Adhesion area estimation using backscatter image gray level masking of uncoated tungsten carbide tools

Alammari, Youssef; Iovkov, Ivan; Berger, Sebastian; Saelzer, Jannis; Biermann, Dirk

doi:10.1016/j.wear.2021.203666

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…e effect measurement algorithm of human motion rehabilitation training based on improved deep reinforcement learning determines the representative gray set of pixels in the dynamic frame [8,9], calculates the distance between the dynamic frame and each frame in the human motion rehabilitation training video, and obtains the key frame of the video according to the calculation results. e specific steps are as follows:…”

Section: Extraction Of Key Framesmentioning

confidence: 99%

Effect Measurement for Human Motion Rehabilitation Training Using Improved Deep Reinforcement Learning and IoT Networks

Zhu

Liu

2022

Security and Communication Networks

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Measuring the effect of human motion rehabilitation training is important to help persons develop motion rehabilitation training plans. The current human motion rehabilitation training effect measurement algorithm has the problems of too large gap between the smoothness of the motion speed curve and the reality, high key frame extraction error rate, low measurement accuracy, long measurement time, and low satisfaction. Therefore, this paper proposes a human motion rehabilitation training effect measurement algorithm using improved deep reinforcement learning and Internet of Things (IoT) networks using IoT network technology to collect human motion rehabilitation training videos. The key frames of the human motion rehabilitation video data are extracted according to the interframe distance, and the metalearning method is used to improve the deep reinforcement learning network, and the obtained key frames are input into the improved deep reinforcement learning network to obtain the motion speed curve of human motion rehabilitation training. The smoothness of the motion velocity curve is calculated, and high smoothness indicates a good human motion rehabilitation training effect, while low smoothness indicates a poor human motion rehabilitation training effect, so as to complete the measure of human motion rehabilitation training effect. The results show that the smoothness of the motion speed curve of the proposed algorithm is closer to reality, the average error rate of key frame extraction is 1.45%, the measurement accuracy of rehabilitation training is more than 90%, the measurement time is controlled below 2.1 s, and the maximum user satisfaction is 93.1, which shows that the practical application of the algorithm is good.

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Section: Extraction Of Key Framesmentioning

confidence: 99%

Effect Measurement for Human Motion Rehabilitation Training Using Improved Deep Reinforcement Learning and IoT Networks

Zhu

Liu

2022

Security and Communication Networks

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“…Adhesion of the workpiece material, such as nickel-based alloys, to the cutting tool is an important aspect that governs a number of physical parameters within the tool-chip interface. By using image processing of discrete gray intensities on backscatter images of uncoated tungsten carbide inserts, Alammari et al [345] enabled the quantification of adhesion area for a variation of the cutting speed, based on a number of orthogonal machining trials carried out on nickel-based superalloy NiCr19-Fe19Nb5Mo3 (2.4668), using uncoated tungsten carbide inserts.…”

Section: Wear In Metalworking and Polishing Processesmentioning

confidence: 99%

A review of advances in tribology in 2020–2021

Meng

et al. 2022

Friction

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Around 1,000 peer-reviewed papers were selected from 3,450 articles published during 2020–2021, and reviewed as the representative advances in tribology research worldwide. The survey highlights the development in lubrication, wear and surface engineering, biotribology, high temperature tribology, and computational tribology, providing a show window of the achievements of recent fundamental and application researches in the field of tribology.

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“…where R(a) is feature fusion output value of medical ultrasound image, S zj is image pixel subset, J(a) represents the gray image [18,19], t 0 is the image initial structure similarity, and (t(a), t 0 ) denotes the similarity degree.…”

Section: Image Segmentation Recognitionmentioning

confidence: 99%

Segmentation for Human Motion Injury Ultrasound Medical Images Using Deep Feature Fusion

Sun

Liu

2022

Mathematical Problems in Engineering

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Image processing technology assists physicians in the analysis of athletes’ human motion injuries, not only to improve the accuracy of athletes’ injury detection but also to improve the localization and recognition of injury locations. It is important to accurately segment human motion injury ultrasound medical images. To address many problems such as poor effect of traditional ultrasonic medical image segmentation algorithm for a sports injury. Therefore, we propose a segmentation algorithm for human motion injury ultrasound medical images using deep feature fusion. First, the accurate estimated value of human posture is extracted and combined with image texture features and image gray value as the target feature value of the ultrasonic medical image of human motion injury. Second, the image features are deeply fused by an adaptive fusion algorithm to enhance the image resolution. Finally, the best segmentation value of the image is obtained by the trained support vector machine to realize the accurate segmentation of human motion injury ultrasonic medical image. The results show that the average accuracy of the posture accurate estimation of the proposed algorithm is 95.97%; the segmentation time of the human motion injury ultrasound medical image of the proposed algorithm is below 150 ms; and the convergence of the algorithm is completed when the number of iterations is 3. The maximum segmentation error rate is 2.68%. The image segmentation effect is consistent with the ideal target segmentation effect. The proposed algorithm has important application value in the field of ultrasonic medical diagnosis of sports injury.

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Adhesion area estimation using backscatter image gray level masking of uncoated tungsten carbide tools

Cited by 6 publications

References 7 publications

Effect Measurement for Human Motion Rehabilitation Training Using Improved Deep Reinforcement Learning and IoT Networks

Effect Measurement for Human Motion Rehabilitation Training Using Improved Deep Reinforcement Learning and IoT Networks

A review of advances in tribology in 2020–2021

Segmentation for Human Motion Injury Ultrasound Medical Images Using Deep Feature Fusion

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