• KSBB Journal
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• v.17 no.1
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• pp.73-79
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• 2002

The method to optimize the microbial production of ethanol from CO using Clostridium ljungdahlii was developed. The kinetic parameter study on CO conversion with Clostridium ljungdahlii was carried out and maximum CO conversion rate of 37.14 mmol/L-hr-O.D. and $K_{m}$ / of 0.9516 atm were obtained. It was observed that method of two stage fermentation, which consists of cell growth stage and ethanol production stage, was effective to produce ethanol. When pH was shifted from 5.5 to 4.5 and ammonium solution was supplied to culture media as nitrogen source at ethanol production stage, the concentration of ethanol produced was increased 20 times higher than that without shift. Ethanol production from CO in a fermenter with Clostridium ljungdahlii was optimized and the concentration of ethanol produced was 45 g/L and maximun ethanol productivity was 0.75 g ethanol/L-hr.

• Journal of Christian Education in Korea
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• v.78
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• pp.21-55
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• 2024

Purpose of study: The purpose of this study is to examine the crisis situation of Christian education experienced by Korean churches and theological education institutions after the COVID-19 pandemic, and to seek new tasks and directions that Korean churches should prepare for the recovery and growth of Christian education. Research Contents and Methods: Before reviewing the current crisis experienced by Korean churches and theological education institutions after the COVID-19 pandemic, the terms and concepts of Christian education are first reflected. In addition, the causes and phenomena of the Korean church and Christian education crisis after the COVID-19 Pandemic are examined through statistics, media, and church field data. The researcher identified the causes of the crisis experienced by Korean churches and theological education institutions as follows: 1. The influence of COVID-19 Pandemic: suspension of faith and educational activities, 2. The age of population cliffs: increase in the elderly vs. decrease in young people, 3. Neoliberalism: marketism, infinite competition, efficiency, winner-take-all, 4. post-religious era: post-Christianity, weakened communitarianism, and reduced number of Christians. The results of this study are four points as follows: 1. Crisis of church education: suspension of operation of Sunday school and feeling of failure, 2. Crisis of faith of young generation: nurturing unstable future church leaders, 3. Faith of adult generation: crisis and hope, 4. The faith of the elderly generation: light and shadow. Conclusion and Recommendations: The new task and direction of Christian education that Korean churches should pursue after the COVID-19 pandemic are as follows: 1. New conversion of Sunday school faith education: From schooling-paradigm education to faith education that integrates family and generation. 2. New conversion of theological education: From knowledge education to spirituality training. 3. Re-ignition of faith education for middle-aged adults: Activation of faith through worship and small groups, 4. New conversion of church elder education: The need to change from caring objects to creative religious life.

• Science of Emotion and Sensibility
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• v.16 no.1
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• pp.29-42
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• 2013

In this paper, we analyze the effect of the depth perception, volume perception and visual discomfort according to the change of the quality of the depth image and the depth of the major object. For the analysis, a 2D image was converted to eighteen 3D images using depth images generated based on the different depth position of a major object and background, which were represented in three detail levels. The subjective test was carried out using eighteen 3D images so that the degrees of the depth perception, volume perception and visual discomfort recognized by the subjects were investigated according to the change in the depth position of the major object and the quality of depth map. The absolute depth position of a major object and the relative depth difference between background and the major object were adjusted in three levels, respectively. The details of the depth map was also represented in three levels. Experimental results showed that the quality of the depth image differently affected the depth perception, volume perception and visual discomfort according to the absolute and relative depth position of the major object. In the case of the cardboard depth image, it severely damaged the volume perception regardless of the depth position of the major object. Especially, the depth perception was also more severely deteriorated by the cardboard depth image as the major object was located inside the screen than outside the screen. Furthermore, the subjects did not felt the difference of the depth perception, volume perception and visual comport from the 3D images generated by the detail depth map and by the rough depth map. As a result, it was analyzed that the excessively detail depth map was not necessary for enhancement of the stereoscopic perception in the 2D-to-3D conversion.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.453-462
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• 2021

A lot of license plate data is required for car number recognition. License plate data needs to be balanced from past license plates to the latest license plates. However, it is difficult to obtain data from the actual past license plate to the latest ones. In order to solve this problem, a license plate recognition study through deep learning is being conducted by creating a synthetic license plates. Since the synthetic data have differences from real data, and various data augmentation techniques are used to solve these problems. Existing data augmentation simply used methods such as brightness, rotation, affine transformation, blur, and noise. In this paper, we apply a style transformation method that transforms synthetic data into real-world data styles with data augmentation methods. In addition, real license plate data are noisy when it is captured from a distance and under the dark environment. If we simply recognize characters with input data, chances of misrecognition are high. To improve character recognition, in this paper, we applied the DeblurGANv2 method as a quality improvement method for character recognition, increasing the accuracy of license plate recognition. The method of deep learning for license plate detection and license plate number recognition used YOLO-V5. To determine the performance of the synthetic license plate data, we construct a test set by collecting our own secured license plates. License plate detection without style conversion recorded 0.614 mAP. As a result of applying the style transformation, we confirm that the license plate detection performance was improved by recording 0.679mAP. In addition, the successul detection rate without image enhancement was 0.872, and the detection rate was 0.915 after image enhancement, confirming that the performance improved.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.119-128
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• 2011

