• Korean Journal of Culture and Social Issue
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• v.12 no.2
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• pp.101-114
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• 2006

This study was examined the effective nonverbal behavior cues of detecting suspects' lies in the investigation scene. In order to search the suspects who drank the alcohol liquor without a permission, 18 soldiers were interviewed. 8 solders had drunken alcohol and had lied when was asked(lie group). The other 10 soldiers hadn't drunken alcohol and had told the truth(truth group). The mean frequencies of nonverbal behaviors were compared lie group with truth group. The following behaviors were measured by frequency: vocal characteristics (high pitch of voice, speech hesitations, speech error, frequency of pauses, period of pauses, latency period), facial characteristics (gaze, smile, touching face, blinking, facial micro-expression), body movement (illustrators, hand and finger movement, leg and foot movement, head movement, trunk movement, shifting position). As results, this study found that deception cues were periods and frequencies of pause, micro-expression, head movements. The lie group had less periods and frequencies of pause, and more micro-expression, head movements than truth group. But, this study didn't found Othello's error cues.

• Journal of Navigation and Port Research
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• v.48 no.3
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• pp.137-145
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• 2024

Recently, there has been an increase in the use of Automatic Identification Systems. Class A AIS is used for ships engaged in international voyages, while Class B AIS is utilized for smaller vessels navigating domestic coastlines. AtoN AIS is used for aids to navigation, AIS is employed for search and rescue aircraft, and AIS-SART is widely used worldwide. Accordingly, in 2022, the Maritime Safety Committee(MSC) of the International Maritime Organization(IMO) revised the performance standards for the satellite emergency positioning radio beacon(EP IRB) to include AIS signals along with 121.5 MHz for aircraft, which has been used as a homing signal. It was recommended to use together as a homing signal, and from July 1, 2022, it was decided that AIS-EP IRB that satisfies the revised performance standards will replace the existing EP IRB. Consequently, starting from July 1, 2022, it was decided that AIS-EPIRB, which meets the revised performance standards, will replace the existing EP IRB. This paper aims to verify the feasibility of implementing AIS-EPIRB, which has not yet been developed domestically. To achieve this, a dedicated chipset for AIS was used to additionally implement frequency generation of 161.975 MHz and 162.025 MHz and GMSK modulation to satisfy the requirements.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Life Science
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• v.18 no.8
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• pp.1132-1139
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• 2008

A CpG island is a short stretch of DNA in which the frequency of the CG dinucleotide is higher than other regions. CpG islands are present in the promoters and exonic regions of approximately $30{\sim}60$% of mammalian genes so they are useful markers for genes in organisms containing 5-methylcytosine in their genomes. Recent evidence supports the notion that the hypermethylation of CpG island, by silencing tumor suppressor genes, plays a major causal role in cancer, which has been described in almost every tumor types. In this respect, CpG island search by computational methods is very helpful for cancer research and computational promoter and gene predictions. I therefore developed a window program (called CpGi) on the basis of CpG island criteria defined by D. Takai and P. A. Jones. The program 'CpGi' was implemented in Visual C++ 6.0 and can determine the locations of CpG islands using diverse parameters (%GC, Obs (CpG)/Exp (CpG), window size, step size, gap value, # of CpG, length) specified by user. The analysis result of CpGi provides a graphical map of CpG islands and G+C% plot, where more detailed information on CpG island can be obtained through pop-up window. Two human contigs, i.e. AP00524 (from chromosome 22) and NT_029490.3 (from chromosome 21), were used to compare the performance of CpGi and two other public programs for the accuracy of search results. The two other programs used in the performance comparison are Emboss-CpGPlot and CpG Island Searcher that are web-based public CpG island search programs. The comparison result showed that CpGi is on a level with or outperforms Emboss-CpGPlot and CpG Island Searcher. Having a simple and easy-to-use user interface, CpGi would be a very useful tool for genome analysis and CpG island research. To obtain a copy of CpGi for academic use only, contact corresponding author.

• Journal of Nutrition and Health
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• v.51 no.6
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• pp.590-598
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• 2018

Purpose: This study was conducted to compare the food involvement scale (FIS) of American and Japanese consumers. In addition, the effects of familiarity, likability, and expectations on willingness to use intentions for block type sauce by nationality were evaluated. Methods: A total of 149 and 112 American and Japanese consumers, respectively, completed the survey. Consumers were asked about familiarity, likability, expectation, willing to use intention, and usage frequency of block type sauce, food involvement scale (FIS), and demographic information. Results: There were differences in the using frequency of block type sauce according to nationality, with consumers in Japan showing significantly higher frequency of using block type sauce than those in the United States (US) (p < 0.001). According to the FIS, US consumers were more focused on how to provide food than food, such as cooking process, table setting, and food shopping, compared to Japanese consumers. In addition, 'expectation' and 'likability' among US consumers and 'expectation' and 'familiarity' among Japanese consumers were positive attributes for willing to use intention (p < 0.01). Conclusion: In the case of the US consumers, 'familiarity' was not significant because the using frequency of the block type sauce was lower than that of Japanese consumers. In the case of the Japanese consumers, 'likability' was not significant because they enjoy cooking itself according to the FIS. Therefore, it is necessary to recognize positive attributes as a key factor for block type sauce, as well as to search for ways to apply marketing strategies based on attributes by nationality.

