• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.157-176
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• 2016

For a long time, mobile phone had a sole function of communication. Recently however, abrupt innovations in technology allowed extension of the sphere in mobile phone activities. Development of technology enabled realization of almost computer-like environment even on a very small device. Such advancement yielded several forms of new high-tech devices such as smartphone and tablet PC, which quickly proliferated. Simultaneously with the diffusion of the mobile devices, mobile applications for those devices also prospered and soon became deeply penetrated in consumers' daily lives. Numerous mobile applications have been released in app stores yielding trillions of cumulative downloads. However, a big majority of the applications are disregarded from consumers. Even after the applications are purchased, they do not survive long in consumers' mobile devices and are soon abandoned. Nevertheless, it is imperative for both app developers and app-store operators to understand consumer behaviors and to develop marketing strategies aiming to make sustainable business by first increasing sales of mobile applications and by also designing surviving strategy for applications. Therefore, this research analyzes consumers' mobile application usage behavior in a frame of substitution/supplementary of application categories and several explanatory variables. Considering that consumers of mobile devices use multiple apps simultaneously, this research adopts multivariate probit models to explain mobile application usage behavior and to derive correlation between categories of applications for observing substitution/supplementary of application use. The research adopts several explanatory variables including sociodemographic data, user experiences of purchased applications that reflect future purchasing behavior of paid applications as well as consumer attitudes toward marketing efforts, variables representing consumer attitudes toward rating of the app and those representing consumer attitudes toward app-store promotion efforts (i.e., top developer badge and editor's choice badge). Results of this study can be explained in hedonic and utilitarian framework. Consumers who use hedonic applications, such as those of game and entertainment-related, are of young age with low education level. However, consumers who are old and have received higher education level prefer utilitarian application category such as life, information etc. There are disputable arguments over whether the users of SNS are hedonic or utilitarian. In our results, consumers who are younger and those with higher education level prefer using SNS category applications, which is in a middle of utilitarian and hedonic results. Also, applications that are directly related to tangible assets, such as banking, stock and mobile shopping, are only negatively related to experience of purchasing of paid app, meaning that consumers who put weights on tangible assets do not prefer buying paid application. Regarding categories, most correlations among categories are significantly positive. This is because someone who spend more time on mobile devices tends to use more applications. Game and entertainment category shows significant and positive correlation; however, there exists significantly negative correlation between game and information, as well as game and e-commerce categories of applications. Meanwhile, categories of game and SNS as well as game and finance have shown no significant correlations. This result clearly shows that mobile application usage behavior is quite clearly distinguishable - that the purpose of using mobile devices are polarized into utilitarian and hedonic purpose. This research proves several arguments that can only be explained by second-hand real data, not by survey data, and offers behavioral explanations of mobile application usage in consumers' perspectives. This research also shows substitution/supplementary patterns of consumer application usage, which then explain consumers' mobile application usage behaviors. However, this research has limitations in some points. Classification of categories itself is disputable, for classification is diverged among several studies. Therefore, there is a possibility of change in results depending on the classification. Lastly, although the data are collected in an individual application level, we reduce its observation into an individual level. Further research will be done to resolve these limitations.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.

• 한국노년학
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• v.37 no.2
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• pp.461-474
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• 2017

The purpose of this study was to develop the caregiver guideline for participation in activities daily living for the elderly with early dementia in home including easily adaptable and professional strategies as early intervetion. The process of this study was 3 stage. First stage, the development of preliminary caregiver guideline as the first stage of this study was to translate to Korean and reclassify the items about caregiver guideline for participation from reports of foreign association or government related dementia, and it consisted of 3 areas, 128 statements for questionnaire for Delphi investigation. Second stage, the guideline was to conduct the content validity, and delete, reclassify, integrate, revise inappropriate items through 2 rounds and 16 Delphi panels. Third stage, the establishment of the final version of caregiver guideline. It consisted of 8 areas: home modification and assistive device, home activities of daily living, health management, communication, psycho-emotional support, leisure activities, social participation, general strategies. All 68 items was arranged in important order. The content validity, stability, agreement index in this study were 0.81, 0.15, 0.79 respectively. When content validity, stability was above 0.49, below 0.5 in Delphi survey with 15 panels, it was not required additional survey. The result of this study meaned that it was not required to additional Delphi survey, and the result was stable and agreeable. This developed guideline was useful and practical to maintain the elderly's with dementia independent and healthy life as education materials for their caregivers, so it will expect to decrease caregiver's burden. Lastly, it stated limitation and suggestion for further study.

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.251-259
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• 2018

