Abstract:Due to the advantages of noncontact and high efficiency, the phase shift profile (PSP) method based on structured light has been widely applied. However, the pixel value of the fringe captured by the industrial camera is distorted due to the gamma effect. It may result in the error of the calculated phase value and reduce the accuracy of measurement. To solve this problem, the mathematical model of phase error is analyzed and studied further. Then, based on the improved model, the projection scheme of the fringe image is designed. The value of shifted phase of the original fringe is π/2, and the mean phase of the original fringe and the phaseshifted fringe is utilized as the final phase. In this way, the purpose of phase compensation can be achieved. Finally, the number of fringe projections is improved. Experimental results show that the proposed method does not need to calculate the distortion value γ. After compensation, the root mean square (RMS) phase error is decreased by 457%.

Abstract:With the development of highprecision fluxmeters, the limitations of existing fluxmeter calibration methods have become increasingly prominent. Aiming at the deficiencies of existing mutual inductance methods and standard coil methods, a fluxmeter calibration device based on voltsecond method is designed. The device adopts a lownoise circuit architecture and uses the FPGA to control the highspeed DAC to output standard magnetic flux. Through testing and analyzing the transient process of voltage, pulse width and level changes of the voltsecond generator, the relative expansion uncertainty of the generated 1 mWb~10 Wb standard magnetic flux is better than 001%. In the cases where the voltsecond products are the same, the larger the output voltage pulse amplitude and the smaller the pulse width, the smaller the relative expansion uncertainty of the output standard magnetic flux will be. However, in calibration process, the voltsecond output combination should be selected reasonably according to the integral response characteristic of the calibrated fluxmeter. The proposed method and traditional mutual inductance method, standard coil method were used to calibrate the same fluxmeter and the results were compared. The results show that the accuracy of the voltsecond method is better than that of the traditional standard coil method. The calibration range of the voltsecond method is better than that of the traditional mutual inductance method, its calibration range reaches 1 mWb~10 Wb, which can meet the calibration requirements of the fluxmeters of class 01 and below.

Abstract:Aiming at the damage phenomena of length standard in ultrasonic cleaning process, the factors influencing the ultrasonic cavitation erosion effect are studied. According to RayleighPlesset equation, the evolution curve of bubble radius is solved, and the maximum temperature and normal stress response in the bubble for the first collapse of the cavitation bubble under different initial radius, frequency and temperature of the gas nucleus are obtained, and then the damage reason of the length standard is analyzed. A new method of deterioration is proposed to study the effects of ultrasonic frequency, temperature and ultrasonic radiation time on the decontamination effects and corrosion stages of the length standard. The experiment results show that the cleaning process of the length standard is divided into three stages: cavitation cleaning stage, cavitation erosion incubation stage and cavitation erosion acceleration stage. In the process of removing oil pollution, the effect of cavitation erosion is very small. When the solution temperature is 60℃, the ultrasonic frequency is 28 kHz and the radiation time is 1 minute, the cleaning effect will be better.

Abstract:In this paper, a high precision absolute timegrating angular displacement sensor using division multiplex access scheme with reflective electrode structure is designed. Based on the incremental timegrating sensor, the singlepole and multipole are combined. The multipole is used as the precise measurement component for high precision, and the singlepole is used as the rough measurement component that achieves absolute positioning. The timedivision multiplex access scheme with reflective electrode structure is proposed. The rough measurement component and the precise measurement component share a set of reflective electrodes and receiving electrodes. In this way, the structure is more compact and better miniaturization. Meanwhile, the rotor does not need lead wires and has more application scenarios. To eliminate the crosstalk between the rough and the precise measurement components, the excitation signal is added to the precise measurement component and the rough measurement component in different time, and grounding the nonworking electrode. A sensor prototype with the 60 mm outer diameter and the 26 mm inner diameter is designed using PCB technology. Through theoretical analysis and structural optimization, experimental results show that the measurement accuracy can achieve ±12″.

Abstract:In order to achieve fast and accurate acoustic power measurement of the focused sound field, a novel acoustic power measurement method based on PVDF pyroelectric sensor is proposed in this study. Firstly, a finite element model is established based on the acoustic power measurement principle of pyroelectric sensor. The simulation analysis is conducted on the influencing factors of acoustic power measurement. Then, a test system with PVDF pyroelectric sensor is established to carry out the study of the acoustic power measurement method. The results show that the proposed measurement method could be applied in both the acoustic field focal region and nonacoustic field focal region, and the output signal and radiation acoustic power show a linear relationship. The maximum measurement error is 82%, which is in accordance with the acoustic metrological requirement. The comparison analysis results of simulation and experiment verify the reliability and accuracy of the proposed method.

