scholarly journals Research on automatic detection technology for the compensation hole shape and position parameters of a brake master cylinder

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110346
Author(s):  
Changfu Zhao ◽  
Hongchang Ding ◽  
Guohua Cao ◽  
Han Hou
Keyword(s):  
Dimensional Accuracy ◽  
Automatic Detection ◽  
Detection Accuracy ◽  
Image Processing Algorithm ◽  
Automatic Equipment ◽  
Master Cylinder ◽  
Position Detection ◽  
Detection Technology ◽  
Automatic Positioning ◽  
Hole Detection

Machine vision is a key technology to achieve high detection accuracy for the compensation hole parameters of automobile brake master cylinders, which influence automobile safety and parking reliability. As an important part of the automobile brake master cylinder, the compensation hole can play an important role in regulating the brake fluid in the reservoir tank and brake chamber, and its dimensional accuracy and processing quality are strictly controlled. Therefore, determining how to accurately obtain images of the compensation hole is a primary problem in the detection of compensation hole parameters. In this paper, fully automatic equipment for compensation hole detection in automobile brake master cylinders is designed using an image processing algorithm to realize the automatic positioning of the compensation hole and automatic detection of size parameters. Experiments show that the automatic positioning and detection time for the compensation hole is less than 8 s, the detection accuracy of the compensation hole size is higher than ±0.021 mm, and the position detection accuracy for the compensation hole is higher than ±0.045 mm. The compensation hole detection technology proposed in this paper provides high real-time performance and good robustness while meeting accuracy requirements and detection speed.

scholarly journals A New Method for Detecting Compensation Hole Parameters of Automobile Brake Master Cylinder Based on Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Changfu Zhao ◽  
Hongchang Ding ◽  
Guohua Cao ◽  
Ying Zhang
Keyword(s):  
Hough Transform ◽  
New Method ◽  
Zernike Moment ◽  
Machining Accuracy ◽  
Detection Accuracy ◽  
Master Cylinder ◽  
Fast Detection ◽  
Detection Technology ◽  
Center Position ◽  
Detection Speed

The machining accuracy of the compensation hole of the automobile brake master cylinder directly determines the safety of the automobile and the reliability of parking. How to detect the parameters of the compensation hole with high precision becomes a crucial issue. In this paper, by analyzing the principle of Hough transform detection technology and several optimization algorithms, a new method combining Zernike moment and improved gradient Hough transform is proposed to detect the circular hole parameters. The simulation experiment shows that the proposed algorithm satisfies 0.1 pixels in the coordinate detection of the center position, and the radius detection accuracy is 0.05 pixels, with fast detection speed and good robustness. Compared with the random Hough transform algorithm and the gradient Hough transform algorithm, the algorithm proposed in this paper has higher detection accuracy, faster detection speed, and better robustness, which meets the online detection accuracy requirements of the brake master cylinder compensation hole.

scholarly journals Automatic Detection of Grammatical Errors in English Verbs Based on RNN Algorithm: Auxiliary Objectives for Neural Error Detection Models

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yizhou He
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Error Detection ◽  
Rapid Development ◽  
Automatic Detection ◽  
Detection Accuracy ◽  
Original Text ◽  
Detection Technology ◽  
Grammatical Errors ◽  
English Verb

With the rapid development of neural network technology, we have widely used this technology in various fields. In the field of language translation, the research on automatic detection technology of English verb grammatical errors is in a hot stage. The traditional manual detection cannot be applied to the current environment. Therefore, this paper proposes an automatic detection technology of English verb grammatical errors based on recurrent neural network (RNN) algorithm to solve this problem. Firstly, the accuracy and feedback speed of traditional manual detection and recurrent neural network RNN algorithm are compared. Secondly, a detection model which can be calculated according to grammatical order combined with context is designed. Finally, when the output verb result is inconsistent with the original text, it can automatically mark the error detection effect. The experimental results show that the algorithm model studied in this paper can effectively improve the detection accuracy and feedback efficiency and is more applicable and effective than the traditional manual detection method.

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

scholarly journals Deep-Learning-Based Pupil Center Detection and Tracking Technology for Visible-Light Wearable Gaze Tracking Devices

2021 ◽  
Vol 11 (2) ◽  
pp. 851
Author(s):  
Wei-Liang Ou ◽  
Tzu-Ling Kuo ◽  
Chin-Chieh Chang ◽  
Chih-Peng Fan
Keyword(s):  
Deep Learning ◽  
Visible Light ◽  
Tracking System ◽  
Recall Rate ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Tracking Errors ◽  
Gaze Tracking ◽  
Detection Technology ◽  
Pupil Tracking