• The Journal of Korean Society for School & Community Health Education
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• v.9 no.1
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• pp.85-97
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• 2008

Objectives: This study was designed to understand the oral health knowledge & conduct of middle-school students, search for the learning objective and the educational method in line with the subjects and of utilizing as the basic data for an effective oral health-care program. Methods: The samples to achieve the purpose of this research are composed of 139 students in middle-school, OO county. Chungcheongbuk-do, the number of male students 64, and female students 75. Data were statistically analyzed by frequency analysis, $x^2$-test or Fisher's exact test by using SPSS WIN Ver. 12.0. Results: Among items on oral-health knowledge in middle-school students. the awareness ratio on a cause and preventive method for oral disease was surveyed to be lower than the awareness ratio on symptoms of oral disease. As a result of examining by comparing knowledge and behavior on the time of tooth brush. both awareness and behavior were the level of 50% or less than it. In particular, 46.2% perceived after lunch. but practice just accounted for 33.0%. The frequency of tooth brush a day was the largest in a case(47.5%) of doing twice a day. However. there was also the response (5.8%) with saying of brushing once or not brushing even once. Thus, the practice of tooth brush was surveyed to be very low even if being a minority of students. The frequency of taking a light meal was 68.8% in less than twice a day. However, even students of taking more than five times were surveyed to be 9.8%. Out of the whole-body health in over 50%-59.9%. the oral health was surveyed to be perceived to be very important. Compared to the awareness level on importance of a tooth, the ratio of visiting a dentistry was analyzed to be very low. Conclusions: The study results suggest that the school oral-health project was examined to have the necessity of being expanded and carried out even in middle-and-high schools, by which the specific oral-health promotion program including oral-health education in this period is developed.

• Economic and Environmental Geology
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• v.27 no.6
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• pp.557-561
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• 1994

Q estimates are made by direct measurements of energy loss per cycle from primary P and S waves, as a function of frequency. Assuming that intrinsic Q is frequency independent and scattering Q is frequency dependent over the frequencies of interest, the relative contributions of each, to a total observed Q, may be estimated. Test examples are produced by computing viscoelastic synthetic seismograms using a pseudo spectral solution with inclusion of relaxation mechanisms (for intrinsic Q) and a fractal distribution of scatterers (for scattering Q). The composite theory implies that when the total Q for S-waves is smaller than that for P-waves (the usual situation), intrinsic Q is dominating; when it is larger, scattering Q is dominating. In the inverse problem, performed by a global least squares search, intrinsic $Q_p$ and $Q_s$ estimates are reliable and unique when their absolute values are sufficiently low that their effects are measurable in the data. Large $Q_p$ and $Q_s$ have no measurable effect and hence are not resolvable. Standard deviation of velocity $({\sigma})$ and scatterer size (A) are less unique as they exhibit a tradeoff as predicted by Blair's equation. For the P-waves, intrinsic and scattering contributions are of approximately the same importance, for S-waves, the intrinsic contributions dominate.

• Korean Chemical Engineering Research
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• v.47 no.2
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• pp.220-229
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• 2009

Strain improvement and morphology investigation in bioreactor cultures were undertaken in suspended cultures of Phellinus linteus mycelia for mass production of protein-bound polysaccharides(soluble ${\beta}$-D-glucan), a powerful immuno-stimulating agent. Phellineus sp. screened for this research was identified as Phellinus linteues through ITS rDNA sequencing method and blast search, demonstrating 99.7% similarity to other Phellinus linteus strains. Intensive strain improvement program was carried out by obtaining large amounts of protoplasts for the isolation of single cell colonies. Rapid and large screening of high-yielding producers was possible because large numbers of protoplasts ($1{\times}10^5{\sim}10^6\;protoplasts/ml$) formed using the banding filtration method with the cell wall-disrupting enzymes could be regenerated in relatively high regeneration frequency($10^{-2}{\sim}10^{-3}$) in the newly developed regeneration medium. It was demonstrated that the strains showing high performances in the protoplast regeneration and solid growth medium were able to produce 5.8~6.4%(w/w) of ${\beta}$-D-glucan and 13~15 g/L of biomass in stable manners in suspended shake-flask cultures of P. linteus mycelia. In addition, cell mass increase was observed to be the most important in order to enhance ${\beta}$-D-glucan productivity during the course of strain improvement program, since the amount of ${\beta}$-D-glucan extracted from the cell wall of P. linteus mycelia was almost constant on the unit biomass basis. Therefore we fully investigated the fungal cell morphology, generally known as one of the key factors affecting cell growth extent in the bioreactor cultures of mycelial fungal cells. It was found that, in order to obtain as high cell mass as possible in the final production bioreactor cultures, the producing cells should be proliferated in condensed filamentous forms in the growth cultures, and optimum amounts of these filamentous cells should be transferred as active inoculums to the production bioreactor. In this case, ideal morphologies consisting of compacted pellets less than 0.5mm in diameter were successfully induced in the production cultures, resulting in shorter period of lag phase, 1.5 fold higher specific cell growth rate and 3.3 fold increase in the final biomass production as compared to the parallel bioreactor cultures of different morphological forms. It was concluded that not only the high-yielding but also the good morphological characteristics led to the significantly higher biomass production and ${\beta}$-D-glucan productivity in the final production cultures.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.195-211
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• 2014