Abstract:The stability and steady speed of the calibration light source directly affect the accuracy and efficiency of spectral radiation calibration of the onboard highresolution spectrometer. To realize the rapidness and stabilization of calibration light source, a fast stabilization regulation method and a system of constant current source based on digital process control are proposed and designed. Based on the constant current source of field effect tube, the rapid constant current source is added with digital process control link to make up for the shortage of feedback oscillation regulation of field effect tube constant current source. The long thermal stabilization time of the system can also be improved. The adjustment method of rapid stabilization can be divided into three stages, including large preset value starting fast preheating to produce heat quickly, buffer fast stabilization to reduce the overshoot oscillation and constant current of simulated closedloop to output the stable current. Comparison experiments between the fast stable constant current source and the field effect tube constant current source show that the time deceases from 670 s to 66s for outputting a current of 1 50000 mA and ripple coefficient 1 decreases from 670s to 66 s. In addition, as the drive circuit of the halogen tungsten lamp of standard light source, the fullspectrum mean baseline drift of the 400 nm~2 500 nm data reduces from 05% to 006% under the output spectrum of the standard light source for 20 minutes after being electrified for 2 minutes.

Abstract:In order to solve the problem of poor linearity of laminar flow meter, a micro gaptype laminar flow meter is proposed. The measurement principle, structure design and parameter calculation of the laminar flow meter are introduced in this paper. A gaptype LFM with a designed flow range of 0～280 mL/min was tested, the used standard equipment was a standard piston device. In the test the combined uncertainty is 012% and the extended uncertainty is 024% (k=2). The test result shows that under the premise of not using any correction factor the maximum reference error of the tested device is -090%, which means that the LFM under test reaches the designed specification: accuracy of 10 class with a flow range of 10∶1. In addition, the tested LFM has a good linearity, and the value Remax de/l is 342 that is greater than the traditional required value of 2～25, which indicates that this structure design can effectively overcome the nonlinear influence caused by sudden expansion and sudden contraction.

Abstract:Currently, the power supply of wireless capsule endoscopy (WCE) is poor, which limits the detection performance for gastrointestinal disease focus. A hollow 3D receiving coil is proposed, in which other modules of the WCE could be placed and the volume of the WCE is reduced further. A single turn analysis method is proposed according to the electromagnetic field theory. The induced electromotive force (EMF) and receiving power of the coil are analyzed. The uniformity performance index is proposed and the optimization model is established. The optimal design parameters of the coil is obtained. An angle attitude rotation platform was built to measure the EMF of the coil. The tested error between the measured EMF and its theoretical value is less than 6%, which verifies the rationality of the single turn analysis method and the coil design. The WCE power supplied with the designed coil was implanted into the intestine of a pig in vivo. The obtained image transmission rate is stably at 30 f/s with resolution of 400*400. The novel coil can provide sufficient and stable power, which is used for the WCE to implement gastrointestinal disease detection. The single turn analysis method can not only be applied to the designed coil, but also be applied to the power analysis of other implantable medical devices with altering relative position and angle between transmitter and receiver. The proposed method can also be used to solve the electromagnetic parameters, such as mutual inductance between transmitting and receiving coils.

Abstract:3D LiDARs and cameras are commonly used in the fields of autonomous vehicles, mobile robots and etc. To fuse the complementary information of both, extrinsic calibration is the prerequisite and foundation. Based on the motion characteristics of planar mobile robot, a twostage extrinsic parameter calibration method is proposed in this paper, which can quickly calibrate the extrinsic parameters with the manual intervention as little as possible. In the first stage, the LiDAR odometry and visual odometry are simultaneously estimated in the robot motion process. Then with the extracted translations and rotations, a coarse estimation of the relative rotation is found by using modified Kabsch algorithm.In the second stage, the trihedron formed by three planes of a box is utilized as the marker.Precise extrinsic parameters are obtained by optimizing the distances of projections of points on trihedron edges in the point cloud to corresponding trihedron edges in the image. This efficient algorithm features just less manual intervention for the calibration without any specialized equipments.Finally, the experiment results show that the mean errors on three coordinate axes are only 367, 111 and 078 mm, and the corresponding mean calibration time on the typical mobile computing platform is only about 635 ms.