BACKGROUND: Various culture media have been used for hydroponic cultures of horticultural plants under the smart greenhouses with natural and artificial light types. Management of the culture medium for the control of medium amounts and/or necessary components absorbed by plants during the cultivation period is performed with ICT (Information and Communication Technology) and/or IoT (Internet of Things) in a smart farm system. This study was conducted to develop the cloud-based data analysis system for effective management of culture medium applying to hydroponic culture and plant growth in smart greenhouses. METHODS AND RESULTS: Conventional inorganic Yamazaki and organic media derived from agricultural byproducts such as a immature fruit, leaf, or stem were used for hydroponic culture media. Component changes of the solutions according to the growth stage were monitored and plant growth was observed. Red and green lettuce seedlings (Lactuca sativa L.) which developed 2~3 true leaves were considered as plant materials. The seedlings were hydroponically grown in the smart greenhouse with fluorescent and light-emitting diodes (LEDs) lights of $150{\mu}mol/m^2/s$ light intensity for 35 days. Growth data of the seedlings were classified and stored to develop the relational database in the virtual machine which was generated from an open stack cloud system on the base of growth parameter. Relation of the plant growth and nutrient absorption pattern of 9 inorganic components inside the media during the cultivation period was investigated. The stored data associated with component changes and growth parameters were visualized on the web through the web framework and Node JS. CONCLUSION: Time-series changes of inorganic components in the culture media were observed. The increases of the unfolded leaves or fresh weight of the seedlings were mainly dependent on the macroelements such as a $NO_3-N$, and affected by the different inorganic and organic media. Though the data analysis system was developed, actual measurement data were offered by using the user smart device, and analysis and comparison of the data were visualized graphically in time series based on the cloud database. Agricultural management in data visualization and/or plant growth can be implemented by the data analysis system under whole agricultural sites regardless of various culture environmental changes.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.122-130
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• 2013

This work proposes a 12b 100MS/s 45nm CMOS four-step pipeline ADC for high-speed digital communication systems requiring high resolution, low power, and small size. The input SHA employs a gate-bootstrapping circuit to sample wide-band input signals with an accuracy of 12 bits or more. The input SHA and MDACs adopt two-stage op-amps with a gain-boosting technique to achieve the required DC gain and high signal swing range. In addition, cascode and Miller frequency-compensation techniques are selectively used for wide bandwidth and stable signal settling. The cascode current mirror minimizes current mismatch by channel length modulation and supply variation. The finger width of current mirrors and amplifiers is laid out in the same size to reduce device mismatch. The proposed supply- and temperature-insensitive current and voltage references are implemented on chip with optional off-chip reference voltages for various system applications. The prototype ADC in a 45nm CMOS demonstrates the measured DNL and INL within 0.88LSB and 1.46LSB, respectively. The ADC shows a maximum SNDR of 61.0dB and a maximum SFDR of 74.9dB at 100MS/s, respectively. The ADC with an active die area of $0.43mm^2$ consumes 29.8mW at 100MS/s and a 1.1V supply.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.37-47
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• 2006

This work proposes a 10b 25MS/s $0.8mm^2$ 4.8mW 0.13um CMOS A/D Converter (ADC) for high-performance wireless communication systems such as DVB, DAB and DMB simultaneously requiring low voltage, low power, and small area. A two-stage pipeline architecture minimizes the overall chip area and power dissipation of the proposed ADC at the target resolution and sampling rate while switched-bias power reduction techniques reduce the power consumption of analog amplifiers. A low-power sample-and-hold amplifier maintains 10b resolution for input frequencies up to 60MHz based on a single-stage amplifier and nominal CMOS sampling switches using low threshold-voltage transistors. A signal insensitive 3-D fully symmetric layout reduces the capacitor and device mismatch of a multiplying D/A converter while low-noise reference currents and voltages are implemented on chip with optional off-chip voltage references. The employed down-sampling clock signal selects the sampling rate of 25MS/s or 10MS/s with a reduced power depending on applications. The prototype ADC in a 0.13um 1P8M CMOS technology demonstrates the measured DNL and INL within 0.42LSB and 0.91LSB and shows a maximum SNDR and SFDR of 56dB and 65dB at all sampling frequencies up to 2SMS/s, respectively. The ADC with an active die area if $0.8mm^2$ consumes 4.8mW at 25MS/s and 2.4mW at 10MS/s at a 1.2V supply.

• Journal of Mushroom
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• v.17 no.4
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• pp.211-217
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• 2019

Currently, cultivation of mushrooms using the Information and Communication Technology (ICT)-based smart farming technique is increasing rapidly. The main environmental factors for growth of mushrooms are temperature, humidity, carbon dioxide (CO₂), and light. Among all the mentioned factors, currently, only temperature has been maintained under automatic control. However, humidity and ventilation are controlled using a timer, based on technical experience.Therefore, in this study, a Pleurotus eryngii first-generation smart farm model was set up that can automatically control temperature, humidity, and ventilation. After installing the environmental control system and the monitoring device, the environmental condition of the mushroom cultivation room and the growth of the fruiting bodies were studied. The data thus obtained was compared to that obtained using the conventional cultivation method.In farm A, the temperature during the primordia formation stage was about 17℃, and was maintained at approximately 16℃ during the fruiting stage. The humidity was initially maintained at 95%, and the farm was not humidified after the primordia formation stage. There was no sensor for CO₂ management, and the system was ventilated as required by observing the shape of the pileus and the stipe. It was observed that, the concentration of CO₂ was between 700 and 2,500 ppm during the growth period. The average weight of the mushrooms produced in farm A was 125 g, and the quality was between that of the premium and the first grade.In farm B. The CO₂ sensor was in use for measurement purposes only; the system was ventilated as required by observing the shape of the pileus and the stipe. During the growth period, the CO₂ concentration was observed to be between 640 and 4,500 ppm. The average weight of the mushrooms produced in farm B was 102 g.These results indicate that the quality of the king oyster mushroom is determined by the environmental conditions, especially by the concentration of CO₂. Thus, the data obtained in this study can be used as an optimal smart farm model, where, by improving the environmental control method of farm A, better quality mushrooms were obtained.