• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.4
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• pp.12-17
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• 2018

A natural cavitation-type high-speed underwater vehicle with solid rocket motor is tested, and its speed and running distance are measured. The outputs from pressure sensors on the surface of the vehicle reveal a pressure-time history reflecting the development of supercavitation. Underwater cameras installed on the wall of the test pool record the entire process from the onset of supercavitation to its full development. CNU-SuperCT, based on two-dimensional inviscid theoretical analysis, is used to simulate test results. Considering CNU-SuperCT does not include the control fins of the vehicle, simulation results agree with test results very well. Additionally, pictures from underwater cameras support the test results.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Atmosphere
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• v.31 no.3
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• pp.341-359
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• 2021

This technical note introduces the new Korea Meteorological Administration (KMA) Global Seasonal forecasting system version 6 (GloSea6) to provide a reference for future scientific works on GloSea6. We describe the main areas of progress and improvements to the current GloSea5 in the scientific and technical aspects of all the GloSea6 components - atmosphere, land, ocean, and sea-ice models. Also, the operational architectures of GloSea6 installed on the new KMA supercomputer are presented. It includes (1) pre-processes for atmospheric and ocean initial conditions with the quasi-real-time land surface initialization system, (2) the configurations for model runs to produce sets of forecasts and hindcasts, (3) the ensemble statistical prediction system, and (4) the verification system. The changes of operational frameworks and computing systems are also reported, including Rose/Cylc - a new framework equipped with suite configurations and workflows for operationally managing and running Glosea6. In addition, we conduct the first-ever run with GloSea6 and evaluate the potential of GloSea6 compared to GloSea5 in terms of verification against reanalysis and observations, using a one-month case of June 2020. The GloSea6 yields improvements in model performance for some variables in some regions; for example, the root mean squared error of 500 hPa geopotential height over the tropics is reduced by about 52%. These experimental results show that GloSea6 is a promising system for improved seasonal forecasts.

• Food Science of Animal Resources
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• v.39 no.1
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• pp.1-12
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• 2019

The objective of this study was to establish an optimized 1D $^1H$ quantitative nuclear magnetic resonance (qNMR) analytical method for analyzing polar metabolites in meat. Three extraction solutions [0.6 M perchloric acid, 10 mM phosphate buffer, water/methanol (1:1)], three reconstitution buffers [20 mM 3-morpholinopropane-1-sulfonic acid, 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid, phosphate buffer], and two pulse programs (zg30, noesypr1d) were evaluated. Extraction with 0.6 M perchloric acid and 20 mM phosphate resulted in a stable baseline and no additional overlap for quantifying polar metabolites in chicken breast. In qNMR analysis, zg30 pulse program (without water-suppression) showed smaller relative standard deviation (RSD) and faster running time than noesypr1d (water-suppression). High-performance liquid chromatography was compared with qNMR analyses to validate accuracy. The zg30 pulse program showed good accuracy and lower RSD. The optimized qNMR method was able to apply for beef and pork samples. Thus, an optimized 1D $^1H$ qNMR method for meat metabolomics was established.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.655-658
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• 2016

Xilinx 7-Series FPGA(Field Programmable Gate Array)s mainly used for the implementation of high-performance digital circuit have SRAM-type configuration memory and can malfunction when soft errors occur in their configuration memory. SEM(Soft Error Mitigation Controller) offered by Xilinx helps users mitigate the influence of soft errors in configuration memory. When soft errors occur, SEM Controller can recover the state of FPGA through partial reconfiguration if the soft errors are correctable by ECC(Error Correction Code) and CRC(Cyclic Redundancy Code). This paper presents the availability analysis of Xilinx 7-Series FPGAs against soft errors under the protection of the SEM Controller. Availability functions are derived and compared according to the correction capability of the SEM Controller. The result may help to estimate the reliability of SRAM-based FPGA running in an environment where soft errors may occur.