Abstract:Aiming at the instability problem of target tracking caused by scale change and severe apparent change in target tracking, in this paper a multichannel feature fused scale estimation strategy is designed, and a multichannel scale adaptive target tracking algorithm based on double correlation filter is proposed. Considering that the features of CN are insensitive to attitude and scale, and the features of HOG have good stability to illumination change and target movement, the features of CN, HOG and gray scale are fused to improve the tracking robustness to target apparent change. On the premise of ensuring minimum error risk, the ridge regression is used to solve the filter. The scale filter is established to realize the multiscale judgment of the target, so that when the target scale changes the tracking of the target remains stable. The TB100 data set was used to test the performance of the algorithm in multiple scenarios. The experiment results show that the algorithm has good tracking effect under the conditions of target apparent change, scale transformation and background interference.

Abstract:The longitudinal displacement of the seamless rail is the longitudinal movement of the rail along the top surface of the track panel or road bed caused by the internal longitudinal stress of the rail. The timely and highprecision measurement of the longitudinal displacement is of great significance for the safe operation of the highspeed railway. In this paper, a method for measuring the longitudinal displacement of seamless rail based on binocular vision technology is proposed. This convenient and efficient noncontact measurement solution consists of a reference target deployed on the trackside fixed facility, a coded mark to be measured deployed on the rail waist and a binocular camera. A coding feature point detection algorithm based on doubleline constraint is proposed, which focuses on solving the problem of accurate detection and matching of coded feature points in complex field acquisition environment. The threedimensional distance measurement algorithm of chain binocular camera is proposed. The highprecision measurement of the longitudinal displacement of seamless rail is achieved with accurate threedimensional reconstruction. The indoor simulation experiment and outdoor field experiment verify the effectiveness of the proposed method. The measurement accuracy of the rail longitudinal displacement in outdoor field experiment reaches 02 mm.

Abstract:Aiming at the problem of poor detection accuracy for moving targets under jitter interference, a moving target detection algorithm based on optical flow method and threeframe difference method is proposed. Firstly the image stabilization algorithm based on LK (LucasKanade) optical flow method is used to dejitter the video, then the threeframe difference method is used to extract the target. Simulation results show that the PSNR (Peak Signal to Noise Ratio) value is increased by 36 dB after image stabilization, and the designed algorithm can accurately extract the target under jitter interference, the average processing speed on the test platform is 28 fps. At the same time, aiming at the problem that traditional KCF (Kernelized Correlation Filter) algorithm has poor tracking performance for scalechanging and partially occluded targets, an improved KCF algorithm is designed, which constructs the image pyramid of the target, then calculates the filter response on different layers of the image pyramid, finds the layer with largest response and updates the target location of next frame. Meanwhile, an occlusion detection mechanism is introduced in the algorithm, which reduces the impact of target occlusion on tracking. Simulation results indicate that the improved algorithm has stronger robustness to the scalechanging and partially occluded targets, and can achieve stable target tracking, the processing speed of the algorithm is 33 fps. Compared with the KCF algorithm, the precision of the proposed algorithm is increased by 42% and the success rate is increased by 118%.

Abstract:Most existing 3D ear recognition methods are based on the iterative closet point (ICP) algorithm that requires large amount of calculation and long matching time. It is easy to fall into the local optimum problem. Meanwhile, the ear region used for registration contains much redundant information. To solve these problems, a fast 3D ear recognition method is proposed in this study, which is based on the local and global information. Firstly, key points are detected according to internal shape features. In this way, the normalization of the ear region is achieved. Secondly, the lowdimensional local features are extracted to realize key points match. The candidate list is obtained, and the registration process is realized by fast point feature histogram and improved ICP algorithm with the normal vector of the point cloud. The proposed algorithm is evaluated by using the UNDJ2 database. Experimental results show that the key point feature extraction only takes 0026 s, and it takes 0015 s for key point match. The identification experiments show that Rankone recognition rate can reach 9855%, which shows that this method has faster recognition speed and higher recognition rate than other stateoftheart algorithms.

