Abstract:Objectification of pulse diagnosis is a process of combining the quantitative principle in traditional Chinese medicine (TCM) to objectively record pulse information, propose analysis methods, and serve for the disease and syndrome identification, by using advanced instruments and equipment. Since the quality of pulse signal directly affects the method and result of pulse analysis, the important content of objectification research of pulse diagnosis is the collection and processing of pulse information, and the key lies in the selection of pulse sensor. Starting from the research progress of the objectification of pulse diagnosis at home and abroad, and the problems that the objectification standards are not unified and can not be widely used in clinical practice, this paper lists and analyzes several typical pulse sensors such as pressure type, photoelectric volumetric type and optical fiber type sensors, and their application status in pulse monitoring system. This paper emphasizingly introduces the flexible and planar pressure sensors of pulse instrument, and its advantages in the performance of multi-site pulse detection, and prospects the development direction and application fields of pulse sensor, which provides a new idea for the further study of pulse sensor and objectification of traditional Chinese medicine.

Abstract:Due to the virtue of directly measure mass flow, the Coriolis mass flowmeter is the first choice of international trade settlement in recent years. It has become one of the most rapidly developing flowmeters. Although the accuracy of Coriolis mass flowmeter is relatively high, it has the weakness of zero-point drift, which could result in long-term instability. Based on the characteristics of amplitude-frequency and phase-frequency of the classic U tube in the sensor, this article analyzes the responses of each order to the working vibration frequency. The model of the initial phase of the sensor is formulated. By prototypes testing, this zero-point model is suitable for thin-walled pipe structure and can be widely used. It reduces the error of Coriolis mass flowmeter in cryogenic flow measurement to be within ±0. 3% , and also provides a theoretical foundation to reduce the zero-point drift of Coriolis mass flowmeter.

Abstract:The output of the traditional inductive particle sensor is pulse signal of inductance or voltage amplitude, which is scalar signal in nature. The metal particle can be distinguished into magnetic metal or non-magnetic particle by the positive and negative of pulse signal. The particle size can only be estimated under the condition of known particle material. However, for oil containing multiple metal particles, the particle identification method based on the scalar signal may be invalid. To solve this problem, a double lock-in amplifier circuit is utilized to convert the complex particle signal into a pair of DC signals. A signal processing method based on fuzzy membership function is proposed, which can realize the material identification and size estimation of various particles under noise interference. In this paper, an experimental system based on a three-coil sensor is established. The fuzzy membership function is formulated and the system is calibrated by using five kinds of metal particles. Finally, two kinds of particles are selected to evaluate the calibrated system. Results show that the particle material can be identified accurately by this system, and the error of particle size estimation is less than 2% .

Abstract:To calibrate the sensitivity coefficient of the triaxial force sensor, a new method for achieving a measurable triaxial impact force pulse with an inclined end Hopkinson bar is proposed. The influence of oblique angle θ on Z-axis measurement error through numerical calculation is analyzed. The vertical end Hopkinson bar is used to calibrate the Z-axis sensitivity of the B25B triaxial force sensor. The frequency characteristics of the sensor under the impact of the two configurations of bullets is analyzed. The relationship between the bullet configuration and the bandwidth of the loaded signal is established. Triaxial synchronous shock calibration for B25B triaxial force sensor is achieved by the inclined end Hopkinson bar, and the decoupling analysis realized by using the least square method. Results show that the sensor has a positive coupling relationship between the X-axis and Y-axis, and a negative coupling relationship between the X-、 Z- and Y- and Z-axis. The sensitivity coefficient varies linearly with the impact speed. Finally, the synchronous calibration result is used to test the force in the Z-axis direction. Compared with the uniaxial calibration, results show that the force calculated by the synchronous calibration result is closer to the measured force. The maximum relative error is 1. 73% .

