• Transactions of the Korean hydrogen and new energy society
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• v.32 no.4
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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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• v.11 no.3
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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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• v.15 no.4
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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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• v.41 no.2
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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.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.43-48
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• 2016

Wireless sensor networks (WSN) can be more reliable and easier to program and use with the help of sensor database management systems (SDMS). SDMS establish a user-friendly SQL-based interface to process declarative user-defined queries over sensor readings from WSN. Typical queries in SDMS are ad-hoc snapshot queries and long-running, continuous queries. In SDMSs queries are flooded to all nodes in the sensor net, and query results are sent back from nodes that have qualified results to a base station. For query flooding to all nodes, and result flooding to the base station, a lot of communication energy consuming is required. This paper suggests an efficient in-network index solution, named Distributed Information Gathering (DIG) to process range queries in a sensor net environment that can save energy by reducing query and result flooding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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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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• v.39 no.2
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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.

• Proceedings of the KIEE Conference
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• 2006.04a
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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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• v.13 no.12
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.909-916
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• 2015

Recently, as shown demands and the need for Cloud dig data, large-scale data centers, as well as factory unit's have also vigor. This study focuses the system design that can integrate various facilities on the data center build upon of the factory and the midsize group. Because the integrated facilities management of midsize groups should have variety of sensors and devices requires integrated monitoring system that can manage them as a whole. In this study, it constitutes a integrated model protocol interface of the various facilities, it suggests ways to avoid the errors that may occur during design to study the development and systematically carried out. For this purpose, this study provides the analysis of a failure information delivery time and integration scenario for efficient analysis and evaluation of an integrated monitoring system.