Abstract:For the type of pleural connected pulmonary nodule, the segmentation accuracy is low due to fuzzy boundary, intensity inhomogeneity and low contrast between the pleural part and pulmonary nodule part. To solve this problem, an improved active contour model is proposed. First, the eigenvector with the combination of wavelet energy and local binary mode (LBP) is constructed to enhance the dissimilarities between pulmonary nodule and other tissues, such as pulmonary parenchyma and pleura. Secondly, the membership degree of robust speed function is calculated by the fuzzy Knearest neighbor method which combines wavelet energy feature and LBP feature. Thirdly, the robust speed function is introduced into the active contour model. At the boundary of the pulmonary nodule, the robust speed function is close to 0. The evolution of the active contour curve stops, and the segmentation results of pleural connected pulmonary nodule are obtained. Experimental results show that the proposed model has a true positive rate of 090, a false positive rate of 006 and a similarity of 085. These results are very close to the ideal result of manual segmentation by clinical experts.

Abstract:The state of charge (SOC) estimation accuracy for the lithiumion battery depends heavily on the battery model. Therefore, a combined model (Shepherdbased combined model, SCM) based on the improved Shepherd model coupled with temperature and cycle numbers is proposed. The thermal modeling and cyclic loss modeling of parameters, such as open circuit voltage, polarization constant, available capacity, and internal resistance in the Shepherd model, are considered in this paper. The identification method for model parameters is simplified to a nonlinear least squares method that only requires two sets of discharge experimental data at different temperatures. The discharge experiment is implemented by using lithiumion batteries of different cycle numbers and the actual working conditions of the electric vehicle are simulated by setting the lithiumion battery at different temperatures. The SOC dynamic estimations of SCM model and ECM model are implemented by using the extended Kalman filter algorithm. Simulation and experimental results show that the relative error of the proposed model is less than 15% and the SOC estimation error is less than 3%. The superiority of the proposed model is verified.

Abstract:Aiming at the problem that robot calibration is highly configuration dependent, considering the correlation of the approximation degree of vectors versus the distances and angles among different position vectors at the end of the robot, as well as the triangular area formed in space, the concept and measurement method of the approximation degree of robot pose and pose error vector are introduced, and the discriminating rules and using conditions based on the approximation degree are formulated. Furthermore, the database samples in the neighborhood of the target pose are screened. On this basis, a robot accuracy compensation method based approximation degree weighted average interpolation is proposed. The sample point pose and error data screened in the workspace are used to predict the interpolation values of the pose error of the target point. The example study results show that the proposed method can effectively improve the spatial adaptability of the robot pose error compensation and meet the requirements in highend field.

Abstract:In this paper, an improved elitist clonal selection algorithm (ECSA) is proposed to realize the automatic knot adjustment of the Bspline curve approximation. In order to improve the search efficiency and solution quality of the algorithm, an adaptive chaotic mutation operator is designed, and an antibody reselection strategy based on the antibody concentration and antigen affinity vectorial moment is proposed. Then Bayesian Information Criterion (BIC) is used as the affinity metric to weigh and judge the goodness of fitting and computational complexity. Further, the improved algorithm achieves a balance between depth search and breadth optimization, and can automatically and accurately calculate the number and locations of internal knots, thus the Bspline curve approximation of the data points is completed. Simulation and experiment results show that the proposed algorithm not only can efficiently and accurately realize the automatic Bspline curve approximation of the noisy complex data with the features of continuity, discontinuity and cusps, but also possesses better global convergence and convergence speed compared with current researches.

Abstract:To ensure the safety of the human occupied vehicle (HOV), an acoustic localization algorithm is proposed based on the virtual moving long baseline (VMLBL) technique. The influence factors on positioning error are analyzed. Instead of deploying transponders on the seafloor in advance, a set of manoeuvres are conducted to localize the HOV. First, based on the property of the innovation, an improved Kalman filter algorithm is used to perform outlier rejection and information correction for the range information. In this way, the influence of outlier can be overcome. Secondly, the VMLBL is constructed by fusing the moving radius vector of the HOV and the range information between the HOV and the ship. Then, the initial position results are calculated by the least squares method. To obtain more accurate position results, a cascade Kalman filter is utilized for trajectory smoothing and the final positioning result can be achieved. Simulations and sea trial data processing results show that the effective rate of data can reach 951% with positioning error less than 10%. The final positioning results are consistent with those of the ultrashort baseline positioning system. Thus, the proposed algorithm can provide an effective auxiliary way to localize the HOV.

