A solid-state capacitor aluminum/carbon composite negative foil with the carbon layer weight less than 3 mg/cm2 and carbon/aluminum layer featuring with good interface adhesion and high specific capacitance is prepared by adding the pressing-in technique and the sol-gel idea based on the carbon coating technology on the paste. The microstructure and phase composition of the surface and interface of the aluminum/carbon composite foil are investigated by means of scanning electron microscope (SEM), and the effects of various composite carbon materials on the specific capacity of the negative electrode foil at different frequencies are discussed. The curve of the specific capacity of the negative electrode carbon foil with frequency is presented under the condition of different carbon slurry ratios. The results show that the pores on the surface of the carbon layer are developed, and the carbon layer and aluminum foils are closely combined, generating Al4C3 on the surface and forming a good metallurgical combination. When the ratio of full-carbon paste is 15:2:1:2, the combination of carbon/aluminum layer is greatly bonded and the specific volume is up to 2950x10-6 F/cm2 under the condition of 1kHz-0.3V. As the test frequency increases, the specific capacity attenuation is small.
KEYWORDS: Skin, Feature selection, Feature extraction, Bismuth, Lithium, Image segmentation, Human vision and color perception, Diagnostics, Medical diagnostics, Machine learning
At present PASI system of scoring is used for evaluating erythema severity, which can help doctors to diagnose psoriasis
[1-3]. The system relies on the subjective judge of doctors, where the accuracy and stability cannot be guaranteed [4].
This paper proposes a stable and precise algorithm for erythema severity estimation. Our contributions are twofold. On
one hand, in order to extract the multi-scale redness of erythema, we design the hierarchical feature. Different from
traditional methods, we not only utilize the color statistical features, but also divide the detect window into small window
and extract hierarchical features. Further, a feature re-ranking step is introduced, which can guarantee that extracted
features are irrelevant to each other. On the other hand, an adaptive boosting classifier is applied for further feature
selection. During the step of training, the classifier will seek out the most valuable feature for evaluating erythema
severity, due to its strong learning ability. Experimental results demonstrate the high precision and robustness of our
algorithm. The accuracy is 80.1% on the dataset which comprise 116 patients’ images with various kinds of erythema.
Now our system has been applied for erythema medical efficacy evaluation in Union Hosp, China.
KEYWORDS: Image segmentation, Skin, RGB color model, Image processing algorithms and systems, Feature extraction, Data modeling, Medical imaging, Light sources and illumination, Lamps, Space reconnaissance
The automatic segmentation of psoriatic lesions is widely researched these years. It is an important step
in Computer-aid methods of calculating PASI for estimation of lesions. Currently those algorithms can
only handle single erythema or only deal with scaling segmentation. In practice, scaling and erythema
are often mixed together. In order to get the segmentation of lesions area,this paper proposes an
algorithm based on Random forests with color and texture features. The algorithm has three steps. The
first step, the polarized light is applied based on the skin’s Tyndall-effect in the imaging to eliminate
the reflection and Lab color space are used for fitting the human perception. The second step, sliding
window and its sub windows are used to get textural feature and color feature. In this step, a feature of
image roughness has been defined, so that scaling can be easily separated from normal skin. In the end,
Random forests will be used to ensure the generalization ability of the algorithm. This algorithm can
give reliable segmentation results even the image has different lighting conditions, skin types. In the
data set offered by Union Hospital, more than 90% images can be segmented accurately.
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