• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.6
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• pp.190-197
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• 2012

The purpose of this study was to evaluate thermal environment and heat budget of light thin layer green roof through an experiment in order to quantify its heat budget. Two concrete model boxes($1.2m(W){\times}1.2m(D){\times}1.0m(H)$) were constructed: One experiment box with Zoysia japonica planted on substrate depth of 10cm and one control box without any plant. Between June 6th and 7th, 2012, outside climatic conditions(air temperature, relative humidity, wind direction, wind speed), evapotranspiration, surface and ceiling temperature, heat flux, and heat budget of the boxes were measured. Daily maximum temperature of those two days was $29.4^{\circ}C$ and $30^{\circ}C$, and daily evapotranspiration was $2,686.1g/m^2$ and $3,312.8g/m^2$, respectively. It was found that evapotranspiration increased as the quantity of solar radiation increased. A surface and ceiling temperature of those two boxes was compared when outside air temperature was the greatest. and control box showed a greater temperature in both cases. Thus it was found that green roof was effective in reducing temperature. As results of heat budget analysis, heat budget of a green roof showed a greater proportion of net radiation and latent heat while heat budget of the control box showed a greater proportion of sensible heat and conduction heat. The significance of this study was to analyze heat budget of green roof temperature reduction. As substrate depth and types, species and seasonal changes may have influences on temperature reduction of green roof, further study is necessary.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.

• Journal of the Korean Society of Safety
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• v.10 no.3
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• pp.81-95
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• 1995

Now, safety assurance in construction sites should be accomplished by its own organization rather than control of the code or government. It is believed that the safety assurance can be considerably improved by a lecture or an education using the existing theories or literatures up to now, but it is thought that fundamental safety assurance we not able to be accomplished without developing safety devices '||'&'||' equipment or taking fundamental measures, based on the result analyzed from workers behaviors. There are various behaviors of the workers showed in construction site, but only tests for hammerusing works such as form, re-bar, stone workers directly related to the grip strength are mainly performed, investigated and measured here for the study. The above works are similar to power grip, 7th picture on seven items which are categorized for hand grip types(Ammermin 1956 ; Jones ; Kobrick 1958). Measurements of grip strength are commonly taken in anthropometric surveys. They are easy to administer but unfortunately it is rather dubious whether they yield any data that are of interest to the engineer. Very fewer controls of tools are grasped and squeesed studies showed very little overall correlation between grip strength and other measures of bodily strength (Laubach, Kromer, and Thordsen 1972), but hammer-using work which is practically progressed in construction site are mainly influenced with grip strength. According to the investigation on work measurement, it is shown that 77% of form worker are using hammer to be related to grip strength. In this study, it is particularly noticed that wearing safety gloves in construction site is required for workers safety but 20% difference between grip strength with safety gloves and without ones are commonly neglected in the site(Fig. 1). Nevertheless, safety operation with consideration of the above 20% difference is not considered in the construction site. Factors of age, kinds of work, working time, with or without safety gloves are in vestigated '||'&'||' collected at the sites for this study. Test, not at each working hour but at 14 : 00 when the almost all of the workers think the most tired, resulting from the questionaires, also when it is shown on the research report has been performed and compared for main kinds of works : form '||'&'||' re-bar work. Tests were performed with both left SE rightand of the workers simultaneously in construction site using Rand Dynamometer(Model 78010, Lafayette Instrument Co., Indiana, U.S.A) by reading grip strength on the gauge while they are pulling, and then by interviewing on their ages, works, experiences and etc., directly. The above tests have been performed for the dates of 15th march-26th May '95 with consideration of site condition. And even if various factors of ambient temperature on the testing date, working condition, individual worker's habit and worker's condition of the previous ate are concerned with the study. Those are considered as constants in this study. Samples are formwork 53, rebar 62, electrician 5, plumber 4, welding 1 from D construction Co., Ltd, ; formwork 12, re-bar 5, electrician 2, from S construction Co., Ltd, , formwork 78, re-bar 18, plumber 31, electrician 13, labor 48, plumber 31, plasterer 15, concrete placer 6, water proof worker 3, maisony 5 from B construction Co., Ltd. As In the previously mentioned, main aspect to be investigated in this study will be from '||'&'||' re-bar work because grip strength will be directly applied to these two kinds of works ; form '||'&'||' re-bar work, eventhough there are total 405 samples taken. It is thought that a frequency of accident occurrence will be mainly two work postures "looking up '||'&'||' looking down" to be mainly sorted, but this factor is not clarified in this study because It will be needed a lot of work more. Tests has been done at possible large scale of horizontally work-extended sites within one hour in order to prevent or decrease errors '||'&'||' discrepancies from time lag of the test. Additionally, the statistical package computer program SPSS PC＋has been used for the study.