Abstract:Greenhouse effect results in the global warming, and huge carbon emissions make the earth overwhelmed. How to reduce carbon emissions has become an urgent problem to be addressed. When the sum of wind power output and thermal power unit minimum output is larger than the load, the power balance can only be achieved through energy storage, heat storage device or abandoned wind. It can be defined as low load operating condition. During the low load period, it is difficult to absorb wind power and high energy storage cost. Firstly, the influence of deep regulation of thermal power units on generation cost and carbon emissions is considered. In this way, a stage output model of thermal power components is formulated. Carbon trading mechanism is introduced into the dispatching model of the system, and a stepbystep carbon trading cost calculation model can be formulated. Secondly, the minimum carbon emissions and generation costs are selected as the optimization objectives. Many constraints of the system are considered comprehensively. A lowcarbon multisource coordinated dispatching model with low load scenarios based on multiobjective is formulated. Then, the improved firefly algorithm is used to achieve the optimal dispatching scheme. Finally, taking a system with 10 wind farms as an example, three different experiments show that the proposed method can effectively reduce carbon emissions and improve the operating economy of the system. The influence of wind power gridconnected permeability on system operating mode is also analyzed, it shows that lowcarbon multisource coordinated dispatch in the lowload scenario is closely related to the penetration of wind power gridconnected.

Abstract:In order to meet the diversity requirements of mobile robot interaction control and improve the speech control performance of mobile robot, a speechbased mobile robot control system is designed. Through analyzing the control signal transmission process and speech signal noise source of the robot in using environment, the composition scheme of the mobile robot speech control system is made up. The main flow for the implementation of front end speech recognition section is given. And the dereverberation algorithm for speech enhancement is emphatically designed. By fully utilizing the potential spectral features of speech, a dereverberation algorithm is proposed based on combined nonnegative matrix factorization and deep neural network. Firstly, the speech signal features are obtained through matrix decomposition, and then the feature vector is generated to train the activation function, which reduces the training complexity of the deep neural network model. The comparison analysis shows that the proposed algorithm possesses superiority in solving the speech reverberation problem. The control software was written, which was embedded in the speech recognition algorithm. A speech control platform of industrial mobile robot was built to verify the effectiveness of the speech control system. In the reverberation environment, speech control experiments were conducted, in which different people performed multiple actions on the robot. The results show that the system can realize the speech control of mobile robots. The proposed speech recognition method can achieve the average correct execution rates of actions of 96%, 95% and 93%, respectively for the mobile robot under the reverberation conditions of 03, 06 and 09 s.

Abstract:Rotating machinery equipment fault detection and identification has always been a research hotspot. Aiming at the deficiency of current fault feature extraction and diagnosis methods, this study proposes a method based on improved complete ensemble empirical mode decomposition (ICEEMD) and adaptive whale optimization algorithm (AWOA) optimized extreme learning machine (ELM). The generation of pseudomodality can be avoided by ICEEMD during the decomposition process, and the residual noise in the mode is small. The extracted fault information is more accurate. ICEEMD is used to decompose the collected signals into intrinsic mode function (IMF). Through analyzing Spearman rank correlation coefficient (SRCC) among IMFs of rolling bearings in different fault states, the conclusion is that the IMF should be screened out when its SRCC is larger than 002. The hybrid entropy (HE) of the screened IMF is further calculated as feature vectors. Compared with other bionic algorithms, the whale optimization algorithm (WOA) has advantages of fewer related parameters to be adjusted, faster convergence speed, and better stability. AWOA improves the convergence accuracy further through optimizing WOA′s local search mode by adaptive weight. Through AWOA optimizing the weight and threshold of ELM, the accuracy of fault diagnosis is improved. Comparison experiments show that AWOAELM has strong learning ability and higher accuracy of fault diagnosis. The AWOAELM method is applied to the fault diagnosis of ball bearings and outer rings of rolling bearings with different sizes. The accuracy of ball fault diagnosis is 995%, and the accuracy of external loop fault diagnosis is 100%.

