• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2024년도 제69차 동계학술대회논문집 32권1호
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• pp.469-470
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• 2024

본 논문에서는 DigComp를 분석하여 데이터 리터러시 수준을 분석하고자 하였다. 이를 위해 DigComp의 구성요소인 데이터 리터러시, 소통 및 협업, 디지털 콘텐츠 제작, 보안, 문제해결 중 데이터 리러터시 영역의 세부 요소를 살펴보았다. 데이터 리터러시는 탐색·검색·필터링, 평가, 관리 3가지로 세분되어 있었고, 각각은 수준에 따라 기초, 중급, 고급, 전문가의 4단계로 구분되어 있었다. 그리고 3가지 영역의 수준을 분석하여 각 수준을 대표하는 핵심 단어를 추출하였다. 향후 이를 바탕으로 한 구체적 적용방안에 관한 연구가 이뤄지길 기대한다.

• 한국수소및신에너지학회논문집
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• 제32권4호
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• pp.219-227
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• 2021

Hydrogen isotopes, which are used as raw materials in fusion reaction, participate in the reaction only in small amount, and most of them are released together with impurities. In order to recover and reuse only hydrogen isotopes from this exhaust gas, a recovery process is required, and most of the hydrogen isotopes can be recovered using a Pd Membrane. In this study, the recovery rate of hydrogen isotopes was measured through the first and second stage Pd membrane experiments. In the case of the experiment using a single stage Pd membrane, about 99.2%, and in the case of the first stage and second stage Pd membrane connection experiments, a recovery rate of 99.9% or more was obtained. Therefore, the recovery rate of Pd membrane process applied to hydrogen can be applied to hydrogen isotopes. In addition, the simulation model was established using aspen custom modeler, a commercial software, and the validity of the simulation was checked by applying the references and experimental data. The simulation results based on the experimental data showed a difference of 2% or less.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.149-155
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• 2023

Since developing digital technology requires new skills in digital literacy, digital competence also has become a keystone in English classes. This study explores three aspects of English as a Foreign Language (EFL) learners' digital confidence: information and data literacy, communication and collaboration, and digital content creation, based on the European Digital Competence Framework for Citizens (DigComp). The participants were 150 Korean college students enrolled in two general English classes, and their English proficiency levels were from basic to advanced; each level consisted of 30 participants. In order to assess their digital confidence, I designed a Google survey form and collected data during two semesters. The survey results revealed that the participants had highest digital confidence in information and data literacy and overall, the female participants showed higher digital confidence than their male counterparts. It also showed that the learners' English proficiency and computer skills are important factors.

• JSTS:Journal of Semiconductor Technology and Science
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• 제15권4호
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• pp.506-517
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• 2015

This paper presents a 1.248 Gb/s - 2.918 Gb/s low-power receiver MIPI-DigRF M-PHY with a fully digital frequency detection loop. MIPI-DigRF M-PHY should be operated in a very short training time which is $0.01{\mu}s$ the for HS-G2B mode. Because of this short SYNC pattern, clock and data recovery (CDR) should have extremely fast locking time. Thus, the quarter rate CDR with a fully digital frequency detection loop is proposed to implement a fast phase tracking loop. Also, a low power CDR architecture, deserializer and voltage controlled oscillator (VCO) are proposed to meet the low power requirement of MIPI-DigRF M-PHY. This chip is fabricated using a $0.11{\mu}m$ CMOS process, and the die area is $600{\mu}m{\times}250{\mu}m$. The power consumption of the receiver is 16 mW from the supply voltage of 1.1 V. The measured lock time of the CDR is less than 20 ns. The measured rms and peak jitter are $35.24ps_{p-p}$ and $4.25ps_{rms}$ respectively for HS-G2 mode.

• International Journal of Oral Biology
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• 제41권2호
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• pp.97-103
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• 2016

