scholarly journals The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Zhen Qi ◽  
Li-ping Tu ◽  
Jing-bo Chen ◽  
Xiao-juan Hu ◽  
Jia-tuo Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Mean Value ◽  
Learning Methods ◽  
Color Classification ◽  
Imaging Device ◽  
Machine Learning Methods ◽  
Profile Correction ◽  
Tongue Color

Background and Goal. The application of digital image processing techniques and machine learning methods in tongue image classification in Traditional Chinese Medicine (TCM) has been widely studied nowadays. However, it is difficult for the outcomes to generalize because of lack of color reproducibility and image standardization. Our study aims at the exploration of tongue colors classification with a standardized tongue image acquisition process and color correction. Methods. Three traditional Chinese medical experts are chosen to identify the selected tongue pictures taken by the TDA-1 tongue imaging device in TIFF format through ICC profile correction. Then we compare the mean value of L*a*b* of different tongue colors and evaluate the effect of the tongue color classification by machine learning methods. Results. The L*a*b* values of the five tongue colors are statistically different. Random forest method has a better performance than SVM in classification. SMOTE algorithm can increase classification accuracy by solving the imbalance of the varied color samples. Conclusions. At the premise of standardized tongue acquisition and color reproduction, preliminary objectification of tongue color classification in Traditional Chinese Medicine (TCM) is feasible.

scholarly journals Learning about the vertical structure of radar reflectivity using hydrometeor classes and neural networks in the Swiss Alps

2019 ◽  
Author(s):  
Floor van den Heuvel ◽  
Loris Foresti ◽  
Marco Gabella ◽  
Urs Germann ◽  
Alexis Berne
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Vertical Profile ◽  
Vertical Structure ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Profile Correction ◽  
Radar Measurements ◽  
Reflectivity Data ◽  
Precipitation Events

Abstract. The use of radar for precipitation measurement in mountainous regions is complicated by many factors, especially beam shielding by terrain features, which, for example, reduces the visibility of the shallow precipitation systems during the cold season. When extrapolating the radar measurements aloft for quantitative precipitation estimation (QPE) at the ground, these must be corrected for the vertical change of the radar echo caused by the growth and transformation of precipitation. Building on the availability of polarimetric data and a hydrometeor classification algorithm, this work explores the potential of machine learning methods to study the vertical structure of precipitation in Switzerland and to propose a more localised vertical profile correction. It first establishes the ground work for the use of machine learning methods in this context: from volumetric data of 30 precipitation events vertical cones with 500 m vertical resolution are extracted. It is shown that these cones can well represent the vertical structure of different types of precipitation events (stratiform, convective, snowfall). The reflectivity data and the hydrometeor proportions from the extracted cones constitute the input for the training of artificial neural networks (ANN), which are used to predict the vertical change in reflectivity. Lower height levels are gradually removed in order to test the ANN's ability to extrapolate the radar measurements to the ground level. It is found that ANN models using the information on hydrometeor proportions can predict from altitudes between 500 and 1000 metres higher than the ANN based on only reflectivity data. In comparison with more traditional vertical profile correction techniques the ANNs show less prediction errors made from all height levels up to 4000 m a.s.l., above which the ANNs lose predictive skill and the performance levels off to a constant value.

Color Normalization for Robust Automatic Bill of Materials Generation and Visual Inspection of PCBs

2020 ◽  
Author(s):  
Olivia P. Paradis ◽  
Nathan T. Jessurun ◽  
Mark Tehranipoor ◽  
Navid Asadizanjani
Keyword(s):  
Machine Learning ◽  
Visual Inspection ◽  
Color Correction ◽  
Learning Methods ◽  
Linear Discriminant ◽  
Bill Of Materials ◽  
Machine Learning Methods ◽  
Profile Correction ◽  
Before And After ◽  
Imaging Conditions

Abstract A Bill of Materials (BoM) is the list of all components present on a Printed Circuit Board (PCB). BoMs are useful for multiple forms of failure analysis and hardware assurance. In this paper, we build upon previous work and present an updated framework to automatically extract a BoM from optical images of PCBs in order to keep up to date with technological advancements. This is accomplished by revising the framework to emphasize the role of machine learning and by incorporating domain knowledge of PCB design and hardware Trojans. For accurate machine learning methods, it is critical that the input PCB images are normalized. Hence, we explore the effect of imaging conditions (e.g. camera type, lighting intensity, and lighting color) on component classification, before and after color correction. This is accomplished by collecting PCB images under a variety of imaging conditions and conducting a linear discriminant analysis before and after color checker profile correction, a method commonly used in photography. This paper shows color correction can effectively reduce the intraclass variance of different PCB components, which results in a higher component classification accuracy. This is extremely desirable for machine learning methods, as increased prior knowledge can decrease the number of ground truth images necessary for training. Finally, we detail the future work for data normalization for more accurate automatic BoM extraction. Index Terms – automatic visual inspection; PCB reverse engineering; PCB competitor analysis; hardware assurance; bill of materials

scholarly journals Learning about the vertical structure of radar reflectivity using hydrometeor classes and neural networks in the Swiss Alps

2020 ◽  
Vol 13 (5) ◽  
pp. 2481-2500
Author(s):  
Floor van den Heuvel ◽  
Loris Foresti ◽  
Marco Gabella ◽  
Urs Germann ◽  
Alexis Berne
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Vertical Profile ◽  
Vertical Structure ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Profile Correction ◽  
Radar Measurements ◽  
Reflectivity Data ◽  
Precipitation Events

Abstract. The use of radar for precipitation measurement in mountainous regions is complicated by many factors, especially beam shielding by terrain features, which, for example, reduces the visibility of the shallow precipitation systems during the cold season. When extrapolating the radar measurements aloft for quantitative precipitation estimation (QPE) at the ground, these must be corrected for the vertical change of the radar echo caused by the growth and transformation of precipitation. Building on the availability of polarimetric data and a hydrometeor classification algorithm, this work explores the potential of machine learning methods to study the vertical structure of precipitation in Switzerland and to propose a more localised vertical profile correction. It first establishes the ground work for the use of machine learning methods in this context: from volumetric data of 30 precipitation events, vertical cones with 500 m vertical resolution are extracted. It is shown that these cones can well represent the vertical structure of different types of precipitation events (stratiform, convective, snowfall). The reflectivity data and the hydrometeor proportions from the extracted cones constitute the input for the training of artificial neural networks (ANNs), which are used to predict the vertical change in reflectivity. Lower height levels are gradually removed in order to test the ANN's ability to extrapolate the radar measurements to the ground level. It is found that ANN models using the information on hydrometeor proportions can predict from altitudes between 500 and 1000 m higher than the ANN based on only reflectivity data. In comparison to more traditional vertical profile correction techniques, the ANNs show less prediction errors made from all height levels up to 4000 m a.s.l., above which the ANNs lose predictive skill and the performance levels off to a constant value.

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