Mr. Li Wang Profile

Li Wang

at Univ of Science and Technology of China

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Publications (3)

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Proceedings Article | 26 January 2021 Paper

Study the preparation method of cathode foil of solid-state aluminum capacitor

Yun Lu, Li Wang, Xiaolong Li

Proceedings Volume 11767, 117670H (2021) https://doi.org/10.1117/12.2592263

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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.

Proceedings Article | 29 October 2014 Paper

Hierarchical feature selection for erythema severity estimation

Li Wang, Chenbo Shi, Chang Shu

Proceedings Volume 9273, 927323 (2014) https://doi.org/10.1117/12.2074369

KEYWORDS: Skin, Feature selection, Feature extraction, Bismuth, Lithium, Image segmentation, Human vision and color perception, Diagnostics, Medical diagnostics, Machine learning

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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.

Proceedings Article | 29 October 2014 Paper

Automatic segmentation of psoriasis lesions

Yang Ning, Chenbo Shi, Li Wang, Chang Shu

Proceedings Volume 9273, 927324 (2014) https://doi.org/10.1117/12.2074372

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

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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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