• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.229-234
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• 1999

We analyzed the reliability characteristics in platinum schottky diodes with variations of n-type silicon substrates concentrations and temperature variations of measurements. The parameters of reliability measurement analysis are saturation current. turn-on voltage and ideality factor in the forward bias, the breakdown voltage in the reverse bias with device shapes. The shape of devices are square type and long rectangular type for edge effect. As a result, we analyzed that the forward turn-on voltage, barrier height, dynamic resistance and reverse breakdown voltage were decreased but ideality factor and saturation current were increased by increased concentration in platinum and n-silicon junction parts. In measurement temperature(RT, $50^{\circ}C$, $75^{\circ}C$), the extracted electrical parameter values of reliability characteristics were increased at the higher temperature under the forward and reverse bias. The long rectangular type devices were more decreased than the square type in reverse breakdown voltage by tunneling effects of edge part.

• Restorative Dentistry and Endodontics
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• v.45 no.3
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• pp.28.1-28.13
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• 2020

Objectives: To evaluate the outcome of in vitro studies comparing the effectiveness of QMix irrigant in removing the smear layer in the root canal system compared with other irrigants. Materials and Methods: The research question was developed by using Population, Intervention, Comparison, Outcome and Study design framework. Literature search was performed using 3 electronic databases PubMed, Scopus, and EBSCOhost until October 2019. Two reviewers were independently involved in the selection of the articles and data extraction process. Risk of bias of the studies was independently appraised using revised Cochrane Risk of Bias tool (RoB 2.0) based on 5 domains. Results: Thirteen studies fulfilled the selection criteria. The overall risk of bias was moderate. QMix was found to have better smear layer removal ability than mixture of tetracycline isonomer, an acid and a detergent (MTAD), sodium hypochlorite (NaOCl), and phytic acid. The efficacy was less effective than 7% maleic acid and 10% citric acid. No conclusive results could be drawn between QMix and 17% ethylenediaminetetraacetic acid due to conflicting results. QMix was more effective when used for 3 minutes than 1 minute. Conclusions: QMix has better smear layer removal ability compared to MTAD, NaOCl, Tubulicid Plus, and Phytic acid. In order to remove the smear layer more effectively with QMix, it is recommended to use it for a longer duration.

• Journal of the Korean Applied Science and Technology
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• v.34 no.4
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• pp.1017-1024
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• 2017

Carbon thin films were deposited by HiPIMS(High Power Impulse Magnetron Sputtering). The properties and microstructures of carbon thin film were investigated with power, pressure, bias voltage and duty cycle. As the HiPIMS power increased, the deposition thickness increased and the surface tended to be rough. The increase in pressure also tended to make the surface rough, but the deposition thickness was not proportional to the pressure. As the bias voltage increased, the surface roughness became worse, the deposition thickness increased and then decreased from the critical bias voltage. Changes in the duty cycle have caused problems such as arcing, which is affected by the chamber structure and the size of the target. The $sp^2/sp^3$ fractions of thin films were estimated by XPS and it was confirmed that the fraction of thin films made by HiPIMS were larger than the fraction of thin films made by DC sputtering.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.681-686
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• 2012

This paper studied the speech parameters less affected by the human emotion for the development of the robust emotional speech recognition system. For this purpose, the effect of emotion on the speech recognition system and robust speech parameters of speech recognition system were studied using speech database containing various emotions. In this study, mel-cepstral coefficient, delta-cepstral coefficient, RASTA mel-cepstral coefficient, root-cepstral coefficient, PLP coefficient and frequency warped mel-cepstral coefficient in the vocal tract length normalization method were used as feature parameters. And CMS (Cepstral Mean Subtraction) and SBR(Signal Bias Removal) method were used as a signal bias removal technique. Experimental results showed that the HMM based speaker independent word recognizer using frequency warped RASTA mel-cepstral coefficient in the vocal tract length normalized method, its derivatives and CMS as a signal bias removal showed the best performance.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2377-2386
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• 2021

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

• Journal of Korean Medicine Rehabilitation
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• v.31 no.2
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• pp.15-24
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• 2021

Objectives Purpose of our study is to investigate the preventive and therapeutic effect of Chuna manual therapy (CMT) for lymphedema. Methods A study search of 10 databases was performed. We included the randomized controlled trials (RCTs) which performed CMT for lymphedema in this study. The keywords used were 'chuna' or 'tuina' and 'lymphedema'. Two independent authors rated study quality and risk of bias using the Cochrane risk of bias tool. Results 9 appropriate RCTs were remained after screening. The therapeutic effects of the experimental group was statistically higher than that of the control group with functional exercise or taking western medicine. Subjective symptom score was also lower in the CMT group. Conclusions These results suggests that CMT has sufficient evidence that it is more effective in prevent or alleviating symptoms of lymphedema than conventional treating methods. However, due to the high risk of bias of included studies, further researches are needed with higher quality of evidence.

