• Nuclear Medicine and Molecular Imaging
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• v.43 no.4
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• pp.323-329
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• 2009

Purpose: Radioimmunoassay (RIA) was usually performed by the batch assay. To improve the efficiency of RIA without increase of the cost and time, random assay could be a choice. We investigated the possibility of the random assay using automatic dispenser by assessing the agreement between batch assay and random assay. Materials and Methods: The experiments were performed with four items; Triiodothyronine (T3), free thyroxine (fT4), Prostate specific antigen (PSA), Carcinoembryonic antigen (CEA). In each item, the sera of twenty patients, the standard, and the control samples were used. The measurements were done 4 times with 3 hour time intervals by random assay and batch assay. The coefficient of variation (CV) of the standard samples and patients' data in T3, fT4, PSA, and CEA were assessed. ICC (Intraclass correlation coefficient) and coefficient of correlation were measured to assessing the agreement between two methods. Results: The CVs (%) of T3, fT4, PSA, and CEA measured by batch assay were 3.2$\pm$1.7%, 3.9$\pm$2.1%, 7.1$\pm$6.2%, 11.2$\pm$7.2%. The CVs by random assay were 2.1$\pm$1.7%, 4.8$\pm$3.1%, 3.6$\pm$4.8%, and 7.4$\pm$6.2%. The ICC between the batch assay and random assay were 0.9968 (T3), 0.9973 (fT4), 0.9996 (PSA), and 0.9901 (CEA). The coefficient of correlation between the batch assay and random assay were 0.9924(T3), 0.9974 (fT4), 0.9994 (PSA), and 0.9989 (CEA) (p<0.05). Conclusion: The results of random assay showed strong agreement with the batch assay in a day. These results suggest that random assay using automatic dispenser could be used in radioimmunoassay.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.91-121
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• 2008

Electronic commerce, commonly known as e-commerce or eCommerce, has become a major business trend in these days. The amount of trade conducted electronically has grown extraordinarily by developing the Internet technology. Most electronic commerce has being conducted between businesses to customers; therefore, the researches with respect to e-commerce are to find customer's needs, behaviors through statistical methods. However, the statistical researches, mostly based on a questionnaire, are the static researches, They can tell us the dynamic relationships between initial purchasing and repurchasing. Therefore, this study proposes dynamic research model for analyzing the cause of initial purchasing and repurchasing. This paper is based on the System-Dynamic theory, using the powerful simulation model with some restriction, The restrictions are based on the theory TAM(Technology Acceptance Model), PAM, and TPB(Theory of Planned Behavior). This article investigates not only the customer's purchasing and repurchasing behavior by passing of time but also the interactive effects to one another. This research model has six scenarios and three steps for analyzing customer behaviors. The first step is the research of purchasing situations. The second step is the research of repurchasing situations. Finally, the third step is to study the relationship between initial purchasing and repurchasing. The purpose of six scenarios is to find the customer's purchasing patterns according to the environmental changes. We set six variables in these scenarios by (1) changing the number of products; (2) changing the number of contents in on-line shopping malls; (3) having multimedia files or not in the shopping mall web sites; (4) grading on-line communities; (5) changing the qualities of products; (6) changing the customer's degree of confidence on products. First three variables are applied to study customer's purchasing behavior, and the other variables are applied to repurchasing behavior study. Through the simulation study, this paper presents some inter-relational result about customer purchasing behaviors, For example, Active community actions are not the increasing factor of purchasing but the increasing factor of word of mouth effect, Additionally. The higher products' quality, the more word of mouth effects increase. The number of products and contents on the web sites have same influence on people's buying behaviors. All simulation methods in this paper is not only display the result of each scenario but also find how to affect each other. Hence, electronic commerce firm can make more realistic marketing strategy about consumer behavior through this dynamic simulation research. Moreover, dynamic analysis method can predict the results which help the decision of marketing strategy by using the time-line graph. Consequently, this dynamic simulation analysis could be a useful research model to make firm's competitive advantage. However, this simulation model needs more further study. With respect to reality, this simulation model has some limitations. There are some missing factors which affect customer's buying behaviors in this model. The first missing factor is the customer's degree of recognition of brands. The second factor is the degree of customer satisfaction. The third factor is the power of word of mouth in the specific region. Generally, word of mouth affects significantly on a region's culture, even people's buying behaviors. The last missing factor is the user interface environment in the internet or other on-line shopping tools. In order to get more realistic result, these factors might be essential matters to make better research in the future studies.

