• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.498-509
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• 2001

Collaborative systems allow users, who may be far removed from each other geographically, to do collaborative work such as 3D animation, computer game, and industrial design in a single virtual space. This paper describes our experience to develop a collaborative system framework that aims at expanding the some functions of a stand-alone visual modeling tool, called 3D Studio Max, into those of the distributed collaborative working environments. The paper mainly deals with design and implementation of a 3D shared-object Plug-In with respect to the 3D Studio Max Plug-In Software Development Kit in the distributed collaborative system developed by the authors. There are two major functions of the proposed scheme; one is to write 3D object-information to the shared memory after extracting it from the 3D Studio Max, the other is to create 3D objects after retrieving them from the shared memory. Also, the proposed scheme provides a simple way of storing 3D objects that have variable size, by means of shared memory which located in between the collaborative system clients and 3D studio Max. One of the remarkable virtures of the Plug-In is to reduce a considerable amount of shared object data which in consequence can mitigate the network overhead. This can be achieved by the fact that the system is able to extract a minimum amount of 3D objects that are required to transmit. Also, using the proposed scheme, user can facilitate 3D Studio Max into distributed collaborative working environments. This, in consequence give many benefits such as saving time as well as eliminating space constraints in the course of 3D modeling when we are under industrial design process.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.1-8
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• 2020

Communication between system modules is applied using the Modbus protocol in industrial sites including smart factories, industrial drones, building energy management systems, PLCs, ships, trains, and airplanes. The existing Modbus was used for serial communication, but the recent Modbus protocol is used for TCP/IP communication.The Modbus protocol supports RTU, TCP and ASCII, and implements and uses protocols in embedded systems. However, the transmission I/O devices for RTU, TCP, and ASCII-based protocols may differ. For example, RTU and ASCII communications transmit on a serial-based communication protocol, but in some cases, Ethernet TCP/IP transmission is required. In particular, since the C language (object-oriented) is used in embedded systems, the complexity of source code related to I/O registers increases. In this study, we designed software that can logically separate I/O functions from embedded devices, and designed the execution logic of each instance requiring I/O processing through a delegate class instance with Modbus RTU, TCP, and ASCII protocol generation. We designed and experimented with software that can separate communication I/O processing and logical execution logic for each instance.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.185-197
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• 2022

Recently, deep learning networks with high performance for object recognition are emerging. In the case of object recognition using deep learning, it is important to build a training data set to improve performance. To build a data set, we need to collect and label the images. This process requires a lot of time and manpower. For this reason, open data sets are used. However, there are objects that do not have large open data sets. One of them is data required for license plate detection and recognition. Therefore, in this paper, we propose an artificial license plate generator system that can create large data sets by minimizing images. In addition, the detection rate according to the artificial license plate arrangement structure was analyzed. As a result of the analysis, the best layout structure was FVC_III and B, and the most suitable network was D2Det. Although the artificial data set performance was 2-3% lower than that of the actual data set, the time to build the artificial data was about 11 times faster than the time to build the actual data set, proving that it is a time-efficient data set building system.

• Journal of Korea Port Economic Association
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• v.38 no.1
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• pp.31-47
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• 2022

While the ship finance industry has long been struggling with diminishing involvements from the private sector, government-run banks have consistently increased their presence in maritime finance. To address such concerns, this research conceptually explores the creation of blockchain technology-driven security token offering (STO) platforms. To suggest a sound platform model, this piece first examines key design principles. Based on the integral perspective on the digital platform, this paper exhibits three core design principles to create a virtuous platform ecosystem, then sets out STO platform design guidelines. This paper further explores an STO platform model by considering conventional ship finance systems and practices in Korea. The STO platform has three main effects; 1) the wider availability of STOs can enlarge both the scope and size of ship finance users, 2) the activation of security token transactions leads to an increase in participation, and 3) possibilities to create complementary innovative financial services can further encourage the participation of private investors. The STO ecosystem may contribute to the shipping, shipbuilding, and ship finance industries by enhancing its attractiveness to the general public and by creating positive externalities for Busan as a maritime finance center.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.2
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• pp.61-73
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• 2021

This study aimed to identify the landscape perception and landscape characteristics of users by utilizing SNS data generated by their experiences. Therefore, how to recognize the main places and scenery appearing on the island, and what are the characteristics of the main scenery were analyzed using online text data and photo data. Text data are text mining and network structural analysis, while photographic data are landscape identification models and color analysis. As a result of the study, First, as a result of frequency analysis of Bakji·Banwol-do topics, we were able to derive keywords for local landscapes such as 'Purple Bridge', 'Doori Village', and location, behavior, and landscape images by analyzing them simultaneously. Second, the network structure analysis showed that the connection between key and undrawn keywords could be more specifically analyzed, indicating that creating landscapes using colors is affecting regional activation. Third, after analyzing the landscape identification model, it was found that artificial elements would be excluded to create preferred landscapes using the main targets of "Purple Bridge" and "Doori Village", and that it would be effective to set a view point of the sea and sky. Fourth, Bakji·Banwol-do were the first islands to be created under the theme of color, and the colors used in artificial facilities were similar to the surrounding environment, and were harmonized with contrasting lighting and saturation values. This study used online data uploaded directly by visitors in the landscape field to identify users' perceptions and objects of the landscape. Furthermore, the use of both text and photographic data to identify landscape recognition and characteristics is significant in that they can specifically identify which landscape and resources they prefer and perceive. In addition, the use of quantitative big data analysis and qualitative landscape identification models in identifying visitors' perceptions of local landscapes will help them understand the landscape more specifically through discussions based on results.