• Journal of Surface Science and Engineering
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• v.54 no.2
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• pp.53-61
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• 2021

The properties of tetrahedral amorphous Carbon (ta-C) film can be determined by multiple parameters and comprehensive effects of those parameters during a deposition process with filtered cathodic vacuum arc (FCVA). In this study, Taguchi method was adopted to design the optimized FCVA deposition process of ta-C for improving deposition efficiency and mechanical properties of the deposited ta-C thin film. The influence and contribution of variables, such as arc current, substrate bias voltage, frequency, and duty cycle, on the properties of ta-C were investigated in terms of deposition efficiency and mechanical properties. It was revealed that the deposition rate was linearly increased following the increasing arc current (around 10 nm/min @ 60 A and 17 nm/min @ 100A). The hardness and I_D/I_G showed a correlation with substrate bias voltage (over 30 GPa @ 50 V and under 30 GPa @ 250 V). The scratch tests were conducted to specify the effect of each parameter on the resistance to plastic deformation of films. The analysis on variances showed that the arc current and substrate bias voltage were the most effective controlling parameters influencing properties of ta-C films. The optimized parameters were extracted for the target applications in various industrial fields.

• Animal Bioscience
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• v.34 no.10
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• pp.1623-1631
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• 2021

Objective: This study aimed to validate and evaluate the dry matter (DM) intake prediction model of the Korean feeding standards for dairy cattle (KFSD). Methods: The KFSD DM intake (DMI) model was developed using a database containing the data from the Journal of Dairy Science from 2006 to 2011 (1,065 observations 287 studies). The development (458 observations from 103 studies) and evaluation databases (168 observations from 74 studies) were constructed from the database. The body weight (kg; BW), metabolic BW (BW^0.75, MBW), 4% fat-corrected milk (FCM), forage as a percentage of dietary DM, and the dietary content of nutrients (% DM) were chosen as possible explanatory variables. A random coefficient model with the study as a random variable and a linear model without the random effect was used to select model variables and estimate parameters, respectively, during the model development. The best-fit equation was compared to published equations, and sensitivity analysis of the prediction equation was conducted. The KFSD model was also evaluated using in vivo feeding trial data. Results: The KFSD DMI equation is 4.103 (±2.994)+0.112 (±0.022)×MBW+0.284 (±0.020)×FCM-0.119 (±0.028)×neutral detergent fiber (NDF), explaining 47% of the variation in the evaluation dataset with no mean nor slope bias (p>0.05). The root mean square prediction error was 2.70 kg/d, best among the tested equations. The sensitivity analysis showed that the model is the most sensitive to FCM, followed by MBW and NDF. With the in vivo data, the KFSD equation showed slightly higher precision (R² = 0.39) than the NRC equation (R² = 0.37), with a mean bias of 1.19 kg and no slope bias (p>0.05). Conclusion: The KFSD DMI model is suitable for predicting the DMI of lactating dairy cows in practical situations in Korea.

• The Journal of Korean Obstetrics and Gynecology
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• v.35 no.2
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• pp.28-41
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• 2022

Objectives: The purpose of this study is to investigate the effectiveness of moxibustion in infertility with In Vitro Fertilization and Embryo Transfer (IVF-ET). Methods: We searched 8 databases (Embase, PubMed, CiNii, CNKI, OASIS, ScienceOn, KMBASE, KISS)to identify eligible studies published before 2021 Oct. We included randomized controlled clinical trials (RCTs) using moxibustion in infertility with IVF-ET. The methodological quality of each RCT was assessed by the Cochrane risk of bias tool. Results: Two RCT studies were eligible in our review. The overall risk of bias was evaluated as unclear. The meta-analysis of 2 trials indicated that favorable results for the use of moxibustion with IVF-ET. Conclusions: This systematic review and meta-analysis of clinical trials suggests that moxibustion with IVF-ET can effect on Infertility patients. However, because of studies included analysis was biased due to unclear risk of bias and unreliable study design, future RCT studies and additional Meta-Analysis are needed to judge the supplementary treatment role of moxibustion in infertility with IVF-ET.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.