Portable Structure Surface Crack Detection System Based on Android Platform

Cracks,potholes,and other defects often occur on infrastructure such as bridges,among which cracks are one of the most frequent defects.They have diverse shapes and are difficult to detect.Traditional manual inspection methods are inefficient and have low accuracy,while automated inspection machines are bulky and inconvenient to carry and use.Based on the shortcomings of existing detection technologies,this paper proposes a portable structural surface crack detection system based on the Android platform using a portable hand-held image acquisition device.The system captures cracks on the structure's surface and obtains high-definition crack images.Then,these images are transmitted to portable smartphone terminals through Wi-Fi.Next,the image is pre-processed using weighted averaging,grayscale linear transformation,and adaptive median filtering.Then,the improved Canny edge detection algorithm is applied to identify crack information,and the edge segmentation algorithm is used to determine the crack width.Finally,based on camera calibration,the pixels are converted into the length data required for actual measurement.The results show that the system is easy to operate,and it not only has crack storage and tracking functions,but also can effectively measure the crack width on the surface of components.The measurement accuracy of this system reaches the sub-pixel level,and in actual testing,compared with the crack width gauge,the maximum relative error does notexceed6.25%.

Modeling and Experimental Analysis of Roughness Effect on Ultrasonic Nondestructive Evaluation of Micro-crack

A high-precision evaluation of ultrasonic detection sensitivity for a micro-crack can be restricted by a corroded rough surface when the surface microtopography is of the same order of magnitude as the crack depth.In this study,a back-surface micro-crack is considered as a research target.A roughness-modified ultrasonic testing model for micro-cracks is established based on a multi-Gaussian beam model and the principle of phase-screen approximation.The echo signals of micro-cracks and noises corresponding to different rough front surfaces and rough back surfaces are obtained based on a reference reflector signal acquired from a two-dimensional simulation model.Further compari-son between the analytical and numerical models shows that the responses of micro-cracks under the effects of dif-ferent corroded rough surfaces can be accurately predicted.The numerical and analytical results show that the echo signal amplitude of the micro-crack decreases significantly with an increase in roughness,whereas the noise ampli-tude slightly increases.Moreover,the effect of the rough front surface on the echo signal of the micro-crack is greater than that of the rough back surface.When the root-mean-square(RMS)height of the surface microtopography is less than 15μm,the two rough surfaces have less influence on the echo signals detected by a focused transducer with a frequency of 5 MHz and diameter of 6 mm.A method for predicting and evaluating the detection accuracy of micro-cracks under different rough surfaces is proposed by combining the theoretical model and a finite element simulation.Then,a series of rough surface samples containing different micro-cracks are fabricated to experimentally validate the evaluation method.

Method to Detect Granary Storage Weight Based on the Janssen Model

This study proposes a new model of granary storage weight detection based on the Janssen model to satisfy the strategic requirements of granary storage quantity detection in China. The model theoretically elucidates the relationship between granary storage weight and bottom/side pressure. A new layout of pressure sensors along the inner and outer rings is also proposed to obtain the pressure value. The experimental results indicate that the detection error of the proposed model is significantly lower than 1% with respect to the low-cost detection system, and this effectively satisfies the actual requirement for real-time monitoring of granary storage quantity.

Analysis on the Characteristics of GPS-PWV during Heavy Rainfall in Huaihua Region

Using GPS precipitable water vapor( GPS-PWV) inverted based on the advanced ZHD model and localized T_m model,as well as hourly meteorological data from automatic weather station,the variation characteristics of atmospheric water vapor and evolution features of GPS-PWV during 14 heavy rainfall events at Huaihua in 2017 were analyzed. As the results shown,GPS-PWV could reveal the variation characteristics of atmospheric water vapor in Huaihua region well. The monthly change of precipitable water vapor-pressure( PWV-P) data pair was evident. The PWV appeared a lower value with a smaller range accompanied by a 14.75 hPa higher surface air pressure than that in summer when precipitation occurred during winter,which gradually increased with a lower surface air pressure while precipitation occurred during spring. In summer,the PWV rose to the annual peak value with the lowest surface air pressure under rainfall,and it scattered to low-value area in autumn. In 14 heavy rainfall events at Huaihua during flood season of 2017,all of the PWV values exceeded corresponding monthly mean,besides there was a well corresponded relationship between the maximum rainfall and the maximum PWV in hourly scale. Before the heavy rainfall occurred,the PWV increased comparatively distinctly with a clear decrease of the surface air pressure,and that could be a preferably reference point in the local strong precipitation nowcasting.

Data Classification Using Combination of Five Machine Learning Techniques

Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.

Figure of Merit Enhancement of Surface Plasmon Resonance Biosensor Using Ga-Doped Zinc Oxide in Near Infrared Range

This work presents a surface plasmon resonance biosensor for the figure of merit enhancement by using Ga-doped zinc oxide(GZO),i.e.,nanostructured transparent conducting oxide as plasmonic material in place of metal at the telecommunication wavelength.Two-dimentional graphene is used here as a biorecognition element(BRE)layer for stable and robust adsorption of biomolecules.This is possible due to stronger van der Waals forces between graphene’s hexagonal cells and carbon-like ring arrangement present in biomolecules.The proposed sensor shows improved biosensing due to fascinating electronic,optical,physical,and chemical properties of graphene.This work analyses the sensitivity,detection accuracy,and figure of merit for the GZO/graphene SPR sensor on using the dielectric layer in between the prism and GZO.The highest figure of merit of 366.7 RIU^(−1) is achieved for the proposed SPR biosensor on using the nanostructured GZO at the 3000 nm dielectric thickness.The proposed SPR biosensor can be used practically for sensing of larger size biomolecules with due availability of advanced techniques for the fabrication of the nanostructured GZO and graphene.