• Food Science of Animal Resources
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• v.26 no.4
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• pp.418-425
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• 2006

This study was conducted to evaluate extraction properties of crude saponin and ginsenosides, and their effects on sensory properties of emulsified pork sausage. Non-dried ginseng root was boiled in 0 (e.g., 100% distilled water), 20, 40, 60, 80 or 100% ethanol, and powdered by a freezing dry method. Weight of dried powder for the 0% ethanol extraction was 20% of initial non-dried ginseng weight, while $20{\sim}80%$ and 100% ethanol extractions resulted in approximately 15 and 10% of their initial weights, respectively. On the other hand, crude saponin content in the dried powder was linearly increased for a higher ethanol content where 100% ethanol extraction resulted in 123.52 mg/g. LC/MS analysis of crude saponin for quantifying ginsenosides showed that Rb1, Rb2 and Rc were significantly (p<0.05) higher levels for both 80 and 100% ethanol extractions. In the case of Rg1 ginsenoside, 60, 80 and 100% ethanol extractions resulted in significantly (p<0.05) higher levels. Emulsified pork sausages containing 0, 1 or 2% ginseng extracts were smoked or non-smoked and their sensory characteristics and preference were evaluated. Smoking process significantly (p<0.05) decreased juiciness and tenderness, but the treatment significantly (p<0.05) improved flavor and consumer preference. It was particularly noticed that a 2% addition of ginseng extract prevented the adverse effects of smoking process on juiciness and tenderness while the 2% addition significantly (p<0.05) improved consumer preference. The current results implied that addition of ginseng extract in emulsified pork sausage could improve sensory quality.

• Annals of Clinical Microbiology
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• v.22 no.1
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• pp.9-13
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• 2019

Background: The isolation of carbapenemase-producing Enterobacteriaceae (CPE) has become increasingly common. Continuous surveillance for these organisms is essential because their infections are closely related to outbreaks of illness and are associated with high mortality rates. The aim of this study was to develop and evaluate multiplex PCR as a means of detecting several important CPE genes simultaneously. Methods: We aimed to develop a multiplex PCR that could detect seven CPE genes simultaneously. The multiplex PCR was composed of seven primer sets for the detection of KPC, IMP, VIM, NDM-1, GES, OXA-23, and OXA-48. We designed different PCR product sizes of at least 100 bp. We evaluated the performance of this new test using 69 CPE-positive clinical isolates. Also, we confirmed the specificity to rule out false-positive reactions by using 71 carbapenem-susceptible clinical strains. Results: A total of 69 CPE clinical isolates showed positive results and were correctly identified as KPC (N=14), IMP (N=13), OXA-23 (N=12), OXA-48 (N=11), VIM (N=9), GES (N=5), and NDM (N=5) by the multiplex PCR. All 71 carbapenem-susceptible clinical isolates, including Enterococcus faecalis, Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa, showed negative results. Conclusion: This multiplex PCR can detect seven CPE genes at a time and will be useful in clinical laboratories.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.12
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• pp.1790-1799
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• 2010

A controlled and double blinded trial was performed to evaluate the effects of OPE supplementation on blood circulation in healthy male smokers. The study population consisted of 92 healthy male smokers aged $44.4{\pm}7.8$ years. The supplement group (n=49) received OPE containing 100 mg quercetin/day for 10 weeks, while the control group (n=43) received placebo powders. The prothrombin time and activated partial thromboplastin time increased significantly after OPE consumption, indicating the attenuated blood clot formation by OPE. In conclusion, these results indicate that the daily consumption of OPE (containing 100 mg quercetin) for 10 weeks exerts significant health benefits through the modulation of physiological functions including the blood circulation in male smokers.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Science Education
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• v.43 no.1
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• pp.157-172
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• 2019

The purpose of this study is to analyze the influence of number of targets on common knowledge generation and brain activity during the common life science discovery task performance. In this study, 35 preliminary life science teachers participated. This study was intentionally made a block designed for EEG recording. EEGs were collected while subjects were performing common discovery tasks. The sLORETA method and the relative power spectrum analysis method were used to analyze the brain activity difference and the role of activated cortical and subcortical regions according to the degree of difficulty of common discovery task. As a result of the study, in the case of the Theta wave, the activity of the Theta wave was significantly decreased in the frontal lobe and increased in the occipital lobe when the difficult difficulty task was compared with the easy difficulty task. In the case of Alpha wave, the activity of Alpha decreased significantly in the frontal lobe when performing difficult task with difficulty. Beta wave activity decreased significantly in the frontal lobe, parietal lobe, and occipital lobe when performing difficult task. Finally, in the case of Gamma wave, activity of Gamma wave decreased in the frontal lobe and activity increased in the parietal lobe and temporal lobe when performing the difficult difficulty task compared to the task of easy difficulty. The level of difficulty of the commonality discovery task is determined by the cingulate gyrus, the cuneus, the lingual gyrus, the posterior cingulate, the precuneus, and the sub-gyral where it was shown to have an impact. Therefore, the difficulty of the commonality discovery task is the process of integrating the visual information extracted from the image and the location information, comparing the attributes of the objects, selecting the necessary information, visual work memory process of the selected information. It can be said to affect the process of perception.