Abstract:Abstract:There are abnormal temperature rise and difficult measurement of loss in the lap joint area of transformer laminated core joint. To solve these problems, this study proposes the joint building factor (JBF) method to obtain the accurate calculation ofloss mutation at the lap joint. Firstly, a threedimensional equivalent model of theoverlapped zoneof core joint isformulatedandtheequivalent rangeof theoverlapped zone isdeterminedbythegradient curve.Meanwhile,thecalculation formula of JBF is determinedandtheinfluencing factors of JBF are studied. Then, the whole temperature rise model of transformer considering JBF is established. The temperature rise and influence factors of different hot spots on iron core and structural parts under open circuit test are calculated and analyzed. Finally, the temperature rise test platform of transformer core is built to measure the temperature of joint lap area and hot spots of clamps. Experimental results show that the relative error between the measured value and the calculated value is less than 250%, which proves the effectiveness of the calculation method. Therefore, this method of equivalent loss in the lap area provides a theoretical basis for transformer core loss assessment and engineering design. It is of great significance for transformer safety operation and online monitoring.

Abstract:Abstract:A lowcost noninvasive coreless current monitoring microsystem is proposed for production and living power safety, which is based on a single monaxial tunneling magnetoresistance sensor. The principle and key points for measuring cable current with the magnetoresistance sensor are demonstrated. A detachable package with limit mechanism is designedto ensurethe reliabilityofthemeasurementtransferrelationship withthe compact structureandconvenientmeasurement. Itdoesnot needthetraditional redundant magneticcoreor multi sensors. The high integration and low power microsystem hardware are realized. The accurate measurement and calibration algorithms are achieved by software. A test environment is built according to the city power specification. Test results show good linearity and consistency of the developed current monitoring microsystem. A maximum error of 15% in the 0~8 A range is verified within the national standard of 05 level. It also has good longtime stability and antiinterference ability to adjacent line, which shows the developed microsystem suitable for most residential power monitoring scenarios.

Abstract:Abstract:It is difficult to achieve fullflow lubricant monitoring by using the traditional single excitation wear debris sensor. Hence, this paper designs an inductive wear debris monitoring sensor based on the multiexcitation structure. Multiple excitation structures are placed in the circumferentialdirection ofthe flow channel, which expand thedetection range.In thisway,the debris detection within the whole diameters canbe realized.By formulatinga threedimensionalfinite elementmodel,thestaticmagnetic fieldandtransientcharacteristics of the induction coil are analyzed. The relationship between the number of excitation structures and the uniformity of the yz plane circumferential magnetic field has been revealed. Meanwhile, the magnetic pole shape of the sensor is optimized. By using the generated signal, experimental results show that intensity and direction of excitation current, and the number of excitation structures have directly influence on the inductive signal. Results verify that the number of excitation structures is an important influencing factor on the effective detection range of the sensor. And the results of inputting wear debris experiment show that the 13 μm ferromagnetic debris in a 40 mm diameter flow channel under the condition of lubricating oil circulation can be detected.

Abstract:Abstract:Stable and accurate attitude estimation is the key to the autonomous control of unmanned aerial vehicle (UAV). The attitude heading reference system(AHRS), using the micro electronic mechanical system inertial measurement unit(MEMSIMU) as the measurement sensors, is an indispensable system forUAV′sattitude estimation. Aiming at the problem oflowprecision usingextendedKalmanfilter(EKF)andunscentedKalmanfilter(UKF) caused bythe nonlinearattitude model,anattitudeheadingreference algorithm basedon nonlinearsliding modefilter is proposed. Meanwhile, aiming at the problem that the traditional attitude heading reference algorithm cannot estimate the motion acceleration, an estimation algorithm of motion acceleration using Kalman Filter is proposed based on the motion characteristics of micro UAV, which realizes the online estimation of motion acceleration. The carbased and flightbased test show that the algorithm proposed in this paper can accurately estimate the carrier′s motion attitude and motion acceleration without GPS. The accuracy of acceleration reaches 015 m/s2, and the accuracy of attitude reaches 1°.

Abstract:Abstract:Stable and accurate attitude estimation is the key to the autonomous control of unmanned aerial vehicle (UAV). The attitude heading reference system(AHRS), using the micro electronic mechanical system inertial measurement unit(MEMSIMU) as the measurement sensors, is an indispensable system forUAV′sattitude estimation. Aiming at the problem oflowprecision usingextendedKalmanfilter(EKF)andunscentedKalmanfilter(UKF) caused bythe nonlinearattitude model,anattitudeheadingreference algorithm basedon nonlinearsliding modefilter is proposed. Meanwhile, aiming at the problem that the traditional attitude heading reference algorithm cannot estimate the motion acceleration, an estimation algorithm of motion acceleration using Kalman Filter is proposed based on the motion characteristics of micro UAV, which realizes the online estimation of motion acceleration. The carbased and flightbased test show that the algorithm proposed in this paper can accurately estimate the carrier′s motion attitude and motion acceleration without GPS. The accuracy of acceleration reaches 015 m/s2, and the accuracy of attitude reaches 1°.