Mammals have 3 pairs of major salivary glands i.e., the parotid, submandibular, and sublingual glands. Saliva secretion of these glands is modulated by taste perception. Salivary glands are composed mainly of acinar and ductal cells. Primary saliva is secreted by acinar cells and modified during ductal flow. Recently, of the murine 35 bitter taste receptors, Tas2r108 was expressed at highest levels in the submandibular gland by qPCR. Further, Tas2r108-transfected cells respond to a range of bitter compounds, such as denatonium, quinine, colchicine, diphenidol, caffeine and dapson. The objective of the present study was to characterize the expression of Tas2r108 mRNA in acinar and/or ductal cells of the submandibular gland using in situ hybridization (ISH). Male 42-60 days old DBA2 mice were used in the study. Messenger RNAs were extracted from the submandibular gland for generating digoxigenin (DIG) labeled-cRNA probes. These probes were transcribed in anti-sense and sense orientation using T7 RNA polymerase. Dot blot hybridization was performed using DIG labeled-cRNA probes, in order to estimate integrity and optimal diluting concentration of these probes. Subsequently, ISH was performed on murine submandibular gland to detect Tas2r108 mRNA. Dot blot hybridization data demonstrated that Tas2r108 DIG labeled-cRNA anti-sense probes specifically detected Tas2r108 cDNA. ISH results showed that the anti-sense probes labeled acinar and ductal cells in the submandibular gland, whereas no staining was visible in sense controls. Interestingly, the Tas2r108 expression levels were higher in acinar than ductal cells. These results suggested that Tas2r108 might be more associated with primary saliva secretion than with ductal modification of saliva composition.

• 한국융합학회논문지
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• 제7권6호
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• pp.43-48
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• 2016

무선센서 네트워크는 센서 데이터베이스 관리 시스템을 통해 보다 효율적으로 개발 및 운용될 수 있다. 센서 데이터베이스 관리 시스템은 무선센서 입력에 대해 선언된 사용자 정의 질의를 처리하기 위해 사용자들에게 익숙한 SQL 유형의 사용자 접속을 지원한다. 무선센서 네트워크상의 전형적 질의 유형은 임의의 스냅 샷 값 검색이나 오래도록 지속되는 연속 질의 형태를 갖는다. 무선센서 네트워크상에서 질의 처리는 베이스스테이션으로부터 여러 노드들로 질의를 보내는 과정과 여러 노드에서 얻어지는 질의 결과를 베이스스테이션으로 회수하는 과정이 있는데 이러한 질의의 파급이나 베이스스테이션으로의 결과 전송은 많은 에너지 소모를 요구한다. 이 논문은 무선센서 네트워크상에서 영역 질의를 처리함에 있어 질의 및 결과를 파급시키는데 소모되는 에너지를 절약시켜 주기 위한 분산정보수집(DIG: Distributed Information Gathering)이라고 이름붙인 효율적 색인 방법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3935-3949
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• 2021

With the rapid development of Internet and big data technology, various online social network platforms have been established, producing massive information every day. Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet. Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation in cross-network scenarios. This paper proposes a novel hot topic discovery method across social network based on an im-proved LDA model, which first integrates the text information from multiple social network platforms into a unified data set, then obtains the potential topic distribution in the text through the improved LDA model. Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic. This paper obtains data from the online social networks and constructs a cross-network topic discovery data set. The experimental results demonstrate the superiority of the proposed method compared to baseline methods.

• KDI Journal of Economic Policy
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• 제39권2호
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• pp.75-98
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• 2017

This paper aims to estimate the social discount rate (SDR) rather than dig into its theoretical foundation. As SDRs can be derived by investigating both the rate of return on investment and the social time preference rate, we estimate the marginal productivity of both private and public capital and the time preference rate based on the Euler equation. In order to provide a single representative SDR, the weighted averages of the marginal productivity and time preference rate, whose weights are determined by the flow of funds data reflecting the social demand of funds, are presented. Based on the empirical results, we argue that the marginal productivity of private capital stands in the middle of the 3% range while that of public capital varies from 4.5% to 8.6%, with the time preference rate showing a decreasing trend from 3.2% in the early 2000s to 1.2% by around 2030. The single representative SDR or the weighted SDR is estimated to be approximately 3.0~4.5% and expected to continue its downward trend for the foreseeable future.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.171-173
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• 2006

Because sensor networks use wireless communication, they are vulnerable to attacks which are more difficult launch in the wired domain. Many wired networks benefit from their inherent physical security properties. It is unlikely that an adversary will dig up the Internet backbone and splice into the line. However, wireless communications are difficult to protect; they are by nature a broadcast medium. In a broadcast medium, adversaries can easily eavesdrop on, intercept, inject, and alter transmitted data. In addition, adversaries are not restricted to using sensor network hardware. We have analyzed the IEEE 802.15.4, ZigBee specification which includes a number of security provisions and options. In this paper, we highlight places where USN security considerations and home network attack scenarios.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.