Abstract:Aiming at the problem that the output signal of a flexible fiber Bragg grating tactile sensor applied to the electronic skin is the nonlinear and multi-dimensional coupling of the position and size information of the applied load, based on the mechanical finite element simulation of the fiber Bragg grating tactile sensing array, the decoupling methods are proposed, in which error back propagation (BP) neural network and radial basis function (RBF) neural network are applied to the haptic signals in simulation and experiment. The neural network decoupling results of experiment data show that compared with the error BP neural network, the RBF neural network has stronger anti-noise ability and can better approximate the mapping relationship between noisy tactile multi-dimensional nonlinear experiment data. After decoupling with the radial basis function neural network, the spatial resolution of the sensor array is 5 mm, and the minimum relative errors of the pressure position and size perception are 3. 00% and 4. 82% . The research results in this paper have certain practical value for the research and promotion of electronic skin flexible tactile sensors.

Abstract:Coal mill is the core equipment of coal pulverizing system in the thermal power plant. It is of great significance for system safety to formulate the abnormity diagnosis model based on a small amount of data when the new coal mill is into production. In this paper, the diagnosis model based on three typical abnormities in the process of coal mill is firstly established. A new real-time updating strategy for the Bayesian network (BN) model based on node identification is proposed. Taking the abnormity diagnosis BN of existing coal mills as the source domain model and using the small amount of new data of the coal mill in the target domain, the nodes that do not match the new information could be found out. Retaining the useful information of the source domain model, the target domain model will be updated and supplemented according to the new data through local updating. To verify the proposed method, the method is applied to the diagnosis process of abnormity. Experimental results show that the updated model has good performance, and the average correct rate of diagnosis is more than 98% .

Abstract:The lithium-ion battery in electric vehicle may decay quickly due to vibration and aging. One purpose of this research is to diagnose the degradation mechanism under such conditions. The other is to realize the state of health ( SOH) prediction. The specific methods are given as follows. First, the attenuation results of the battery affected by vibration are analyzed. Secondly, the degradation mode is identified. Then, the incremental capacity-differential voltage ( IC-DV) curve is used to quantify the degradation mode. The results of group Z are illustrated as follows. The loss of active material is 36. 94% , the loss of lithium ions is 35. 12% , and the conductivity loss is 1. 9% . Finally, the quantified results are taken as the input to formulate the GA-Elman model to realize SOH prediction. The errors are within 5% , which can meet the requirements of battery management system (BMS). This research provides a basis for the diagnosis and SOH prediction of lithium-ion batteries under vibration and aging conditions. It could help BMS formulate relevant strategies to extend battery life.

Abstract:In recent years, the great progress has been achieved in the estimation of lithium battery state using the electrochemical impedance spectroscopy (EIS). In this paper, an EIS equivalent circuit model parameter extraction algorithm is proposed to analyze the battery equivalent circuit parameters in different states, which has advantages of simplicity and good convergence. Firstly, the initial values of equivalent circuit model parameters are obtained by combining the physical meaning of the model. The mapping relationship between the equivalent circuit and EIS is used for iteration to extract parameters. Compared with the theoretical reference value, the error of the key parameter is less than 4. 4% . To obtain the EIS data of lithium-ion battery under different states of charge, the electrochemical workstation is used to measure the EIS data of lithium-ion phosphate battery under constant temperature. Finally, by comparing the equivalent model parameters under different states of charge, the feasibility of using EIS to quickly estimate the state of lithium battery is verified to a certain degree.

Abstract:An adaptive convolutional graph neural network fault diagnosis method is proposed to diagnose aero-engine accessory gearbox faults under intense background noise conditions. Wavelet packet coefficient matrixes, which decompose from the gearbox′s vibration signals by wavelet packets, are defined as graphs containing nodes and edges. An adaptive graph convolution operation is designed based on Chebyshev polynomials, the adaptive graph convolutional kernel is constructed in the graph convolutional networks to improve the fault feature extraction ability of nodes and edges,and enhance the generalization of the model under strong noise conditions. Finally, the fully connected layer is used for feature extraction to achieve fault diagnosis of aero-engine accessory gearbox. The application case shows that the proposed the adaptive graph convolutional networks has an average diagnostic accuracy of 86. 42% for aero-engine accessory magazine fault diagnosis under strong background noise, which is higher than LeNet, ResNet and GCNet models, and can effectively identify faults and be applied to aero-engine accessory magazine fault diagnosis.

