Abstract:Robot teleoperation is the key technology to implement tasks in space, telesurgery, deep ocean areas. Robot teleoperation based on virtual reality is an efficient way to overcome the time delay problem, which can provide with a teleoperation with hightransparency and strongstability performance, and it is currently the main way to realize robot teleoperation. Firstly, the key components of the robot teleoperation system based on virtual reality are analyzed, and the function of each module is introduced. Secondly, methods to realize virtual reality environment modelling are reviewed, including the geometric modelling and the dynamic modelling methods. The performance of each modelling method is analyzed. Besides, the construction methods and application areas of virtual fixtures are discussed. Furthermore, current problems in this area are summarized and analyzed, and some research directions are proposed.

Abstract:In the current robot aided rehabilitation training methods based on patient’s active participation, the perception of the patients’ active participation are mostly from the viewpoint of “motor” while neglecting the “psychological” involvement. Moreover, the interactive control methods do not incorporate robotic continuous variable motion control and therapist discrete event decision control into a unified framework. To solve this problem, a robotassisted clinical rehabilitation training method is proposed based on anxiety emotion recognition and hybrid theory. Firstly, the significance and difference of anxiety with different arousal are analyzed; Secondly, an anxiety emotion classifier using radial basis function based support vector machine is developed. Besides, humanrobot interactive controller coinciding with the arousal of the patient’s anxiety is developed by using hybrid control theory. Finally, the proposed method is verified with three recruited stroke patients on a constructed clinical experimental platform featuring a Barrett WAMTM manipulator.

Abstract:Biological intelligence (human intelligence) and artificial intelligence (machine intelligence) have their good points, respectively and they also have strong advantage complementarity. With the aid of all kinds of humanmachine interface technologies and methods, human intelligence and artificial intelligence are combined to form a new powerful hybrid intelligence, which will provide a significant theory innovation and breakthrough in technology. This paper firstly introduces the main research directions, key technology problems and major research contents of humanmachine intelligence. Then, further introduction is made to illustrate the applications and promoting actions of humanmachine intelligence synergy technology in the fields of service, medical and rehabilitation robot. Finally, the possible problems and challenges existing in the applications are analyzed, and conclusion is made.

Abstract:In order to improve the coordinated motion ability of the limbs of patients with hemiplegia, a new type of a sitting singleinput rehabilitation robot for coordinated movement of limbs rehabilitation training robot is developed. This robot can provide the synchronous and asynchronous cooperation training for hemiplegic patients under the active and passive training mode. Based on the 7R linkage metamorphic mechanism, a rehabilitation training mechanism is designed to realize the coordinated movement of the upper and lower limbs. By the transmission system，the crank initial phase of the training mechanism can be adjusted, which can realize the synchronous or asynchronous movement of the left and right sides of the limbs. The kinematics analysis of the training mechanism is conducted, and it is proved theoretically that the mechanism can achieve the coordination movement of the limbs. The upper and lower computer control system is exploited by intelligent mobile terminal and microcontroller. Finally, the experiments are carried out on the prototype of the robot. The test results are as follows: The robot can realize cooperative motion of upper and lower limbs that the shoulder joint of the subject can move between -16.9° and 15.1°, and the knee joint can move between 121.5° and 172.5°; The robot can achieve passive training of synchronous and asynchronous limbs with the speed limit (0~6 r/min) and active training of asynchronous limbs with plantar pressure range (61.82~227 N). Synchronous and asynchronous training modes can be switched automatically.

Abstract:The haptic rendering technique provides the haptic interaction between the operator and virtual environment by means of a haptic humanmachine interface device, which enables the operator to touch, perceive and manipulate the virtual objects actively. The haptic rendering method enhances the realism and immersion of the virtual reality system, and further expands the application field of virtual reality technology. Traditional wearable haptic rendering devices and force feedback joystick haptic rendering devices have some limitations in portability, operating space and other aspects. As the rapid development of computer technology and consumer electronics, the haptic rendering methods for natural humancomputer interaction gain wide concern. Firstly, this paper introduces the traditional haptic rendering devices, such as wearable device and force feedback joystick device. Then, the haptic rendering methods for natural humancomputer interaction, including the ungrounded method and untethered method, are elaborated. After comparing and analyzing the differences of different haptic rendering methods, the future development direction of the haptic rendering methods for natural interaction is discussed.

