• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.217-219
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• 2001

플래쉬 메모리는 데이터 저장 및 변경이 가능한 비휘발성 메모리로 가벼운 무게, 낮은 전력 소모, 충격에 대한 저항성과 빠른 데이터 처리 능력 때문에 이동형 컴퓨터 시스템에서 사용하기에 적당하다. 그러나 플래쉬 메모리는 덮어쓰기(update-in-place)가 불가능하고 각 메모리 셀에 대해 초기화 작업(erasing operation)의 수가 제한되어 있다. 이러한 단점들을 고려하여 세그먼트의 데이터 중 유효 데이터의 비율과 hot 데이터(가까운 시간 안에 update가 될 것이라는 예상되는 data)의 수, 세그멘트가 초기화되었던(easing) 횟수 등을 고려한 새로운 초기화 기법(cleaning policy)을 제안하고자 한다.

• Journal of Korean society of Dental Hygiene
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• v.14 no.6
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• pp.903-913
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• 2014

Objectives: The purpose of the study was to investigate the content analysis of daily tooth cleaning service records by caregivers in a long-term care facility. Methods: The data were analyzed by qualitative research based on content analysis of the daily records of the processes and results of daily tooth cleaning service. Twenty caregivers provided tooth, gum and denture cleaning service after breakfast, lunch, and dinner to 48 elderly residents. The study lasted about two weeks(from August 4 to August 20, 2014). The researcher reconstructed the language by repeatedly reviewing the caregivers statements in the records. The content categories were derived from the records through a reiterative manual comparative analysis. Using constant comparison method, reconstructed meanings were incorporated into various meanings and reanalyzed by final categories called as analytic coding. In order to validate the reliability, 6 times of discussion made the common meanings through a master's degree student and a dental hygiene professor. Results: The caregivers identified lack of understanding and ability to recognize the functional physical and mental changes in the elderly. The elderly had difficulty in recognizing silent communication and daily tooth cleaning. The caregivers were so strenuous in taking care of the daily tooth cleaning service for the elderly. At last, they gave up the daily tooth cleaning service and took on it to the guardians. They found that there was no social supporting network for oral health of the elderly residents. Conclusions: Caregivers had insufficient understanding of the functional physical and mental changes in the elderly residents, and they had difficulty providing daily tooth cleaning service to the elderly due to poor skill and abilities.

• Agribusiness and Information Management
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• v.9 no.1
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• pp.18-22
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• 2017

It takes lots of time and labor if a worker have to measure the water quality at a certain but designated time every day in an un-automated aqua farm. In addition, if the equipment is soaked in the sea water consistently, it will be contaminated by diverse floating matters and barnacles, and it often becomes mal-functional within 2~3 months. Therefore, we need to develop a system with which the sensed data could be checked in real time and operated automatically, while preventing the contamination of the sensor, a crucial component for water quality measuring equipment, as much as possible, and increasing the replacement cycle. We have developed a non-cleaning water quality measuring equipment and its software which are used in the fishery household of offshore aqua farms. By providing the workers with a mobile application which has a function of monitoring the water quality in real time, they can check the situation directly without going to the fishery household.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.16 no.7
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• pp.569-572
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• 2003

In this study, we evaluated the dilute HF cleaning to reduce residual defects made by metal CMP process. The purpose of this test is to observe the existence of barrier metal damage during DHF cleaning on condition that it should not affect metal thin film reliability, so we will get rid of slurry residual particles as a main defect of the metal CMP process for the better yield. In-line defect data showed us that slurry residual particles were removed by DHF application. The HF rinse significantly reduced metal contamination levels and surface roughness. The best effect by additional oxide loss was discovered when Dilute HF condition is 10".

• Journal of Biosystems Engineering
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• v.19 no.1
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• pp.22-32
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• 1994

The combine harvester is considered as an important but complicated and costly machine. The appropriate size of combine has to be developed to use efficiently in Korea. But the combine is such a complicated machine that a complete design model to develop a new type is impossible without understanding the relationship between each factor. The combine capacity is generally limited by the cleaning shoe performance. So a design model for a cleaning shoe has to be developed first for the complete combine design. The objective of this research was to develop a cleaning model of head-feeding combine to predict grain separation from chaff and broken straw on a sieve. A developed physically based model can explain the situation which can happen during separation process. A test apparatus based on the field going machine was developed. The test materials were paddy rice and barley. The data obtained were analyzed by the hand and the video camera. The developed model was verified as an adequate model through the test with $R^2$ of 0.934 and 0.837. The model can be used to evaluate design and operation alternatives of combine and also applied to the automatic control of separation unit of combine with a loss monitering sensors.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1379-1386
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• 2018

