• Journal of the Korean Society of Radiology
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• v.84 no.2
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• pp.441-453
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• 2023

Purpose To evaluate the development, location, and volume of a VX2 carcinoma using four inoculation methods in a rabbit brain. Materials and Methods Inoculation of a VX2 cell suspension was performed 1) on the appointed day, 2) seven days after storing a VX2 carcinoma in a freezer or 3) seven days after storing a VX2 carcinoma in a deep freezer after sacrificing the donor rabbits. 4) Without sacrificing the rabbits, the VX2 cell suspension was obtained using a gun biopsy, inoculation was performed on the appointed day. MR imaging was performed 10 days after inoculation. Brain tissues were obtained the day after. The development, location, and volume of the tumor were evaluated. Results Seventeen of the 18 rabbits inoculated on the appointed day developed tumors (average tumor volume, 106.32 mm³). One of five inoculated seven days after storing the VX2 tumor in the freezer, and three of five inoculated seven days after storing the VX2 tumor in the deep freezer developed tumors. Inoculation with a VX2 cell suspension obtained with a gun biopsy from five rabbits revealed development of tumors in only two rabbits. The tumors mostly developed in the superficial cortex. Conclusion TVX2 rabbit brain tumor model is easy to develop and revealed variable reproducibility. This model can be applicable in radiologic imaging, treatment planning, interventional treatment and drug delivery research. VX2 cell can be successfully innoculated into the brain using variable methods under researcher's variable conditions.

• Journal of the Daesoon Academy of Sciences
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• v.49
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• pp.401-432
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• 2024

This article examines the types of "wind (바람)" accepted in sijo (three-verse poems), focusing on the accepted pattern, and investigating its role and meaning. The summary is as follows: first of all, the works of "wind" were accepted in the sijo, and the total number of works was 620. There were 459 short sijo, and 161 long sijo. On the other hand, 148 authors wrote on "The Wind," and in the Late Joseon Dynasty, 90 authors composed 265 poems, the most. In the Early Joseon Dynasty, 50 poets composed 111 poems. Most of them were civil officials, and 170 poems were identified. Next, the aspect of jang (章) was confirmed to occur 684 times in total, with 632 instances of being single uses and 52 instances of duplication. Meanwhile, the core of the sijo, the first sentence of the last chapter, contains 'wind (바람)' 34 times in 25 words. And in terms of the aspect of the particle combined with 'wind,' the nominative particle appeared the most at 113 instances, and the auxiliary particle 'eun/neun (은/는)' was the most numerous at 58 instances. As for the types of wind contained in sijo, there are 6 major categories: 106 medium categories, with the total frequency is 688. 'Singular' appears 133 times in 6 words, and 'combination' appears 121 times in terms of total frequency. The combination with terrestrial objects was the most frequent at 79 times, and the combination with 'heavenly' objects was 75 times with 3 words, and 'mixture' indicated a mixture of several objects, with 7 words occurring 42 times. Second the literary acceptance and role of 'wind' in Sijo was examined. First, 'acceptance' and the role as a medium for conveying ideas, acceptance and the role as the development of ideas, and acceptance and role of literary expression. Through this, it can be seen that 'wind' in Sijo was accepted in literature and played a major role. Lastly, the role and meaning of wind in Sijo can be seen in the fact that it remains differentiated from other form of ancient literature or other genres. It serves as a literary device that effectively expresses the theme, and the scope of the material accepted in Sijo was expanded through wind.

• 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.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.1
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• pp.45-52
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• 2015