Abstract:Abstract:As an important functional material, magnetic fluid has been widely used in the sensor field. An innovative magnetic fluid micropressure difference sensor is designed based on Hall elements in this study. Magnetic fluid is absorbed at the end of the cylindrical permanent magnet placedin the middle of transparentglass tube. In thisway, circular rings are formed,whichcanbeused to lubricate, sealandmake thefrictionhappenbetweensolidandliquid.Theringpermanentmagnet fixed onthe bottomofthe transparentglasstube isutilized to provide restoring force. When the permanent moves in the tube, the magnetic field of the Hall elements that acts as a transformation component will change with the displacements. Then, the voltage signals of the system are output. Finally, the optimized sensor has a diameter of 10 mm and a length of 80 mm. When it is combined with a 3 mm magnetic ring and a 50 mm permanent magnet, the measurement accuracy is 1 Pa within the measurement range of 0~1 000 Pa. The linearity error is 58%, the hysteresis error is 53%, and the repeatability error is 29%. The sensor is small in size and low in cost, which shows a strong practical value.

Abstract:Abstract:As an important functional material, magnetic fluid has been widely used in the sensor field. An innovative magnetic fluid micropressure difference sensor is designed based on Hall elements in this study. Magnetic fluid is absorbed at the end of the cylindrical permanent magnet placedin the middle of transparentglass tube. In thisway, circular rings are formed,whichcanbeused to lubricate, sealandmake thefrictionhappenbetweensolidandliquid.Theringpermanentmagnet fixed onthe bottomofthe transparentglasstube isutilized to provide restoring force. When the permanent moves in the tube, the magnetic field of the Hall elements that acts as a transformation component will change with the displacements. Then, the voltage signals of the system are output. Finally, the optimized sensor has a diameter of 10 mm and a length of 80 mm. When it is combined with a 3 mm magnetic ring and a 50 mm permanent magnet, the measurement accuracy is 1 Pa within the measurement range of 0~1 000 Pa. The linearity error is 58%, the hysteresis error is 53%, and the repeatability error is 29%. The sensor is small in size and low in cost, which shows a strong practical value.

Abstract:Abstract:For the ship health monitoring, it needs to ensure the accuracy and reliability of the strain sensing. To achieve this objective, a fiber Bragg grating (FBG) based on the traceable strain sensor is designed in this study. The strain sensor is optimized with high precision, and it can berepeatedly installed and calibrated. Aspecial calibration devicefor FBGstrainsensorisdesigned.Its measurement valuecan betracedback tothe nationallengthstandard.Theperformanceofthetraceablestrainsensoris evaluatedbyloadingexperiments.Results show that the maximum nonlinear error of the strain sensor is 34 με, and the repeatability is 31 με. These results prove the high accuracy of the proposed strain sensor. In addition, the wave load tests are also implemented in the standard pool simulating, which includes the actual wind and wave environment by installing the proposed traceable strain sensor on the ship structure model. It proves the reliability of the proposed strain sensor. The static loading experiment of the sensor is carried out, and the measured data of the sensor arein agreement with the theoretical calculation results. Therefore, this studyprovides an effective tool for strain sensing, especially forthe ship online health monitoring system.

Abstract:Abstract:For the ship health monitoring, it needs to ensure the accuracy and reliability of the strain sensing. To achieve this objective, a fiber Bragg grating (FBG) based on the traceable strain sensor is designed in this study. The strain sensor is optimized with high precision, and it can berepeatedly installed and calibrated. Aspecial calibration devicefor FBGstrainsensorisdesigned.Its measurement valuecan betracedback tothe nationallengthstandard.Theperformanceofthetraceablestrainsensoris evaluatedbyloadingexperiments.Results show that the maximum nonlinear error of the strain sensor is 34 με, and the repeatability is 31 με. These results prove the high accuracy of the proposed strain sensor. In addition, the wave load tests are also implemented in the standard pool simulating, which includes the actual wind and wave environment by installing the proposed traceable strain sensor on the ship structure model. It proves the reliability of the proposed strain sensor. The static loading experiment of the sensor is carried out, and the measured data of the sensor arein agreement with the theoretical calculation results. Therefore, this studyprovides an effective tool for strain sensing, especially forthe ship online health monitoring system.

Abstract:Abstract:To improve the detection sensitivity of spinexchangerelaxationfree (SERF) atomic magnetometer, a method is proposed by increasing the optical range of the detection light, which interacts with the pump light in the gas chamber. This method usesthe technique of establishing Herriott multipass cell outside theatomic gas chamber.And thewhole system is placedinsidetheshieldingcylinder.Themagneticfreedetectionsystemis designedandmanufactured by 3Dsoftware.Comparedwith thetraditionalSERF atomicmagnetometer, itachieves the sensitivity that is not lower than 34 fT/Hz1/2 at multiple frequency points. The problem of combining the complex optical structure with atomic magnetometer can be solved. It provides a good foundation for further improving the performance of the magnetometer by using FP integral cavity in the future.

