• The Journal of Natural Sciences
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• v.12 no.1
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• pp.31-39
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• 2002

Second harmonic generation was measured in porous silicon superlattices surface which were made by changing and anodization current density and the anodization time periodically in the process of obtaining porous silicon. The technique of second harmonic generation is used as a probe to the surface of porous silicon superlattice by using Nd:YAG laser. We have investigated the surface structure and nonlinear-optics properties of porous silicon superlattice.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.25-29
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• 2019

Microstructure pattern generation on light guides in backlight unit (BLU) is an essential process for designing flat panel display, but efficient designing algorithm is still limited to achieve uniform luminescence while maintaining fully random distribution to avoid interference effects. In this study, a molecular dynamics model based pattern generation algorithm has been developed. The proposed algorithm allows a fast and efficient distribution of patterns at specified density within the user-defined computational cells, and its efficiency and performance has been demonstrated with sample cases.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1318-1330
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• 2018

Video captioning refers to the process of extracting features from a video and generating video captions using the extracted features. This paper introduces a deep neural network model and its learning method for effective video captioning. In this study, visual features as well as semantic features, which effectively express the video, are also used. The visual features of the video are extracted using convolutional neural networks, such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction network proposed in this paper. Further, an attention-based caption generation network is proposed for effective generation of video captions using the extracted features. The performance and effectiveness of the proposed model is verified through various experiments using two large-scale video benchmarks such as the Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.1
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• pp.1-6
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• 2021

Next-generation sequencing (NGS) technologies have changed the process of genetic diagnosis from a gene-by-gene approach to syndrome-based diagnostic gene panel sequencing (DPS), diagnostic exome sequencing (DES), and diagnostic genome sequencing (DGS). A priori information on the causative genes that might underlie a genetic condition is a prerequisite for genetic diagnosis before conducting clinical NGS tests. Theoretically, DPS, DES, and DGS do not require any information on specific candidate genes. Therefore, clinical NGS tests sometimes detect disease-related pathogenic variants in genes underlying different conditions from the initial diagnosis. These clinical NGS tests are expensive, but they can be a cost-effective approach for the rapid diagnosis of rare disorders with genetic heterogeneity, such as the glycogen storage disease, familial intrahepatic cholestasis, lysosomal storage disease, and primary immunodeficiency. In addition, DES or DGS may find novel genes that that were previously not linked to human diseases.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.6
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• pp.269-279
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• 2020

This paper presents the design and implementation of embedded systems and autonomous path generation for autonomous speed sprayer. Autonomous Orchard Systems can be divided into embedded controller and path generation module. Embedded controller receives analog sensor data, on/off switch data and control linear actuator, break, clutch and steering module. In path generation part, we get 3D cloud point using Velodyne VLP16 LIDAR sensor and process the point cloud to generate maps, do localization, generate driving path. Then, it finally generates velocity and rotation angle in real time, and sends the data to embedded controller. Embedded controller controls steering wheel based on the received data. The developed autonomous speed sprayer is verified in test-bed with apple tree-shaped artworks.

• Microbiology and Biotechnology Letters
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• v.50 no.1
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• pp.122-125
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• 2022

The cellulose-fed microbial fuel cell (MFC) is a biotechnological process that directly converts lignocellulosic materials to electricity without combustion. In this study, the cellulose-fed, MFC-integrated thermophilic bacterium, Bacillus sp. WK21, with endoglucanase and exoglucanase activities of 1.25 ± 0.08 U/ml and 0.95 ± 0.02 U/ml, respectively, was used to generate electricity at high temperatures. Maximal current densities of 485, 420, and 472 mA/m² were achieved when carboxymethyl cellulose, avicel cellulose, and cellulose powder, respectively, were used as substrates. Their respective maximal power was 94.09, 70.56, and 89.30 mW/m³. This study demonstrates the value of the novel use of a cellulase-producing thermophilic bacterium as a biocatalyst for electricity generation in a cellulose-fed MFC.

• New & Renewable Energy
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• v.18 no.2
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• pp.18-25
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• 2022

In this study, we propose a wind power generation prediction system that applies machine learning and data mining to predict wind power generation. This system increases the utilization rate of new and renewable energy sources. For time-series data, the data set was established by measuring wind speed, wind generation, and environmental factors influencing the wind speed. The data set was pre-processed so that it could be applied appropriately to the model. The prediction system applied the CNN (Convolutional Neural Network) to the data mining process and then used the LSTM (Long Short-Term Memory) to learn and make predictions. The preciseness of the proposed system is verified by comparing the prediction data with the actual data, according to the presence or absence of data mining in the model of the prediction system.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.784-793
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• 2022

Grid-connected distributed power generation has been widely used in green energy generation. However, due to the distributed characteristics, distributed power generation is difficult to be dynamically allocated and monitored in the electrical control process. In order to solve this problem, this research combined the Internet of Things (IoT) with the automatic control system of electrical engineering to improve the control strategy of the power grid inverter according to the characteristics of the IoT system. In the research, a connection system of the power grid inverter and the IoT controller were designed, and the application effect was tested by simulation experiments. The results showed that the power grid inverter had strong tracking control ability for current and power control. Meanwhile, the electrical control system of the IoT could independently and dynamically control the three-phase current and power. The given value was reached within 50 ms after the step signal was input, which could protect the power grid from being affected by the current. The overall system could realize effective control, dynamic control and protective control.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.683-688
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• 2018

Recently, the demand for electric energy has been increasing due to the spread of hybrid electric vehicles. In this study, to meet this demand, the ANSYS MAXWELL electromagnetic simulation system was used to compare the power generation characteristics of three types of suspension system that can generate electricity using energy harvesting technology. Next, the optimal design was determined for each model by using the commercial PIDO (Process Integration and Design Optimization) tool, PIANO (Process Integration, Automation and Optimization). We selected three design variables and constructed an approximate model based on the experimental design method through electromagnetic analysis for 18 experimental points derived from Orthogonal Arrays among the experimental design methods. Then, we determined the optimal design by applying the Evolutionary Algorithm. Finally, the optimal design results were verified by electromagnetic simulation of the optimum design result model using the same analysis conditions as those of the initial model. After comparing the power generation characteristics for the optimal structure for each linear generator model, the maximum power generation amounts in the 8pole-8slot, 12pole-12slot, and 16pole-16slot structures were 366.5W, 466.7W and 579.7W, respectively, and it was found that as the number of slots and poles increases, the power generation increases.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1017-1024
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• 2017

The development of technology has brought changes in content media. Starting from voice and sound media in the oral era, through text and painting, the realism has led to the development of visual media plus sound and image media. What we should consider here is not only the one-sided influence of change in the media due to the development of technology, but also the understanding, concentration, and commitment of information depending on which generation has access to the media Therefore, we focus on the digital native generation that uses digital as main media. The features of the digital native generation include the ability to process visual information quickly, multi-tasking, and divisionism. In this paper, we propose a moving pictogram for the digital native generation, and a moving pictogram for exit pictogram which shows limitation. The new dynamic pictograms that fit to the characteristics of the digital native generation, as well as interactive dynamic pictograms, are areas of thought and research on which this paper can be regarded as the first step.