Conventional underwater cameras used in fish farms can only shoot limited areas and are vulnerable to underwater contamination. There is also a problem with contaminated farms as surplus residues are deposited as a result of feed supply to farms' nets. This paper proposes underwater drones for underwater monitoring of fish farms and cleaning nets. If underwater drones are used for management of fish farms, underwater imaging, monitoring and cleaning of fish farms' nets can be possible. By using this technology, data can be collected by detecting changes in the environment of a fish farm and responding to changes that occur within a fish farm based on the data. In addition, the establishment of an integrated control system will enable to build efficient and stable smart farms.

• Transportation Technology and Policy
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• v.10 no.2
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• pp.47-58
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• 2013

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.117-130
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• 2003

It is not unusual to observe that there Is a great amount of redundant or inconsistent data even within an e-business system such as CRM(Customer Relationship Management) system. This problem becomes aggravate when we construct a system of which information are gathered from different sources. Data quality management is indeed needed to avoid any possible redundant or inconsistent data in such information system. A data quality process, in general, consists of three phases: data cleaning (scrubbing), matching, and integration phase. In this paper, we introduce and categorize data quality operators for each phase. Then, we describe our distance function used in the matching phase, and present a matching algorithm PRIMAL (a PRactical Matching Algorithm). And finally, we present a related work and future research.

• 보존과학연구
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• s.31
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• pp.43-57
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• 2010

This study intends to provide quantitative data about the extraction characteristics of major elements of earthenware by executing soaking test of cleaning agents. It aims at providing basic data for the stability assessment when applying the cleaning agents for conserving earthenware. The data will be extracted from the analysis of co-relationship between the physical characteristics and the ion extraction characteristics. XRD analysis displayed that AT-1, AT-2 and AT-3 which did not generate mullite were fired at lower than 1,000 whereas AT-3 and AT-5 that included mullite were higher than 1,000. The degree of absorption was AT-4 > AT-2 > AT-1 > AT-3 > AT-5 in order and the correlation between the degree of absorption and firing temperature of earthenware displayed a positive correlation. Extraction amount of oxalic acid which was used for the removing iron oxide was AT-1 > AT-2 AT-4 > AT-3 > AT-5 in order. and the ion extraction data displayed that there is a positive correlation with absorption level. However AT-1 and AT-2 which were fired at lower temperature showed that there was no correlation between the ion extraction characteristics and absorption level. Ion extraction of citric acid produced little amount compared with the one of oxalic acid, yet it caused less damage to earthenware than oxalic acid when it applied. The result of ion extraction level in the absorption test displayed that Fe had higher level than in Si, Al from the test for both oxalic acid and citric acid. Based on the regression analysis of the data from the previous studies, the physical characteristics of the earthenware and ion extraction level, further studies will be conducted on the predicting technique on the extraction characteristics of major elements of earthenware samples for the conservation in future.

• Journal of Environmental Health Sciences
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• v.47 no.5
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• pp.462-471
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• 2021

Background: The number of synthesized chemicals has rapidly increased over the past decade. For many chemicals, there is a lack of information on toxicity. With the current movement toward reducing animal testing, the use of toxicity big data and deep learning could be a promising tool to screen potential toxicants. Objectives: This study identified potential chemicals related to reproductive and estrogen receptor (ER)-mediated toxicities for 1135 cleaning products and 886 laundry products. Methods: We listed chemicals contained in cleaning and laundry products from a publicly available database. Then, chemicals that potentially exhibited reproductive and ER-mediated toxicities were identified using the European Union Classification, Labeling and Packaging classification and ToxCast database, respectively. For chemicals absent from the ToxCast database, ER activity was predicted using deep learning models. Results: Among the 783 listed chemicals, there were 53 with potential reproductive toxicity and 310 with potential ER-mediated toxicity. Among the 473 chemicals not tested with ToxCast assays, deep learning models indicated that 42 chemicals exhibited ER-mediated toxicity. A total of 13 chemicals were identified as causing reproductive toxicity by reacting with the ER. Conclusions: We demonstrated a screening method to identify potential chemicals related to reproductive and ER-mediated toxicities utilizing chemical toxicity big data and deep learning. Integrating toxicity data from in vivo, in vitro, and deep learning models may contribute to screening chemicals in consumer products.