• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.2
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• pp.159-162
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• 2017

Along with the popular use of commercial drones, there are increased concerns on the possible threats from drones intruding into secured areas. The difficulty of drone detection is attributed to its stealthy operation flying at low altitude with low level signature. Consequently, the anti-drone technique has been of major research topic in recent years and among others, the radar detection is considered as the most promising technique. However, the use of conventional radar detection may not be effective due to the low level radar cross sections of the commercial drones. In this paper, ISAR technique has been employed to implement drone detection in urban area. To this purpose, a pulsed radar system is set up on the ground to track flying drones and the corresponding ISAR images are produced by coherent processing.

• Journal of Powder Materials
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• v.14 no.2 s.61
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• pp.123-126
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• 2007

In this work, the dispersion behavior of $Y_2O_3$ particles in binary aluminum (Al)-copper (Cu) cast alloy was investigated with respect to Cu contents of 20 (hypoeutertic), 33 (eutectic) and 40 (hypereutectic) wt.%. In cases of hypo and hypereutectic compositions, SEM images revealed that the primary Al and ${\theta}$ phases were grown up at the beginning, respectively, and thereafter the eutectic phase was solidified. In addition, it was found that some of $Y_2O_3$ particles can be dispersed into the primary Al phase, but none of them are is observed inside the primary 6 phase. This different dispersion behavior of $Y_2O_3$ particles is probably due to the difference in the val- ues of specific gravity between $Y_2O_3$ particles and primary phases. At eutectic composition, $Y_2O_3$ particles were well dispersed in the matrix since there is few primary phases acting as an impediment site for particle dispersion during solidification. Based on the experimental results, it is concluded that $Y_2O_3$ particles are mostly dispersed into the eutectic phase in binary Al-Cu alloy system.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.105-111
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• 2019

Core technologies that lead the Fourth Industrial Revolution era, such as artificial intelligence, big data, and autonomous driving, are implemented and serviced through the rapid development of computing power and hyper-connected networks based on the Internet of Things. In this paper, we implement two different models for drivable area segmentation in various environment, and propose a better model by comparing the results. The models for drivable area segmentation are using DeepLab V3+ and Mask R-CNN, which have great performances in the field of image segmentation and are used in many studies in autonomous driving technology. For driving information in various environment, we use BDD dataset which provides driving videos and images in various weather conditions and day&night time. The result of two different models shows that Mask R-CNN has higher performance with 68.33% IoU than DeepLab V3+ with 48.97% IoU. In addition, the result of visual inspection of drivable area segmentation on driving image, the accuracy of Mask R-CNN is 83% and DeepLab V3+ is 69%. It indicates Mask R-CNN is more efficient than DeepLab V3+ in drivable area segmentation.

• Journal of Multimedia Information System
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• v.7 no.2
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• pp.115-124
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• 2020

This paper is a study of deep learning among artificial intelligence technology which has been developing many technologies recently. Especially, I am talking about emotional computing that has been mentioned a lot recently during deep learning. Emotional computing, in other words, is a passive concept that is dominated by people who scientifically analyze human sensibilities and reflect them in product development or system design, and a more active concept that studies how devices and systems understand humans and communicate with people in different modes. This emotional signal extraction, sensitivity, and psychology recognition technology is defined as a technology to process, analyze, and recognize psycho-sensitivity based on micro-small, hyper-sensor technology, and sensitive signals and information that can be sensed by the active movement of the autonomic nervous system caused by human emotional changes in everyday life. Chapter 1 talks about overview and Chapter 2 shows related research. Chapter 3 shows the problems and models of real emotional computing and Chapter 4 shows this paper as a conclusion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• Journal of Industrial Convergence
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• v.18 no.6
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• pp.147-154
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• 2020

It is difficult to educate the overall operation of public and private blockchains with different characteristics. Recently, most education for blockchain is targeted at public blockchains such as Bitcoin and Ethereum. However, in an actual business environment, a private blockchain such as HyperLedger Fabric is used because access to corporate data is controlled through user authentication. In the case of HLF-based education, it is necessary to understand various components that are not in the public blockchain, such as peers, orderers, and channels. In this paper, a lab framework for HLF is designed for an efficient and systematic understanding of the functions and operations. The framework consists of HLF network, chaincode, and decentralized software control functions. Through the framework, the network configuration, distribution and activation of chaincode, and dApp execution process were checked step by step, and it was very easy to understand the overall flow for blockchain services. In addition, it is expected that a systematic understanding of the overall flow will be possible even in future network expansion.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• ETRI Journal
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• v.39 no.4
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• pp.502-513
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• 2017

For video understanding, namely analyzing who did what in a video, actions along with objects are primary elements. Most studies on actions have handled recognition problems for a well-trimmed video and focused on enhancing their classification performance. However, action detection, including localization as well as recognition, is required because, in general, actions intersect in time and space. In addition, most studies have not considered extensibility for a newly added action that has been previously trained. Therefore, proposed in this paper is an extensible hierarchical method for detecting generic actions, which combine object movements and spatial relations between two objects, and inherited actions, which are determined by the related objects through an ontology and rule based methodology. The hierarchical design of the method enables it to detect any interactive actions based on the spatial relations between two objects. The method using object information achieves an F-measure of 90.27%. Moreover, this paper describes the extensibility of the method for a new action contained in a video from a video domain that is different from the dataset used.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.12
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• pp.4925-4928
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• 2014

Background: This study aimed to evaluate the methylation of RASSF1A and CDH13 gene promoter regions as a marker for monitoring chemotherapeutic efficacy with personalized medicine for patients with NSCLC, in the hope of providing a new direction for NSCLC individualized chemotherapy. Materials and Methods: 42 NSCLC patients and 40 healthy controls were included. Patient blood samples were collected in the whole process of chemotherapy. Methylation of RASSF1A and CDH13 gene promoter regions was detected by the methylation specific polymerase chain reaction (MSP). Results: The rate of RASSF1A and CDH13 gene methylation in 42 cases of NSCLC patients was significantly higher than in 40 healthy controls (52.4% to 0.0%, 54.8% to 0.0%, p<0.05). After the chemotherapy, the hyper-methylation of RASSF1A and CDH13 genes in PR group and SD group decreased significantly (p<0.05), and was significantly different from that in PD group (p<0.05), but not as compared with healthy controls (P>0.05). With chemotherapy, RASSF1A and CDH13 promoter region methylation rate in 42 cases of patients showed a declining trend. Conclusions: The methylation level of RASSF1A and CDH13 gene promoter region can reflect drug sensitivity of tumors to individualized treatment.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.289-303
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• 2022

In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.