• New Physics: Sae Mulli
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• v.68 no.11
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• pp.1268-1274
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• 2018

Aircraft-warning lights are lights that are used to inform pilots in flight about the presence of buildings or dangerous objects. Currently, the light sources of most aircraft-warning lights have been replaced by light-emitting diodes (LEDs). However, the aircraft-warning lights that are installed do not meet the optical performance standards and may cause airplane collisions. Therefore, the use of such light poses a risk to aviation safety. In order to solve this problem, we designed a Fresnel lens with the same luminous intensity distribution ovef $360^{\circ}$ direction; thus, we collimated the light beam from the LED light source with a narrow beam divergence angle in the form of an array of aspheric pieces. After that, we designed and simulated an aircraft-warning-light optical system with a center luminous intensity of 20,000 cd and a vertical divergence angle of $3^{\circ}$ or more by optimizing the lens' tilt and the distance between the LED and the Fresnel lens.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.107-117
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• 2020

With the recent development of autonomous vehicles, various sensors installed in vehicles have become common, and big data generated from those sensors is increasingly being used in the transportation field. In this study, we proposed a grid index method to efficiently process real-time vehicle sensing big data and public data such as road weather. The applicability and effect of the proposed grid space division method and grid ID generation method were analyzed. We created virtual data based on DTG data and mapped to the road link based on coordinates. As a result of analyzing the data processing speed in grid index method, the data processing performance improved by more than 2,400 times compared to the existing link unit processing method. In addition, in order to analyze the efficiency of the proposed technology, the virtually generated data was mapped and visualized.

• Journal of Platform Technology
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• v.8 no.4
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• pp.11-19
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• 2020

In an autonomous driving environment, dynamic recognition of objects is essential as the situation changes in real time. In addition, as the number of sensors and control modules built into an autonomous vehicle increases, the amount of data the central control unit has to process also rapidly increases. By minimizing the output data from the sensor, the load on the central control unit can be reduced. This study proposes a dynamic object recognition algorithm solely using the embedded processor on a LiDAR sensor. While there are open source algorithms to process the point cloud output from LiDAR sensors, most require a separate high-performance processor. Since the embedded processors installed in LiDAR sensors often have resource constraints, it is essential to optimize the algorithm for efficiency. In this study, an embedded processor based object recognition algorithm was developed for autonomous vehicles, and the correlation between the size of the point clouds and processing time was analyzed. The proposed object recognition algorithm evaluated that the processing time directly increased with the size of the point cloud, with the processor stalling at a specific point if the point cloud size is beyond the threshold

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.29-35
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• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.

• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.299-304
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• 2021

Existing human activity recognition systems detect activities through devices such as wearable sensors and cameras. However, these methods require additional devices and costs, especially for cameras, which cause privacy issue. Using WiFi signals that are already installed can solve this problem. In this paper, we propose a CNN-based human activity recognition system using channel state information of WiFi signals, and present results of designing and implementing accelerated hardware structures. The system defined four possible behaviors during studying in indoor environments, and classified the channel state information of WiFi using convolutional neural network (CNN), showing and average accuracy of 91.86%. In addition, for acceleration, we present the results of an accelerated hardware structure design for fully connected layer with the highest computation volume on CNN classifiers. As a result of performance evaluation on FPGA device, it showed 4.28 times faster calculation time than software-based system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.907-913
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• 2022

Approximately 70% of smartphone users around the world use Android operating system-based smartphones, and malicious apps targeting these Android platforms are constantly increasing. Google has provided "Google Play Protect" to respond to the increasing number of Android targeted malware, preventing malicious apps from being installed on smartphones, but many malicious apps are still normal. It threatens the smartphones of ordinary users registered in the Google Play store by disguising themselves as apps. However, most people rely on antivirus programs to detect malicious apps because the average user needs a great deal of expertise to check for malicious apps. Therefore, in this paper, we propose a method to classify unnecessary malicious permissions of apps by using only the categories and permissions that can be easily confirmed by the app, and to easily detect malicious apps through the classified permissions. The proposed method is compared and analyzed from the viewpoint of undiscovered rate and false positives with the "commercial malicious application detection program", and the performance level is presented.

• Journal of Advanced Navigation Technology
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• v.26 no.1
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• pp.9-14
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• 2022

In this paper we investigate the advantages when the GPS Antenna is installed on the rear missile body. In conventional design the GPS antenna locates on the front part of missile. However it causes degraded GPS positioning performance since the missile body blocks the GPS signals. This paper proposes the GPS array antenna design which locates on the rear part of missile body and has the tilted antenna patches to achieve the maximum area of receiving GPS signals. We simulate LOS region of receiving signals and conducted anechoic chamber test to define the effective signal receiving region. And we conduct field test and flight test to check out the enhancement of signal receiving area.

• Journal of IKEEE
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• v.26 no.3
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• pp.380-388
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• 2022

The traditional malware detection technologies collect known malicious programs and analyze their characteristics. Then such a detection technology makes a blacklist based on the analyzed malicious characteristics and checks programs in the user's system based on the blacklist to determine whether each program is malware. However, such an approach can detect known malicious programs, but responding to unknown or variant malware is challenging. In addition, since such detection technologies generally monitor all programs in the system in real-time, there is a disadvantage that they can degrade the system performance. In order to solve such problems, various methods have been proposed to analyze major behaviors of malicious programs and to respond to them. The main characteristic of ransomware is to access and encrypt the user's file. So, a new approach is to produce the whitelist of programs installed in the user's system and allow the only programs listed on the whitelist to access the user's files. However, although it applies such an approach, attackers can still perform malicious behavior by performing a DLL(Dynamic-Link Library) injection attack on a regular program registered on the whitelist. This paper proposes a method to respond effectively to attacks using DLL injection.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.5
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• pp.280-287
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• 2022

An open rack vaporizer is a facility that vaporizes liquefied natural gas using sea water. When a vaporization efficiency of the open rack vaporizer decreases, liquefied natural gas can leak, which can cause great damage to the facility. Operators have to monitor the operation state of the facility in real-time to prevent the accident. However, operators have visited the site and have checked the state by looking at the value of sensors installed in the open rack vaporizer through indicators. For the safe operation of the open rack vaporizer, a monitoring system is needed to monitor the operation state of the open rack vaporizer in real-time without the need for operators to visit the site. In this paper, we developed a long term evolution based monitoring system to monitor the operation state of the open rack vaporizer. The developed system can monitor the real-time operation state of the open rack vaporizer at a control center far from the facility. For the system development, data transmission infrastructure using long term evolution was built. Afterwards a software was developed to monitor the operation state of the open rack vaporizer in real-time using the transmitted data. Finally, performance evaluation was conducted to confirm that the developed system operated successfully without data transmission delay or data missing.

• Applied Chemistry for Engineering
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• v.33 no.1
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• pp.96-102
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• 2022

Reactants of PEMFC are hydrogen and oxygen in gas phases and fuel cell overpotential could be reduced when reactants are smoothly transported. Numerous studies to modify cathode flow field design have been conducted because oxygen mass transfer in high current density region is dominant voltage loss factor. Among those cathode flow field designs, a block in flow field is used to forced supply reactant gas to porous gas diffusion layer. In this study, the block was installed on a simple fuel cell model. Using computational fluid dynamics (CFD), effects of forced convection due to blocks on a polarization curve and local current density contour were studied when different air flow rates were supplied. The high current density could be achieved even with low air supply rate due to forced convection to a gas diffusion layer and also with multiple blocks in series compared to a single block due to an increase of forced convection effect.