Abstract:Abstract:To improve the detection sensitivity of spinexchangerelaxationfree (SERF) atomic magnetometer, a method is proposed by increasing the optical range of the detection light, which interacts with the pump light in the gas chamber. This method usesthe technique of establishing Herriott multipass cell outside theatomic gas chamber.And thewhole system is placedinsidetheshieldingcylinder.Themagneticfreedetectionsystemis designedandmanufactured by 3Dsoftware.Comparedwith thetraditionalSERF atomicmagnetometer, itachieves the sensitivity that is not lower than 34 fT/Hz1/2 at multiple frequency points. The problem of combining the complex optical structure with atomic magnetometer can be solved. It provides a good foundation for further improving the performance of the magnetometer by using FP integral cavity in the future.

Abstract:Abstract:To reduce the influence of thermal error on the machining accuracy of the electric spindle, it is necessary to establish a thermal error compensation system for the electric spindle. Its performance mainly depends on the accuracy of the thermal errorprediction model and the temperaturequality of themodel input. To ensurethe temperature qualityofthe inputmodel,a comprehensivealgorithm that fusesfuzzyCmeansclusteringand graycorrelation analysis isusedtooptimizethe temperaturemeasurement points. The numberoftemperature measurement points is reduced from 10 to 3. The main spindle of the electric spindle is the test object. The temperature variable of the electric spindle speed of 7 000 r/min is used as the input, and the thermal error variable is the output. The adaptive neural fuzzy inference system is used to establish the thermal error prediction model of the electric spindle. The experimental data of 5 000 and 9 000 r/min are used as evaluation. Experimental results show that the formulated ANFIS thermal error prediction model can effectively predict the thermal error of the electric spindle. The residual error of the prediction model is less than 1 μm. Finally, compared with the back propagation neural network, results show that the prediction model has higher accuracy and antiinterference ability.

Abstract:Abstract:To reduce the influence of thermal error on the machining accuracy of the electric spindle, it is necessary to establish a thermal error compensation system for the electric spindle. Its performance mainly depends on the accuracy of the thermal errorprediction model and the temperaturequality of themodel input. To ensurethe temperature qualityofthe inputmodel,a comprehensivealgorithm that fusesfuzzyCmeansclusteringand graycorrelation analysis isusedtooptimizethe temperaturemeasurement points. The numberoftemperature measurement points is reduced from 10 to 3. The main spindle of the electric spindle is the test object. The temperature variable of the electric spindle speed of 7 000 r/min is used as the input, and the thermal error variable is the output. The adaptive neural fuzzy inference system is used to establish the thermal error prediction model of the electric spindle. The experimental data of 5 000 and 9 000 r/min are used as evaluation. Experimental results show that the formulated ANFIS thermal error prediction model can effectively predict the thermal error of the electric spindle. The residual error of the prediction model is less than 1 μm. Finally, compared with the back propagation neural network, results show that the prediction model has higher accuracy and antiinterference ability.

Abstract:Abstract:The measurement accuracy of silicon piezoresistive pressure sensor is subject to the influence of temperature drift, nonlinear error and etc., existing measurement error numerical compensation methods have low realtime performance. A stepbystepdifferent frequency compensation method basedon twoelement interpolationis proposed. The methodisimplemented asfollows: firstly,the cubic splineinterpolation algorithmis usedto interpolatethe outputvoltage andworkingtemperature ofthe sensor, and suppresstemperaturedrift;andthen, Lagrange interpolation algorithm is used to interpolate the pressure and voltage piecewisely, and reduce the nonlinear error; meanwhile, the frequency of the temperature interpolation is decreased to reduce the calculation burden in each pressure interpolation period, and overcome the shortcoming that traditional interpolation compensation method calculates two variables in one interpolation period and the calculation is timeconsuming. Experiments on sensor calibration and error compensation were conducted, the results verify that the compensation accuracy meets the error requirement of ± 005% FS in the temperature range of 0～60℃, and the data output rate of 1 kHz is achieved in the designed acquisition system; the experiment results indicate that the proposed method can effectively improve the measurement accuracy of silicon piezoresistive pressure sensor, has good real time performance and can realize error online compensation. The proposed method possesses a certain engineering value in the gas pressure measurement in aeroengine tests.

Abstract:Abstract:The measurement accuracy of silicon piezoresistive pressure sensor is subject to the influence of temperature drift, nonlinear error and etc., existing measurement error numerical compensation methods have low realtime performance. A stepbystepdifferent frequency compensation method basedon twoelement interpolationis proposed. The methodisimplemented asfollows: firstly,the cubic splineinterpolation algorithmis usedto interpolatethe outputvoltage andworkingtemperature ofthe sensor, and suppresstemperaturedrift;andthen, Lagrange interpolation algorithm is used to interpolate the pressure and voltage piecewisely, and reduce the nonlinear error; meanwhile, the frequency of the temperature interpolation is decreased to reduce the calculation burden in each pressure interpolation period, and overcome the shortcoming that traditional interpolation compensation method calculates two variables in one interpolation period and the calculation is timeconsuming. Experiments on sensor calibration and error compensation were conducted, the results verify that the compensation accuracy meets the error requirement of ± 005% FS in the temperature range of 0～60℃, and the data output rate of 1 kHz is achieved in the designed acquisition system; the experiment results indicate that the proposed method can effectively improve the measurement accuracy of silicon piezoresistive pressure sensor, has good real time performance and can realize error online compensation. The proposed method possesses a certain engineering value in the gas pressure measurement in aeroengine tests.