Cloud computing services provide IT resources as services on demand. This is considered a key concept, which will lead a shift from an ownership-based paradigm to a new pay-for-use paradigm, which can reduce the fixed cost for IT resources, and improve flexibility and scalability. As IT services, cloud services have evolved from early similar computing concepts such as network computing, utility computing, server-based computing, and grid computing. So research into cloud computing is highly related to and combined with various relevant computing research areas. To seek promising research issues and topics in cloud computing, it is necessary to understand the research trends in cloud computing more comprehensively. In this study, we collect bibliographic information and citation information for cloud computing related research papers published in major international journals from 1994 to 2012, and analyzes macroscopic trends and network changes to citation relationships among papers and the co-occurrence relationships of key words by utilizing social network analysis measures. Through the analysis, we can identify the relationships and connections among research topics in cloud computing related areas, and highlight new potential research topics. In addition, we visualize dynamic changes of research topics relating to cloud computing using a proposed cloud computing "research trend map." A research trend map visualizes positions of research topics in two-dimensional space. Frequencies of key words (X-axis) and the rates of increase in the degree centrality of key words (Y-axis) are used as the two dimensions of the research trend map. Based on the values of the two dimensions, the two dimensional space of a research map is divided into four areas: maturation, growth, promising, and decline. An area with high keyword frequency, but low rates of increase of degree centrality is defined as a mature technology area; the area where both keyword frequency and the increase rate of degree centrality are high is defined as a growth technology area; the area where the keyword frequency is low, but the rate of increase in the degree centrality is high is defined as a promising technology area; and the area where both keyword frequency and the rate of degree centrality are low is defined as a declining technology area. Based on this method, cloud computing research trend maps make it possible to easily grasp the main research trends in cloud computing, and to explain the evolution of research topics. According to the results of an analysis of citation relationships, research papers on security, distributed processing, and optical networking for cloud computing are on the top based on the page-rank measure. From the analysis of key words in research papers, cloud computing and grid computing showed high centrality in 2009, and key words dealing with main elemental technologies such as data outsourcing, error detection methods, and infrastructure construction showed high centrality in 2010~2011. In 2012, security, virtualization, and resource management showed high centrality. Moreover, it was found that the interest in the technical issues of cloud computing increases gradually. From annual cloud computing research trend maps, it was verified that security is located in the promising area, virtualization has moved from the promising area to the growth area, and grid computing and distributed system has moved to the declining area. The study results indicate that distributed systems and grid computing received a lot of attention as similar computing paradigms in the early stage of cloud computing research. The early stage of cloud computing was a period focused on understanding and investigating cloud computing as an emergent technology, linking to relevant established computing concepts. After the early stage, security and virtualization technologies became main issues in cloud computing, which is reflected in the movement of security and virtualization technologies from the promising area to the growth area in the cloud computing research trend maps. Moreover, this study revealed that current research in cloud computing has rapidly transferred from a focus on technical issues to for a focus on application issues, such as SLAs (Service Level Agreements).

• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.37-44
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• 2008

Data entered into RFID tags are used for saving costs and enhancing competitiveness in the development of applications in various industrial areas. RFID readers perform the identification and search of hundreds of objects, which are tags. RFID technology that identifies objects on request of dynamic linking and tracking is composed of application components supporting information infrastructure. Despite their many advantages, existing applications, which do not consider elements related to real.time data communication among remote RFID devices, cannot support connections among heterogeneous devices effectively. As different network devices are installed in applications separately and go through different query analysis processes, there happen the delays of monitoring or errors in data conversion. The present study implements a RFID database handling system in semantic Web environment for integrated management of information extracted from RFID tags regardless of application. Users’ RFID tags are identified by a RFID reader mounted on an application, and the data are sent to the RFID database processing system, and then the process converts the information into a semantic Web language. Data transmitted on the standardized semantic Web base are translated by a smart browser and displayed on the screen. The use of a semantic Web language enables reasoning on meaningful relations and this, in turn, makes it easy to expand the functions by adding modules.