• Proceedings of the Korean Vacuum Society Conference
• /
• 2013.08a
• /
• pp.77.2-77.2
• /
• 2013

In this talk, I will briefly review recent results of my group related to application of atomic layer deposition (ALD) for fabricating environmental catalysts and organic solar cells. ALD was used for preparing thin films of TiO2 and NiO on mesporous silica with a mean pore size of 15 nm. Upon depositing TiO2 thin films of TiO2 using ALD, the mesoporous structure of the silica substrate was preserved to some extent. We show that efficiency for removing toluene by adsorption and catalytic oxidation is dependent of mean thickness of TiO2 deposited on silica, i.e., fine tuning of the thickness of thin film using ALD can be beneficial for preparing high-performing adsorbents and oxidation catalysts of volatile organic compound. NiO/silica system prepared by ALD was used for catalysts of chemical conversion of CO2. Here, NiO nanoparticles are well dispersed on silica and confiend in the pore, showing high catalytic activity and stability at 800oC for CO2 reforming of methane reaction. We also used ALD for surface modulation of buffer layers of organic solar cell. TiO2 and ZnO thin films were deposited on wet-chemically prepared ZnO ripple structures, and thin films with mean thickness of ~2 nm showed highest power conversion efficiency of organic solar cell. Moreover, performance of ALD-prepared organic solar cells were shown to be more stable than those without ALD. Thin films of oxides deposited on ZnO ripple buffer layer could heal defect sites of ZnO, which can act as recombination center of electrons and holes.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.14 no.1
• /
• pp.13-21
• /
• 2015

This study focuses on operational analysis of 2 types of intersection shared lanes. First, the analysis showed that a through & right-turn shared lane is always less used than the adjacent through-only lanes and as a result, operational efficiency deteriorates. To improve the efficiency fine-tuning in signal timing optimization using lane-by-lane traffic volume data is required. Further improvement can be achieved by guiding drivers to equally use the shared lane. For left-turn & U-turn shared lanes, it was found that saturation flow rate is affected by interference between U-turn and conflicting right-turn movements. However, since such interference does not occur in every cycle, a statistical model must be established to develop realistic adjustment factor for saturation flow rate of the shared lane.

• Journal of Microbiology and Biotechnology
• /
• v.28 no.12
• /
• pp.2046-2056
• /
• 2018

Phospholipase D has great commercial value due to its transphosphatidylation products that can be used in the food and medicine industries. In order to construct a strain for use in the production of PLD, we employed a series of combinatorial strategies to increase PLD expression in Bacillus subtilis WB600. These strategies included screening of signal peptides, selection of different plasmids, and optimization of the sequences of the ribosome-binding site (RBS) and the spacer region. We found that using the signal peptide amyE results in the highest extracellular PLD activity (11.3 U/ml) and in a PLD expression level 5.27-fold higher than when the endogenous signal peptide is used. Furthermore, the strain harboring the recombinant expression plasmid pMA0911-PLD-amyE-his produced PLD with activity enhanced by 69.03% (19.1 U/ml). We then used the online tool \RBS Calculator v2.0 to optimize the sequences of the RBS and the spacer. Using the optimized sequences resulted in an increase in the enzyme activity by about 26.7% (24.2 U/ml). In addition, we found through a transfer experiment that the retention rate of the recombinant plasmid after 5 generations was still 100%. The final product, phosphatidylserine (PS), was successfully detected, with transphosphatidylation selectivity at 74.6%. This is similar to the values for the original producer.