Abstract:Abstract:Robot automatic drilling and riveting system has got more and more extensive applications in the manufacture and assembly of aeronautical parts. This paper proposes a fast onsite selfpositioning method of the robot, which uses the single industrial camera carried in the end effectorof the drillingand rivetingrobot tocarry outthe fastonsiteselfpositioning. The method onlyneedstodrive the industrialcameraat theend ofthe drillingand rivetingrobot in teaching modetosequentially image morethansixlocating points whose coordinates are known in advance in the workpiece coordinate system, the transformation relationship between the robot base coordinate system and workpiece coordinate system can be determined quickly and easily. The results show that the deviation of the rotation vector between coordinate systems is less than 0004, and the absolute deviations of the translation vectors in X, Y and Z directions are less than 05 mm, the highprecision selfpositioning of the robot is achieved. The mathematical model of the robot selfpositioning method based on sequentially, individually imaging of the locating points is derived. Aiming at the new model, a high efficient and steady solving algorithm is put forward. The proposed method directly makes use of the industrial camera carried in the automatic drilling and riveting system itself, is not restricted by the locating point distribution. Since no additional coordinate measuring system, for instance a laser tracker, is necessary to carry out tedious onsite threedimensional measurement and data processing, the onsite alignment process of the robot and workpiece is much more convenient and efficient. The proposed robot positioning model and solving method established aiming at sequentially, individually imaging of multiple locating points can be widely used to solve the selfpositioning problem of various kinds of eyeinhand robotic systems.

Abstract:Abstract:Robot automatic drilling and riveting system has got more and more extensive applications in the manufacture and assembly of aeronautical parts. This paper proposes a fast onsite selfpositioning method of the robot, which uses the single industrial camera carried in the end effectorof the drillingand rivetingrobot tocarry outthe fastonsiteselfpositioning. The method onlyneedstodrive the industrialcameraat theend ofthe drillingand rivetingrobot in teaching modetosequentially image morethansixlocating points whose coordinates are known in advance in the workpiece coordinate system, the transformation relationship between the robot base coordinate system and workpiece coordinate system can be determined quickly and easily. The results show that the deviation of the rotation vector between coordinate systems is less than 0004, and the absolute deviations of the translation vectors in X, Y and Z directions are less than 05 mm, the highprecision selfpositioning of the robot is achieved. The mathematical model of the robot selfpositioning method based on sequentially, individually imaging of the locating points is derived. Aiming at the new model, a high efficient and steady solving algorithm is put forward. The proposed method directly makes use of the industrial camera carried in the automatic drilling and riveting system itself, is not restricted by the locating point distribution. Since no additional coordinate measuring system, for instance a laser tracker, is necessary to carry out tedious onsite threedimensional measurement and data processing, the onsite alignment process of the robot and workpiece is much more convenient and efficient. The proposed robot positioning model and solving method established aiming at sequentially, individually imaging of multiple locating points can be widely used to solve the selfpositioning problem of various kinds of eyeinhand robotic systems.

Abstract:Abstract:As an important step to calibrate the static working radius of a 10-6 scale precision centrifuge, the accuracy of the rotation axis determination will directly affect the measurement uncertainty of the output acceleration. According to the definition, the rotation axis used as a virtualdatum cannot bedirectly obtained withmeasurement. Aiming attheabove problems,a measurement method fortherotation axisof precisioncentrifuge basedonparameterestimation isproposed usingGaussian filtering, eccentricity andtiltparameter estimation, and automatic adjustment. The method can make the geometric center line of standard cylinder rod coincide with the rotation axis, and achieves the purpose of datum conversion. Roundness meter and emulation experiments were used to verify the correctness and effectiveness of the filtering algorithm and the parameter estimation. A rotation axis measurement unit was built using precision turntable, standard cylinder rod, spectral confocal sensor and six axis parallel positioning motion platform. The test results demonstrate that the eccentricity between the standard cylinder rod and the rotation axis can be adjusted to 02 μm, and the tilt can also be adjusted to 0000 2°.

Abstract:Abstract:The automatic fluxgate theodolite is an important instrument for measuring the geomagnetic declination and geomagnetic inclination in the absolute observation of geomagnetism. There are nonorthogonal errors of the horizontal axes and magnetic axes,zero offset error of the fluxgate sensor, motor stopdeviation andverticalaxis tiltdeviation duringthe measurement.In order to improve themeasurementaccuracy ofthe instrument,the multibodysystem theoryis usedto establishthe output valuemodelof thefluxgatesensorin thispaper. Based on the model and the "fourposition measurement method", the multiple parameter error compensation algorithm for magnetic declination and inclination is proposed. The compensation algorithm performs measurement through the sensor pointing to four specific positions perpendicular to the geomagnetic vector, which can eliminate nonorthogonal error and sensor zero offset. The compensation algorithms can effectively correct the motor stop deviation and vertical axis tilt deviation during the measurement process. Simulation experiments on simulation data show that the algorithm can effectively compensate the motor stop error and vertical axis tilt error within ±10′. The actual measurement experiments completed at the geomagnetic station show that the compensation algorithm reduces the measurement error to within 3″ and meets the instrument measurement requirements.