Abstract:The paper proposes a heat generation rate measurement method for large-size pouch lithium-ion batteries used in electric vehicles ( EV) , namely, the thermal compensation method. The curve relationship between the battery heat generation rate and operating current, temperature was studied, the effectiveness of the thermal compensation method was verified with the conventional method using heat flow meter. Finally, combining these two methods, the heat generation characteristics of the battery under high and low temperature rise conditions were studied. The study results show that the average calculated deviation of battery heat generation rate based on thermal compensation method is less than 5. 6% . The battery heat generation rate increases with the increase of operating current, and the relationship between the two shows quadratic nonlinear relationship. The heat generation rate of the battery under high temperature rise condition first decreases and then rises with the depth of discharge as a U-shaped curve. The average heat generation rate of the battery under low temperature rise condition is about 13. 7% higher than that under high temperature rise condition. The thermal compensation method proposed in this paper has the advantages of high accuracy, low cost, simplicity and flexibility. The research results can provide a guidance for the establishment of thermal model and thermal management system design of large-size pouch lithium-ion batteries.

Abstract:Aiming at the complex distribution difference caused by two domains under different working conditions, an adaptation regularization based transfer learning method for rolling bearing fault diagnosis under different working conditions is proposed. Firstly, the training base classifier predicts the pseudo label for the target domain, and the joint distribution is used to align two domain distributions to reduce the distribution difference. Secondly, the target domain data are further utilized through the manifold regularization to mine the potential distribution geometry of the data and learn the target domain data distribution information. Finally, the classifier is established under the framework of structural risk minimization combined with the above two-step learning principle. The optimal coefficient matrix is obtained by iteratively updating pseudo labels to complete the fault diagnosis of rolling bearing under different working conditions. The experimental validation is implemented on two rolling bearing datasets. Results show that the identification accuracy values of the proposed method are 96. 38% and 94. 18% , respectively. It shows that the method can effectively deal with the complex distribution differences caused by multiple working conditions, and has good effectiveness and feasibility.

Abstract:The component of rolling bearing vibration is difficult to be determined and the instantaneous rotational frequency is hard to be estimated accurately under varying conditions without a tachometer. Most studies mainly rely on the prior speed knowledge and focus on the spectrum distortion caused by time-varying impacts, while little attention has been paid to tacholess bearing fault feature extraction under varying conditions. A novel tacholess quantitative characterization method is proposed for rolling bearing fault feature extraction under varying conditions. The vibration Hilbert envelope is utilized to extract the bearing fault feature. To quantitatively describe the relationship among vibration envelope components, a model based on source hypothesis for feature extraction as well as a quantitative characterization method is proposed. The instantaneous frequencies of spectrogram ridges are estimated consecutively based on the maximum energy-minimum curvature criterion by resorting to the time-frequency reassignment and reconstruction capability of the synchrosqueezed wavelet transform. To reduce the influence of various interference in the generalized demodulated vibration envelope on the quantitative results, a method of interference suppression and stationarization reset under varying conditions is proposed based on the selective reconstruction and the generalized demodulation. The proposed method is applied to analyze the simulated signal and bearing vibration data. It takes about 3 s to extract the features of envelope component with length of 10 k points under different source hypotheses. Meanwhile, the fault characteristics of inner race fault bearing with a speed change of about 300 r/ min in 2 s and that of the compound fault bearing with a speed change of about 200 r/ min in 2 s are quantitatively characterized without a tachometer.

