Machine Vision Detection Method for Surface Defects of Automobile Stamping Parts

A new system for detecting surface defects of automobile stamping parts is designed. Set up a defect detection system, which includes light source, camera and lens. Through this system, auto body surface image is collected, defect data set is established and manual annotation is performed. The marked training set was used to train the convolutional neural network, and then the convolutional neural network was used to segment the surface defects at the pixel level to obtain the defect regions of different categories. The experimental results show that the surface defect detection method proposed in this paper can effectively segment the surface defects of the workpiece, and the accuracy of scratch and rust segmentation is high

Pointer Temperature and Humidity Meter Detection Based on Machine Vision

A new method is proposed to achieve the accurate segmentation and reading of different types of pointer temperature and humidity instruments. The Canny edge detection operator is applied to detect edges of the meter gray image, and the scale line distribution arc and scale lines is obtained according to distribution characteristics of the scale line. The pointer is fitted by concentric method within the radius of the arc. OCR identifies the scale value of the dial, and then determines the corresponding reading number of each scale line and dividing value by mapping relation. Three different types of meters are used to detect, and experiments results demonstrate that the proposed method can achieve accurate segmentation of pointer and scales, as well as accurately calculate the readings of instrument

Detection on the Fertility of Hatching Eggs Based on Heart Rate Threshold

The fertility detection and classification of hatching eggs is extremely significant in the production of Avian influenza vaccine. A novel method by setting rational heart rate threshold to solve the problem that it’s difficult to separate the dead eggs from the normal eggs during the incubation process is proposed in this paper, which is critical to ensure the quality of vaccine. The object of our research is the 9-day-later hatching eggs, which are divided into two types, namely fertile eggs and dead eggs. Firstly, we collect heartbeat signal of the 9-day-later hatching eggs by the method of PhotoPlethysmoGraphy(PPG). Secondly, in order to reduce noise interference, we design a butterworth high-pass filter to filter the collected signal and remove baseline drift. Finally, two classification algorithms based on heart rate threshold and frequency spectral amplitude threshold are designed to detect the fertility of hatching eggs from time domain and frequency domain respectively. The experimental results demonstrate that the method we proposed successfully achieved the goal of high detection accuracy of hatching eggs, which also indicate that our approach is feasible for classification of hatching eggs

Pointer Temperature and Humidity Meter Detection Based on Machine Vision

A new method is proposed to achieve the accurate segmentation and reading of different types of pointer temperature and humidity instruments. The Canny edge detection operator is applied to detect edges of the meter gray image, and the scale line distribution arc and scale lines is obtained according to distribution characteristics of the scale line. The pointer is fitted by concentric method within the radius of the arc. OCR identifies the scale value of the dial, and then determines the corresponding reading number of each scale line and dividing value by mapping relation. Three different types of meters are used to detect, and experiments results demonstrate that the proposed method can achieve accurate segmentation of pointer and scales, as well as accurately calculate the readings of instrument

Adaptive multi-view semi-supervised nonnegative matrix factorization

Multi-view clustering, which explores complementary information between multiple distinct feature sets, has received considerable attention. For accurate clustering, all data with the same label should be clustered together regardless of their multiple views. However, this is not guaranteed in existing approaches. To address this issue, we propose Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization (AMVNMF), which uses label information as hard constraints to ensure data with same label are clustered together, so that the discriminating power of new representations are enhanced. Besides, AMVNMF provides a viable solution to learn the weight of each view adaptively with only a single parameter. Using L2,1 -norm, AMVNMF is also robust to noises and outliers. We further develop an efficient iterative algorithm for solving the optimization problem. Experiments carried out on five well-known datasets have demonstrated the effectiveness of AMVNMF in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information

Laser-interferometer gravitational-wave optical-spring detectors

Using a quantum mechanical approach, we show that in a gravitational-wave interferometer composed of arm cavities and a signal recycling cavity, e.g., the LIGO-II configuration, the radiation-pressure force acting on the mirrors not only disturbs the motion of the free masses randomly due to quantum fluctuations, but also and more fundamentally, makes them respond to forces as though they were connected to an (optical) spring with a specific rigidity. This oscillatory response gives rise to a much richer dynamics than previously known, which enhances the possibilities for reshaping the LIGO-II's noise curves. However, the optical-mechanical system is dynamically unstable and an appropriate control system must be introduced to quench the instability.Comment: 7 pages, 3 figures; to appear in the Proceedings of 4th Edoardo Amaldi Conference on Gravitational Waves, Perth, Australia, 8-13 July 200