Abstract:Abstract:Aiming at the problem that the angle value of the magnetoelectric encoder is easily affected by highfrequency noise and affects the output accuracy of the angle value, based on the Kalman filter and the motion state equation, the paper proposes amethod is propised for actively moni

Abstract:Abstract:To achieve the 3D measurement of deep sea insitu objects, a laser scanning binocular stereo vision imaging method is proposed. Based on the principle of binocular stereo vision, an underwater binocular stereo imaging measurement model is formulatedin the form of a fourdimensional light fieldrepresentation. Thelaser line scanning light bar is used as thepixelmatchingclue. Thepixelmatchingalgorithmimprovestheaccuracybetweentwocameras,whichisbasedoncoplanarconstraints.Through the design of highpressureresistantstructural, electrical and software, the corresponding engineering prototype is developed. Hydrostatic pressure experiments show that the prototype can adapt to the pressure in a depth of 4 000 meters in deep sea. In the laboratory precision experiment, a standard bat is 3 meters away from the prototype to evaluate the measurement accuracy. After multiple scans and 3D reconstructions, the standard deviation of the center distance between two balls is 228 mm. The South China Sea actual experiments show that the prototype has the capacity to implement the deep sea insitu scanning 3D measurement and reconstruction work. The standard deviation of the standard bat is 222 mm at a distance of 2 meters from the prototype. The above researches and experiments indicate that the prototype has the capacity of insitu measurement in deep sea. The higher threedimensional measurement accuracy can be realized.

Abstract:Abstract:The indoor positioning technology has broad application prospects in autonomous navigation of agents. To obtain the position and attitude angle of the moving target in the suspended state, a novel Yconfiguration optical indoor positioning sensor (YPS) is designed. It consists of threelinear chargecoupleddevice(CCD) and three cylindricallenses.According to themovableand the receiving terminalcharacteristicsofYPS fixedon themovingtarget, a threedimensionalreconstruction modelofspatialpointlightsourcesand a quaternion pose calculation algorithm based on cooperative points on YPS are established. It also utilizes the image coordinates on YPS of three visible point light sources in turn.are established When the image coordinates of three visible point light sources are collected in turn. Then, the coordinates and attitude angle of the moving target can be solved when the moving target is within the measurable view field of YPS. Simulation and experiment analyze the influence of different parameters on YPS. In the center of cylinder lens with good linearity, it is verified that that the new YPS can achieve accurate single point positioning. The error of point coordinate is not larger than 3 mm. The pose information of indoor moving target can be determined.

Abstract:Abstract:To achieve highprecision calibration of line structured light, a new line structure cursor calibration algorithm based on twodimensional planar target is proposed. First of all, the light bar straight line equation in the image coordinate systemis fitted. The light bar straight line equationin the imageof thecamera coordinatesystem issolvedby transformingrelationshipbetween theimage coordinatesystemandthecameracoordinate system.Theplaneequationdeterminedbytheoriginofthecameracoordinatesystem andthe straight line is solved. Then, the plane equation of the twodimensional target plane is determined. The intersection of two planes is the equation of the light bar straight line on the calibration plate of the camera coordinate system. Each calibration plate image is processedEach calibration board image is processed as above. The laser plane equation is fitted by the finally extracted multiple groups of light bar centerlines. In the general environment, experimental results show that this algorithm can make up for the shortcomings of the existing algorithms with fewer calibration points. It has advantages of high accuracy and easy operation. The maximum difference is 0006 mm.

Abstract:Abstract:The light intensity variation may bring some differences among vehicle face images which are captured at different times such as vehicle color difference, headlight status difference, etc. To make the recognition method universal to multiple lightingconditions, a novel siamese nonnegative matrixfactorization (NMF)model isformulated. First,theoriginal featuresofeachpairof vehicle face trainingimagesare split andtakingas theinputoftwo NMF models.Then, asiameseNMFmodelisestablishedbyfusingtheerrorloss,the intraclass loss and the interclass loss. The same feature basis vectors are shared by these two NMF models. Finally, the model is solved by using the gradient descent algorithm. Thus, the shared feature basis vectors can be acquired, and the reidentification of vehicle face images can be achieved based on the cosine distance. Experimental results show that the proposed algorithm can achieve accurate reidentification results even when two vehicle face images are captured under different lighting conditions. Both the false accept rate and the false reject rate can be reduced to be below 6%.

Abstract:Abstract:Autosegmentation of the knee joint in magnetic resonance (MR) images is significant for clinical requirements. However, it is challenging due to that the segmentation targets have dramatically different sizes. In this study, an endtoend DRD UNet is proposed, which is based on the deep learningframework. Theresidualmodule isused asthebasic modulein theUNetmodel, whichincreasestheabilityof reusingfeature maps. Theparalleldilated convolution modulesareusedtoachieve differentreceptivefields,which can overcomethe limitations of single receptive field in the UNet model and effectively improve the segmentation capability with targets of different sizes. The multioutput fusion deep supervision module is designed to directly utilize the feature maps of different levels. In this way, the information complementarity is obtained, the consistency and accuracy of the segmented regions are improved. The proposed algorithm is evaluated by using the public OAIZIB data set. The average segmented surface distance is 02 mm, the root mean square surface distance is 043 mm, the Hausdorff distance is 522 mm, the average dice similarity coefficient (DSC) is 9305%, and the volume overlap error is 386%. Compared with the conventional UNet and other currently available models, the proposed DRD UNet has better segmentation accuracy.