Abstract:With regard to the traditional laser microdissection using the relative motion between the single laser focus and the workpiece, there are limitations in forming the machining trajectory, such as the low cutting efficiency and the difficulty in closing the cutting trajectory. To address these issues, a laser microdissection method based on digital micromirror device without mechanical movement is proposed. By adjusting the digital micromirror array, the amplitude of the laser beam can be adjusted, and the arbitrary light field intensity distribution can be obtained in the sample area with the optical path design. In this way, the parallel cutting of plane projection is realized with almost the same intensity. Meanwhile, the process of key parameter design, simulation, and system construction is described. Specifically, the polyester film is first cut with various patterns such as straight line, hollow ring and hollow rectangle. Then, the frozen section of cell tissue is cut. Experiments show that, by one projection, designable patterned structures can be cut in parallel with accurate target graphics and high cutting efficiency. Under the 20× objective lens, the minimum cutting line width reaches submicron, and the minimum ring diameter is smaller than the single cell diameter. This method can provide new ideas and new directions for improving the efficiency and accuracy of the laser microdissection system.

Abstract:The total temperature measurement precision is a key index of the measurement and control system of the wind tunnel. The technical index requirement of total temperature measurement is up to 0. 1℃ , and the temperature measurement error with velocity is the main consideration of the index requirement. To achieve the reliable performance assessment of the total temperature probe and high precision compensation of the temperature measurement error with velocity, a total temperature probe with double stagnation cover is designed. The evaluation method of the recovery coefficient is proposed, and evaluation experiments are implemented in the pilot wind tunnel. Experimental results show that the total temperature probe with double stagnation hood is less disturbed by the flow field than the traditional total temperature probe. And the test performance is much more stable. The proposed method for the recovery coefficient could compensate the temperature measurement error due to the high-speed flow effectively. Compared with the current engineering method, the compensation accuracy of the proposed evaluation method for the traditional total temperature probe recovery coefficient is 8 times higher. The research results can be applied to the aerodynamics geoclimatic test facilities, which provide good fundaments for the progress of the measurement technology of the wind tunnel.

Abstract:Laser interferometer is a typical measuring instrument in high-precision absolute gravity measurement. During the conventional process of single measurement, it generally involves motor drive, data acquisition, gravity acceleration absolute value calculation, and measurement results display and storage. During the process of sequential control, it requires a lot of time and a large output time error. In this study, an innovative time-optimization method is proposed. A time optimization model of the two-process time-reuse mechanisms of motor drive and post-acquisition data processing before data acquisition is formulated. The steps in the sequential control mode are optimized and reconfigured to achieve the goal of reduce the measurement time of conventional measurements and the error of result output time. The practical measurement results show that the time optimization method could reduce the time from the original 34. 3 s±0. 3 s to be the precise 22 s, and the error is 5 milliseconds. The measurement efficiency is greatly improved. Meanwhile, the precise control of a single measurement time is achieved. However, it increases the feasibility of fusion analysis with similar observation data.

Abstract:Aiming at the on-line calibration problem of in-service strain monitoring systems ( SMS) for bridge structures under all-day working conditions, an on-line calibration method based on passive excitation is proposed, and an on-line calibration system model is established. In this model, the dynamic load of normal passing vehicles on the bridge is used as the excitation source. Through synchronously measuring the structural strain response parameters of the SMS and the reference system, the traceability chain of calibration is constructed to realize the on-line calibration under the continuous working state of the in-service SMS. According to the requirements of the measurement performance evaluation of the SMS, a quantitative analysis model of period measurement error, basic error and confidence interval based on large sample data is established. Experiment verification was carried out on Jiujiang Bridge in Guangdong Province. The results show that the proposed method has feasibility for field implementation, and the on-line calibration results calculated from different data sets have good consistency. When the coverage probability is greater than 90% , the half-width deviation of the basic error interval is less than ±0. 005.

