• International Journal of Advanced Culture Technology
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• 제8권4호
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• pp.299-305
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• 2020

With the advancement of the 4th Industrial Revolution(4IR), microprocessor education is on the rise due to the explosive demand for IoT (Internet of Things) and M2M devices. However, it is difficult due to many constraints to efficiently transfer training on hardware assembly and implementation through online training. Thus, we developed a cost-effective online content based on Arduino simulations, Atmel Studio 7, and WinAvr simulator that are required for the utilization of AVR 128. These Camtasia videos overcame the limitation of theory focused on-line education by visually introducing the practical utilization of an actual AVR 128. In this paper, the proposed educational content was provided to university students, and the results of student feedback show that it has a strong effect.

• International Journal of Industrial Entomology and Biomaterials
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• 제3권1호
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• pp.75-81
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• 2001

We have constructed a novel recombinant Bombyx mori nuclear polyhedrosis virus (BmNPV) producing the green fluorescent polyhedra. For the production of the fluorescent polyhedra, partial polyhedrin gene containing KRKK as nuclear localization site from the BmNPV polyhedrin gene and the green fluorescent protein (gfp) gene were introduced under the control of p10 promoter of BmNPV. The recombinant BmNPV was stably produced fluorescent polyhedra in the infected Bm5 cells and the morphology of the fluorescent polyhedra was similar to that of wild-type BmNPV. The fluorescent polyhedra had 32 kDa native polyhedrin and 41 kDa fusion protein. From these data, we have further developed a novel BmNPV p10-based transfer vector producing recombinant polyhedra with foreign gene Product. The novel BmNPV P10-based transfer vector is composed of partial polyhedrin gene, factor Xa, and multiple cloning sites.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.175-181
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• 2022

COVID-19 is an acute respiratory syndrome that affects the host's breathing and respiratory system. The novel disease's first case was reported in 2019 and has created a state of emergency in the whole world and declared a global pandemic within months after the first case. The disease created elements of socioeconomic crisis globally. The emergency has made it imperative for professionals to take the necessary measures to make early diagnoses of the disease. The conventional diagnosis for COVID-19 is through Polymerase Chain Reaction (PCR) testing. However, in a lot of rural societies, these tests are not available or take a lot of time to provide results. Hence, we propose a COVID-19 classification system by means of machine learning and transfer learning models. The proposed approach identifies individuals with COVID-19 and distinguishes them from those who are healthy with the help of Deep Visual Embeddings (DVE). Five state-of-the-art models: VGG-19, ResNet50, Inceptionv3, MobileNetv3, and EfficientNetB7, were used in this study along with five different pooling schemes to perform deep feature extraction. In addition, the features are normalized using standard scaling, and 4-fold cross-validation is used to validate the performance over multiple versions of the validation data. The best results of 88.86% UAR, 88.27% Specificity, 89.44% Sensitivity, 88.62% Accuracy, 89.06% Precision, and 87.52% F1-score were obtained using ResNet-50 with Average Pooling and Logistic regression with class weight as the classifier.

• International Journal of Advanced Culture Technology
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• 제3권1호
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• pp.31-38
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• 2015

Coking accumulations via dry methane reforming (DMR) over 10NAM monolithic catalyst and pelletized catalyst was investigated. 10NAM catalyst was synthesized and coated on a wall of monolithic reactor. Pelletized catalyst of 10NAM was also prepared for the comparison. Consequently, catalyst was characterized by BET, $H_2-TPR$ and $H_2-TPD$. The catalytic reaction was undergone at $600^{\circ}C$ under atmospheric pressure and $CH_4$ to $CO_2$ reactant ratio of 1:2. The coking formation over spent catalyst was then carried out in the hydrogen flow using temperature programmed technique (TPH). According to the results, DMR over 10NAM monolithic catalyst exhibits a minimized coking formation comparing to the use of pelletized catalyst. This could be attributed to a prominent heat transfer efficiency of the monolithic catalyst.

• 한국도로학회논문집
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• 제19권4호
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• pp.37-44
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• 2017

PURPOSES : Conductive and convective heat transfer simulations for an asphalt mixture were made by using discrete element method (DEM) and similarity principle. METHODS : In this research, virtual specimens composed of discrete element method particles were generated according to four different predetermined particle size distribution curves. Temperature variations of the four different particles for a given condition were estimated and were compared with measurements and analytical solutions. RESULTS : The virtual specimen with mixed particles and with the smallest particle show very good agreement with laboratory test results and analytical solutions. As particle size decreases, better heat transfer simulation can be performed due to smaller void ratio and more contact points and areas. In addition, by utilizing the similarity principle of thermal properties and corresponding time unit, analytical time can be drastically reduced. CONCLUSIONS : It is concluded that the DEM asphalt mixture specimens with similarity principle could be used to predict the temperature variation for a given condition. It is observed that the void ratio has critical effect on prediction of temperature variation. Comparing the prediction for a 4 mm particle specimen with a mixed particle specimen, it is also concluded that predicting the mixed particle specimen temperature is much more efficient considering the number of particles that are directly associated with computational time in DEM analysis.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.182-190
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• 2021