• The Korean Journal of Pharmacology
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• v.20 no.2
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• pp.15-21
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• 1984

It was aimed to study the effects of ${\alpha}_2-adrenoceptor$ agonists on the sleeping time in $one{\sim}two-day-old$ chickens. Furthermore, it was also evaluated whether ${\alpha}_1-adrenoceptor$ agonist and antagonist might affect the sleeping in the chickens and discussed in relation with opiate receptor. 1) Guanabenz, clonidine, guanfacine and B-HT 933 decreased the latency of the loss of righting reflex in a dose-dependent manner, but B-HT 920 and oxymetazoline slightly prolonged it. 2) ${\alpha}_2-Adrenoceptor$ agonists produced dose·related increase in sleeping time. The potency was guanabenz>clonidine>oxymetazoline${\geq}$B-HT 933${\geq}$B-HT 920>guanfacine in this order. 3) ${\alpha}_2-Adrenoceptor$ antagonists decreased guanabenz-induced sleeping time in a dose ·dependent manner. The rank order of ${\alpha}_2-adrenoceptor$ antagonists was yohimbine>rauwolscine>piperoxan${\geq}$RX 781094. 4) Sleeping time caused by both ethanol and hexobarbital was not affected by yohimbine in chickens. 5) Methoxamine and phenylephrine showed little significant effect on the guanabenz-induced sleeping time. However, prazosin increased it. Paradoxically, corynanthine rather caused to decrease it. These results suggest that the stimulation of central ${\alpha}_2-adrenoceptor$ mediates sleeping, however it is remained uncertain in the role of central ${\alpha}_1-adrenoceptor$ in chickens. In addition, the one~two-day-old chickens may be considered as a useful, inexpensive and simple experimental model to evaluate the in vivo pharmacological action of the ${\alpha}_2-adrenoceptor$ agonist and antagonist related to sedation.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.152-161
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• 2023

In general, large-capacity hydrogen storage vessels, typically in the form of vertical cylindrical vessels, are constructed using steel materials. These vessels are anchored to foundation slabs that are specially designed to suit the environmental conditions. This anchoring method involves pre-installed anchors on top of the concrete foundation slab. However, it's important to note that such a design can result in concentrated stresses at the anchoring points when external forces, such as seismic events, are at play. This may lead to potential structural damage due to anchor and concrete damage. For this reason, in this study, it selected an vertical hydrogen storage vessel based on site observations and created a 3D finite element model. Artificial seismic motions made following the procedures specified in ICC-ES AC 156, as well as domestic recorded earthquakes with a magnitude greater than 5.0, were applied to analyze the structural behavior and performance of the target structures. Conducting experiments on a structure built to actual scale would be ideal, but due to practical constraints, it proved challenging to execute. Therefore, it opted for an analytical approach to assess the safety of the target structure. Regarding the structural response characteristics, the acceleration induced by seismic motion was observed to amplify by approximately ten times compared to the input seismic motions. Additionally, there was a tendency for a decrease in amplification as the response acceleration was transmitted to the point where the centre of gravity is located. For the vulnerable components, specifically the sub-system (support columns and anchorages), the stress levels were found to satisfy the allowable stress criteria. However, the concrete's tensile strength exhibited only about a 5% margin of safety compared to the allowable stress. This indicates the need for mitigation strategies in addressing these concerns. Based on the research findings presented in this paper, it is anticipated that predictable load information for the design of storage vessels required for future shaking table tests will be provided.