Abstract:Datadriven based fault detection method has become important means for the fault detection of practical industrial processes, however, in practical application it is often limited by the size of process historical data, so that it is difficult to achieve satisfactory fault detection accuracy. In this paper, aiming at this problem a sample space reconstruction strategy is proposed, which constructs the sample pairs of the same or different categories based on random sampling. While the data size is expanded, the strategy transforms complex classification modeling problem into the comparison problem of the similarity among the samples, which effectively reduces the complexity of the task and the amount of the data needed for modeling. Based on the reconstruction strategy, the siamese CNN is introduced and improved, a chemical industrial process fault detection method based on Multiscale Siamese Convolutional Neural Networks (Multiscale Siamese CNN) is proposed. The test results on the TennesseeEastman (TE) process dataset verify the effectiveness of the proposed algorithm. The test results show that the average fault detection accuracy of the proposed algorithm reaches 8966%, which is improved by 8% above compared with that of conventional datadriven fault detection algorithm.

Abstract:To meet the requirements of high stability precision control to an inertially stabilized platform (ISP), a single neuron/proportion integration differentiation (PID) adaptive compound control method based on the improved bacterial foraging optimization algorithm is proposed. Firstly, the single neuron and PID control are fused to formulate a single neuron/PID adaptive controller to realize the adaptive control of ISP. In this way, the control accuracy of the ISP is improved. Secondly, to solve the problem that the optimal parameters of the controller are hard to be achieved by the trial method, an improved bacterial foraging optimization algorithm is used to optimize the parameters of the compound controllers. Finally, simulations and experiments are carried out. Experimental results show that the proposed method can significantly improve the system performance such as stability accuracy and disturbance rejection ability. After utilizing the compound control with parameter optimization, the stabilization accuracy of the platform under the condition of static and dynamic base are 0003 8° and 0290 4°, which are 191% and 399% higher than the traditional PID control.

Abstract:To enhance precision control of piezoelectric actuator, this paper proposes an active disturbance rejection repetitive control (ADRRC) method. Firstly, the dynamic model of piezoelectric drive system is formulated. Then, an output feedback integral controller and a plugin repetitive controller are merged into the linear active disturbance rejection control (LADRC). One kind of ADRRC strategy is proposed, which has capabilities of step, ramp and periodic signal tracking/suppression. Furthermore, the stability of the closed loop system and the design method of the control system are analyzed by using the smallgain theorem. Finally, the proposed method is applied to the piezoelectric actuator system. Experimental results show that the proposed method can achieve better control performance than LADRC method. In addition, the method can track and reject many kinds of external signals with high precision.

Abstract:To address the problem of obstacle avoidance and path planning of intelligent robots in twodimensional static environment, a realtime obstacle avoidance algorithm based on BP neural network is proposed. Firstly, multiple sectors are used to represent the environment around the robot, and lidar is utilized to detect the distance information of obstacles in each sector. With the distance information of obstacles in each sector, BP neural network is used to calculate the score of the sector selected as obstacle avoidance direction. Then, the Euclidean distance between the midpoint coordinate of each sector and the midpoint coordinate of the closest sector to the obstacle at the current moment is used to calculate the conditional probability. Each sector is selected as the direction of obstacle avoidance under the current pose of the robot. Finally, the sector with the largest product of score and conditional probability is taken as the obstacle avoidance direction of the robot. Experimental results show that the convergence time of the proposed algorithm is 50% less than that of the grid method, and the obstacle avoidance trajectory of the robot is shorter than that of the artificial potential field method. It can be better applied to complex multiobstacle scenarios.

Abstract:For realtime prediction on device degradation, the existing algorithms are difficult to update the trained model and the prediction results are easy to be distorted due to the outlier and noise. To solve these problems, a novel method named as online sequential regularized correntropy criterion extreme learning machine (OSRCCELM) is proposed to generate high robust prediction model and provide dynamic updating ability. Firstly, based on the regularized correntropy criterion ELM, the updating method for the model is realized. Secondly, the outlier and noise are detected with the dynamic Mestimator that is integrated with the error codebook. Finally, the corresponding influence of the outlier and noise are removed from the current model. Experiments using simulated data and CTR of optical couples show that OSRCCELM can achieve higher prediction accuracy without the effect of outliers and noises than other methods while provide accurate prediction with high speed.