• Journal of Practical Engineering Education
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• v.9 no.1
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• pp.7-13
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• 2017

This study will provide a practical plan of engineering education through the study of major activities connected with the production of works to accomplish the graduation conditions by completing the comprehensive design subject and the result of the performance. The designed subject is to measure the minimum safety distance during driving using the obstacle detection function of the ultrasonic sensor and to perform the avoidance algorithm based on the measurement value of the acceleration gyro sensor. It is proposed an access surveillance system that minimizes the damage of drones, surrounding objects, and people, and improves air mobility. Experimental results show that the obstacles around the drone are detected by five ultrasonic sensors and the difference of output value is applied to each motor of the drone and obstacle avoidance is confirmed. In addition, the content and level of the data for measuring the achievement of learning achievement in the engineering education certification program were used and the results were confirmed to be consistent with the description of the engineering problem level required for the graduates of 4-year engineering college.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.77-86
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• 2019

This paper proposes using machine-learning technology to analyze and classify historical collected documents based on them. Data is collected based on keywords associated with a specific domain and the non-conceptuals such as special characters are removed. Then, tag each word of the document collected using a Korean-language morpheme analyzer with its nouns, verbs, and sentences. Embedded documents using Doc2Vec model that converts documents into vectors. Measure the similarity between documents through the embedded model and learn the document classifier using the machine running algorithm. The highest performance support vector machine measured 0.83 of F1-score as a result of comparing the classification model learned.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.9-14
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• 2019

A small millimeter-wave tracking radar is a pulse-based radar that searches, detects, and tracks a target in real time through a TWS (Track While Scan) method for a traps target on the sea with a large RCS running at low speed. It is necessary to develop a board equipped with a high-speed CPU to acquire and track target information through LPRF, DBS, and HRR signal processing techniques for a trap target operating various kinds of dexterous objects such as chaff and decoy, We designed a signal processor structure including DFT (Discrete Fourier Transform) module design that can perform real - time FFT operation using FPGA (Field Programmable Gate Array) and verified the signal processor implemented through performance test.

• Smart Structures and Systems
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• v.23 no.5
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• pp.449-466
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• 2019

Cable-stayed bridges are attractive due to their beauty, reducing material consumption, less harm to the environment and so on, in comparison with other kinds of bridges. As a massive structure with long period and low damping (0.3 to 2%) under many dynamic loads, these bridges are susceptible to fatigue, serviceability disorder, damage or even collapse. Tuned Mass Damper (TMD) is a suitable controlling system to reduce the vibrations and prevent the threats in such bridges. In this paper, Multi Tuned Mass Damper (MTMD) system is added to the Ahvaz cable stayed Bridge in Iran, to reduce its seismic vibrations. First, the bridge is modeled in SAP2000 followed with result verification. Dead and live loads and the moving loads have been assigned to the bridge. Then the finite element model is developed in OpenSees, with the goal of running a nonlinear time-history analysis. Three far-field and three near-field earthquake records are imposed to the model after scaling to the PGA of 0.25 g, 0.4 g, 0.55 g and 0.7 g. Two MTMD systems, passive and active, with the number of TMDs from 1 to 8, are placed in specific points of the main span of bridge, adding a total mass ratio of 1 to 10% to the bridge. The parameters of the TMDs are optimized using Genetic Algorithm (GA). Also, the optimum force for active control is achieved by Fuzzy Logic Control (FLC). The results showed that the maximum displacement of the center of the bridge main span reduced 33% and 48% respectively by adding passive and active MTMD systems. The RMS of displacement reduced 37% and 47%, the velocity 36% and 42% and also the base shear in pylons, 27% and 47%, respectively by adding passive and active systems, in the best cases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.169-170
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• 2019

Lately, as artificial intelligence becomes a source of global competitiveness, the government is strategically fostering artificial intelligence that is the base technology of future new industries such as autonomous vehicles, drones, and robots. Domestic artificial intelligence research and services have been launched mainly in Naver and Kakao, but their size and level are weak compared to overseas. Recently, deep learning has been conducted in recent years while recording innovative performance in various pattern recognition fields including speech recognition and image recognition. In addition, deep running has attracted great interest from industry since its inception, and global information technology companies such as Google, Microsoft, and Samsung have successfully applied deep learning technology to commercial products and are continuing research and development. Therefore, we will look at artificial intelligence which is attracting attention based on previous research.