• Title/Summary/Keyword: in-flight simulation

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Development of an Image Processing System for the Large Size High Resolution Satellite Images (대용량 고해상 위성영상처리 시스템 개발)

  • 김경옥;양영규;안충현
    • Korean Journal of Remote Sensing
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    • v.14 no.4
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    • pp.376-391
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    • 1998
  • Images from satellites will have 1 to 3 meter ground resolution and will be very useful for analyzing current status of earth surface. An image processing system named GeoWatch with more intelligent image processing algorithms has been designed and implemented to support the detailed analysis of the land surface using high-resolution satellite imagery. The GeoWatch is a valuable tool for satellite image processing such as digitizing, geometric correction using ground control points, interactive enhancement, various transforms, arithmetic operations, calculating vegetation indices. It can be used for investigating various facts such as the change detection, land cover classification, capacity estimation of the industrial complex, urban information extraction, etc. using more intelligent analysis method with a variety of visual techniques. The strong points of this system are flexible algorithm-save-method for efficient handling of large size images (e.g. full scenes), automatic menu generation and powerful visual programming environment. Most of the existing image processing systems use general graphic user interfaces. In this paper we adopted visual program language for remotely sensed image processing for its powerful programmability and ease of use. This system is an integrated raster/vector analysis system and equipped with many useful functions such as vector overlay, flight simulation, 3D display, and object modeling techniques, etc. In addition to the modules for image and digital signal processing, the system provides many other utilities such as a toolbox and an interactive image editor. This paper also presents several cases of image analysis methods with AI (Artificial Intelligent) technique and design concept for visual programming environment.

Study on the neutron imaging detector with high spatial resolution at China spallation neutron source

  • Jiang, Xingfen;Xiu, Qinglei;Zhou, Jianrong;Yang, Jianqing;Tan, Jinhao;Yang, Wenqin;Zhang, Lianjun;Xia, Yuanguang;Zhou, Xiaojuan;Zhou, Jianjin;Zhu, Lin;Teng, Haiyun;Yang, Gui-an;Song, Yushou;Sun, Zhijia;Chen, Yuanbo
    • Nuclear Engineering and Technology
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    • v.53 no.6
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    • pp.1942-1946
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    • 2021
  • Gadolinium oxysulfide (GOS) is regarded as a novel scintillator for the realization of ultra-high spatial resolution in neutron imaging. Monte Carlo simulations of GOS scintillator show that the capability of its spatial resolution is towards the micron level. Through the time-of-flight method, the light output of a GOS scintillator was measured to be 217 photons per captured neutron, ~100 times lower than that of a ZnS/LiF:Ag scintillator. A detector prototype has been developed to evaluate the imaging solution with the GOS scintillator by neutron beam tests. The measured spatial resolution is ~36 ㎛ (28 line pairs/mm) at the modulation transfer function (MTF) of 10%, mainly limited by the low experimental collimation ratio of the beamline. The weak light output of the GOS scintillator requires an enormous increase in the neutron flux to reduce the exposure time for practical applications.

Application of neural network for airship take-off and landing mode by buoyancy control (기낭 부력 제어에 의한 비행선 이착륙의 인공신경망 적용)

  • Chang, Yong-Jin;Woo, Gui-Ae;Kim, Jong-Kwon;Lee, Dae-Woo;Cho, Kyeum-Rae
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.33 no.2
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    • pp.84-91
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    • 2005
  • For long time, the takeoff and landing control of airship was worked by human handling. With the development of the autonomous control system, the exact controls during the takeoff and landing were required and lots of methods and algorithms were suggested. This paper presents the result of airship take-off and landing by buoyancy control using air ballonet volume change and performance control of pitch angle for stable flight within the desired altitude. For the complexity of airship's dynamics, firstly, simple PID controller was applied. Due to the various atmospheric conditions, this controller didn't give satisfactory results. Therefore, new control method was designed to reduce rapidly the error between designed trajectory and actual trajectory by learning algorithm using an artificial neural network. Generally, ANN has various weaknesses such as large training time, selection of neuron and hidden layer numbers required to deal with complex problem. To overcome these drawbacks, in this paper, the RBFN (radial basis function network) controller developed. The weight value of RBFN is acquired by learning which to reduce the error between desired input output through and airship dynamics to impress the disturbance. As a result of simulation, the controller using the RBFN is superior to PID controller which maximum error is 15M.