• Analytical Science and Technology
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• v.28 no.6
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• pp.385-397
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• 2015

Harmful and potentially harmful constituents (HPHCs) are chemical compounds in tobacco smoke that cause harm to smokers and non-smokers. This study established and validated methods for the analysis of HPHCs from mainstream cigarette smoke. The analyzed HPHCs were categorized into seven groups: aromatic amines, volatile organic compounds (VOCs), heavy metals, tobacco specific nitrosamines (TSNAs), benzo[a]pyrene (B[a]P), ammonia, and carbonyl compounds. The methods were validated by specificity, linearity, limit of detection (LOD), accuracy, precision, and recovery. These validated methods were then applied to the reference cigarettes (1R5F, 3R4F). The correlation coefficients (r²) for the calibration curves of the seven groups were over 0.995. The LODs showed values of 0.01-0.04 ng/cig cig for aromatic amines, 0.01-0.16 μg/cig for VOCs, 0.01-1.27 ng/cig for heavy metals, 0.06-0.28 ng/cig for TSNAs, 0.04 ng/cig for benzo[a]pyrene, 0.08 μg/cig for ammonia, and 0.78-1.77 μg/cig for carbonyl compounds. The precisions obtained from the intra and inter-day batches were less than 15%. The accuracy and the recovery range were less than 15% and 79.2-117.5%, respectively. The proposed methods can therefore be applied for determining HPHCs in tobacco mainstream smoke.

• Analytical Science and Technology
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• v.28 no.5
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• pp.323-330
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• 2015

Alkaptonuria, a rare inherited metabolic disease, is characterized by a lack of homogentisate dioxygenase and accumulation of homogentisic acid (HGA), leading to homogentisic aciduria, arthritis, and ochronosis. In this study, a rapid analytical method, without an expensive and tedious solid phase extraction step, was developed to quantify HGA in plasma using GC-MS. HGA-spiked pooled plasma samples were subjected to liquid-liquid extraction (LLE) with ethyl acetate, followed by trimethylsilyl derivatization (TMS) and GC-MS quantification using selected ion monitoring. The formation of TMS derivative of the 1 carboxylic and 2 hydroxyl functional groups was performed by reacting BSTFA (with 10% TMCS) for 5 min at 80 ℃. For selected ion monitoring, quantification and confirmation ions were determined based on specific ions (m/z 384, m/z 341 and m/z 252) of the TMS derivative of HGA. Calibration curves of pooled normal plasma specimens showed a linear relationship in the range of 1-100 ng/µL. The precision and accuracy were within a relative standard deviation (RSD) of 1 to 15% and a bias of -5 to 25%. Recoveries were obtained in the range of 99-125% and 95-115% for intra-day and inter-day assay, respectively, at 2, 20 and 80 ng/µL. The limit of detection (LOD) and limit of quantification (LOQ) were 0.4 ng/µL and 4 ng/µL, respectively. No homogentisic acid was excreted from normal Korean plasma samples. Collectively, the results from the present study suggest that this method could be useful for routine diagnosis and therapeutic monitoring of alkaptonuria patients with excellent sensitivity and rapidity.

• Management & Information Systems Review
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• v.35 no.3
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• pp.57-79
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• 2016

It is required to derive various conclusions by identifying the type of power and the relationship between SCMs and presenting practical implications. Thus, we can identify the differential effects of each type of power on SCM performance. We can contribute to develop the practical implications at more sophisticated multi-dimension by comparing results of this study with various SCM theories. Through previous studies, the source of power is largely divided into binding power and non-binding power. Binding power is classified into behavior coercion, binding reward and relationship legitimacy. Non-binding power is classified into work expertise, information superiority and value compliance. Enterprises should fully understand and recognize partners within supply chains including understanding of the source of power, imbalance and results. Thus, we look into types of power and effects on trust and commitment, and identify a causal relationship leading to collaboration and SCM performance. Specific research results are as follows. First, the binding power did not give a significant effect to the trust. However, the binding power gave a positively(+) significant effect to the commitment. Second, non-binding power showed a significant effect on both trust and commitment. As a result of analysis on total effects, it was shown that non-binding power gave indirect effects to collaboration and SCM performance. Third, it was shown that both trust and commitment significantly affected collaboration. From the perspectives of social exchange theory and trading cost theory among inter-organizational relationship theory, it may lead to SCM performance of trust, commitment and collaboration. Moreover, it was found that association of each attribute of power led to the significant result. Fourth, it was shown that trust and collaboration significantly affected SCM performance. However, commitment did not directly affect SCM performance, but it indirectly significantly affected SCM performance through collaboration. Proper use of this power can firmly build partnerships between members of the supply chain and induce the improvement on supply chain performance and satisfaction of members.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.20 no.3
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• pp.131-140
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• 2015