Abstract:Abstract:Due to the indeterminacy of the influence of the curvature radius of the curved detection surface on the radiation sound field of angled SV wave EMAT, the deviations would occur in the defect detection capability and defect quantification/positioningaccuracy of the thickwalled pipe linefocusing angled SVwave EMAT. Thefinite element model for the radiation soundfield of linefocusingangledSV waveEMAT basedon plane/convex detectionsurfacesis established, the influences of the factorssuchasthe curvatureradius of the convex detection surface, number of turns of meanderline coil and design initial angle on the main lobe peak and width are studied. Subsequently, the detection results for the convex detection surface are compared with those for the plane detection surface. The results show that compared with the plane detection surface, the convex detection surface plays a role of focusing ultrasonic wave, which is specifically manifested by the increase of the main lobe peak on the focusing side by 2276% and the decrease of the main lobe width by 1056%. When the number of turns of the meanderline coil is increased to more than 28 turns, the increase of the number of turns of the coil has a limited effect on strengthening the main lobe peak. When the curvature radius is 1485×10-1 m, the design initial angle of the coil is 10 ° and the number of turns of the meanderline coil is increased from 10 to 28, the main lobe peak of the linefocusing SV wave increases by 10256%, and the main lobe width decreases by 5799%.

Abstract:Abstract:The defect monitoring, leak identification and location of buried thermal pipeline are studied in this article. According to the similarity principle, the leakage test platform is established. The infrared thermal imager equipment is used to obversethe surface above the buried pipelineunder three differentworkingconditions. The variation characteristics oftemperaturedistributionof infrared thermal image, maximum differenceradiation temperatureand differenceofthesurfaceabovetheheatedpipeline areanalyzed.Results showthatthe infrared thermal image appears high temperature area, temperature gradient area and natural temperature region around the leakage when the pipeline leaks. The maximum temperature point in the high temperature zone usually corresponds to the location of the leakage point. The temperature gradient area represents the scope of the spill. In addition, the maximum radiation temperature raises fast when the pipeline leaks. The rate is 2 times and 4 times of damaged insulation layer and nondamaged pipeline, respectively. The temperature distribution histogram is changed from normal distribution to positive skew distribution. According to the pixels of the thermal image, the proposed method can calculate the actual area of the affected area of buried pipeline leakage. According to the coordinates of the maximum temperature point, the location of the leakage point can realize pipeline leakage impact assessment and accurate leak location.

Abstract:Abstract:To improve the accuracy and antijamming capability of the gas pipeline leakage location, a twostep method based on crossspectral beamforming with exclusion of autospectral is proposed by using linear array. A series of tests are implemented toanalyze the performance of leakage location andvelocity estimationfor Melement (M=3, 5, 7)arrays and the subarrays.Thedifferentantijamming capability among those threearraysare discussed.Experimentalresults showthat the4kHzcomponentofleakagesignalis lessinfluenced bymultipath and its velocity is in the range of 1 600 m/s ～1 700 m/s. Compared with the 2element and 4element subarrays, the 6element subarray has better velocity estimation stability and antijamming capability. Location accuracy and antijamming capability of the 7element array are also better. When the interference is small, mean errors of location results at multiple leakage positions are less than 1% by utilizing 7element array. Under the SNR of -15 dB, mean errors of 7element array are remained less than 2%. Furthermore, location cannot be completed by using the existing acoustic method that is based on generalized cross correlation analysis under noise condition. This study applies the array technique on pipeline leakage location and the performance of location method is enhanced.

Abstract:Abstract:The pulsed eddy current testing (PECT) attracts increasing attention because it is sensitive to different depth defects and has advantages of low power dissipation and abundant frequency spectrum. Recent studies apply current excitation based pulsededdy current for metal material testing. Due tothe inductance to the coil,it leads to a largereverseinducedelectromotiveforceina drivercoilattherisingand fallingedges ofpulseexcitation.Thephenomenon meansthathighperformanceofpower and stability shouldbe utilized for testing. To solve the aforementioned problems of the excitation circuit, a voltage excitation based ferromagnetic casing PECT method is proposed. The timedomain analytical solution of the testing is formulated. The influences of the selfinductance of the driver coil and the casing eddy current on the excitation current have been analyzed. Then, a more precise pulse excitation function is used for the calculation of the analytical solution. And the analytical solutions of the induced electromotive force to pickup coils are formulated based on the superposition of the excited and induced magnetic field. Finally, experimental results indicate that better accuracy is achieved based on the proposed analytical solutions. The relative errors of excitation current and induced electromotive force between the theoretical and experimental results are 29% and 96%, respectively.