Abstract:The robot faces different shapes and sizes of objects in the task of grasping. The scattered objects in the scene may have different poses and positions, which make the task of recognizing positions and poses of objects more difficult. In view of this, a threedimensional scene recognition method for robotic grasping is proposed. It makes up a defect that the 2D detection method is sensitive to the field of view in robotic grasping task. Firstly, the convolutional neural network is designed to detect the object in the RGB image. Eight corner points of the three-dimensional bounding box of objects, and the center point of the object are generated. Secondly, a method is proposed to calculate the best position and pose for robotic grasping. Finally, the robot is controlled to grasp objects. In real scene, the detection accuracy reaches 88% , and the grasping success rate based on the designed three-dimensional recognition network is up to 94% . In summary, the designed network can effectively find a suitable grasping pose. The grasping success rate is improved. It is able to meet higher requirements.

Abstract:In order to improve the perception ability of Unmanned Surface Vehicles ( USV) to typical small water surface targets, cooperative environment perception method for nautical radar and photoelectric pod facing to Unmanned Surface Vehicles is studied in this paper. Firstly, Gaussian filtering and morphological filtering are used to process the image of the nautical radar; Secondly, the deep learning target detection algorithm is studied and an improved single shot multibox detector ( SSD) target detection algorithm model is proposed based on multi-scale convolution fusion structure and spatial attention enhancement. The improvement was designed to improve the feature retention of small target with weak texture. The proposed method was verified in the VOC207 data set and typical water surface scene. In addition, the paper proposes the flow chart of joint target perception with nautical radar and photoelectric pod. The perception target distribution maps of nautical radar and photoelectric pod are fused to get the final target distribution map, which includes the information such as categories, orientations, distances, etc. of the targets. The experiment shows that the improved algorithm reaches the mean average precision (mAP) of 75. 3% and maintains the real-time detection speed of 64. 4 FPS. Besides, the nautical radar and photoelectric pod can detect the sea target cooperatively and effectively.

Abstract:The binocular vision measurement of coal flow is a key technology to realize energy-saving and safe operation control of belt conveyors. However, the texture and color features of coal samples are single and repeated. The coal particles′ internal gaps are distributed uneven. These factors have seriously influence on the accuracy and real-time performance of coal flow measurement. To address these issues, a binocular vision measurement method for coal flow of belt conveyors is proposed, which is based on deep learning. Firstly, the coal image is preprocessed through correction, segmentation and enhancement. Secondly, a PSM-Net model for coal stereo matching is formulated, which is also based on deep learning. The fine-tuning learning mechanism is adopted to train the PSM-Net model to obtain the coal material volume. Then, based on the two-dimensional characteristics of coal material, a calculation method for coal packing rate based on discrete element method is proposed to achieve coal packing density. Finally, the coal flow of the belt conveyors is calculated, which is based on the obtained volume and packing density. Experiment results show the effectiveness of the proposed algorithm. The accuracy of the binocular vision measurement of coal flow reaches 98. 704 3% , and the calculation rate reaches 1 127 ms per frame.

Abstract:To recover image quality from images with rain, a rain removal algorithm for sea surface images is proposed, which is based on the residual block network. The algorithm combines two types of residual block networks for extracting deep-level information of images with rain. During the training process, the residuals between the image with rain and the original image are learned. In this way, the target value domain of the image operated by the algorithm is reduced and the sparsity is enhanced. For the training dataset, we use the outdoor rain image dataset and the sea surface rain image dataset simulated by two rain addition algorithms to expand the training samples. For the test images, three different types of rain scene images are selected, and the types of rain include rain lines and raindrops. Experimental results show that the proposed derain algorithm can be applied to different rain scenes. The signal-to-noise ratio evaluation index of the images after derain processing is above 35, and the structural similarity is above 0. 97. The clarity of the images is generally improved.

