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이젝터 시스템은 지상에서 초음속 엔진 고공모사 시험을 위해 유용하다. 본 연구에서는 해석적 연구를 통해 이젝터 주요 구성품에 대한 최적 설계안과 작동 조건을 도출하였다. 마하 4~6, 고도 10~20km 의 작동조건을 모사하기 위한 초음속 이젝터를 설계하기 위하여 공기 이젝터 성능해석 및 설계 기법을 적용하여 1차원 기본설계를 수행하였다. 제한된 크기에서 원하는 요구성능을 만족하기 위하여 파라메트릭 설계를 통하여 최적화된 이젝터 형상을 도출하였다. 그리고 설계된 이젝터 형상에 대하여 유동해석 및 시험평가를 수행하여 이젝터 내부의 유동특성을 파악하고 기본 설계 결과값의 타당성을 검증하였다. The ejector system is a useful device for creating high altitude conditions for ground tests of supersonic engines. In this study, the effects of ejector component geometries and inflow conditions on ejector operational mode are investigated by numerical analysis. A one-dimensional preliminary design was performed by applying the ejector performance and design procedure to design supersonic ejector to simulate operating condition of 10~20km altitude Mach number 4~6. CFD analysis and ground test were performed on the manufactured high altitude test facility with ejector system. Finally the character on performance on the ejector was verified comparing to test results.
In this paper, spoofing effects on a GNSS receiver were analyzed. The spoofer (spoofing device) was classified to two categories. One is an active spoofer and the other is a passive spoofer. The active spoofer was considered for analysis. For the analysis of spoofing effects on a GNSS receiver, a real-time GNSS spoofing simulator was developed. The simulator was consisted with two parts which are a baseband signal generation part and a RF up-conversion part. The first GNSS baseband signal was generated according to spoofing parameters such as range, range rate, GNSS navigation data, spoofing to GNSS signal ratio, and etc. The generated baseband signal was up-converted to GNSS L1 band. Then the signal transmitted to a GNSS signal. For a perfect spoofing, a spoofer knew an accurate position and velocity of a spoofing target. But, in real world, that is not nearly possible. Although uncertainty of position and velocity of the target was existed, the spoofer was operated as an efficient jammer.
Vehicle localization is essential for autonomous driving. Basically, the position information of the autonomous vehicle is obtained from the Global Positioning System (GPS). More accurate localization can be performed by using maps and various sensors that are mounted on the autonomous vehicle. GPS receivers cannot receive GPS signals in tunnels, so dead reckoning (DR) is used for vehicle localization. However, the error from DR continuously accumulates. Therefore, this error must be corrected by using vehicle-mounted sensors, such as Light Detection and Ranging (LIDAR) and cameras. Tunnels have very specific shape information, which is usually an ellipse, and several emergency facilities exist in tunnels. Some facilities are separated from the tunnel wall, which can be detected by using 3D LIDAR. In particular, fire extinguisher lamps are periodically installed at intervals of 50 m, which can serve as good landmarks. First, the point cloud for the tunnel wall must be removed to effectively detect fire extinguisher lamps. This process can be easily conducted by using shape information. After this removal, we detect the fire extinguisher lamps. In this paper, we propose a 3D LIDAR-based vehicle-localization method that uses ellipse parameters and fire extinguisher lamps in road tunnels. These experiments are conducted at the Maseong tunnel in South Korea. The experimental results show that the root mean square (RMS) position errors in the lateral and longitudinal directions were 0.06 m and 0.23 m, respectively, exhibiting precise localization accuracy.
With GPS being the primary navigation system, Loran use is in steep decline. However, according to the final report of vulnerability assessment of the transportation infrastructure relying on the global positioning system prepared by the John A. Volpe National Transportation Systems Center, there are current attempts to enhance and re-popularize Loran as a GPS backup system through the characteristic of the ground based low frequency navigation system. To advance the Loran system such as Loran-C modernization and eLoran development, research is definitely needed in the field of Loran-C receiver signal processing as well as Loran-C signal design and the technology of a receiver. We have developed a set of Matlab tools, which implement a software Loran-C receiver that performs the receiver's position determination through the following procedure. The procedure consists of receiving the Loran-C signal, cycle selection, calculation of the TDOA and range, and receiver's position determination through the Least Square Method. We experiences the effect of an incorrect cycle selection and various error factors (ECD, ASF, sky wave, CRI, etc.) from the result of the Loran-C signal processing. It is apparent that researches which focus on the elimination and mitigation of various error factors need to be investigated on a software Loran-C receiver. These aspects will be explored in further work through the method such as PLL and Kalman filtering.