To set up unicellular cyanobacterial strains with photo-biological $H_2$ production potential, live samples were repeatedly collected from 68 stations in the coastal zone of Korea for the four years since 2005. Among 77 cyanobacterial strains established six (KNU strains, CB-MAL002, 026, 031, 054, 055 and 058) were finally chosen as the excellent strains for $H_2$ production with $H_2$ accumulation over 0.15 mL $H_2\;mL^{-1}$ under general basic $H_2$ production conditions as well as positive $H_2$ production for more than 60 hr. To explore optimum procedures for higher $H_2$ production efficiency of the six cyanobacterial strains, the inter-strain differences in the growth rate under the gradients of water temperature and salinity were investigated. The maximum daily growth rates of the six strains ranged from 1.78 to 2.08, and all of them exhibited $N_2-fixation$ ability. Based on the similarity of the 16S rRNA sequences, all the test strains were quite close to Cyanothece sp. ATCC51142 (99%). The six strains, however, were grouped into separate clades from strain ATCC51142 in the molecular phylogeny diagram. Chlorophyll- a content was 3.4~7.8% of the total dried weight, and the phycoerythrin and phycocyanin contents were half of those in the Atlantic strain, Synechococcus sp. Miami BG03511. The growth of the six strains was significantly suppressed at temperatures above the optimal range, $30{\sim}35^{\circ}C$, to be nearly stopped at $40^{\circ}C$. The growth was not inhibited by high salinities of 30 psu salinity in all the strains while strain CB055 maintained its high growth rate at low salinities down to 15 psu. The euryhaline strains like CB055 might support massive biotechnological cultivation systems using natural basal seawater in temperate latitudes. base seawater. The biological and ecophysiological characteristics of the test strains may contribute to designing the optimal procedures for photo-biological $H_2$ production by unicellular cyanobacteria.

• Korean Journal of Weed Science
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• v.30 no.4
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• pp.330-339
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• 2010

In order to suggest correct direction of researches on Miscanthus spp. which are promising bioenergy crop, authors had reviewed and summarized various literature about botanical taxonomy, morphology and present condition of breeding, cultivation and utilization of miscanthus. Among the genus of Miscanthus which are known 17 species, the most important species are M. sinensis and M. sacchariflorus which origin are East Asia including Korea, and M. x giganteus which is inter-specific hybrid of tetraploid M. sacchariflorus and diploid M. sinensis. Miscanthus is superior to other energy crops in resistance to poor environments including cold, saline and damp soil, nitrogen utilization efficiency, budget of input energy and carbon which are required for producing biomass and output which are stored in biomass. The major species for production of energy and industrial products including construction material in Europe, USA and Canada is M. x giganteus which was introduced from Japan in 1930s. In present, many breeding programs are conducted to supplement demerits of present varieties and to develop "Miscanes" which is hybrid of miscanthus and sugar cane. In Korea, the researches on breeding and cultivation of miscanthus were initiated in 2007 by collecting germplasms, and developed "Goedae-Uksae 1" which is high biomass yield and "mass propagation method of miscanthus" which can improve propagation efficiency in 2009. In order to develop "Korean miscanthus industry" in future, the superior varieties available not only domestic but also foreign market should be developed by new breeding method including molecular markers. Researches on production process of cellulosic bio-ethanol including pre-treatment and saccharification of miscanthus biomass also should be strengthen.

• Journal of Animal Science and Technology
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• v.53 no.4
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• pp.303-310
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• 2011

Phylogenetic relationships of Jeju dogs to other domestic and foreign dog breeds were assessed using mtDNA D-loop sequences. Neighbor-joining trees were constructed using complete sequences (970 bp excluding the tandem repeat region) determined for five Cheju, four Jindo, four Sapsaree, five Pungsan, two of each East and West Laika dogs (Canis familiaris), two gray wolves (Canis lupus) and two coyotes (Canis latrans) and also published complete sequences for dogs. Coyote sequences were used as outgroups. In addition, a total of 214 haplotypes of 598bp D-loop sequences from 30 dog breeds were collected from GenBank and used to investigate genetic structure of population. In the analyses of full D-loop sequence variation and the phylogenetic trees constructed by neighbor-joining method, neither haplotypes nor clades specific for any domestic dog breeds were observed. The inter-species sequence variation (4.51%) between domestic dogs and wolves was much higher than the intra-species sequence variation within domestic dogs (1.63%) and wolves (3.64%). The divergence of the dog and wolf occurred approximately 1~2 million years ago based on these values. The taxa of Jeju dog breed in the phylogenetic tree are clustered separately and intermingled with other taxa of breeds, suggesting that active crossbreeding of Jeju dogs with other domestic breeds.