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Journal of Liaoning Petrochemical University
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2008, Vol.28 No.3  Publication date:20 September 2008
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  • Intermittent Transportation Technology of Buried Heat Oil Pipelines
  • LI Jin, WU Ming
  • 2008, 28 (3): 35-37.
  • Abstract ( ) PDF ( 231KB ) ( )   
  • The low flow rate of crude oil pipelines is ubiquitous. When the transportation quantity becomes lower than allowed minimum quantity, intermittent transportation technique can be used to solve this problem. In the course of intermittent transportation, if shutdown time is too long and crude oil temperature of pipeline decreases to a certain value, pipeline restart confronts tremendous difficulties, and further probably results in the accidents of pipeline condensation. According to of heating and water power character of pipelines from Tieling to Dalian, based on intermittent transportation, the mathematical models of temperature drop, restart temperature rising and restart pressure were established. By adopting finite difference method, partial differential equation of heat conduction is transferred to linear equations system and comes into solution by iterative method. Taking two sections of pipelines covering Anshan to Da Shiqiao and Da Shiqiao to Xiongyue as examples, simulation calculations of shut-down and restart were conducted. The result shows that pipeline may work safely during winters by this way that pipelines restart and continue work for two days after 8 days shut-down when the oil flow is 23 300 t/d and outgoing temperature is 45 ℃. The method is successfully applied for Tieling-Dalian pipeline.
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  • Iris Image Algorithm for Real-Time Pre-Estimation Based on SVM
  • WANG Wei-min,TONG He
  • 2008, 28 (3): 56-60.
  • Abstract ( ) PDF ( 377KB ) ( )   
  • There exist frequently different types of bad sample images in an iris identification application system. When these bad images are imported into the identification process, generally it results in increased enrollment failure rate and localization errors or identification errors. According to the articulation and resolution of the iris part, previous image quality evaluation methods estimate whether an image is a bad or not after having calculated the iris location of an input image. So, only part of bad samples can be handled, and it is time-consuming. The reasons and characteristics that bad sample images were analyed. A real-time pre-estimation method for supporting vector machine's associated estimation network was proposed. Before the localization or rough localization process, sample images temporarily saved in memory are detected. According to the output results from pre-estimation network, the system determines re-acquisition or to turn into the next step. The experimental result shows that the method can detect most types of the bad sample images. Detection speed is fast and error rate is comparatively low. The method can satisfy the pre-estimation requirements of a real-time iris identification system.
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