In x-ray scanning system, scatter images are obtained to provide information on material density. The forward and
backward scatter is related to solid angle. Scatter is therefore dependent on the distance of the scanned object from the
x-ray source. In the real world, an object may be placed anywhere on the conveyer belt, so the measured intensity will
contain errors relative to the ideal intensity. This makes classification results less reliable. Extraction of characteristic
values L associated with the density; need to know the gray levels of scatter images, so how to base on forward scatter
and back scatter images to determine the scatter image gray level is first necessary to solve the problem. The author
combined with the forward scatter and backscatter images,then established higher order gray-level mathematical model
of scattering images, to eliminate the impact of distance on the scatter images, to obtain more accurate gray level of
scatter image. Then compare the error use of LMS algorithm and the LS algorithm to solving mathematical model
parameters, LS algorithm ultimately prove less error and experimental validation of the superiority of the LS algorithm.
X-ray security device cannot detect accurately for some volatile of toxic harmful or flammable easy explosion products, this article takes electronic noses as secondary detection means of x-ray security equipment embedded in the x-ray security equipment. Using different mode recognition methods process experimental data, focus research on neural network mode recognition method, last established effective BP network model, identification accurate rate is 100% for gas species. Take the X-ray detection technology and electronic nose odor recognition technology combination to achieve the integration of multi-testing information, provide effective methods and theoretical basis for research and development of new security equipment, and realize the integration of a variety of information technology and multi-stage checking.
In material classification, distilling eigenvalue will use the object's true gray levels. The problem is objects in a bag
almost always overlap with others. Being able to identify the object of interest and remove the overlap effects becomes
the key issue that needs to be solved. First, the paper took an n-object-overlapping problem simplified to a
two-object-overlapping problem. So the research focus turned to computing true gray levels for two-object-overlapping
problem. It was necessary to develop models that can be used to remove the background object overlapping effects.
The author took back scatter images for example, discussed the development of the mathematical model for removing
the overlapping effects, solved the model parameter by experiment and analyzed model error. This method has been used
in x-ray security inspection equipment of DT Inspection equipment limited company. The results of application show
that the algorithm is feasible. This is a unique contribution to the explosive detection community.
In this paper, for explosives classification problem in safety inspection field, feature extraction and recognition from the
radiation data as the core, combined with dual-energy X-ray transmission technology, low-energy forward scatter and
low-energy back scatter technology to get eigenvalue R associated with effective atomic number and eigenvalue L
associated with density. Synthesized R and L, get the discriminate, decision-making plane and distinguish rule based on
the least mistake probability. The distribution of materials with regularity in this plane, there is a curve, most illicit
materials drop into the area under the curve. Then get the criterion to classify the explosives. A given object may be
randomly placed anywhere on the conveyor belt, resulting in a variation in the detected signals. Both an adaptive
modeling technique and least squares method are used to decrease this distance effect. This is the magnitude contribute
for recognition of solid explosive.
Starting from exploiting the applied detection system of gas transmission pipeline, a set of X-ray image processing
methods and pipeline flaw quantificational evaluation methods are proposed. Defective and non-defective strings
and rows in gray image were extracted and oscillogram was obtained. We can distinguish defects in contrast with
two gray images division. According to the gray value of defects with different thicknesses, the gray level depth
curve is founded. Through exponential and polynomial fitting way to obtain the attenuation mathematical model
which the beam penetrates pipeline, thus attain flaw deep dimension. This paper tests on the PPR pipe in the
production of simulated holes flaw and cracks flaw, 135KV used the X-ray source on the testing. Test results show
that X-ray image processing method, which meet the needs of high efficient flaw detection and provide quality
safeguard for thick oil recovery, can be used successfully in detecting corrosion of insulated pipe.
In material recognition, distilling eigenvalue will use an object's true gray levels. The problem is objects in a bag
almost always overlap with others. Being able to identify the object of interest and remove the overlap effects
becomes the key issue that needs to be solved. First, the author took an n-object-overlapping problem simplified to a
two-object-overlapping problem. So the research focus turned to computing true gray levels for
two-object-overlapping problem. It was necessary to develop models that can be used to remove the background
object overlapping effects. The author took transmission images for example, discussed the development of the
mathematical model for removing the overlapping effects, solved the model parameter by experiment and analyzed
model error. The mathematical models for forward-scatter and backscatter overlap models were much more
complicated than transmission overlap models. However, these formulas could still be derived in a similar manner as
was employed to create transmission models. This method has been used in DEX9080B x-ray security inspection
equipment of DT Inspection equipment limited company. The results of application show that the algorithm is
feasible. This is a unique contribution to the explosive detection community.
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