Abstract:Section forming is an important process in the tunneling process. Traditional section trajectory only aims at the shortest trajectory and it will not change once determined, which restricts the development of tunneling robots. For this reason, this paper proposes a multi-objective optimization method for the cutting trajectory of the cantilever roadheader for common and complex structural sections. Firstly, taking efficiency and safety as the goal, a multi-objective optimization model of the cutting trajectory is established. Considering the actual cutting conditions, the decision variables, objective functions and constraints in the model are determined. Secondly, in order to further improve the convergence and distribution of the optimized solution, a multi-objective particle swarm optimization based on fitness distance to simplify knowledge base ( FDMOPSO) is proposed. Finally, the multi-objective optimization model of the cutting trajectory is solved based on the FDMOPSO algorithm. Simulation results show that the convergence and distribution of the algorithm are improved by about 90% and 40% , respectively. It also verified that the cutting trajectory solution set can be planned for complex structure roadway sections of different shapes and sizes. Based on efficiency, safety and cutting smoothness, the optimal trajectory can be finally determined. The optimized cutting trajectory not only improves the cutting efficiency, but also avoids dirt band trapping and increases the safety of cutting.

Abstract:In the microfluidic system, the utilization of superparamagnetic particles to achieve the capture, separation and detection of targets has broad application prospect in the field of biological analysis. However, the rapid and accurate automatic transmission of particles is a major challenge for the on-chip biological analysis. To solve this problem, this paper studies the modeling and motion control of particles in the microfluidic system. First, an electromagnetic coil with a tapered tip iron core is designed, and a model of the magnetic field distribution of the electromagnetic coil is formulated. Then, a dynamic model of the particles in the microfluidic chip is established, which uses a high-gain extended state observer. To estimate and suppress the uncertainty of model parameters and environmental interference, a closed-loop motion control system is designed. Finally, it is evaluated through experiments. Experimental results show that the model and control method proposed in this paper can realize the identification, tracking and motion control of superparamagnetic particles in microfluidic channels in different environments. The maximum average error of motion is 3. 37 μm, and the maximum root mean square error is 4. 29 μm.

Abstract:In this paper, the stability of DC-DC converter in the DC microgrid is studied. A linear switching system model is formulated, which is based on the switching action of the converter. A new switching control method based on the characteristics of PWM wave is proposed, which depends both on time and system state. First, the switching time within each period is determined by the system state error. The switching system must be switched at the end of the period. Then, the duty cycle of the period is achieved by summing the activation time of the subsystems within the period. Simulation and experiment comparison show that this method limits each switching cycle to only one switching, which can reduce the switching frequency of the system. It can respond quickly when the load demand fluctuates. The output voltage is stabilized at the desired value within the ± 1% . The superiority of the switching control method is evaluated. The implementation of this method could reduce the loss of the DC microgrid to a certain extent and improve its stability.

Abstract:A sea-cucumber underwater identification based on active electrolocation is proposed to solve the problems of the existing underwater identification technology affected by underwater visual conditions and heavy equipment. Firstly, by measuring the electrical conductivity of sea-cucumber and other materials, the feasibility of using active electrolocation technology for sea-cucumber identification is evaluated. Then, a sea-cucumber identification test platform is built by simulating the sea cucumber habitat environment. And the influence of lift-off distance and recognition signal characteristics on the recognition effect of sea-cucumber is studied. Secondly, the recognition effect of sea cucumber in different posture and different positions is studied. Finally, the underwater discrimination effect of stone and steel block with sea-cucumber is explored. Results show that, at the lift-off distance of 50 mm, the perturbation potential change rate caused by sea cucumbers is about 5% , which is obviously different from the influence mechanism of other objects on the active electric field. This technology can effectively identify sea cucumbers. The resolution of the recognition effect is related to the liftoff distance of the sensor and the characteristics of the electrical signal. This technology can effectively identify sea-cucumbers in different postures and different environmental backgrounds.