Purpose : The dose distribution of organ at risk (OAR) and normal tissue is affected by treatment technique in postoperative radiation therapy for prostate cancer. The aim of this study was to compare dose distribution characteristic and to evaluate treatment efficiency by devising VMAT plans according to applying differed number of arc and IMRT plan for postoperative patient of prostate cancer radiation therapy using a rectal balloon. Materials and Methods : Ten patients who received postoperative prostate radiation therapy in our hospital were compared. CT images of patients who inserted rectal balloon were acquired with 3 mm thickness and 10 MV energy of HD120MLC equipped Truebeam STx (Varian, Palo Alto, USA) was applied by using Eclipse (Version 11.0, Varian, Palo Alto, USA). 1 Arc, 2 Arc VMAT plans and 7-field IMRT plan were devised for each patient and same values were applied for dose volume constraint and plan normalization. To evaluate these plans, PTV coverage, conformity index (CI) and homogeneity index (HI) were compared and $R_{50%}$ was calculated to assess low dose spillage as per treatment plan. $D_{25%}$ of rectum and bladder Dmean were compared on OAR. And to evaluate the treatment efficiency, total monitor units(MU) and delivery time were considered. Each assessed result was analyzed by average value of 10 patients. Additionally, portal dosimetry was carried out for accuracy verification of beam delivery. Results : There was no significant difference on PTV coverage and HI among 3 plans. Especially CI and $R_{50%}$ on 7F-IMRT were the highest as 1.230, 3.991 respectively(p=0.00). Rectum $D_{25%}$ was similar between 1A-VMAT and 2A-VMAT. But approximately 7% higher value was observed on 7F-IMRT compare to the others(p=0.02) and bladder Dmean were similar among the all plan(P>0.05). Total MU were 494.7, 479.7, 757.9 respectively(P=0.00) for 1A-VMAT, 2A-VMAT, 7F-IMRT and at the most on 7F-IMRT. The delivery time were 65.2sec, 133.1sec, 145.5sec respectively(p=0.00). The obvious shortest time was observed on 1A-VMAT. All plans indicated over 99.5%(p=0.00) of gamma pass rate (2 mm, 2%) in portal dosimetry quality assurance. Conclusion : As a result of study, postoperative prostate cancer radiation therapy for patient using a rectal balloon, there was no significant difference of PTV coverage but 1A-VMAT and 2A-VMAT were more efficient for dose reduction of normal tissue and OARs. Between VMAT plans. $R_{50%}$ and MU were little lower in 2A-VMAT but 1A-VMAT has the shortest delivery time. So it is regarded to be an effective plan and it can reduce intra-fractional motion of patient also.

• Journal of agricultural medicine and community health
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• v.26 no.2
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• pp.15-41
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• 2001

Since medical clients and the community they live in are expected to be center of future public health and medical care system, new service programs must be developed with patients focused on in line with widening public access of information and social participation. Patients- focused service shall mean the area- oriented provision of public health service. In this study, health officers working at public health centers, public health sub- centers and medical offices in Jeonbuk- do area were taken for population in order to investigate their attitudes toward and knowledge about the service area assigning system under the public health programs. Findings from the survey to 260 health officers, divided by general category, are as follows : Government officers at public health organizations appeared to have high grade of understanding to the service area assigning system and also great appreciation for the necessity of it. Regarding the timing for the system to be introduced, they support the gradual implementation and, as for the type of service to be provided, they preferred home nursing and treatment of chronic diseases. Highly positive responses were centered on the health classes under the health promotion projects, and as far as health projects for the old are concerned, services for home nursing, for the disabled and for home- alone people are favored most. On the other hand, budgeting, manpower and reorganization are rated as prerequisite to establishment of the service area assigning system. From the viewpoint of system side, the improvement of working conditions is rendered as most urgent, while the information system for establishing the service area assigning system is conceived far from satisfactory. Proper assignment of specialists was noted as mostly important to establish the delivery system for medical service through the service area assigning system by team. As merits of the service area assigning system, it is pointed out that, through the system, health clients can better be managed and the nursing quality will be improved thank to the enhanced specialization. It is also perceived that the district health service is not well prepared to respond to the increased and diversified needs of community people and, furthermore, service programs of health centers have not been fully developed. The most serious problem standing in the way to expansion of health projects is, it is noted, uniformity (formality) of the project. Based on the results of the survey which suggest time has ripen to introduce the service area assigning system, following strategies are proposed to anchor down the system as soon as possible: First, we should introduce the system gradually, starting from the area selected, and in consideration of area specialities, refraining from the hitherto stereotyped way of providing health service. Second, we should seek to properly assign the specialists and improve the working conditions of the assigned officers by securing sufficient budget, since it is a most urgent step to lay foundation for the service area assigning system. Third, best service program should be developed to meet the satisfaction of community people by responding to their needs and solidifying the management of medical clients. Fourth, wide scope of study should further be conducted in order to help this system take roots in the central living of community residents since pilot project on the experimental base attended by specialists only can not win popularity among the masses.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.