Abstract:In order to make the arm rehabilitation patients accurately feel the endofarm force, a soft measurement method of endofarm force is proposed based on random forest regression. The data from the electromyography (EMG) sensors and the arm gesture sensors are integrated to describe the comprehensive feature information of the arm. The comprehensive feature information is used as the input of Random Forest Regression with the endofarm force as the output. According to the basic actions of arm rehabilitation, two groups of experiments ‘pushpull’ and ‘liftdrop’ are specifically designed. The Random Forest Regression is trained offline with the comprehensive feature information and the endofarm force measured with a force sensor, then prediction of endofarm force is conducted and compared with the real force. The method is verified by the experimental results and the effectiveness of the method is proved.

Abstract:In order to realize the reuse of the main components of the general handeye system and the structuredlight seam tracking system, the installation of a laser, filter and protective device on the basis of the general handeye system makes it used as a seam tracking sensor. Calibration results of the original handeye system were experimentally modified to replace the calibration of the structuredlight seam tracking sensor. The influence caused by the installation of laser and filter on the original handeye calibration result is analyzed in detail. The method of data approximation is used to improve the handeye calibration accuracy of the line laser structured light seam tracking sensor. In the experiment, the handeye system is calibrated firstly to obtain the original matrix of the camera coordinate system to the tool coordinate system. The error of welding point data obtained from the original extrinsic matrix and the actual coordinate of the solder joint is analyzed to correct the handeye calibration result. Experimental results show that the average precision of the handeye calibration of the lightseam tracking sensor based on the calibration results of the general handeye system is 0.53mm, which can meet the requirements of engineering welding.

Abstract:Diseases or accidents often lead to hand dysfunction. At present, the assessment of hand rehabilitation mainly depends on the experience and judgment of doctors. Most of hand rehabilitation products on the market are complex and expensive. The daily training data for further diagnosis of patients cannot be recorded by the existed products. In this paper, a data glove with multisensors is designed for hand rehabilitation. The least square approach is adopted to correct the accelerometer and magnetometer. The complementary filter algorithm is used for sensor data fusion. Realtime hand gesture can be obtained and demonstrated in Unity 3D platform. Four kinds of gestures are captured and reconstructed by the proposed approach. It reveals that the data glove can effectively improve the hand rehabilitation procedure for both patients and doctors.

Abstract:Current masterslave hand mapping in the humancomputer interaction has shortcomings such as lack of feedback, poor mapping accuracy and poor wearability. A masterslave mapping system based on fabric strain sensor and feedback mechanism is presented to address these issues. The strain sensitive unit is constituted of Lycra fabric surface spinning graphene/polyaniline composite conductive materials, in which the silver conductive adhesive is used as the electrode. The fabric strain sensor is layout in masterslave hand to construct 2×5 array. The gesture recognition model is obtained by combining stretch sensor information and the improved DH algorithm. The BP neural network is used to model the information of the masterslave strain sensor. Combining with the online optimization algorithm, the feedback mechanism of the strain sensor is introduced to realize the efficient and accurate mapping of the masterslave hand gestures, and the gesture mapping with feedback mechanism is established. The strain characteristics of the sensors are tested and the mapping accuracy of the gesture mapping system with and without feedback mechanism is compared. The experimental results indicate that the masterslave control system based on fabric strain sensor and feedback mechanism can improve mapping precision and wearability.

Abstract:In order to improve the accuracy of the scanning wavelength of the portable grating scanning near infrared spectrometer, on the basis of the dispersion formula of plane diffraction grating and the position & mechanical transmission relationship among the grating, swing rod and screw in the scanning mechanism, a mathematical model of spectrometer wavelength correction is built, and a wavelength correction method is proposed. The method includes three processes. First, the parameters of the wavelength correction model are determined based on particle swarm optimization algorithm. Second, the swing rod length and the correction value of the wavelength are determined based on the swing rod length ratio of the wavelength correction model and full spectrum comparison optimization. Third, the hardware structure adjustment and soft parameter compensation are conducted on the spectrometer. The characteristic wavelength of a standard light source was used to perform wavelength correction experiment on the selfdeveloped portable nearinfrared spectrometer. The experiment results indicate that after wavelength correction the wavelength accuracy of the spectrometer is better than ±1 nm, which satisfies the spectrometer wavelength accuracy requirement, and the results verify the correctness of the spectrometer wavelength error analysis and the effectiveness of the wavelength correction method.

