• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.12
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• pp.878-881
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• 2013

In this study, we have fabricated the dye sensitized solar cell (DSSC) composed by a transparent conductive oxide (TCO), a nanocrystalline semiconductor film usually $TiO_2$, a sensitizer adsorbed on the surface of the semiconductor, an electrolyte containing a redox mediator and a counter electrode. The $TiO_2$ nanopowder was prepared by sol-gel methode. The HCl (hydrochloric acid) and TBAOH (Tetrabutyl amonium hydroxide) was added for improving the catalyst and distributed properties of $TiO_2$ nanopowder. Ammonium hydroixde was added in order to control the morphology and size of $TiO_2$ nano crystal. A $TiO_2$ paste for working electrode was prepared with the addition of HPC (hydroxypropyl cellulos) used as a binder of which volume was controled as 1.3, 1.5, 1.7, and 2.0%. The measured I-V curves of assembled DSSC showed that the cell with 1.7% HPC binder had the best efficiency of 6.79%.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.698-701
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• 2005

This paper examines that is extracted certain information in forest areas within high resolution imagery based on wavelet transformation. First of all, study areas are selected one more species distributed spots refer to forest type map. Next, study area is cut 256 x 256 pixels size because of image processing problem in large volume data. Prior to wavelet transformation, five texture parameters (contrast, dissimilarity, entropy, homogeneity, Angular Second Moment (ASM≫ calculated by using Gray Level Co-occurrence Matrix (GLCM). Five texture images are set that shifting window size is 3x3, distance .is 1 pixel, and angle is 45 degrees used. Wavelet function is selected Daubechies 4 wavelet basis functions. Result is summarized 3 points; First, Wavelet transformation images derived from contrast, dissimilarity (texture parameters) have on effect on edge elements detection and will have probability used forest road detection. Second, Wavelet fusion images derived from texture parameters and original image can apply to forest area classification because of clustering in Homogeneous forest type structure. Third, for grading evaluation in forest fire damaged area, if data fusion of established classification method, GLCM texture extraction concept and wavelet transformation technique effectively applied forest areas (also other areas), will obtain high accuracy result.

• BMB Reports
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• v.43 no.12
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• pp.824-829
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• 2010

Melanin synthesis is regulated by melanocyte specific enzymes and related transcription factors. $\beta$-carboline alkaloids including harmaline and harmalol are widely distributed in the environment including several plant families and alcoholic beverages. Presently, melanin content and tyrosinase activity were increased in melanoma cells by harmaline and harmalol in concentration- and time-dependent manners. Increased protein levels of tyrosinase, tyrosinase-related protein-1 (TRP-1), and TRP-2 were also evident. In addition, immunofluorescence and Western blot analyses revealed harmaline and harmalol increased cAMP response element binding protein phosphorylation and microphthalmia-associated transcription factor expression. In addition to studying the signaling that leads to melanogenesis, roles of the p38 MAPK pathways by the harmaline and harmalol were investigated. Harmaline and harmalol induced time-dependent phosphorylation of p38 MAPK. Harmaline and harmalol stimulated melanin synthesis and tyrosinase activity, as well as expression of tyrosinase and TRP-1 and TRP-2 indicating that these harmaline and harmalol induce melanogenesis through p38 MAPK signaling.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.79-84
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• 2023

Apache Spark, which provides the fastest processing speed among recent distributed and parallel processing technologies, provides real-time functions and machine learning functions. Although official documentation guides for these functions are provided, a method for fusion of functions to predict a specific value in real time is not provided. Therefore, in this paper, we conducted a study to predict the value of data in real time by fusion of these functions. The overall configuration is collected by downloading stock price data provided by the Python programming language. And it creates a model of regression analysis through the machine learning function, and predicts the adjusted closing price among the stock price data in real time by fusing the real-time streaming function with the machine learning function.

• Tribology and Lubricants
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• v.26 no.1
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• pp.14-20
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• 2010

Tribological properties of nanoporous structured alumina film was investigated. Alumina film (AAO: anodic aluminum oxide) of $60{\mu}m$ thickness having nanopores of 45 nm diameter with 105 nm interpore-diatance was fabricated by mild anodization process. Reciprocating ball-on-flat sliding friction tests using 1 mm diameter steel ball as a counterpart were carried out with wide range of normal load from 1 mN to 1 N in an ambient environment. The morphology of worn surfaces were analyzed using scanning electron microscopy. The friction coefficient was strongly influenced by the applied normal load. Smooth layer patches were formed on the worn surface of both AAO and steel ball at relatively high load (100 mN and 1 N) due to tribochemical reaction and compaction of wear debris. These tribolayers contributed to the lower friction at high loads. Extremely thin layer patches, due to mild plastic deformation of surface layer, were sparsely distributed on the worn surface of AAO at low loads (1 mN and 10 mN) without the evidence of tribochemical reaction. Delaminated wear particles were generated at high loads by fatigue due to repeated loading and sliding.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.505-518
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• 2023

