Precise in-situ characterization and cross-validation of the electromagnetic properties of a switched reluctance motor

Liu, Xiao; Gong, Liang; Li, Bingchu; Liu, Chengling

doi:10.1016/j.aiia.2020.05.002

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…When the condition of constant excitation (i.e., when the MMF is constant is considered), the incremental mechanical work done is the same as the rate of change of coenergy, which is nothing but the complement of the field energy. Hence, the incremental mechanical work done is expressed as [26]: and 𝜕𝜕 1 the change in coenergy is occurred. Hence, the air gap torque in terms of the coenergy represented as a function of rotor position and current is given by…”

Section: Torque Calculation In Switched Reluctance Motormentioning

confidence: 99%

Recent Trends in Minimization of Torque Ripples in Switched Reluctance Machine

Deepa¹,

Krishnaveni²,

Mallesham³

2022

Preprint

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Increased definite power, reduced cost, and dynamic construction are the important features required by electric motors for various applications. One such motor accommodating all the requisites mentioned above is Switched reluctance. Applications pertaining to small, commercial, and EV find Switched reluctance machine as a developing competitor to the conventional induction and permanent magnet motors. Though SRMS contribute a considerable number of advantages over induction motors and permanent magnet motors, the primary disadvantages pertaining to SRMs are the torque ripple, especially at high-speed operation, and the resulting acoustic noise. These disadvantages may not always be detrimental to the systems in entirely all cases, but depending on the application to be employed, torque ripple and acoustic noise form are detrimental to the system. This paper reviews the technology status and recent trends in the minimization of torque ripples switched reluctance machines.

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Section: Torque Calculation In Switched Reluctance Motormentioning

confidence: 99%

Recent Trends in Minimization of Torque Ripples in Switched Reluctance Machine

Deepa¹,

Krishnaveni²,

Mallesham³

2022

Preprint

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show abstract

“…Figure 4 illustrates how the noise from the background is eliminated from captured photos during preprocessing to obtain the fruit image. Most researchers then change the image from RGB to greyscale before changing it to binary (Ling et al, 2020). After dataset capture and before the advent of deep learning (DL), the extraction of features has been a common processing step.…”

Section: Introduction Of Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Modern techniques in detecting, identifying and classifying fruits according to the developed machine learning algorithm

Khalid

2024

JART

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Recent developments in machine vision have opened up a wide range of applications, and farming is no exception. Deep learning (DL) has a wide range of applications because of its capacity to extract robust features from photos. Shape, color, and feel of many fruit species make it difficult to discover and classify fruits. When examining the effects of artificial intelligence on fruit identification and classification, we noted that, up until 2018, the majority of approaches relied on traditional machine learning (ML) techniques, while just a few ways took use of DL techniques for recognizing fruits and categorization. In this post, we thoroughly covered the datasets that many academics utilized, the useful descriptors, the application of model, and the difficulties of utilizing DL to identify and classify fruits. Finally, we compiled the outcomes of various DL techniques used in earlier research to identify and categorize fruits. This work examines the use of models based on DL for fruit categorization and recognition in recent studies. In order to make it simpler for beginning agricultural researchers to comprehend the importance of ML in the agricultural domain, we have developed a DL model for apple categorization using the well-known dataset "Fruit 360" starting scratch.

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“…At the same time, secure grasping requires soft contact, force control, and slip prevention while the object is being handled [1,9]. Among the main tasks in agricultural applications, the harvesting of fruits and vegetables is one of the most time-consuming and labor-intensive tasks and suffers from low efficiency and limited competitiveness [10,11]. Bac et al and Lehnert et al utilized a suction gripper and an oscillating blade in the end executor of an agricultural robotic harvester to reap sweet peppers in a cropping environment [12,13].…”

Section: Introductionmentioning

confidence: 99%

Fruit Classification Utilizing a Robotic Gripper with Integrated Sensors and Adaptive Grasping

Zhang

Lai

et al. 2021

Mathematical Problems in Engineering

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As the core component of agricultural robots, robotic grippers are widely used for plucking, picking, and harvesting fruits and vegetables. Secure grasping is a severe challenge in agricultural applications because of the variation in the shape and hardness of agricultural products during maturation, as well as their variety and delicacy. In this study, a fruit identification method utilizing an adaptive gripper with tactile sensing and machine learning algorithms is reported. An adaptive robotic gripper is designed and manufactured to perform adaptive grasping. A tactile sensing information acquisition circuit is built, and force and bending sensors are integrated into the robotic gripper to measure the contact force distribution on the contact surface and the deformation of the soft fingers. A robotic manipulator platform is developed to collect the tactile sensing data in the grasping process. The performance of the random forest (RF), k-nearest neighbor (KNN), support vector classification (SVC), naive Bayes (NB), linear discriminant analysis (LDA), and ridge regression (RR) classifiers in identifying and classifying five types of fruits using the adaptive gripper is evaluated and compared. The RF classifier achieves the highest accuracy of 98%, while the accuracies of the other classifiers vary from 74% to 97%. The experiment illustrates that efficient and accurate fruit identification can be realized with the adaptive gripper and machine learning classifiers, and that the proposed method can provide a reference for controlling the grasping force and planning the robotic motion in the plucking, picking, and harvesting of fruits and vegetables.

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Precise in-situ characterization and cross-validation of the electromagnetic properties of a switched reluctance motor

Cited by 4 publications

References 18 publications

Recent Trends in Minimization of Torque Ripples in Switched Reluctance Machine

Recent Trends in Minimization of Torque Ripples in Switched Reluctance Machine

Modern techniques in detecting, identifying and classifying fruits according to the developed machine learning algorithm

Fruit Classification Utilizing a Robotic Gripper with Integrated Sensors and Adaptive Grasping

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