<P>Penetrating craniofacial injuries with chopsticks in children are peculiar accidents in the Oriental culture. All 10 cases previously reported were caused by wooden chopsticks that required surgical operations. However, there are no reported injuries with metal chopsticks in the past literature which should have been as common as that of wooden chopstick injuries in Asia. We evaluated the difference of injury patterns and clinical observations between wooden and metal chopstick injuries. We reviewed 6 treated children with penetrating craniofacial injuries from chopsticks: one wooden and five metal chopsticks. One child who had penetration through the nasal cavity presented with temporary rhinorrhea, another with mild hemiparesis, and one child with temporary upward gaze limitation of the left eye. Radiological examination revealed 1 patient with epidural hemorrhage, 1 patient with minimal subdural hemorrhage, and 4 with intracerebral hemorrhage that were fortunately too small to receive surgery. We performed surgical procedure only for a child who had a wooden chopstick that had impacted into the temporal cortex. We followed up all 6 children for more than 1 year, and found that all had fully recovered to near-normal neurological status. We observed that penetrating craniofacial injuries with metal chopsticks rarely require surgical intervention and usually results in good outcome because the resultant wound is usually small without broken fragments compared to injuries with wooden chopsticks.</P><P>Copyright © 2006 S. Karger AG, Basel</P>
In this paper, a detection method of a pedestrian safety road marking was proposed. The proposed algorithm uses laser range and reflectivity of a range finder (LRF). For a detection of crosswalk marking and stop line, the DFT (Discrete Fourier Transform) of reflectivity and cross-correlation method between the reference replica and the measured reflectivity are used. A speed bump is detected through measuring an altitude difference of two LRFs which have the different tilted angle. Furthermore, we proposed a velocity constrained a detection method of a speed bump. Finally, the proposed methods are tested in on-line, on the pavement of a road. The considered road markings are wholly detected. The localization errors of both road markings are smaller than 0.4 meter.
Tall buildings in urban areas obstruct the GPS satellite signal reception, thus the GPS position accuracy can be very poor. However, buildings in urban areas can be used to generate a landmark map for precise vehicle localization. This map's information can be represented in various forms. We focused on the outer wall of a building that was erected vertically from the ground and almost flat. Therefore, vertical corners that meet the vertical planes are present everywhere in urban areas. These corners can provide very good landmarks and can be extracted using LIDAR. Additionally, traffic signs can be used for localization. The traffic signs are present in the road and reflect light well. Therefore, traffic signs can be clearly distinguished from other structures using LIDAR reflectivity. In this paper, we used building's vertical corner and traffic signs for vehicle localization in an urban area. In addition, we proposed a vertical structure map with information for vertical corners and traffic signs. The experiment was carried out in the Gangnam area of Seoul, South Korea. The traveling distance was approximately 4.5 km and the maximum traveling speed was approximately 80 km/h. The lateral and longitudinal RMS position errors were 0.118 m and 0.231 m, respectively.
The position of autonomous driving vehicle is basically acquired through the global positioning system (GPS). However, GPS signals cannot be received in tunnels. Due to this limitation, localization of autonomous driving vehicles can be made through sensors mounted on them. In particular, a 3D Light Detection and Ranging (LIDAR) system is used for longitudinal position error correction. Few feature points and structures that can be used for localization of vehicles are available in tunnels. Since lanes in the road are normally marked by solid line, it cannot be used to recognize a longitudinal position. In addition, only a small number of structures that are separated from the tunnel walls such as sign boards or jet fans are available. Thus, it is necessary to extract usable information from tunnels to recognize a longitudinal position. In this paper, fire hydrants and evacuation guide lights attached at both sides of tunnel walls were used to recognize a longitudinal position. These structures have highly distinctive reflectivity from the surrounding walls, which can be distinguished using LIDAR reflectivity data. Furthermore, reflectivity information of tunnel walls was fused with the road surface reflectivity map to generate a synthetic reflectivity map. When the synthetic reflectivity map was used, localization of vehicles was able through correlation matching with the local maps generated from the current LIDAR data. The experiments were conducted at an expressway including Maseong Tunnel (approximately 1.5 km long). The experiment results showed that the root mean square (RMS) position errors in lateral and longitudinal directions were 0.19 m and 0.35 m, respectively, exhibiting precise localization accuracy.