3D point cloud semantic segmentation is a computer vision task that involves dividing the point cloud into different objects and regions by predicting the class label of each point. Existing 3D semantic segmentation models have some limitations in performing sufficient fusion of multi-modal features while ensuring both characteristics of 2D visual features extracted from RGB images and 3D geometric features extracted from point cloud. Therefore, in this paper, we propose MMCA-Net, a novel 3D semantic segmentation model using 2D-3D multi-modal features. The proposed model effectively fuses two heterogeneous 2D visual features and 3D geometric features by using an intermediate fusion strategy and a multi-modal cross attention-based fusion operation. Also, the proposed model extracts context-rich 3D geometric features from input point cloud consisting of irregularly distributed points by adopting PTv2 as 3D geometric encoder. In this paper, we conducted both quantitative and qualitative experiments with the benchmark dataset, ScanNetv2 in order to analyze the performance of the proposed model. In terms of the metric mIoU, the proposed model showed a 9.2% performance improvement over the PTv2 model using only 3D geometric features, and a 12.12% performance improvement over the MVPNet model using 2D-3D multi-modal features. As a result, we proved the effectiveness and usefulness of the proposed model.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.1 s.24
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• pp.24-32
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• 2006

This paper is to analyze the effectiveness of multistatic sonar network based on detection performance. The multistatic sonar network is a distributed detection system that places a source and multi-receivers apart. So it needs a detection technique that relates to decision rule and optimization of sonar system to improve the detection performance. For this we propose a data fusion procedure using Bayesian decision and optimal sensor arrangement by optimizing a bistatic sonar. Also, to analyze the detection performance effectively, we propose the environmental model that simulates a propagation loss and target strength suitable for multistatic sonar networks in real surroundings. The effectiveness analysis on the multistatic sonar network confirms itself as a promising tool for effective allocation of detection resources in multistatic sonar system.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11b
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• pp.3-6
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• 2001

In common globular proteins, the native form is in its most stable state. However, the native form of inhibitory serpins (serine protease inhibitors) and some viral membrane fusion proteins is in a metastable state. Metastability in these proteins is critical to their biological functions. Our previous studies revealed that unusual interactions, such as side-chain overpacking, buried polar groups, surface hydrophobic pockets, and internal cavities are the structural basis of the native metastability. To understand the mechanism by which these structural defects regulate protein functions, cavity-filling mutations of a 1-antitrypsin, a prototype serpin, were characterized. Increasing conformational stability is correlated with decreasing inhibitory activity. Moreover, the activity loss appears to correlate with the decrease in the rate of the conformational switch during complex formation with a target protease. We also increased the stability of a 1-antitrypsin greatly via combining various stabilizing single amino acid substitutions that were distributed throughout the molecule. The results showed that a substantial increase of stability, over 13 kcal/mol, affected the inhibitory activity with a correlation of 11% activity loss per kcal/mol. The results strongly suggest that the native metastability of proteins is indeed a structural design that regulates protein functions and that the native strain of a 1-antitrypsin distributed throughout the molecule regulates the inhibitory function in a concerted manner.

• Electrical & Electronic Materials
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• v.14 no.12
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• pp.3-6
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• 2001

In common globular proteins, the native form is n its most stable state. However, the native form of inhibitory serpins (serine protease inhibitors) and some viral membrane fusion proteins is in a metastable state. Metastability in these proteins is critical to their biological functions. Our previous studies revealed that unusual interactions, such as side-chain overpacking, buried polar groups, surface hydrophobic pockets, ad internal cavities are the structural basis of the native metastability. To understand the mechanism by which these structural defects regulate protein functions, cavity-filling mutations of $\alpha$1-antitrypsin, a prototype serpin, were characterized. Increasing conformational stability is correlated with decreasing inhibitory activity. Moreover, the activity loss appears to correlate with the decrease in the rate of the conformational switch during complex formation with a target protease. We also increased the stability of $\alpha$1-antitrypsin greatly via combining various stabilizing single amino acid substitutions that were distributed throughout the molecule. The results showed that a substantial increase of stability, over 13 kcal/mol, affected the inhibitory activity with a correlation of 11% activity loss per kcal/mol. The results strongly suggest that the native metastability of proteins is indeed a structural design that regulates protein functions and that the native strain of $\alpha$1-antitrypsin distributed throughout the molecule regulates the inhibitory function in a concerted manner.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1051-1058
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• 2011

Unmanned autonomous forklifts have a great potential to enhance the productivity of material handling in various applications because these forklifts can pick up and deliver loads without an operator and any fixed guide. Especially, automation of pallet loading and unloading technique is useful for enhancing performance of logistics and reducing cost for automation system. There are, however, many technical difficulties in developing such forklifts including localization, map building, sensor fusion, control, and so on. This is because the system requires numerous sensors, actuators, and controllers that need to be connected with each other, and the number of connections grows very rapidly as the number of devices grows. This paper presents a vision sensorbased autonomous loading and unloading for network-based unmanned forklift where system components are connected to a shared CAN network. Functions such as image processing and control algorithm are divided into small tasks that are distributed over a number of microcontrollers with a limited computing capacity. And the experimental results show that proposed architecture can be an appropriate choice for autonomous loading in the unmanned forklift.