In this study, for the application of visible-light wearable eye trackers, a pupil tracking methodology based on deep-learning technology is developed. By applying deep-learning object detection technology based on the You Only Look Once (YOLO) model, the proposed pupil tracking method can effectively estimate and predict the center of the pupil in the visible-light mode. By using the developed YOLOv3-tiny-based model to test the pupil tracking performance, the detection accuracy is as high as 80%, and the recall rate is close to 83%. In addition, the average visible-light pupil tracking errors of the proposed YOLO-based deep-learning design are smaller than 2 pixels for the training mode and 5 pixels for the cross-person test, which are much smaller than those of the previous ellipse fitting design without using deep-learning technology under the same visible-light conditions. After the combination of calibration process, the average gaze tracking errors by the proposed YOLOv3-tiny-based pupil tracking models are smaller than 2.9 and 3.5 degrees at the training and testing modes, respectively, and the proposed visible-light wearable gaze tracking system performs up to 20 frames per second (FPS) on the GPU-based software embedded platform.

scholarly journals Automatic Detection Technology of Sonar Image Target Based on the Three-Dimensional Imaging

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Wanzeng Kong ◽  
Jinshuai Yu ◽  
Ying Cheng ◽  
Weihua Cong ◽  
Huanhuan Xue
Keyword(s):  
Data Storage ◽  
3D Imaging ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Automatic Detection ◽  
Classification Model ◽  
Image Detection ◽  
Practical Applications ◽  
Sonar Image ◽  
Detection Technology

With 3D imaging of the multisonar beam and serious interference of image noise, detecting objects based only on manual operation is inefficient and also not conducive to data storage and maintenance. In this paper, a set of sonar image automatic detection technologies based on 3D imaging is developed to satisfy the actual requirements in sonar image detection. Firstly, preprocessing was conducted to alleviate the noise and then the approximate position of object was obtained by calculating the signal-to-noise ratio of each target. Secondly, the separation of water bodies and strata is realized by maximum variance between clusters (OTSU) since there exist obvious differences between these two areas. Thus image segmentation can be easily implemented on both. Finally, the feature extraction is carried out, and the multidimensional Bayesian classification model is established to do classification. Experimental results show that the sonar-image-detection technology can effectively detect the target and meet the requirements of practical applications.

scholarly journals The nanofluidic confinement apparatus: studying confinement-dependent nanoparticle behavior and diffusion

2018 ◽  
Vol 9 ◽  
pp. 301-310 ◽  
Author(s):  
Stefan Fringes ◽  
Felix Holzner ◽  
Armin W Knoll
Keyword(s):  
Polymer Surface ◽  
Diffusion Constant ◽  
Numerical Aperture ◽  
Lateral Diffusion ◽  
Vertical Position ◽  
Detection Accuracy ◽  
Illumination Time ◽  
Electroviscous Effect ◽  
Position Detection ◽  
Hydrodynamic Effects

The behavior of nanoparticles under nanofluidic confinement depends strongly on their distance to the confining walls; however, a measurement in which the gap distance is varied is challenging. Here, we present a versatile setup for investigating the behavior of nanoparticles as a function of the gap distance, which is controlled to the nanometer. The setup is designed as an open system that operates with a small amount of dispersion of ≈20 μL, permits the use of coated and patterned samples and allows high-numerical-aperture microscopy access. Using the tool, we measure the vertical position (termed height) and the lateral diffusion of 60 nm, charged, Au nanospheres as a function of confinement between a glass surface and a polymer surface. Interferometric scattering detection provides an effective particle illumination time of less than 30 μs, which results in lateral and vertical position detection accuracy ≈10 nm for diffusing particles. We found the height of the particles to be consistently above that of the gap center, corresponding to a higher charge on the polymer substrate. In terms of diffusion, we found a strong monotonic decay of the diffusion constant with decreasing gap distance. This result cannot be explained by hydrodynamic effects, including the asymmetric vertical position of the particles in the gap. Instead we attribute it to an electroviscous effect. For strong confinement of less than 120 nm gap distance, we detect the onset of subdiffusion, which can be correlated to the motion of the particles along high-gap-distance paths.

The Development of Near-source Electromagnetic Methods in China

2018 ◽  
Vol 23 (1) ◽  
pp. 115-124 ◽  
Author(s):  
Guo-qiang Xue
Keyword(s):  
High Efficiency ◽  
Academic Research ◽  
Electromagnetic Method ◽  
Detection Accuracy ◽  
Wide Field ◽  
Transient Electromagnetic Method ◽  
Transient Electromagnetic ◽  
Detection Technology ◽  
Deep Depth ◽  
Central Loop