Abstract:Internal combustion engine fault diagnosis using vibration response signal meets the challenging of strong coupling and weak fault characteristics. A fault diagnosis method is proposed based on texture feature extraction of internal combustion engine vibration spectrum. In order to clearly characterize the nonstationary timevarying components in the timefrequency distribution of internal combustion engine vibration signal, the variational mode decomposition (VMD) is combined with the Rihaczek complex energy density distribution method. Thus, the vibration spectrum image can be obtained with good timefrequency clustering and no crossterm interference. Considering the parameter selection in the VMD decomposition process, Shannon entropy is introduced as the objective function and the successive grid search technique is employed to identify the optimal model parameters, to improve the adaptability of VMD decomposition. To realize the automatic recognition and fault diagnosis of the vibration spectrum of internal combustion engine, an improved local binary model (ILBP) is presented to analyze the texture information contained in the vibration spectrum. The lowdimensional feature parameters are extracted and the nearest neighbor classifier is adopted to identify the vibration spectrum under different working conditions. The proposed method is applied to the fault diagnosis of internal combustion engine. The results show that the method can effectively extract the weak fault characteristics of the vibration signal and realize the automatic diagnosis of internal combustion engine failure.

Abstract:Aiming at the magnetic interference problem of threeaxis magnetometers in magnetic field measurement process, a new magnetic field measurement error compensation method based on damped particle swarm optimization (DPSO) algorithm is proposed. A comprehensive error compensation model for magnetometer error and geomagnetic interference field is established. DPSO algorithm and twostep method are used to carry out the parameter estimation work of nonlinear observation model, respectively. The scalar value of the proton magnetometer is taken as the true value and a nonmagnetic turntable is employed in the experiment for continuous sampling. Experiment results demonstrate that the DPSO algorithm has good suppression capability to magnetic field measurement error. After compensation, the root mean square errors (RMSE) are reduced from 1 025.7 to 60.304 4 and 581 nT for DPSO algorithm and twostep method, respectively. The results indicate that better compensation performance is achieved with DPSO algorithm, and the compensation accuracy is improved at least by one order of magnitude, which provides an efficient compensation method for magnetic field measurement error.

Abstract:Time grating sensor is a novel sensor and it can achieve very high accuracy owing to a special way that measure time using pulse with high frequency. In this paper, the reasons of signal distortion have been introduced firstly, including the imbalance of amplitude, imperfect quadrature, inductive of harmonics. In particular, aim to the angular deviation from the imbalance of amplitude, the letter introduces the dq transform from electrical machinery to solve it and deduces the angular deviation. The process of measuring is analyzed using rotating coordinate system where an error compensation algorithm has been proposed. The error would be corrected with a simply way due to the fact that the algorithm utilizes the characteristic of signal completely. Finally, the proposed method was applied to the experimental system and the error of sensor was obtained under different circumstances. Experimental results indicate the curve of error consist of 2th harmonic because of the imbalance of amplitude. The deviation of angular information from the imbalance of amplitude will be eliminated if the algorithm is applied .it will get good result with the algorithm and it will be make sense for timegrating sensor.

Abstract:The accuracy of RSSIbased ranging is severely affected by multipath effect and external radio interference in underground mine tunnel environments. To solve this problem, a novel RSSIbased ranging algorithm is proposed which overcomes the multipath effect by applying geometrical optics rules in analyzing RSSI measurements in both Lineof Sight and NonLineofSight scenario. The proposed algorithm can reconstruct a function for path loss and the physical distance which can help minimize the ranging error caused by multipath transport. Extensive real experiments are conducted in underground tunnel environments and the accuracy of the proposed ranging algorithm is analyzed. The impact of representative system parameters on the ranging, including multipath attenuation factor and path loss exponent, are discussed. Experiment and simulation results demonstrate that the proposed algorithm outperforms conventional RSSIbased ranging method without using any radio map information nor a calibration stage.

