scholarly journals Optimization of Magnetic-Grating-Like Stroke-Sensing Cylinder Based on Response Quality Evaluation Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Yanqing Guo ◽  
Lu Liu ◽  
Yongling Fu ◽  
Chuangchuang Li ◽  
Liang Guo

The measurement of hydraulic cylinder displacement has been addressed from different fields. The detection principle of magnetic grating is able to realize the high integration and accuracy. In this paper, a signal response quality evaluation algorithm for devising and optimizing a high-accuracy displacement measuring system is proposed. On the basic of signal response quality evaluation method, structure variables are optimized to enhance the working performance. By defining the parameters, an optimum structure cylinder prototype is made and tested to provide better estimates. Experimental results on working characteristic are presented to verify the effectiveness of the optimized structure. The efficiency of the proposed signal response quality evaluation function is therefore demonstrated through the working performance.

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhong Han

To address the limitations of the university digital teaching quality assessment algorithms as well as the large evaluation mistakes in the existing algorithms, this paper presents a unique university digital teaching quality evaluation method based on multilevel analysis. First, the existing state of digital teaching quality evaluation in colleges and universities is studied to develop an evaluation index for digital teaching quality. Then, to identify and compute the weight of digital teaching quality indicators, an index weight evaluation matrix is built and the weight of digital teaching quality assessment indicators is plotted using a multilevel structure tree model. Then, from the top to the bottom of the tree, this paper computes the hierarchical ranking of assessment indicators. Additionally, this paper computes the membership degree of index evaluation, normalises the evaluation indicators, and completes the digital teaching quality assessment with the digital teaching confidence calculation. The experimental results demonstrate that the proposed method’s digital teaching quality assessment index has a high degree of accuracy and low evaluation error.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhao Changbi ◽  
Wang Jinjuan ◽  
Ke Li

The quality of boxing video is affected by many factors. For example, it needs to be compressed and encoded before transmission. In the process of transmission, it will encounter network conditions such as packet loss and jitter, which will affect the video quality. Combined with the proposed nine characteristic parameters affecting video quality, this paper proposes an architecture of video quality evaluation system. Aiming at the compression damage and transmission damage of leisure sports video, a video quality evaluation algorithm based on BP neural network (BPNN) is proposed. A specific Wushu video quality evaluation algorithm system is implemented. The system takes the result of feature engineering of 9 feature parameters of boxing video as the input and the subjective quality score of video as the training output. The mapping relationship is established by BPNN algorithm, and the objective evaluation quality of boxing video is finally obtained. The results show that using the neural network analysis model, the characteristic parameters of compression damage and transmission damage used in this paper can get better evaluation results. Compared with the comparison algorithm, the accuracy of the video quality evaluation method proposed in this paper has been greatly improved. The subjective characteristics of users are evaluated quantitatively and added to the objective video quality evaluation model in this paper, so as to make the video evaluation more accurate and closer to users.


2013 ◽  
Vol 32 (3) ◽  
pp. 710-714
Author(s):  
Jin-jin WEI ◽  
Su-mei LI ◽  
Wen-juan LIU ◽  
Yan-jun ZANG

2021 ◽  
pp. 1-13
Author(s):  
Yanjie Qi ◽  
Zehui Yang ◽  
Lin Kang

Due to the limitation of dynamic range of the imaging device, the fixed-voltage X-ray images often produce overexposed or underexposed regions. Some structure information of the composite steel component is lost. This problem can be solved by fusing the multi-exposure X-ray images taken by using different voltages in order to produce images with more detailed structures or information. Due to the lack of research on multi-exposure X-ray image fusion technology, there is no evaluation method specially for multi-exposure X-ray image fusion. For the multi-exposure X-ray fusion images obtained by different fusion algorithms may have problems such as the detail loss and structure disorder. To address these problems, this study proposes a new multi-exposure X-ray image fusion quality evaluation method based on contrast sensitivity function (CSF) and gradient amplitude similarity. First, with the idea of information fusion, multiple reference images are fused into a new reference image. Next, the gradient amplitude similarity between the new reference image and the test image is calculated. Then, the whole evaluation value can be obtained by weighting CSF. In the experiments of MEF Database, the SROCC of the proposed algorithm is about 0.8914, and the PLCC is about 0.9287, which shows that the proposed algorithm is more consistent with subjective perception in MEF Database. Thus, this study demonstrates a new objective evaluation method, which generates the results that are consistent with the subjective feelings of human eyes.


Fast track article for IS&T International Symposium on Electronic Imaging 2021: Image Quality and System Performance XVIII proceedings.


2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>


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