• Tuberculosis and Respiratory Diseases
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• v.54 no.5
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• pp.551-562
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• 2003

Background : NF-${\kappa}B$ is a characteristic transcriptional factor which has been shown to regulate production of acute inflammatory mediators and to be involved in the pathogenesis of many inflammatory lung diseases. There has been some evidence that PI3K/Akt pathway could activate NF-${\kappa}B$ in human cell lines. However, the effect of PI3K/Akt pathway on the activation of NF-${\kappa}B$ varied depending on the cell lines used in the experiments. In this study we evaluated the effect of PI3K/Akt pathway on the activation of NF-${\kappa}B$ in human respiratory epithelial cell lines. Methods : BEAS-2B, A549 and NCI-H157 cell lines were used in this experiment. To evaluate the activation of Akt activation and I${\kappa}B$ degradation, cells were analysed by western blot assay using phospho-specific Akt Ab and $I{\kappa}B$ Ab. To block PI3K/Akt pathway, cells were pretreated with wortmannin or LY294002 and transfected with dominant negative Akt (DN-Akt). For IKK activity, immune complex kinase assay was performed. To evaluate the DNA binding affinity and transcriptional activity of NF-${\kappa}B$, electrophoretic mobility shift assay (EMSA) and luciferase assay were performed, respectively. Results : In BEAS-2B, A549 and NCI-H157 cell lines, Akt was activated by TNF-$\alpha$ and insulin. Activation of Akt by insulin did not induce $I{\kappa}B{\alpha}$ degradation. Blocking of PI3K/Akt pathway via wortmannin/LY294002 or DN-Akt did not inhibit TNF-$\alpha$-induced $I{\kappa}B{\alpha}$ degradation or IKK activation. Inhibition of PI3K/Akt did not affect TNF-$\alpha$-induced NF-${\kappa}B$ activation. Overexpression of DN-Akt did not block TNF-$\alpha$-induced transcriptional activation of NF-${\kappa}B$, but wortmannin enhanced TNF-$\alpha$-induced in NF-${\kappa}B$ transcriptional activity. Conclusion : PI3K/Akt was not involved in TNF-$\alpha$-induced $I{\kappa}B{\alpha}$ degradation or transcriptional activity of NF-${\kappa}B$ in human respiratory epithelial cell lines.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Nutrition and Health
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• v.47 no.3
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• pp.214-220
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• 2014

Purpose: The purpose of this study was to assess hygiene status of meals for poorly-fed children through microbiological quality. Methods: Meals were provided by two social enterprises, one franchise, and one convenience store. There were a total of six meal samples; two samples (social enterprise meal 1; SEM 1, social enterprise meal 2; SEM 2) from two social enterprises, respectively, two samples (franchise meal 1; FM 1, franchise meal 2; FM 2) from one franchise, and two samples (convenience store meal 1; CSM 1, convenience store meal 2; CSM 2) from one convenience store. Microbiological analysis and assessment were performed by Korean food standards codex (KFSC). Results: General bacteria and E. coli in SEM 1 were detected, but the levels were not over KFSC, and Coliform less than $9.2{\times}10$ CFU/g was also detected in seasoned bean sprouts of SEM 1. General bacteria was detected at $1.6{\times}10^6$ CFU/g in cabbage kimchi of SEM 2. Coliform was detected in cabbage kimchi, squid cutlet, stir-fried pork, and fried chicken of FM1 and 2, but the levels were not over KFSC. In addition, S. aureus was detected in cabbage kimchi and seasoned dried white radish of FM 1 and 2 ($9.8{\times}10^2$ CFU/g, $9.4{\times}10^3$ CFU/g respectively), thus was over KFSC. B. cereus was detected in stir-fried pork and fried chicken ($1.2{\times}10^3$ CFU/g, $1.5{\times}10^3$ CFU/g respectively) of FM 1 and 2, thus was over KFSC. Finally, S. aureus was detected in stir-fried dried squid, seasoned spicy chicken, and stir-fried kimchi of CSM 1 and 2, and was over KFSC too ($9.5{\times}10^4$ CFU/g, $2.4{\times}10^2$ CFU/g, $1.3{\times}10^3$ CFU/g respectively). Conclusion: Results of this study suggest that systemic management of hygiene is necessary to safely providing meals to poorly-fed children.