Automated design of squirrel-cage induction machines by predefined torque-speed-characteristic

Hecker, Q.; Ccoa, J. A. Butron; Meyer, W E; Herzog, Hans-Georg

doi:10.1109/iemdc.2013.6556306

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…These interferences showed some holes in the binary image, which will affect the vertical projection function, and then affect the recognition rate of the sugarcane stem node. In this paper, morphological closed operation is used to fill holes, morphological closed operation can not only smooth the contours of the image, but also break the narrow discontinuities, eliminate the smaller holes (GONZALEZ et al, 2007), The formula is (4).…”

Section: Binarization and Morphological Processingmentioning

confidence: 99%

Sugarcane stem nodes based on the maximum value points of the vertical projection function

Chen

et al. 2020

Cienc. Rural

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In order to solve the problem that the stem nodes are difficult to identify in the process of sugarcane seed automatic cutting, a method of identifying the stem nodes of sugarcane based on the extreme points of vertical projection function is proposed in this paper. Firstly, in order to reduce the influence of light on image processing, the RGB color image is converted to HSI color image, and the S component image of the HSI color space is extracted as a research object. Then, the S component image is binarized by the Otsu method, the hole of the binary image is filled by morphology closing algorithm, and the sugarcane and the background are initially separated by the horizontal projection map of the binary image. Finally, the position of sugarcane stem is preliminarily determined by continuously taking the derivative of the vertical projection function of the binary image, and the sum of the local pixel value of the suspicious pixel column is compared to further determine the sugarcane stem node. The experimental results showed that the recognition rate of single stem node is 100%, and the standard deviation is less than 1.1 mm. The accuracy of simultaneous identification of double stem nodes is 98%, and the standard deviation is less than 1.7 mm. The accuracy of simultaneous identification of the three stem nodes is 95%, and the standard deviation is less than 2.2 mm. Compared with the other methods introduced in this paper, the proposed method has higher recognition and accuracy.

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Section: Binarization and Morphological Processingmentioning

confidence: 99%

Sugarcane stem nodes based on the maximum value points of the vertical projection function

Chen

et al. 2020

Cienc. Rural

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show abstract

“…These problems can be solved by image opening operations. [17] However, the image processed by the open operation result in the edge of the image was not smooth, and various types of noise affected the image quality and structure. Therefore, we used median filtering to process the blade binary image which can remove the noise and does not affect the main outline of the image, although it will blur the image.…”

Section: Noise Processingmentioning

confidence: 99%

Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder

Peng

Wang²

2019

International Journal of Food Properties

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Most leaf chlorophyll predictions based on digital image analyzes are modeled by manual extraction features and traditional machine learning methods. In this study, a series of image preprocessing operations, such as image threshold segmentation, noise processing, and background separation, were performed based on digital image processing technology to remove the background and noise interference. The intrinsic features of the leaf RGB image were automatically learned through a stacked sparse autoencoder (SSAE) network to obtain concise data features. Finally, a prediction model between the RGB image features of a leaf and its SPAD value (arbitrary units) was established to predict the chlorophyll content in the plant leaf. The results show that the accuracy and automation of the detection of chlorophyll content of the deep neural network in this study are higher than those of traditional machine learning methods.

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“…Nodal Point: Any of two positions on the axis of a lens or added visual association, resolute by increasing an incident oblique beam and the equal refracted beam to the axis designed for the pair of beams that is parallel external the visual format [5].…”

Section: Implementation Of Face Recognition System Following Stages: mentioning

confidence: 99%

Reference Face Based Technique for Unconstrained Face Recognition from Images Gallery

Tyagi¹

2019

IJCG

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This paper represents the Id Quality of un-constrained face inside an unconstrained environment. It provides capabilities like discrepancy, illumination, and expression difference techniques. It might be useful for both retrieve image (Face) and identification (Recognition). At present plenty of way of top view face recognition can be found. In last few years, for Computer vision, numerous face recognition techniques have been organized. But, actualworld face detection demands a difficult works. The curiosity about unconstrained helpful face recognition keeps growing utilizing the detonation of online press for instance community methods, and video surveillance recording wherever experience analysis is of great importance. In this analysis, it is appeared to handle status inside the scenario of chart assumption. We're in a position to determine a magical experience employing a Varied Technique. This study lights out the choices suggested for unconstrained face recognition quality area and suggesting the solution to be utilized by Reference Face Based Technique (From Gallery Image) centered face recognition. RFG recognition is used in grouping with DCT locality sensitive hashing for efficient recovery to guarantee scalability. Objective of this research is Reference Face Based Technique focused on unconstrained face recognition to enhance the demonstration quality. The Simulation of proposed techniques will be completed through the use of MATLAB.

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Automated design of squirrel-cage induction machines by predefined torque-speed-characteristic

Cited by 7 publications

References 3 publications

Sugarcane stem nodes based on the maximum value points of the vertical projection function

Sugarcane stem nodes based on the maximum value points of the vertical projection function

Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder

Reference Face Based Technique for Unconstrained Face Recognition from Images Gallery

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