Deep learning is an advanced technology for large-scale data analysis, with numerous promising cases like image processing, object detection and significantly more. It becomes customarily to use transfer learning and fine-tune a pre-trained CNN model for most image recognition tasks. Having people taking photos and tag themselves provides a valuable resource of in-data. However, these tags and labels might be noisy as people who annotate these images might not be experts. This paper aims to explore the impact of noisy labels on fine-tuning pre-trained CNN models. Such effect is measured on a food recognition task using Food101 as a benchmark. Four pre-trained CNN models are included in this study: InceptionV3, VGG19, MobileNetV2 and DenseNet121. Symmetric label noise will be added with different ratios. In all cases, models based on DenseNet121 outperformed the other models. When noisy labels were introduced to the data, the performance of all models degraded almost linearly with the amount of added noise.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.121-127
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• 2023

A 500 km/h linear motor high speed terrestrial transportation service is planned to launch 2027 in Japan. In order to support 5G service in the train, the Sub-carrier spacing frequency of 30 kHz is planned to be used instead of common 15 kHz sub-carrier spacing to mitigate Doppler effect in such high-speed transportation. In addition, to increase the cell size of 5G mobile system, plural Base Station antenna will transmit the identical Down Link (DL) signal to form the expanded cell size along the train rail. In this situation, forward and backward antenna signals will be Doppler shifted by reverse direction respectively and the receiver in the train might suffer to estimate accurate Channel Transfer Function (CTF) for its demodulation. In this paper, Delay and Doppler Profiler (DDP) based Channel Estimator is proposed and it is successfully implemented in signal processing simulation system. Then the simulated performances are compared with the conventional Time domain linear interpolated estimator. According to the simulation results, QPSK modulation can be used even under severe channel condition such as 500 km/h, 2 path reverse Doppler Shift condition, although QPSK modulation can be used less than 200 km/h with conventional Channel estimator.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제4B권4호
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• pp.206-210
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• 2004

This paper deals with a parameter estimation of the DC-DC converter system for its diagnosis. Especially, we present the results of parameter estimation for the DC-DC converter model by the system identification method. The parameter estimation for the DC-DC converter system aims at the diagnosis of its operating status. For the operating status diagnosis of the DC-DC converter system, we assume that the DC-DC converter system is an equivalent model of the Buck converter and estimate the main parameter for on-line diagnosis. In addition, for verification of an estimated parameter, we compare a bode plot of the estimated system transfer function and measurement results of the HP4194 instrument. It is a control system analyzer for system transfer function measurement. Our results confirm that the main parameter for diagnosis of the DC-DC converter system can be estimated by the system identification method and that the aging status of the system can be predicted by these results on operating status.

• Clinical and Experimental Reproductive Medicine
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• 제44권1호
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• pp.47-51
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• 2017

Objective: This study was designed to report the status of assisted reproductive technology (ART) therapy in South Korea between January 1, 2012 and December 31, 2012. Methods: A localized online survey, originally developed by the International Committee Monitoring Assisted Reproductive Technologies, was first launched and provided to all available ART centers via email in 2015. Fresh embryo transfer (FET) cases were categorized as standard in vitro fertilization, intracytoplasmic sperm injection (ICSI), or half-ICSI. Thawed embryo transfer (TET) and other related procedures, including surgical sperm retrieval, were surveyed. Results: Data from 33,956 ovum pick-up procedures were provided by 75 clinics in 2012. Of the 33,088 cycles in which ovums were retrieved, a complete transfer was performed in 90.5% (29,932 cycles). In addition, 10,079 FET cycles were confirmed to have resulted in clinical pregnancy, representing a pregnancy rate of 30.5% per ovum pick-up and 33.7% per ET. The most common number of embryos transferred in FET was 2 (41.6%), followed by 3 (34.0%), and non-elective single ETs (10.0%). Of the 10,404 TET cycles in which transfer was completed, 3,760 clinical pregnancies (36.1%) were confirmed by ultrasonography. Conclusion: The overall clinical pregnancy rate for FET and TET cycles in 2012 was higher than in 2011 (33.7% vs. 33.2% and 36.1% vs. 31.1%, respectively). The most common number of embryos transferred in FET cycles was 2, unlike in 2011.

• International Journal of Advanced Culture Technology
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• 제9권2호
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• pp.86-90
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• 2021

Sentiment refers to a person's thoughts, opinions, and feelings toward an object. Sentiment analysis is a process of collecting opinions on a specific target and classifying them according to their emotions, and applies to opinion mining that analyzes product reviews and reviews on the web. Companies and users can grasp the opinions of public opinion and come up with a way to do so. Recently, natural language processing models using the Transformer structure have appeared, and Google's BERT is a representative example. Afterwards, various models came out by remodeling the BERT. Among them, the Facebook AI team unveiled the XLM-R (XLM-RoBERTa), an upgraded XLM model. XLM-R solved the data limitation and the curse of multilinguality by training XLM with 2TB or more refined CC (CommonCrawl), not Wikipedia data. This model showed that the multilingual model has similar performance to the single language model when it is trained by adjusting the size of the model and the data required for training. Therefore, in this paper, we study the improvement of Korean sentiment analysis performed using a pre-trained XLM-R model that solved curse of multilinguality and improved performance.