Quantum noise in laser-interferometer gravitational-wave detectors with a heterodyne readout scheme

We analyze and discuss the quantum noise in signal-recycled laser interferometer gravitational-wave detectors, such as Advanced LIGO, using a heterodyne readout scheme and taking into account the optomechanical dynamics. Contrary to homodyne detection, a heterodyne readout scheme can simultaneously measure more than one quadrature of the output field, providing an additional way of optimizing the interferometer sensitivity, but at the price of additional noise. Our analysis provides the framework needed to evaluate whether a homodyne or heterodyne readout scheme is more optimal for second generation interferometers from an astrophysical point of view. As a more theoretical outcome of our analysis, we show that as a consequence of the Heisenberg uncertainty principle the heterodyne scheme cannot convert conventional interferometers into (broadband) quantum non-demolition interferometers.Comment: 16 pages, 8 figure

Quantum noise in second generation, signal-recycled laser interferometric gravitational-wave detectors

It has long been thought that the sensitivity of laser interferometric gravitational-wave detectors is limited by the free-mass standard quantum limit, unless radical redesigns of the interferometers or modifications of their input/output optics are introduced. Within a fully quantum-mechanical approach we show that in a second-generation interferometer composed of arm cavities and a signal recycling cavity, e.g., the LIGO-II configuration, (i) quantum shot noise and quantum radiation-pressure-fluctuation noise are dynamically correlated, (ii) the noise curve exhibits two resonant dips, (iii) the Standard Quantum Limit can be beaten by a factor of 2, over a frequency range \Delta f/f \sim 1, but at the price of increasing noise at lower frequencies.Comment: 35 pages, 9 figures; few misprints corrected and some references adde

Spectroscopic identification of active sites of oxygen-doped carbon for selective oxygen reduction to hydrogen peroxide

The electrochemical synthesis of hydrogen peroxide (H2O2) via a two-electron (2 e−) oxygen reduction reaction (ORR) process provides a promising alternative to replace the energy-intensive anthraquinone process. Herein, we develop a facile template-protected strategy to synthesize a highly active quinone-rich porous carbon catalyst for H2O2 electrochemical production. The optimized PCC900 material exhibits remarkable activity and selectivity, of which the onset potential reaches 0.83 V vs. reversible hydrogen electrode in 0.1 M KOH and the H2O2 selectivity is over 95 % in a wide potential range. Comprehensive synchrotron-based near-edge X-ray absorption fine structure (NEXAFS) spectroscopy combined with electrocatalytic characterizations reveals the positive correlation between quinone content and 2 e− ORR performance. The effectiveness of chair-form quinone groups as the most efficient active sites is highlighted by the molecule-mimic strategy and theoretical analysis

Spectroscopic Identification of Active Sites of Oxygen-Doped Carbon for Selective Oxygen Reduction to Hydrogen Peroxide

The electrochemical synthesis of hydrogen peroxide (H2O2) via a two-electron (2 e−) oxygen reduction reaction (ORR) process provides a promising alternative to replace the energy-intensive anthraquinone process. Herein, we develop a facile template-protected strategy to synthesize a highly active quinone-rich porous carbon catalyst for H2O2 electrochemical production. The optimized PCC900 material exhibits remarkable activity and selectivity, of which the onset potential reaches 0.83 V vs. reversible hydrogen electrode in 0.1 M KOH and the H2O2 selectivity is over 95 % in a wide potential range. Comprehensive synchrotron-based near-edge X-ray absorption fine structure (NEXAFS) spectroscopy combined with electrocatalytic characterizations reveals the positive correlation between quinone content and 2 e− ORR performance. The effectiveness of chair-form quinone groups as the most efficient active sites is highlighted by the molecule-mimic strategy and theoretical analysis