The Sensor technology and portable device capability able to collect recent user information and the information about the surrounding environment haven been highly developed. A user can be made use of various contents and the option is also extending with this technology development. In particular, the initial portable device had simply a call function, but now that has evolved into 'the 4th screen' which including movie, television, PC ability. also, in the past, a portable device to provided only the services of a SMS, in recent years, it provided to interactive video service, and it include technology which providing various contents. Also, it is rising as media which leading the consumption of contents, because it can be used anytime, anywhere. However, the contents available for the nature of user's handheld devices are limited. because it is very difficult for making the contents separately according to various device specification. To find a solution to this problem, the study on one contents from several device has been progressing. The contents conversion technology making use of the profile of device out of this study comes to the force and profile study has been progressing for this. Furthermore, Demand for a user is also increased and the study on the technology collecting, analyzing demands has been making active progress. And what is more, Grasping user's demands by making use of this technology and the study on the technology analyzing, providing contents has been making active progress as well. First of all, there is a method making good use of ZigBee, Bluetooth technology about the sensor for gathering user's information. ZigBee uses low-power digital radio for wireless headphone, wireless communication network, and being utilized for smart energy, automatic home system, wireless communication application and wireless sensor application. Bluetooth, as industry standards of PAN(Personal Area Networks), is being made of use of low power wireless device for the technology supporting data transmission such as drawing file, video file among Bluetooth device. With analyzing the collected information making use of this technology, it utilizes personalized service based on network knowledge developed by ETRI to service contents tailor-made for a user. Now that personalized service builds up network knowledge about user's various environments, the technology provides context friendly service constructed dynamically on the basis of this. The contents to service dynamically like this offer the contents that it converses with utilizing device profile to working well. Therefore, this paper suggests the system as follow. It collects the information, for example of user's sensitivity, context and location by using sensor technology, and generates the profile as a means of collected information as sensor. It collects the user's propensity to the information by user's input and event and generates profile in the same way besides the gathered information by sensor. Device transmits a generated profile and the profile about a device specification to proxy server. And proxy server transmits a profile to each profile management server. It analyzes profile in proxy server so that it selects the contents user demand and requests in contents server. Contents server receives a profile of user portable device from device profile server and converses the contents by using this. Original source code of contents convert into XML code using the device profile and XML code convert into source code available in user portable device. Thus, contents conversion process is terminated and user friendly system is completed as the user transmits optimal contents for user portable device.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.578-589
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• 2014

Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.

• Clean Technology
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• v.22 no.3
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• pp.175-180
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• 2016

Tyrosinases catalyze the hydroxylation of a monophenol (monophenolase activity) and the conversion of an o-diphenol to o-quinone (diphenolase activity), which are mainly involved in the modification of tyrosine residues into 3,4-dihydroxyphenyl-alanine (DOPA) and DOPA/DOPAquinone-derived intermolecular cross-linking. Previously, we obtained a slightly acidic and cold-active tyrosinase, tyrosinase-CNK, by our recombinant protein approach. The enzyme showed optimal activity at pH 6.0 and 20 ℃ with an abnormally high monophenolase/diphenolase activity ratio and still had approximately 50% activity compared with the highest activity even in ice water. Here, we investigated reaction stability of the recombinant tyrosinase-CNK as a psychrophilic enzyme. The enzyme showed remarkable thermal stability at 0 ℃ and the activity was well conserved in repeated freeze-thaw cycles. Although water-miscible organic solvent as reaction media caused the activity decrease of tyrosinase-CNK as expected, the enzyme activity was not additionally decreased with increased concentration in organic solvents such as ethanol and acetonitrile. Also, the enzyme showed high salt tolerance in chaotropic salts. It was remarkably considered that 2⁺ metal ions might inhibit the incorporation of Cu²⁺ into the active site. We expect that these results could be used to design tyrosinase-mediated enzymatic reaction at low temperature for the production of catechols through minimizing unwanted self-oxidation and enzyme inactivation.

• KSBB Journal
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• v.25 no.4
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• pp.337-343
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• 2010

It has been well known that organic solvents like t-butanol and n-hexane can protect lipases from the inhibition by short-chain alcohols in the enzymatic transesterification. However, use of the organic solvents should be minimized considering their negative effects on environment and human health. Therefore, use of the greener solvents has been pursued in various are as including the enzymatic biotranformation. In this study, the liquid carbon dioxide ($LCO_2$) was employed as an alternative media for the enzymatic transesterification of canola oil. The conversion in the $LCO_2$ was comparable with those in organic solvents and the supercritical carbon dioxide, and under optimum conditions, the value reached 99.7%. It is expected that this method can provide a new type of biodiesel production process with higher energy efficiency and lower environmental impact.