Near-source electromagnetic technology has been developed and applied in the exploration of petroleum, metallic ore, coal, and engineering geology due to its high efficiency, high detection accuracy, and deep depth of investigation. In this paper, research and applications of the frequency-domain electromagnetic sounding method (FDEM), wide-field electromagnetic method (WFEM), modified central-loop transient electromagnetic method (TEM), and short-offset grounded-wire TEM (SOTEM) with obvious near-source characteristics, were reviewed and analyzed. From the 1960s to 1990s, the FDEM method and equipment were extensively developed in China. These methods have played important roles in the exploration of coal resources. Based on controlled source audio-frequency magnetotelluric (CSAMT) and FDEM methods, a new method has been developed by deriving a new expression to calculate apparent resistivity. This method, which is referred to as WFEM, has been studied, applied, and received great attention in China. To increase work efficiency and reduce the influence of local transverse anisotropy on the detection processes, a modified central-loop TEM detection technology based on the central loop transient electromagnetic method was developed in China. The advantages of SOTEM in near-source surveys with high resolution and increased depth detection stimulated academic research interest to further develop grounded-wire TEM techniques. [Figure: see text]

A deep Residual U-Net algorithm for automatic detection and quantification of ascites on the abdominopelvic computed tomography acquired in the emergency department (Preprint)

2021 ◽  
Author(s):  
Hoon Ko ◽  
Jimi Huh ◽  
Kyung Won Kim ◽  
Heewon Chung ◽  
Yousun Ko ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Detection ◽  
High Specificity ◽  
Ct Images ◽  
Free Fluid ◽  
Detection Accuracy ◽  
Abdominopelvic Ct ◽  
Segmentation Accuracy ◽  
Detection And Quantification

BACKGROUND Detection and quantification of intraabdominal free fluid (i.e., ascites) on computed tomography (CT) are essential processes to find emergent or urgent conditions in patients. In an emergent department, automatic detection and quantification of ascites will be beneficial. OBJECTIVE We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). METHODS 2D deep learning models (DLMs) based on a deep residual U-Net, U-Net, bi-directional U-Net, and recurrent residual U-net were developed to segment areas of ascites on an abdominopelvic CT. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and non-ascites images. The AI algorithms were trained using 6,337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1,635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. RESULTS The segmentation accuracy was the highest in the deep residual U-Net with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bi-directional U-Net, and recurrent residual U-net (mIoU values 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest in the deep residual U-net (0.96), followed by U-Net, bi-directional U-net, and recurrent residual U-net (0.90, 0.88, and 0.82, respectively). The deep residual U-net also achieved high sensitivity (0.96) and high specificity (0.96). CONCLUSIONS We propose the deep residual U-net-based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance.

scholarly journals On Using XMC R-CNN Model for Contraband Detection within X-Ray Baggage Security Images

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yong Zhang ◽  
Weiwu Kong ◽  
Dong Li ◽  
Xudong Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Rate ◽  
Automatic Detection ◽  
User Needs ◽  
Labor Intensity ◽  
Detection Rates ◽  
X Ray ◽  
Detection Technology ◽  
Security Inspection

We present an X-ray material classifier region-based convolutional neural network (XMC R-CNN) model for detecting the typical guns and the typical knives in X-ray baggage images. The XMC R-CNN model is used to solve the problem of contraband detection in overlapped X-ray baggage images by the X-ray material classifier algorithm and the organic stripping and inorganic stripping algorithm, and better detection rate and the miss rate are achieved. The detection rates of guns and knives are 96.5% and 95.8%, and the miss rates of guns and knives are 2.2% and 4.2%. The contraband detection technology based on the XMC R-CNN model is applied to X-ray baggage images of security inspection. According to user needs, the safe X-ray baggage images can be automatically filtered in some specific fields, which reduces the number of X-ray baggage images that security inspectors need to screen. The efficiency of security inspection is improved, and the labor intensity of security inspection is reduced. In addition, the security inspector can screen X-ray baggage images according to the boxes of automatic detection, which can improve the effect of security inspection.

scholarly journals Automatic detection of pharyngeal fricative in cleft palate speech

2018 ◽  
Vol 246 ◽  
pp. 03007
Author(s):  
Fei He ◽  
Geyi Zhou ◽  
Xinyi He ◽  
Heng Yin ◽  
Ling He
Keyword(s):  
Cleft Palate ◽  
Nearest Neighbor ◽  
Automatic Detection ◽  
Acoustic Parameter ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Acoustic Feature ◽  
Nearest Neighbor Classifier ◽  
Cleft Palate Speech ◽  
Neighbor Classifier

Pharyngeal fricative occurs during the production of consonants, which makes the consonants lose or weaken in cleft palate speech. In clinical application, the automatic detection of pharyngeal fricative in cleft palate speech could provide objective and effective assistant aids for speech language pathologists. In this paper, a novel acoustic parameter is proposed to detect the existence of pharyngeal fricative in cleft palate speech. This proposed acoustic feature ICPD (Independent Consonant Prominent Distribution) reflects the movement of mouth and tongue. The experimental results show that normal fricative has the higher ICPD. The extracted ICPD feature is combined with k-nearest neighbor classifier to achieve the automatic detection of pharyngeal fricative. The proposed system is tested on 127 speech samples recorded by cleft palate patients and 94 by normal speakers of controls. The overall pharyngeal fricative detection accuracy is around 90%.

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