Simulation Analysis on Polyurethane Coating of Wind Blade Abstract The distribution regularities of stress and displacement in the interior and on the surface of polyurethane coating are analyzed by the software of finite element analysis with the condition that the polyurethane coating of wind blade is impacted by the sand particle with different velocities and angles. The analysis results indicate that at the same impacting angle, the coating produces plastic yield more easily when the velocity of sand particle is faster and the range of the yield area is connected with the contacting time; plastic deformation wear and cutting wear on the surface of polyurethane coating are concurrent but have different effects; the sand particle impacting the coating can produce large tensile stress on the surface of the coating which causes the coating cracks to occur easily; the shear stress is symmetrically distributed on both side of the contacting point, and the coating at the contacting points lower left and lower right can be easily damaged by the shear.

Software Reliability Growth Model Considering First-Step and Second-Step Fault Dependency Abstract As one of the most important indexes to evaluate the quality of software, software reliability experiences an increasing development in recent years. We investigate a software reliability growth model (SRGM). The application of this model is to predict the occurrence of the software faults based on the non-homogeneous Poisson process (NHPP). Unlike the independent assumptions in other models, we consider fault dependency. The testing faults are divided into three classes in this model: leading faults, first-step dependent faults and second-step dependent faults. The leading faults occurring independently follow an NHPP, while the first-step dependent faults only become detectable after the related leading faults are detected. The second-step dependent faults can only be detected after the related first-step dependent faults are detected. Then, the combined model is built on the basis of the three sub-processes. Finally, an illustration based on real dataset is presented to verify the proposed model.

Optimization of Demosaicing Algorithm for Autofluorescence Imaging System Abstract Autofluorescence imaging (AFI) systems are widely used in the detection of precancerous lesions. Fluorescence images of precancerous tissue are usually red (R) or blue (B), so this kind of system has high requirement for colour recovery, especially in R and B channels. Besides, AFI system requires bulk data transmission with no time delay. Existing colour recovery algorithms focus more on green (G) channel, overlooking R and B channels. Although the state-of-art demosaicing algorithms can perform well in colour recovery, they often have high computational cost and high hardware requirements. We propose an efficient interpolation algorithm with low complexity to solve the problem. When calculating R and B channel values, we innovatively propose the diagonal direction to select the interpolation direction, and apply colour difference law to make full use of the correlation between colour channels. The experimental results show that the peak signal-to-noise ratios (PSNRs) of G, R and B channels reach 37.54, 37.40 and 38.22 dB, respectively, which shows good performance in recovery of R and B channels. In conclusion, the algorithm proposed in this paper can be used as an alternative to the existing demosaicing algorithms for AFI system.

Multi-Criteria Decision Making Based on Correlation Coefficient of Triangular Intuitionistic Fuzzy Numbers Abstract We study a multi-criteria fuzzy decision-making method based on weighted triangular intuitionistic fuzzy number correlation coefficients. Under the scenario that criteria weights for alternatives are completely unknown, triangular intuitionistic fuzzy method can not only supplement the insufficiency of the method based on the distance but also endow more information to the estimation and reduce the loss of evaluation information. Among the triangular numbers, two boundary numbers are the maximum and minimum values of the interval respectively, and the medium number is the most possible value under subjective estimation. Using this method, we propose a new way to obtain the criteria weights with more information quantity. By ranking the relative closeness of the weighted correlation coefficients between each alternative, and the critical and ideal alternatives, we show the method to figure out the most suitable alternative based on the expected criteria. An illustrative example is also taken into account to prove the effectiveness of the model.

Hierarchy-Based Adaptive Generalized Predictive Control for Aerial Grasping of a Quadrotor Manipulator Abstract In this paper, an adaptive generalized predictive control (GPC) based on hierarchical control strategy is designed for a quadrotor with a robotic arm. For this nonlinear and coupled system, a two-layer control structure is adopted to achieve more precise trajectory tracking and keep the tracking performance after aerial grasping. The inner-layer controller is a proportional-derivative (PD) controller. The outer-layer subsystem is linearized by input-output linearization first and an adaptive generalized predictive controller is applied. The effectiveness of this approach is verified through the simulation using MATLAB/Simulink. A PD controller with feedforward control input is applied on such a system for a comparative study. Simulation results show that a better tracking performance can be achieved by the proposed strategy.

Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering Abstract In the field of magnetic tile surface detection, artificial detection efficiency is low, and the traditional image segmentation algorithm cannot show good performance when the gray scale of the magnetic tile itself is small, or the image is affected by uneven illumination. In view of these questions, this paper puts forward a new clustering segmentation algorithm based on texture feature. This algorithm uses Gabor function spectra to represent magnetic tile surface texture and then uses a user-defined local product coefficient to modify Gabor energy spectra to get the center number of fuzzy C-means (FCM) clustering. Moreover, the user-defined Gabor energy spectra image is segmented by clustering algorithm. Finally, it extracts the magnetic tile surface defects according to the changes of regional gray characteristics. Experiments show that the algorithm effectively overcomes the noise interference and makes a good performance on accuracy and robustness, which can effectively detect crack, damage, pit and other defects on the magnetic tile surface.

Simulation on Remanufacturing Cost by Considering Quality Grade of Returns and Buffer Capacity Abstract Selecting the remanufacturing system as object of the study, the buffer capacity is served as control variable, and design of experiment (DOE) and simulation are used to analyze the effect that uncertain quality of returns acts on system performance. The remanufacturing time and the recovery rate in each station are used to represent the quality level of the returns, and the variance of remanufacturing time is used to denote the variability of returns’ quality. Three factors (the variability, the proportion and the recovery rate) of different quality levels in returns are considered. By analyzing the variance and the range of the simulation results, some important conclusions are obtained: recovery rate affects the remanufacturing cost by far, and the variability has the minimum influence; furthermore, for the returns, the more obvious of the dispersion degree, the higher proportion of the high-level quality, and the higher of the recovery rate, the lower the cost of remanufacturing will be.

Automatic Detection of Lung Nodules Using 3D Deep Convolutional Neural Networks Abstract Lung cancer is the leading cause of cancer deaths worldwide. Accurate early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules, the potential precursors to lung cancer, is paramount. In this paper, a computer-aided lung nodule detection system using 3D deep convolutional neural networks (CNNs) is developed. The first multi-scale 11-layer 3D fully convolutional neural network (FCN) is used for screening all lung nodule candidates. Considering relative small sizes of lung nodules and limited memory, the input of the FCN consists of 3D image patches rather than of whole images. The candidates are further classified in the second CNN to get the final result. The proposed method achieves high performance in the LUNA16 challenge and demonstrates the effectiveness of using 3D deep CNNs for lung nodule detection.

Design of Micropipette System with High Precision for Small Enzyme Immunoassay Analyzer Abstract A small auto micropipette system is developed to improve the reliability and accuracy of the automatic enzyme immunoassay analyzer's microscale pipetting system. A sophisticated injection mechanism is designed by the means of dislocation parallel distribution of the screw and injector piston rod. It possesses the function of pipetting, taking and removing the pipette tips. In the control system, STM32 controller is used, controlling the single-axis S-type acceleration/deceleration algorithm and multi-threaded coordinated motion. The acceleration/deceleration curves are analyzed and optimized by using the method of segmentation; a minimum injection rate of 1 μL and a step rate of 0.05 μL are realized. The method of digital image processing is used to detect the amount of pipetting in micro-pipetting quantitatively. The liquid area is extracted by background contrast method, and the liquid volume in the tip is obtained by combining the geometric characteristics of the disposable tip, when the pipetting capacity is not qualified to carry out specific guidance on the pipetting system, and avoid the blocking needle, bubble and other abnormal pipetting phenomenon on the impact of pipetting accuracy. The experimental results show that the combination of the automatic sampling system and the image flow detection system can effectively improve the precision and reliability of the micropipetting system. Finally, the injection accuracy of the system at the test points with 10, 50 and 100 μL liquid volumes reaches 1.8%, 1.28% and 1.15% respectively.

Optimum Consecutive Preventive Maintenance Scheduling Model Considering Reliability Abstract As an effective means for improving system condition and reducing failures, a reasonable preventive maintenance scheduling guarantees the stability and safety of the system. This paper studies a consecutive maintenance scheduling problem for single-component systems with imperfect maintenance, and an optimum model with minimum reliability constraint for minimal cost rate is developed based on the renewal theory. The age-based and reliability-based maintenance strategies are modeled and compared according to a numerical example based on the degradation data of the actual system, which also verifies the optimality of the reliability-based strategy. At last, the influence of some key parameters is discussed by sensitive analysis.