Abstract:Pallet picking is one of the important links of automated warehousing. An algorithm was proposed for estimating the localization and angle of pallet with lake information, based on 2D Lidar. It aims at improving the recognition pallet ability of forklift and the flexibility in automated warehousing. A detection model was built to define the geometry relationship between detection region the number of lidar point (detecting the pallet foot), the width of pallet foot and the resolution of Lidar. The algorithm extracted the lines as candidate pallet foot with the improved incremental algorithm and calculate the angle of pallet. Then the dynamic template was created and matched with sliding window, to calculated the position of the pallet. The result shows that the detect region was expend with smaller the threshold number of laser, longer the width of pallet foot and higher resolution of Lidar; the detect region met the result calculated based on the pallet detect model; error on x axis and y axis were ±60 mm mm and ±59 mm respectively; pallet angle error was . With the flexible pallet detecting algorithm, the requirement of localization of warehouse equipment and human was reduced and effect would be enhanced.

Abstract:Nonlinear Vector Network Analyzer (NVNA) is a measurement setup for characterizing the nonlinear behavior of RF/Microwave devices. It uses a phase reference to achieve the phase spectrum measurement of a complex test target, and is mainly used for the power amplifier characterization and modeling at present. This paper presents an improved twophasereference NVNA setup, and investigates its performance for mixer characterization with nonlinear behaviors. This novel NVNA setup uses a “stepped” multisine as the primary phase reference, to achieve multiband measurement on a dense spectral grid, and uses a second “auxiliary” phase reference on a coarse grid to ensure the synchronization between multiband phase measurements. As a result, the signaltonoise ratio of the test bench can be largely improved, and a complex test target with multiband and “modulated multiharmonic” components can be characterized. According to the experimental results, the proposed method can guarantee a phase measurement stability of less than ±0.2° fluctuation, and achieve the phase and harmonic distortion analysis of a mixer under complex modulation.

Abstract:In order to improve the accuracy of 5axis machine tool, a geometric error detection method for rotary feed drive using laser interferometer is presented, which realizes geometry error measurement for rotary axis. This method can be implemented in general rotary feed drive such as AC table tilting type and BC tilting table type. Synchronized motion with linear axis and rotary axis is applied in the measurement. The dependence of traditional methods on rotary center coordinate can be avoided during the measurement. The constraints between the angular error of the linear axis and the geometric error of the rotation axis are deduced. The time cost of setup and measurement can be decreased guaranteeing with the measurement accuracy. Compensation and fast measurement are is realized on measurement of positioning error, resettle error and backlash in rotary axis. An experiment of geometric error measurement is conducted based on XKR50A 5axis machine tool. The accuracy of the rotary feed drive is largely improved after the compensation. The feasibility and engineering application of the method is proved.

Abstract:In order to improve the modeling accuracy of piezoelectric ultra precision positioning stage, the hysteresis model of BacklashLike is used to describe the hysteresis characteristics of piezoelectric ultra precision positioning stage. The recursive least squares method based on genetic algorithm is utilized to identify the parameters of BacklashLike model. Combined with the dynamic characteristics of the piezoelectric ultra precision positioning stage, the secondorder dynamic hysteresis model is established for the piezoelectric ultra precision positioning stage. The experimental results show that, compared with the BacklashLike model under frequencies of 30 and 40 Hz, the maximum output displacement error of the dynamic hysteresis model decreases from 1.21 and 1.39 μm to 0.32 and 0.44 μm, and the maximum relative error are 3.5% and 4.4%. The average displacement error decreases from 0.53 and 0.76 μm to 0.17 and 0.21 μm, the average relative error decreases from 1.93% and 3.38% to 1.11% and 1.37%. The experimental results verify the dynamic hysteresis model and prove that the proposed model can not only reduce the system error caused by the dynamic characteristics of the piezoelectric ultra precision positioning stage, but also simulate the hysteresis characteristic and dynamic characteristic, and avoid the repeated model parameters identification under different frequencies. The accuracy of piezoelectric ultra precision positioning stage is improved in high frequency, fast, and large stroke location. This method is simple and has strong adaptability, easy to implement in engineering.

