Research on a beef tenderness detection method using a bionic mastication system based on a pressure sensor

2017 ◽  
Vol 9 (32) ◽  
pp. 4695-4701 ◽  
Author(s):  
Xiaodan Wang ◽  
Hongmei Wang ◽  
Yingming Cai ◽  
Jiahui Jin ◽  
Lingtao Zhu ◽  
...  
Keyword(s):  
Pressure Sensor ◽  
Detection Method ◽  
High Accuracy ◽  
Meat Tenderness ◽  
Beef Tenderness ◽  
Novel Method

A novel method using bionic mastication system based on a pressure sensor was developed to predict beef tenderness with convenience, stability and high accuracy. What's more, this method can be applied to detect other meat tenderness such as those of chicken and pork as well, which indicates a universality of this method.

A Novel Method of Spike Detection for Interferograms Based on Two-Step Filtering

2014 ◽  
Vol 599-601 ◽  
pp. 1364-1368
Author(s):  
Yao Pu Zou ◽  
Chang Pei Han ◽  
Lei Zhang ◽  
Wen Gui Pan ◽  
Chao Wang
Keyword(s):  
Computer Simulation ◽  
Theoretical Analysis ◽  
Detection Method ◽  
High Accuracy ◽  
Spike Detection ◽  
Novel Method

According to the characteristics of interferogram, we design a new spike detection method, which firstly filters an interferogram with two different ways and then detects spikes based on both results. Theoretical analysis and computer simulation shows that this algorithm performs well in detecting spikes in any position of an interferogram with high accuracy, and can be easily implemented in hardware.

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

High-Accuracy Ultrasound Contrast Agent Detection Method for Diagnostic Ultrasound Imaging Systems

2015 ◽  
Vol 41 (12) ◽  
pp. 3120-3130 ◽  
Author(s):  
Koichi Ito ◽  
Kazumasa Noro ◽  
Yukari Yanagisawa ◽  
Maya Sakamoto ◽  
Shiro Mori ◽  
...  
Keyword(s):  
Contrast Agent ◽  
Ultrasound Imaging ◽  
Detection Method ◽  
Ultrasound Contrast Agent ◽  
Diagnostic Ultrasound ◽  
High Accuracy ◽  
Imaging Systems ◽  
Ultrasound Contrast

Measuring fiber orientations in nonwoven web images using corner detection by Bézier fitting curves

2017 ◽  
Vol 88 (18) ◽  
pp. 2120-2131 ◽  
Author(s):  
Jue Hou ◽  
Bugao Xu ◽  
Hanchao Gao ◽  
RongWu Wang
Keyword(s):  
Curve Fitting ◽  
Fiber Orientation ◽  
Euclidean Distance ◽  
High Accuracy ◽  
Corner Detection ◽  
Bézier Curve ◽  
Corner Points ◽  
Novel Method ◽  
Web Images ◽  
Orientation Statistics

This paper describes a novel method for measuring fiber orientations in nonwoven web images by using Bézier fitting curves to detect corners of fiber edges and to separate crossing fiber edges. First, the Canny detector was adopted to extract fiber edges. Second, Bézier curve fitting was used to fit each fiber edge for calculating the curvature of every point on the edge. Third, corner points were detected by locating points where the curvatures were minimal on various edges and below the threshold to divide edges into segments for orientation calculations. Last, a formula calculating the fiber orientation statistics based on the Euclidean distance was established. The experiment results demonstrated that the proposed method is robust for analyzing different nonwoven web images, and has a high accuracy for corner detection and fiber orientation calculation.

scholarly journals High Accuracy MEMS Pressure Sensor Based on Quartz Crystal Resonator

Proceedings ◽  
2017 ◽  
Vol 1 (4) ◽  
pp. 379 ◽  
Author(s):  
J. Wang ◽  
C. Zhao ◽  
D. X. Han ◽  
X. F. Jin ◽  
S. M. Zhang ◽  
...  
Keyword(s):  
Pressure Sensor ◽  
Quartz Crystal ◽  
High Accuracy ◽  
Quartz Crystal Resonator ◽  
Crystal Resonator

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

scholarly journals Design Optimization and Fabrication of a Novel Structural SOI Piezoresistive Pressure Sensor with High Accuracy

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 439 ◽  
Author(s):  
Chuang Li ◽  
Francisco Cordovilla ◽  
R. Jagdheesh ◽  
José Ocaña
Keyword(s):  
Design Optimization ◽  
Pressure Sensor ◽  
High Accuracy ◽  
Piezoresistive Pressure Sensor
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