Abstract:Aiming at the difficulties of weld stress concentration and magnetic memory quantitative identification on weld latent defect levels, a dynamic fuzzy clustering model based on immune optimization algorithm is presented. The steel Q235 plate specimens with preformed incomplete penetration weld were used as the test materials, and the fatigue tensile experiments were carried out. By comparing with the Xray synchronous test results and quantitative standard, the metal magnetic memory(MMM) signal characteristic parameter vectors for different defect levels are extracted. Considering the fuzzy and uncertainty of MMM test data in critical state identification of different weld defect levels, a dynamic fuzzy clustering algorithm (DFCA) is introduced. By outputting the threshold λ, the initial fuzzy clustering classification is obtained. Furthermore, considering the problem that the DFCA is easy to fall into the local optimum, the immune algorithm with global search and parallel ability is used to optimize the DFCA to obtain an optimal threshold λ. Finally, the dynamic fuzzy clustering model based on immune optimization is established. The verification results show that the defect level prediction accuracy of the proposed model reaches 90%, which provides a new idea for the evaluation on the weld defect levels and the quantitative evaluation on the equipment safety in practical engineering.

Abstract:CO as the incomplete combustion product in flue gas of coalfired unit is an important basis for combustion efficiency monitoring, which has the characteristics of high concentration, strong fluctuation and uneven distribution. In this paper, the mixed sampling online measurement of CO concentration in flue gas is carried out using wavelength modulation spectroscopy in TDLAS technology combined with dilution method. Firstly, based on the harmonic theory, the measurement formulas of the fourth harmonic and first harmonic calibrationfree method were deduced. Secondly, the phase difference between frequency modulation and light intensity modulation, the current tuning coefficient of the CO laser were calibrated, and the optical path of Herriott cell was measured through standard gas combined with direct absorption method. On this basis, the curve of concentration versus harmonic ratio was numerically calculated and verified in experiment. Finally, the monitoring device was applied in a coalfired power plant. During installation, the outlet flues of the economizer were divided into six areas, and a dilution sampling probe was installed in each area. During the dilution sampling process, because the dew point of flue gas was reduced, no dewatering was needed. The diluted flue gases in the six channels were mixed and then entered into the Herriott cell to realize the calibrationfree and highprecision measurement. The monitoring device realized continuous and stable operation, and in the operation Testo350 portable analyzer was used to make measurement and the measurement results were compared with the measurement data obtained from the field measurement device. The comparison results show that the two measurement results are consistent and the error between them is less than 1×10-6.

Abstract:The acousticbased leakage localization error for gas pipelines using crosscorrelation analysis is large due to the error of acoustic speed and time delay estimation. A leakage localization method based on delayandsum is proposed in this study. Two acoustic sensors are deployed in two sides of the leakage position to obtain two kinds of acoustic speed by using delayandsum to estimate the time delay of the signal in each side. Then, the time delay between two sensors is determined by leakage position. The weight vector can be obtained by using timedelay expression to weight the output of linear array. Hence, localization can be determined by searching the peak of weighted output. Experimental results show that a more stable output can be achieved by using delayandsum to estimate the time delay of 37 kHz component. The delayandsum method has a mean error rate of 204% while the rate of the crosscorrelation method is 909%. The accuracy of the acoustic method can be improved by calculating acoustic speed and localizing based on delayandsum with linear array.

Abstract:The antiskid valve is a key part in the antiskid braking system. At present, the comprehensive research on the dynamic characteristics of antiskid valves is relatively missing. Especially, the simulation results and test results cannot be unified. Firstly, a simulation model of antiskid valve based on MATLAB/Simulink software is formulated. The simulation curve of antiskid valve dynamic characteristics is obtained. Secondly, the dynamic characteristics test of the antiskid valve is achieved according to the data acquisition technology and the pneumatic control technology. In this way, the detection of dynamic characteristics is realized. The correctness of the simulation model and the reliability of the detection system are interactively verified with the highly fitted simulation curve and experimental curve. Finally, to comprehensively analyze the influence of relevant parameters on the dynamic characteristics, the saliency analysis theory and the regression analysis theory are utilized. The response surface method is used to complete the mathematical analysis of the experimental parameters and the dynamic characteristics. Experimental results show that the effect determination coefficients R2 are 0999 9 and 0996 7, the correction determination coefficients R2adj are 0998 5 and 0995 2, and the prediction determination coefficients R2pre are 0997 7 and 0992 7. These numerical values verify the rationality of the experiment design, which provide a reasonable and feasible method for optimizing the structure of the antiskid valve.