVG experiment showed patterns similar to CTL, with differences for maximum air temperature. Both experiments showed faster rising of 10-m air temperature during the morning growth hours, presumably due to the rapid growth of daytime mixed layers in the Yonsei University (YSU) boundary layer scheme. The DVG experiment decreased errors in simulating 10-m wind and precipitation. 3) As horizontal resolution increases, the models did not show practical improvement in simulation performance for surface fluxes, air temperature, wind and precipitation, and required three-dimensional observation for more agricultural land spots as well as consistency in model topography and land cover data.

• The Korean Journal of Malacology
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• v.31 no.2
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• pp.83-92
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• 2015

The effect of different intermediated rearing method by expanding the attached floor space in order to highly density culture on the growth characteristics and survival rate of the juvenile abalone, Haliotis discus hannai, were investigated in land-based tanks. The intermediated culture methods was determined thru the shelter counts and layer for 10 month with two replicates : the single layer shelter (SLS), the double layer shelter (DLS), the triple layer shelter (TLS) and the single layer shelter under net cage (SLSNC). In addition, the culture on shallow race way tank had to set up as culture of the ditch raceway tank (CDRT) and the floor race way tank (CFRT). In the growth performance of reared abalone (initial mean shell length $54.18{\pm}7.39mm$ and weight $1.93{\pm}0.14g$) at experimental tanks, that the absolute growth rate (ARG), daily growth rate (DGR) and specific growth rate (SGR) to the shell length and shell breadth was not significant at each experimental tanks except SLSNC. As well as too, weight gain (WG), daily weight gain (DWG) and specific weight gain (SWG) to weight was not significant at each experimental tanks except SLSNC, too. Survival rates of CDRT and CFRT was lower than those of different experimental tanks (P < 0.05). Therefore, these results is showed that high density different intermediated rearing method by expanding the attached floor space for juvenile H. discus hannai was not have difference as growth performance and survival rate both one layer shelter and multi layer shelter. Also, it is considered that shallow race way tank was not useful rearing for the juvenile intermediate culture of H. discus hannai in land based.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2539-2548
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• 2015

Overcoming inter-cell interference and spectrum scarcity are major issues in heterogeneous cellular networks. Static Frequency reuse schemes have been proposed as an effective way to manage the spectrum and reduce ICI(Inter cell Interference) in cellular networks. In a kind of static frequency reuse scheme, the allocations of transmission power and subcarriers in each cell are fixed prior to system deployment. This limits the potential performance of the static frequency reuse scheme. Also, most of dynamic frequency reuse schemes did not consider small cell and the network environment when the traffic load of each cell is heavy and non-uniform. In this paper, we propose an inter-cell resource allocation algorithm that dynamically optimizes subcarrier allocations for the multi-cell heterogeneous networks. The proposed dynamic frequency reuse scheme first finds the subcarrier usage in each cell-edge by using the exhaustive search and allocates subcarrier for all the cells except small cells. After that it allocates subcarrier for the small cell and then iteratively repeats the process. Proposed dynamic frequency reuse scheme performs better than previous frequency reuse schemes in terms of the throughput by improving the spectral efficiency due to it is able to adapt the network environment immediately when the traffic load of each cell is heavy and non-uniform.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.506-514
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• 2013

This study examined the feasibility on the construction of a wood industrialization service center for a wood industry cluster establishment in Jeollanam-do. Construction of the wood industrialization service center is based on a discount rate of 3.5%, an investment period of 4 years, a business operations period of 16 years and an investment cost of 24600 million won; the total amount of the net present value, the cost-benefit ratio and the internal rate of return were assumed to be 2.579 million won, 2.51%, and 10.1%, respectively. In addition, the production inducement coefficient, the induced production effect, the income-induced coefficient, the income inducement effect, the employment inducement coefficient, and the employment inducement effect were estimated 1.4345, 35287 million won, 0.1655, 4000.7 million won, and 0.4665, 1,145 people, in the effects of the wood related industries using the multi-regional input-output model, respectively. Financial independence of operating income to cover its own costs incurred in accordance with the operating project might be practicable.

• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.115-125
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• 2016

Recently, many attentions have been paid to conducting convergence research across diverse disciplines. Along with this convergence era, an IT-based multi-disciplinary convergence project, called EDISON (EDucation-research Integrated Simulation On the Net), has been launched to support the studies of researchers engaged in several computational science and engineering (CSE) fields and to boost learning motivations of CSE students. Since 2011, we have been successfully carrying out the EDISON project. EDISON as a cyber-learning platform enables CSE researchers to share their own high-performance computing (HPC) simulation softwares developed to solve their research problems accompanying large-scale computation and I/O and users to run the softwares with little constraints on the web. Also, the EDISON platform has been utilized as lecture material by many universities in Korea. This article introduces the construction and service statistics of this EDISON platform. Specifically, we explicate several distinctions between EDISON and existing other HPC service platforms and discuss a three-layered technical architecture of the EDISON platform. We then present the up-to-date service statistics of EDISON over the past four years. Finally, we conclude this article and describe future plans.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.5
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• pp.748-754
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• 2010

The contents of soil organic matter (SOM) and $Al_o+1/2Fe_o$ in soils are important criteria for the classification of new Andisols in Soil Taxonomy system. There are many soil types in Jeju Island with various soil forming environments. This paper was conducted to estimate the contents of soil organic matter and the content of ammonium oxalate extracted Al and Fe ($Al_o+1/2Fe_o$) using various environmental variables and to make soil property maps using a statistical analyses. The soil samples were collected from 321 locations and analyzed to measure the contents of SOM and $Al_o+1/2Fe_o$. It was analyzed the relationships among them and various environmental variables such as temperature, precipitation, net primary product, radiation, evapotranspiration, altitude, soil forming energy, topographic wetness index, elevation, difference surrounded area, and distances from the shore and the peak. We can exclude multi-collinearity among environmental variables with principal component analysis and reduce all the variables to 3 principal components. The contents of SOM and $Al_o+1/2Fe_o$ were estimated by multiple regression models and maps of them were made using the models.

• Journal of KIISE:Information Networking
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• v.34 no.6
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• pp.514-524
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• 2007

Recent advances in the speed of multi-rate wireless local area networks (WLANs) and the proliferation of WLAN devices have made rate adaptive, opportunistic scheduling critical for throughput optimization. As WLAN traffic evolves to be more symmetric due to the emerging new applications such as VoWLAN, collaborative download, and peer-to-peer file sharing, opportunistic scheduling at the downlink becomes insufficient for optimized utilization of the single shared wireless channel. However, opportunistic scheduling on the uplink of a WLAN is challenging because wireless channel condition is dynamic and asymmetric. Each transmitting client has to probe the access point to maintain the updated channel conditions at the access point. Moreover, the scheduling decisions must be coordinated at all clients for consistency. This paper presents JUDS, a joint uplink/downlink opportunistic scheduling for WLANs. Through synergistic integration of both the uplink and the downlink scheduling, JUDS maximizes channel diversity at significantly reduced scheduling overhead. It also enforces fair channel sharing between the downlink and uplink traffic. Through extensive QualNet simulations, we show that JUDS improves the overall throughput by up to 127% and achieves close-to-perfect fairness between uplink and downlink traffic.