• Tuberculosis and Respiratory Diseases
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• v.46 no.5
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• pp.697-708
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• 1999

Background : Leukotriene (LT) $C_4$, $D_4$, and $E_4$, the main components of slow-reacting substance of anaphylaxis (SRS-A), have been suggested to play an important role in bronchial asthma such as antigen-induced bronchoconstriction, airway hyperreactivity, and pulmonary eosinophil accumulation. The purpose of this study was to evaluate the effects of treatment with the cysteinyl-LTs (cys-LTs) antagonist, pranlukast on allergen-induced guinea pig asthma model. Methods : Guinea pigs of treatment and placebo groups were sensitized by subcutaneous injection of ovalbumin(OVA) and challenged by inhalation of aerosolized OVA (1% weight/volume OVA). Normal control group did not sensitize with OVA. Oral ingestion of pranlukast and normal saline to the treatment and placebo groups was performed. In the treatment and placebo groups, airway resistance was measured before and after oral ingestion. Serum $LTC_4$ and eosinophilic infiltration of the bronchiolar and peribronchiolar tissues were measured after ingestion in the treatment and placebo groups. Results : Allergen-induced airway constriction developed in 20 (8 in treatment group, 12 in placebo group) among 35 guinea pigs. Airway resistance was significantly decreased at 3 and 6 minutes after OVA challenge in the pranlukast treatment group. In the placebo group, there was no difference of airway resistance between before and after saline ingestion. Serum $LTC_4$ levels showed 348.4 pg/ml in the treatment group, 373.9 pg/ml in the placebo group, and 364.4 pg/ml in the control group. There were no statistically significant difference between treatment and placebo group (p=0.232), and treatment and control group (p=0.501). Eosinophilic infiltrations in the peribronchiolar region per one-microscopic field ($\times$400 high power fields) demonstrated 7.06 in the treatment group, 19.2 in the placebo group, and 4.50 in the control group. There was significant decrement of eosinophilic infiltration in the treatment group which was compared with placebo group (p=0.001). Conclusion : These results demonstrate that pranlukast, a cys-LTs receptor antagonist, can attenuate allergen induced early-phase bronchoconstriction and eosinophilic infiltration in the bronchiolar tissues.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Journal of Embryo Transfer
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• v.22 no.3
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• pp.161-165
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• 2007

Oxytocin is a neurohypophyseal hormone which has multiple functions in mammals. Mainly, oxytocin regulates milk ejection and has an effect on uterine contraction and is related to maternal behavior. Maternal behavior is believed to be suppressed by stress and facilitated by oxytocin. In the cesarean section, oxytocin may be administrated into uterus to promote uterine involution. The present study aimed to test the effect of oxytocin into uterus on maternal behavior of rats with cesarean section. It was measured the effects on maternal behavior of oxytocin infused into uterus in rats with cesarean section as a stressor. In the first experiment, pup survival rate of between a control group and a group with laparotomy as a stress in natural parturition rats was compared. In the second experiment, survival rate for 2 weeks and maternal pup searching behavior (MPSV) were observed in one cesarean sectioned group without oxytocin and the other cesarean sectioned group with oxytocin. Infanticide was observed in stressed group in the first experiment while a normal maternal behavior was observed in a control one. In the second experiment, MPSV was only observed in a cesarean sectioned group with oxytocin and infanticide was observed in two groups except one rat which is thought to be affected by oxytocin as operated relatively late. This is the first study to show that the administration of oxytocin into uterus in the cesarean section is not involved in the regulation of maternal behavior in rats. In conclusion, this study proves the needs of oxytocin into brain in cesarean section related rats model and further study of maternal behavior list, like MPSV.