• BMB Reports
• /
• v.52 no.6
• /
• pp.403-408
• /
• 2019

Dorsoventral patterning of body axis in vertebrate embryo is tightly controlled by a complex regulatory network of transcription factors. Ventx1.1 is known as a transcriptional repressor to inhibit dorsal mesoderm formation and neural differentiation in Xenopus. In an attempt to identify, using chromatin immunoprecipitation (ChIP)-Seq, genome-wide binding pattern of Ventx1.1 in Xenopus gastrulae, we observed that Ventx1.1 associates with its own 5'-flanking sequence. In this study, we present evidence that Ventx1.1 binds a cis-acting Ventx1.1 response element (VRE) in its own promoter, leading to repression of its own transcription. Site-directed mutagenesis of the VRE in the Ventx1.1 promoter significantly abrogated this inhibitory autoregulation of Ventx1.1 transcription. Notably, Ventx1.1 and Xcad2, an activator of Ventx1.1 transcription, competitively co-occupied the VRE in the Ventx1.1 promoter. In support of this, mutation of the VRE down-regulated basal and Xcad2-induced levels of Ventx1.1 promoter activity. In addition, overexpression of Ventx1.1 prevented Xcad2 from binding to the Ventx1.1 promoter, and vice versa. Taken together, these results suggest that Ventx1.1 negatively regulates its own transcription in competition with Xcad2, thereby fine-tuning its own expression levels during dorsoventral patterning of Xenopus early embryo.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.1
• /
• pp.44-49
• /
• 2021

Recently, due to COVID-19, studies have been popularly worked to apply neural network to mask wearing automatic detection system. For applying neural networks, the 1-stage detection or 2-stage detection methods are used, and if data are not sufficiently collected, the pretrained neural network models are studied by applying fine-tuning techniques. In this paper, the system is consisted of 2-stage detection method that contain MTCNN model for face recognition and ResNet model for mask detection. The mask detector was experimented by applying five ResNet models to improve accuracy and fps in various environments. Training data used 17,217 images that collected using web crawler, and for inference, we used 1,913 images and two one-minute videos respectively. The experiment showed a high accuracy of 96.39% for images and 92.98% for video, and the speed of inference for video was 10.78fps.

• Journal of Biomedical Engineering Research
• /
• v.42 no.6
• /
• pp.268-276
• /
• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• Journal of Broadcast Engineering
• /
• v.26 no.6
• /
• pp.778-789
• /
• 2021

Recently, various lightweight methods for using Convolutional Neural Network(CNN) models in mobile devices have emerged. Weight quantization, which lowers bit precision of weights, is a lightweight method that enables a model to be used through integer calculation in a mobile environment where GPU acceleration is unable. Weight quantization has already been used in various models as a lightweight method to reduce computational complexity and model size with a small loss of accuracy. Considering the size of memory and computing speed as well as the storage size of the device and the limited network environment, this paper proposes a method of compressing integer weights after quantization using a video codec as a method. To verify the performance of the proposed method, experiments were conducted on VGG16, Resnet50, and Resnet18 models trained with ImageNet and Places365 datasets. As a result, loss of accuracy less than 2% and high compression efficiency were achieved in various models. In addition, as a result of comparison with similar compression methods, it was verified that the compression efficiency was more than doubled.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.1
• /
• pp.25-38
• /
• 2022

With the diversification of payment methods and games, related financial accidents are causing serious problems for users and game companies. Recently, game companies have introduced an Fraud Detection System (FDS) for game payment systems to prevent financial incident. However, FDS is ineffective and cannot provide major evidence based on judgment results, as it requires constant change of detection patterns. In this paper, we analyze abnormal transactions among payment log data of real game companies to generate related features. One of the unsupervised learning models, Autoencoder, was used to build a model to detect abnormal transactions, which resulted in over 85% accuracy. Using X-FDS (Explainable FDS) with XAI-SHAP, we could understand that the variables with the highest explanation for anomaly detection were the amount of transaction, transaction medium, and the age of users. Based on X-FDS, we derive an improved detection model with an accuracy of 94% was finally derived by fine-tuning the importance of features that adversely affect the proposed model.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.3
• /
• pp.483-490
• /
• 2022

Malaria is a disease caused by a parasite and it is prevalent in all over the world. The usual method used to recognize malaria cells is a thick and thin blood smears examination methods, but this method requires a lot of manual calculation, so the efficiency and accuracy are very low as well as the lack of pathologists in impoverished country has led to high malaria mortality rates. In this paper, a malaria cell image recognition model using transfer learning is proposed, which consists in the feature extractor, the residual structure and the fully connected layers. When the pre-training parameters of the VGG-19 model are imported to the proposed model, the parameters of some convolutional layers model are frozen and the fine-tuning method is used to fit the data for the model. Also we implement another malaria cell recognition model without residual structure to compare with the proposed model. The simulation results shows that the model using the residual structure gets better performance than the other model without residual structure and the proposed model has the best accuracy of 97.33% compared to other recent papers.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3904-3922
• /
• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.