• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.521-526
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• 2020

In this paper, we propose a design method and process for hybrid V2X communication platform that combines WAVE communication and LTE-V2X communication which are C-ITS communication protocols for vehicle environments and Legacy LTE communication which is a commercial mobile communication for evaluating the autonomous platooning platform of commercial vehicles. For a safe and efficient autonomous platooning platform, an low-latency communication function based on C-ITS communication is required, and to control it, commercial communication functions such as Legacy LTE, which can be connected at all times, are required. In order to evaluate such a system, the evaluation equipment must have the same level of communication performance or higher. The main design contents presented in this paper will be applied to the implementation of hybrid V2X terminals for functional evaluation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.774-784
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• 2021

The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.1
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• pp.30-44
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• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.147-157
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• 2022

Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1723-1728
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• 2021

C-ITS is an intelligent transportation system that can improve transportation convenience and traffic safety by collecting, managing, and providing traffic information between components such as vehicles, road infrastructure, drivers, and pedestrians. In Korea, road infrastructure is being built across the country through the C-ITS project, and various services such as real-time traffic information provision and bus operation management are provided. However, the current state-of-the-art road infrastructure and information linkage system are insufficient to build C-ITS. In this paper, considering the continuity of time in various spatial aspects, we proposed a group-type network-based traffic situation recognition system that can recognize traffic flows and unexpected accidents through information linkage between traffic infrastructures. It is expected that the proposed system can primarily respond to accident detection and warning in the field, and can be utilized as more diverse traffic information services through information linkage with other systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1477-1482
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• 2022

Haze is a local weather phenomenon in which very small droplets float in the atmosphere, and the amount and characteristics of haze may vary depending on the region. In particular, these haze reduce visibility, which can cause air traffic interference and vehicle traffic accidents, and degrade the quality of security CCTVs and so on. Therefore, in the past 10 years, research on haze removal has been actively conducted to reduce damage caused by haze. In this study, local haze removal is performed by weight generation using a haziness degree evaluator to adaptively respond to haze-free, homogeneous haze, and non-homogeneous haze cases. And the proposed method improves the limitations of the existing static haze removal method, which assumes that there is haze in the input image and removes the haze. We also demonstrate the superiority of the proposed method through quantitative and qualitative performance evaluations with benchmark algorithms.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.250-259
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• 2021

Path planning is one of the important technologies for automated parking. It requires to plan a collision-free path considering the vehicle's kinematic constraints such as minimum turning radius or steering velocity. In a complex parking lot, Rapidly-exploring Random Tree^* (RRT^*) can be used for planning a parking path, and Reeds-Shepp or Hybrid Curvature can be applied as a tree-extension method to consider the vehicle's constraints. In this case, each of these methods may affect the computation time of planning the parking path, path-tracking error, and parking success rate. Therefore, in this study, we conduct comparative analysis of two tree-extension functions: Reeds-Shepp (RS) and Hybrid Curvature (HC), and show that HC is a more appropriate tree-extension function for parking path planning. The differences between the two functions are introduced, and their performances are compared by applying them with RRT^*. They are tested at various parking scenarios in simulation, and their advantages and disadvantages are discussed by computation time, cross-track error while tracking the path, parking success rate, and alignment error at the target parking spot. These results show that HC generates the parking path that an autonomous vehicle can track without collisions and HC allows the vehicle to park with lower alignment error than those of RS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1486-1494
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• 2021

Character recognition is a technology required in various platforms, such as smart parking and text to speech, and many studies are being conducted to improve its performance through new attempts. However, with low-quality image used for character recognition, a difference in resolution of the training image and test image for character recognition occurs, resulting in poor accuracy. To solve this problem, this paper designed an end-to-end learning neural network that combines image super-resolution and character recognition so that the character recognition model performance is robust against various quality data, and implemented an alternative whole learning algorithm to learn the whole neural network. An alternative end-to-end learning and recognition performance test was conducted using the license plate image among various text images, and the effectiveness of the proposed algorithm was verified with the performance test.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.321-327
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• 2023

With the development of artificial intelligence, the prediction system has become one of the essential technologies in our lives. Despite the growth of these technologies, traffic congestion at intersections in the 21st century has continued to be a problem. This paper proposes a system that predicts intersection traffic jams using a Convolutional LSTM (Conv-LSTM) algorithm. The proposed system models data obtained by learning traffic information by time zone at the intersection where traffic congestion occurs. Traffic congestion is predicted with traffic volume data recorded over time. Based on the predicted result, the intersection traffic signal is controlled and maintained at a constant traffic volume. Road congestion data was defined using VDS sensors, and each intersection was configured with a Conv-LSTM algorithm-based network system to facilitate traffic.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.178-180
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• 2020

기존의 철도차량용 무선전력전송 시스템은 Medium-voltage (MV) 60 Hz 중전압 AC 계통 전압을 Low-voltage (LV) DC로 변환하기 위해 저주파 변압기와 정류기를 사용한다. 하지만 수 MW급의 대전력을 낮은 DC 전압으로 전송하려면 인버터는 수백 A - 수 천 A 이상의 전류용량을 가져야하므로 정류기의 출력 단에 직렬 또는 병렬로 연결된 여러 개의 고주파 변압기를 필요하게 된다 (그림 1참조). 이러한 저주파 변압기, 정류기 및 고주파 변압기는 크고 무거우므로 낮은 전력밀도 및 높은 시스템 가격의 원인이 된다. 본 논문에서는 이러한 저주파변압기, 정류기, 고주파 변압기를 사용하지 않는, 여자 코일을 이용한 새로운 멀티레벨 무선전력전송 시스템의 토폴로지를 제안한다. 제안된 멀티레벨 무선전력전송 시스템은 멀티레벨 인버터의 각 출력 단에 여자코일 (excitation coil) 이 연결되어 있다. 이 여자코일들은 급전코일 (transmitter coil) 에 전기적으로는 절연되었지만 자기적으로 강하게 결합된다. 여자코일들이 발생시킨 자기장은 급전코일에 유도전압을 발생시키고, 급전코일에서 수백 A 이상의 큰 전류를 흐르게 하여 급전코일에서 강한 자기장을 발생하도록 한다. 이 자기장은 급전코일과 수 cm 이상 떨어져 자기적으로 약하게 결합된 집전코일 (receiver coil) 에 다시 유도전압을 발생시켜 전력을 전달하게 된다. 제안한 새로운 멀티레벨 무선 전력 전송 시스템은 시뮬레이션을 통해 검증했다.