Abstract:A shielded cylinder microwave resonant cavity is adopted to realize the precise measurement of permittivity of microwave dielectric ceramics and other kinds of low and medium loss materials, the modematching technique is employed to realize the precise solution of the electromagnetic field in the cavity under dielectricloading condition, and the rigorous relationship between the cavity resonant frequency and material permittivity is obtained. The measurement results of various typical dielectric materials and the test data compared with those of several other methods show that compared with traditional methods, the proposed method achieves higher measurement accuracy with an estimated uncertainty as low as 0.1%; and the permittivity measurement range is extended wider. This method can not only measure the microwave dielectric ceramics with high permittivity, but also measure the low permittivity materials such as crosslinked polystyrene, teflon and etc. Furthermore, as the radiation loss is eliminated in shielded cavity, and the electric current loss in metal surface is restrained, a more accurate quality factor measurement can be realized for low loss materials; the lower limit of the loss angle tangent measurement range can be lower than 1×10-6. This method is suitable for the accurate permittivity measurement of various dielectric materials with the permittivity range of εr=1~100 and above, and the loss angle tangent of tanσ=1×10-3~1×10-6.

Abstract:Residual stress is an internal stress, which remains existing after eliminating the external force or uneven temperature field and other effects. This residual stress has a significant effect on the mechanical properties of the components, which can cause the warp, distortion, or even cracking in parts. Quantitative measurement of the residual stress is of great significance to change the fatigue life and prevent the brittle failure of the components. A variety of test methods of residual stress measurement. This paper introduces the principle and the research progress of destructive and nondestructive testing methods. The accuracy influencing factors and the improvement in measuring instruments are also investigated. The advanced instruments and their suppliers are surveyed on the market. Aiming at an improvement for the deformation of the blind hole and the borehole eccentricity, plastic area radius, internal microstructure, zero drift, measurement, the improved methods are put forward and the prospect of stress testing technology is also discussed.

Abstract:Aiming at the fact that there is no preimage in nonlinear mapping and fault variable cannot be identified which result in that it is difficult for engineering application of kernel principle analysis, an improved KPCA residual direction gradient algorithm is proposed to overcome the above drawbacks in this paper. By use of the correlation between the partial differential of principle statistic and residual statistic, the gram matrix partial differential intermediate computation process is simplified and the KPCA residual direction gradient index is obtained, combined with residual statistic a new fault detection method is proposed. Nonlinear system simulation computation shows that improved KPCA residual direction gradient method has excellent capability of fault variable identification while computational complexity is greatly decreased and the calculation time is shortened. Furthermore, largescale thermodynamic system application shows that the proposed method has better capability in fault detection whenever in case of single fault or multiple faults and there is no residual contamination while it is very suitable for engineering realization.

Abstract:Aiming at the problem that the intelligent recognition algorithm is too complicated in the fault diagnosis technology of circuit breaker, a fault diagnosis method of frametype circuit breaker is proposed based on grey correlation degree. This method uses the grey comprehensive correlation degree to describe the geometric similarity among the fault characteristic sequence curves to judge the type of the fault and makes full use of the changing trend of the fault feature itself. Firstly, the method extracts the local mean decomposition energy moment of the contact vibration signal or the ensemble empirical mode decomposition energy moment of switching coil current signal, which are normalized to form the energy moment sequence. Secondly, the grey comprehensive correlation analysis between the resulting energy moment sequence and the referring sequences under different fault states. Finally, according to the principle of maximum correlation degree, the fault type is determined, and the weight coefficients of various indices of the grey comprehensive correlation degree are obtained with entropy weight method. The diagnosis and testing on the switching mechanical structure fault of the circuit breaker contact and the switching coil circuit fault of operating accessory were conducted, and the results show that the proposed method can achieve the diagnosis of different faults for the frametype circuit breaker accurately and effectively.