Abstract:Abstract:Capacitiveeddy current dual modality integrated nondestructive testing method combines the advantages of capacitive inspection technique in the inspection of insulators and eddy current inspection technique in the inspection of conductors, and can fulfill the overall inspection requirement of“insulatorconductor” hybrid structures suchasglass fiber compositerepairstructure.The principles ofthecapacitivemodeand eddy current modeofdual modalityintegratedinspectiontechniqueareanalysed, respectively,theinfluential factors of the inspection performance for dual modality integrated probe are identified. The influences of excitation signal frequency, liftoff distance, probe scan direction and coil design parameters on the inspection performance are studied in experiment. Normalized variation ratio (NVR) at the center of the target defect in the inspection characteristic signal variation ratio curve is taken as a quantitative indicator to intuitively characterize the influential pattern and degree of various factors on the inspection performance. The results indicate that experiment conditions and design parameters have different influential patterns on the inspection performance, among which the liftoff distance and coil parameters have obvious influence. Comprehensive considering the influential patterns of various factors may hopefully achieve the inspection performance improvement and guide the optimal probe design.

Abstract:Abstract:Glass fiber reinforced plastics (GFRP) has been widely used in many industrial fields. However, there are still many problems to be solved in acoustic emission (AE) dynamic monitoring of the composites. A focusing enhancement technique with virtual loading based on time reversal theoryis proposed forthe strong anisotropy of GFRP, which can be applied to locate thesoundsource. First, thetheoreticalmodelof signalfocusing enhancementtechniqueinthe process ofvirtual loading is derivedaccording to the principleof timereversal.Then, the sensor array are arranged on GFRP, and the sound velocity is measured at different positions in the monitoring area for obtaining the material average sound velocity 2 43232 m/s. Finally, the simulated AE signals collected in experiment are processed by the proposed model The vibration amplitude of each pixel in the monitoring area is calculated and fluctuation image is reconstructed. The position of the AE source is determined by the maximum amplitude. After threshold processing, the location of AE source can be seen intuitively from the figure. Compared with the existing AE instrument, experimental results show that this method has positioning accuracy within 4%, which can meet the requirements of engineering application.

Abstract:Abstract:Surface electromyography (sEMG) occurs before the action. When the action is active, its willingness can be predicted in advance. However, traditional classification methods usually face problems that realtime and accuracy are difficult to be compatible. To make the EMG signal betterapplied tothe machineand equipment, this paperproposesa fast action classification methodfortheLMSrandom forest EMGsignal. It canclassify andidentifyknee bend, hip bend,knee bend,kneebendandknee stretch.Compared with thetraditionalclassification algorithm, this study only needs to collect the data before 120 ms for classification. LMS is used to filter and assign corresponding weight to the original data. Its weight represents the importance of data features. In this way, the classification of traditional surface EMG signals can be improved. The lack of realtime performance provides a solution for the integration of human and exoskeleton devices. Compared with the traditional support vector machine, back propagation neural network and other algorithms, experimental results show that the proposed algorithm takes less time and the speed is 78 times that of the linear fence method. It has high accuracy and stability, and the recognition accuracy is 973%.

Abstract:Abstract:Surface electromyography (sEMG) occurs before the action. When the action is active, its willingness can be predicted in advance. However, traditional classification methods usually face problems that realtime and accuracy are difficult to be compatible. To make the EMG signal betterapplied tothe machineand equipment, this paperproposesa fast action classification methodfortheLMSrandom forest EMGsignal. It canclassify andidentifyknee bend, hip bend,knee bend,kneebendandknee stretch.Compared with thetraditionalclassification algorithm, this study only needs to collect the data before 120 ms for classification. LMS is used to filter and assign corresponding weight to the original data. Its weight represents the importance of data features. In this way, the classification of traditional surface EMG signals can be improved. The lack of realtime performance provides a solution for the integration of human and exoskeleton devices. Compared with the traditional support vector machine, back propagation neural network and other algorithms, experimental results show that the proposed algorithm takes less time and the speed is 78 times that of the linear fence method. It has high accuracy and stability, and the recognition accuracy is 973%.

Abstract:Abstract:The development of the depth camera makes it more convenient to achieve gesture skeletal information. To obtain useful information from the big data of multidimensional gesture skeletal nodes and realize the recognition of common twohanded staticinteractive actions in the complex indoor environment and close range conditions, a static gesturerecognitionmethodis proposed.Itisbasedon multifeaturefusionand multiclassification supportvector classifier(multiclassSVC). To achieve better results,multiclassSVCis optimized by the bioheuristic genetic algorithm. By using gesture skeletal data, a new gesture feature is designed and a more comprehensive gesture feature sequence is established through the feature combination strategy. In this way, the influence of redundant features is reduced and the ability of data processing is enhanced. The optimal kernel function and penalty parameters are obtained by optimizing the kernel function and penalty parameters of multiclassSVC with the bioheuristic genetic algorithm. The issue of low gesture recognition accuracy is addressed, which is caused by the random selection of the kernel function and penalty parameters. Comprehensive evaluations of the gesture recognition model are carried out by using P, R, F1 and A. Comparison experiments with KNN, MLP, MLR, XGboost and other models verify that the proposed gesture recognition model can effectively improve the accuracy of gesture recognition. This paper analyzes the influence of sample size on gesture recognition accuracy through model training by adding gesture sample data iteratively. It provides an effective method to improve gesture recognition accuracy. Experimental results show that the gesture recognition accuracy can reach 984%. And the average precision rate, recall rate and F1 performance evaluation indexes of the recognition algorithm are not lower than 098.