Abstract:Aiming at the difficulty of non-contact measurement of the casting and forging′ s internal temperature field under high temperatures and severe conditions, a reconstruction method for the temperature field of high-temperature castings and forgings with noncontact oblique incident shear wave ( S-wave) electromagnetic acoustic transducer ( EMAT) based on the bending effect and the triangular forward expansion method is proposed. Combining the numerical simulations and EMAT testing experiments at 730℃ , the triangle tracking mathematical model of direct incident S-waves is evaluated. The effects of sound beam incident angle and temperature gradient on the propagation path and flight time of oblique incident S-wave are studied, and the temperature sensitivity coefficients of oblique incident S-wave with different incident angles are compared. Results show that the reconstruction error of direct incident S-waves in the temperature field of the high-temperature forging based on the triangular forward expansion method is within 3% . When the oblique incident S-wave is used, the propagation path of sound ray is greatly affected by the temperature gradient and the incidence angle, and it is more sensitive to the change of temperature gradient. The internal temperature field of high-temperature casting can be reconstructed by the flight time of the S-wave and the offset of the exit point of the S-wave.

Abstract:To solve the problem of incomplete measurement and ill-posed in finite element model updating, a model updating method based on the Neumann series expansion of frequency response function (FRF) is proposed. First, the complete test FRF is calculated by modal data from test and finite element analysis. Then, equations between test FRF and analysis FRF are established according to Neumann series. In this way, the objective function of model updating is formulated. Finally, the modified whale optimization algorithm is applied to obtain the updating results. Numerical examples of a plane truss show that the proposed method is robust to noise and has good precision. With 15% noise, the average relative error after model updating is less than 1% . The method is further validated experimentally with the 4-story shear frame structure. Results show that the updated model can reflect the true status of the structure.

Abstract:Aiming at the problems of low accuracy and unstable results of the spectrophotometer based on Lambert-Beer law in measuring dye concentration in high concentration and dark color dye solution, a soft sensor model is presented. The monochromatic light is regarded as light quantum, the dye molecule is regarded as a one-dimensional harmonic oscillator barrier, Schrödinger equation is used to solve the transmission of light quantum in tunneling penetrating through dye solution, and a mathematical model of the relationship among absorbance, dye concentration and the wavelength of the tested light is obtained. Furthermore, the model is simplified in the range of 400~700 nm of testing wavelength and a soft sensor model of dye solution concentration versus absorbance is obtained from the maximum absorbance and corresponding wavelength. The least square method is used to determine the model coefficients of a certain dye, at last, measuring dye concentration from maximum absorbance is achieved. In this paper, the soft sensor model of single-component dye concentration based on optical quantum tunneling penetration explains the selective absorption of Lambert-Beer law from the microscopic interaction between optical quantum and dye molecules. The model has a good effect on the concentration prediction of deep color and high concentration dye solution, expands the dye solution concentration measurement range of spectrophotometer, and improves the measurement accuracy.

Abstract:Aiming at the pipeline stress detection and quantitative research, a pipeline stress detection method based on electromagnetic detection principle is proposed. Based on the J-A model and molecular current theory, a mechanical magnetic coupling model of the pipe is formulated. Combined with the magnetization curve of ferromagnetic materials under non-hysteresis condition, the influence of external magnetic field and stress on the magnetic field of the pipe wall in the magnetization direction is calculated by using the mathematical model, and a systematic experimental study is carried out. Results show that with the increase of the stress intensity, the electromagnetic stress detection signal of the pipeline increases firstly. Then, the magnetization direction decreases ( the direction of change is reversed). There is a linear relationship before and after the flip point. However, when the rate of magnetic signal changes, the magneto dynamic sensitivity changes with the increase of external magnetic field. When the magnetization curve is at magnetic saturation (magnetic field intensity is greater than 20 KA/ m), the stress has little effect on the magnetization of ferromagnetic materials. When the magnetic field intensity is about 5 000 A/ m, the stress has more significant effect on the magnetization.