Abstract:In order to identify the flow regime of gasliquid twophase flow effectively, air and water are taken as the experiment media and a vortex flowmeter is employed as component to induce the flow around bluffbody. The ductwall differential pressure method is adopted to acquire the wake fluctuation signals of the flow around bluffbody in the gasliquid twophase flow. The signals are decomposed with the ensemble empirical mode decomposition, and then the Hilbert marginal spectrum is obtained with Hilbert transform. The maximum crosscorrelation coefficient method is used to sift the characteristic intrinsic mode functions. The energy ratios of the characteristic intrinsic mode functions are selected, and used to construct the flowpattern maps together with the volumetric void fraction and the twophase Reynolds number respectively. The results show that the two newly constructed flowpattern maps can successfully identify the typical flow patterns such as singlephase water, bubble flow, plug flow and slug flow with the recognition rate up to 91.67% and 88.89% respectively, which can meet the needs of most practical engineering applications nicely.

Abstract:Aiming at the deficiency of high cost and large volume existing in the position sensor of permanent magnet servo motor, as well as the disadvantage of the computation complexity and the unreliability due to relying on the motor parameters existing in the newly developed sensorless technology, the time grating displacement sensor research group proposed a method for detecting the motor rotation position through winding time grating coil. However, because the detection method through winding time grating coil has the shortcomings of complex signal acquisition, poor measurement stability and increasing error due to the uneven coil winding. On the basis of above, this paper proposes a new embedded shaft position detection method for permanent magnet servo motor based on tunnel magnetoresistance (TMR) effect and time grating technology. The position detection principle is analyzed in detail. According to the theoretical deduction for travelling wave expression, the measurement error characteristics caused by the unequal amplitudes of one and two channel standing waves are analyzed, which lays a foundation for the detection structure optimization and further improving the measurement accuracy. At last, the experiment verifies the correctness of the embedded position detection theoretical analysis and the superiority of the proposed detection method. Compared with the detection method through winding time grating coil, the proposed method improves the measurement accuracy nearly by three times and improves the stability by five times.

Abstract:From the magnetoelastic effect principle, it is known that diversity exists in the hysteresis loops of rodelike ferromagnetic material under different tension. So it can be employed to develop an improved method for tension estimation of rodelike structure. Firstly, the hysteresis loop signals of rodlike structure are acquired by an EM sensor which is composed of two coaxial solenoid coils. Then, a curve of hysteresis loop change (CHLC) is defined to reflect the tension influence on each point of the hysteresis loop. Secondly, lowresolution CHLC is obtained by using wavelet analysis. Finally, neural network is used to establish the relationship between the lowresolution CHLC and tension after the data with different tension is used to train the neural network. The experimental results show that CHLC can reflect the tension influence on each point of hysteresis loop intuitively. The lowresolution CHLC not only has the characteristics of including the entire information of tension but also has the characteristics of low dimensionality. The relationship between the lowresolution CHLC and tension can be obtained by using neural network, without analysis of the sensitivity curve and coefficient of determination curve. The simple linear interpolation based RBF neural network has a better performance than BP neural network and RBF neural network. The tension measurement method based on lowresolution CHLC and simple linear interpolation based RBF neural network is applied to two coaxial solenoid coils based EM sensor to measure the tension and its average prediction error and coefficient of determination are 0.11% and 1, respectively. This method is effective and can meet the actual measurement requirements.

Abstract:With the rapid growth of DLP projection technology in the late years, DLP projection technology has been gradually applied to numerous disciplines. Phaseshifting method often works with DLP projection technology due to its flexibility and high precision. As a nonlinear instrument, the projector could hardly project ideal fringe patterns, and could hardly achieve high projection speed. The binary defocusing provides a new solution for high speed and high precision measurement. This paper analyses the current status and development trend of 3D shape measurement by using projector defocusing at home and aboard. A survey is presented for the working principle of DMD, phaseshifting algorithms, unwrappingalgorithms, error compensation methods, the modulating methods for fringe pattern, the calibration methods and some of the details about projector defocusing further. This work can provide references for the researchers engaged in 3D shape measurement by using projector defocusing.