Abstract:Abstract:The development of the depth camera makes it more convenient to achieve gesture skeletal information. To obtain useful information from the big data of multidimensional gesture skeletal nodes and realize the recognition of common twohanded staticinteractive actions in the complex indoor environment and close range conditions, a static gesturerecognitionmethodis proposed.Itisbasedon multifeaturefusionand multiclassification supportvector classifier(multiclassSVC). To achieve better results,multiclassSVCis optimized by the bioheuristic genetic algorithm. By using gesture skeletal data, a new gesture feature is designed and a more comprehensive gesture feature sequence is established through the feature combination strategy. In this way, the influence of redundant features is reduced and the ability of data processing is enhanced. The optimal kernel function and penalty parameters are obtained by optimizing the kernel function and penalty parameters of multiclassSVC with the bioheuristic genetic algorithm. The issue of low gesture recognition accuracy is addressed, which is caused by the random selection of the kernel function and penalty parameters. Comprehensive evaluations of the gesture recognition model are carried out by using P, R, F1 and A. Comparison experiments with KNN, MLP, MLR, XGboost and other models verify that the proposed gesture recognition model can effectively improve the accuracy of gesture recognition. This paper analyzes the influence of sample size on gesture recognition accuracy through model training by adding gesture sample data iteratively. It provides an effective method to improve gesture recognition accuracy. Experimental results show that the gesture recognition accuracy can reach 984%. And the average precision rate, recall rate and F1 performance evaluation indexes of the recognition algorithm are not lower than 098.

Abstract:Abstract:Image reconstruction with electrical impedance tomography (EIT) is a highly nonlinear, underdetermined and morbid inverse problem. Since traditional methods cannot achieve high accuracy and the reconstruction process is usually timeconsuming, a radial basis function neural network based on adaptive particleswarmoptimization (APSORBFNN) method isproposedand used forthe imagereconstruction.15 000 simulation samplesareestablished throughnumericalsimulation,whichare dividedinto the trainingset and test set. After network training, the image correlation coefficient (ICC) on the test set is 095, and the simulation results verify the effectiveness of the proposed APSORBFNN method. When the Gaussian white noises of 30, 40 and 50 dB are added to the test set, the ICCs are 090, 092 and 093, respectively, which proves the robustness of the proposed method. The reconstruction results for the samples with more targets show that the proposed method has good generalization ability. In addition, the experiment data test results of an 8electrode EIT system show that the proposed APSORBFNN method has better image reconstruction results compared with the Tikhonov and RBFNN methods.

Abstract:Abstract:Image reconstruction with electrical impedance tomography (EIT) is a highly nonlinear, underdetermined and morbid inverse problem. Since traditional methods cannot achieve high accuracy and the reconstruction process is usually timeconsuming, a radial basis function neural network based on adaptive particleswarmoptimization (APSORBFNN) method isproposedand used forthe imagereconstruction.15 000 simulation samplesareestablished throughnumericalsimulation,whichare dividedinto the trainingset and test set. After network training, the image correlation coefficient (ICC) on the test set is 095, and the simulation results verify the effectiveness of the proposed APSORBFNN method. When the Gaussian white noises of 30, 40 and 50 dB are added to the test set, the ICCs are 090, 092 and 093, respectively, which proves the robustness of the proposed method. The reconstruction results for the samples with more targets show that the proposed method has good generalization ability. In addition, the experiment data test results of an 8electrode EIT system show that the proposed APSORBFNN method has better image reconstruction results compared with the Tikhonov and RBFNN methods.

Abstract:Abstract:Whirling is one kind of abnormal vibration in deep hole drilling. During the drilling process, it may enlarge the roundness error, and even damage the tool and hole wall. Thus, it is important to monitor the drilling condition and identify the whirling timely. To be specific, the whirling shouldbe suppressedtimelyand thequality ofdeep hole machining should be improved. In thisstudy, anonlinewhirlingdetectionmethodin deep holedrillingisproposedbased onvibrationsignal.Firstly,thevibration signal isdecomposedby empirical wavelet transform, and the high multiple frequency components of spindle rotation are extracted. Secondly, the energy ratio between the extracted component and the original signal is calculated. Finally, the energy ratio is viewed as the detection index to identify the tool condition. The proposed method is evaluated with BTA deephole drilling tests. Experimental results show that the proposed method can effectively identify the whirling which lead to the roundness error that is larger than 0035 mm during the deep hole drilling.

Abstract:Abstract:Whirling is one kind of abnormal vibration in deep hole drilling. During the drilling process, it may enlarge the roundness error, and even damage the tool and hole wall. Thus, it is important to monitor the drilling condition and identify the whirling timely. To be specific, the whirling shouldbe suppressedtimelyand thequality ofdeep hole machining should be improved. In thisstudy, anonlinewhirlingdetectionmethodin deep holedrillingisproposedbased onvibrationsignal.Firstly,thevibration signal isdecomposedby empirical wavelet transform, and the high multiple frequency components of spindle rotation are extracted. Secondly, the energy ratio between the extracted component and the original signal is calculated. Finally, the energy ratio is viewed as the detection index to identify the tool condition. The proposed method is evaluated with BTA deephole drilling tests. Experimental results show that the proposed method can effectively identify the whirling which lead to the roundness error that is larger than 0035 mm during the deep hole drilling.