Abstract:Moving object detection in indoor environment is a research hotspot in the field of computer vision. The dynamic background caused by moving the camera is a challenge in moving target detection. In this paper, a moving object detection algorithm based on ORBSLAM (oriented FAST and rotated BRIEFSimultaneous Localization and Mapping) is proposed. Firstly, the entire indoor environment is traversed using a moving camera, ORBSLAM is used to establish the 3D feature cloud model of the global background. Then, based on the environmental information, local 3D feature point cloud is built. By embedding the local 3D feature points into the global 3D background feature cloud, 3D Meanshift is applied to extracting the foreground points from the local 3D feature points. Finally, deep convolution neural network is utilized to confirm the moving target of the candidate region where the extracted foreground feature points are located. The experimental results on multi indoor scenes show that the proposed method has high detection accuracy and recall rate. The proposed moving object detection algorithm makes full use of the background information, and the depth convolution neural network is used to confirm candidate regions, which effectively improves the detection accuracy and robustness.

Abstract:A monocular vision system with straight line structured light is proposed to address cylindrical surface reconstruction problem. The vision system is based on a virtual image plane camera model. The distortion is corrected by computing the virtual projection image on a virtual image plane. The projective line of a world point is determined by its virtual image point and the camera center, which can be localized by a proposed method. The structured light is calibrated from the virtual images of two stripes on two parallel planes. In accordance with the principle that the stripe of the straight line structured light on a cylindrical surface is an ellipse arc, it firstly calculates the projection point’s coordinates on the light plane for each feature point of stripe from the virtual image. Then the ellipse is extracted from the arc points. Secondly the cylinder’s diameter is acquired because it equals to the length of the ellipse’s minor axis, then the cylinder is localized in 3D space. Finally each projective line connected by the camera’s optical center and the virtual image point is calculated. All intersections of the projection lines and the cylinder construct the point cloud of the cylindrical surface. Then the surface image can be reconstructed. In real experiments the accuracy of the vision system is similar to that using binocular vision system. This proved the effectiveness of the virtual image plane method.

Abstract:Binocular stereo vision and 3D laser scanning techniques are the common sensor measurement methods in environment detection and modeling for mobile robots. In order to realize the data fusion application of the two systems, it is necessary to establish the mathematic relationship between the two local measurement coordinate systems, which is the position and pose joint calibration between the sensors. This paper proposes a new method for the joint calibration based on the distance matching among 3D feature points. A calibration board is designed, which is basically a blackwhite chess board with hollow holes. The binocular stereo vision cameras are used to extract the 3D coordinate information of the corner points of the chess board, and a laser ranging radar is used to scan and acquire the 3D coordinates of the centers of the hollow area. Through minimizing the squared difference of the theoretical and measured distances of two groups of the feature points, the rotation matrix and translation vector of the two sensor coordinate systems are obtained. The joint measurement experiment results show the accuracy and reliability of the proposed method.

Abstract:A topology analysis method of transformer insulation is proposed, which considers free relaxation process. This method introduces free relaxation factor βi from microscopic dynamics, based on the return voltage line relaxation parameter information. The branch lines of different dielectric relaxation response information is obtained, and the topological structure of transformer insulation is determined. At the same time, the relationship between more than 30 sets of transformer insulation relaxation parameters and condition assessment are studied. The results show that number of relaxation mechanism number N, average time constant and free relaxation factor βi can reflect insulation status. As the aging degree of transformer oilpaper insulation increasing, the number of relaxation mechanism N and the free relaxation factor βi are increasing, on the contrary, the average time constant is decreasing. In addition, free relaxation factor has a different scale in the deterioration of oil and paper, which can provide a new idea for transformer insulation aging evaluation.

Abstract:A Stackelberg game model is proposed to study TOUbased trading modes in multiV2M system. According to DG’s power output and load distribution, multiV2M is classified and Stackelberg game model is established. Stackelberg game’s equilibrium with sellers and purchasers is strictly proved. The 24 hours price for the system is calculated. Taking 5 V2Ms system as a an example, the Nash equilibrium explains the system’s working process. The results on the traditional pricing model and Stackelberg game model are compared, and demonstrate the effectiveness of the proposed game model. The proposed model as well as the results have good adaptiveness, which can provide a theoretical basis for the development and management measures of multiV2M system.