To improve the accuracy of image quality assessment， a full-reference image quality assessment （IQA） model is proposed based on the phase consistency of color appearance scale. First， the image structure information is extracted from vividness， which is an index of color appearance in the CIELAB color space， to obtain a color appearance-based phase consistency value. Subsequently， the contrast similarity map is calculated using the root-mean-square method to obtain the chroma similarity map through the color channel of the color space. Finally， the three image features of phase consistency， contrast， and chromaticity are combined， and the standard deviation method is used for pooling. Consequently， the full-reference IQA computing model is realized. To verify the reliability of this model， experiments were conducted on distorted images in four common image databases， whereby prediction accuracy， computational complexity， and generalization were determined based on four criteria. In the experimental results， the Pearson linear correlation coefficient of this model was the lowest for TID2013 at 0.8781 and highest for LIVE at 0.9616. The Spearman rank correlation coefficient was the lowest for TID2013 at 0.8592 and highest for LIVE at 0.9653. Compared with many existing methods， the proposed IQA model has higher prediction accuracy for visual relationships.

To address the low accuracy of the single-geometric-feature-based matching method for cultural relic fragments， an alternative multi-feature-parameter-fusion-based automatic matching method is proposed herein. For this， first， a segmentation algorithm is used to extract the fracture surfaces of cultural relic fragments， and the following four characteristic parameters of points on these fracture surfaces are computed： the average distance from a point to its neighborhood points， distance from a point to the gravity center of its neighborhood， curvature， and average value of the normal included angle of the neighborhood. Following this， the four feature parameters are fused to obtain feature discrimination parameters， and a feature point set is extracted by judging the value of these feature discrimination parameters. Finally， the iterative closest point algorithm based on the scale factor is used to match the feature point set， and consequently， accurate fracture surface matching of cultural relic fragments is achieved. In the experiment， a point cloud data model of Terracotta Warriors fragments is used to verify the performance of the multi-feature-parameter-fusion-based matching method for cultural relic fragments. The results reveal that the proposed matching method can overcome the low accuracy of the single-geometric-feature-based matching method. Compared with the matching accuracy of the existing algorithm， that of the proposed algorithm is improved by more than 15%， while its time efficiency is improved by more than 20%. Therefore， the multi-feature-parameter-fusion-based matching method is effective for cultural relic fragment matching.

Considering the problem of missed pedestrian detection in dense pedestrian images， a multi-branch non-anchor frame network （MBAN） detection method is proposed to detect various posture changes and serious human occlusion in multi-person traffic scenes， such as streets. First， a multi-branch network structure is added after model backbone network detection to detect the local features of multiple key areas with pedestrians. Subsequently， the distance loss function between key areas is designed to guide the branch network to differentially learn the local detection position of pedestrians. Thereafter， four up-sampling blocks are added to the tail of the ResNet50 network to form an hourglass structure， thereby improving the branch network’s ability to understand the spatial information of local features of pedestrians. Finally， a local feature selection network is designed to adaptively suppress the non-optimal values of the multi-branch output and eliminate the redundant feature box in prediction. In the experimental results， the mAP， F1， Prec， and Recall values of the MBAN method for pedestrian detection in multi-person scenes reached 85.22%， 0.87， 80.07%， and 94.39%， respectively. Therefore， this method is effective in detecting pedestrians in dense crowds and has higher recall rate compared with other pedestrian detection algorithms.

In order to solve the problems of the loss of detail information， blurred edges， and artifacts in infrared and visible image fusion， this paper proposes a fast alternating guided filter， which significantly increases the operation efficiency while ensuring the quality of the fused image. The proposed filer combines a convolutional neural network （CNN） and infrared feature extraction effective fusion. First， quadtree decomposition and Bessel interpolation are used to extract the infrared brightness features of the source images， and the initial fusion image is obtained by combining the visible image. Second， the information of the base layer and the detail layer of the source images is obtained through fast alternating guided filtering. The base layer obtains the fused base image through the CNN and Laplace transform， and the detail layer obtains the fused detail image through the saliency measurement method. Finally， the initial fusion map， basic fusion map， and detail fusion map are added to obtain the final fusion result. Because of the fast alternating guided filtering and feature extraction performance of this algorithm， the final fusion result contains rich texture details and clear edges. The experimental results indicate that the fusion results obtained by the algorithm have good fidelity in vision， and its objective evaluation indicators are compared with those of other methods. The information entropy， standard deviation， spatial frequency， wavelet feature mutual information， visual fidelity， and average gradient show improvements by 9.9%， 6.8%， 43.6%， 11.3%， 32.3%， and 47.1%， respectively， on average.

A convolutional neural network （CNN） can be used in the industrial production environment to identify and classify textile defects. To overcome the problems in the visual discrimination of small defect types and imbalance of textile defect categories in actual scenes， a textile defect recognition network （TDRNet） based on label embedding method is proposed. First， the backbone structure is adjusted to improve the classification accuracy of the model. Then， a label embedded module （LEM） is constructed to generate the category weight offset of the model. Subsequently， a distribution perception loss function （DP loss） is proposed to adjust the class distribution of the algorithm； this reduces the distance of homogenous defect features and increases the distance of heterogeneous features. Finally， the seesaw loss function is introduced to dynamically balance the gradient update for different samples during the model training process by suppressing the negative sample gradient of a few categories and increasing the sample loss during misclassification， thereby alleviating the misclassification rate of a few categories. In the self-made "Guangdong intelligent manufacturing" cloth defect classification dataset， the top1 error rate of our framework for rough-grained and fine-grained classifications reached 16.35% and 17.12%， respectively， whereas the top5 error rate of fine-grained classification was as low as 5.20%. Compared with other classification models， TDRNet achieved the best results. In addition， TDRNet was compared with the classical fine-grained classification model in recent five years and achieved state-of-the-art （SOTA） performance， fully demonstrating the enhancements provided.

During the fabrication of inertial switches via micro-electroforming， casting layer warping is frequently encountered owing to the low bonding strength at the interface. Such low bonding strength between the micro-electrocasting layer and substrate is expected to reduce the production yield and prolong the production cycle while increasing production costs. To address this problem， in this paper， methods that involve "removing a passivated film using electrolytic activation" and "introducing a Cu transition layer" are proposed from an interface passivated film perspective. To explore the impact of the passivation film on the interface binding strength， the interface binding energies of different passivation film removal models are simulated using the Materials Studio software. The calculation results reveal that the higher the passivation film removal rate， the better the interface binding strength. The interface binding strength increases by 197% after the passivation film is completely removed. To examine the influence of transition metals on the interface binding strength， Cu， Cr， and Ti are used as transition layers to establish a bonding layer system with a stainless-steel substrate and nickel cast layer， and the binding energy of the system is determined. The calculation results reveal that the binding energy between Cu and the substrate， as well as between Cu and the cast layer， is higher. The interfacial bonding strength increases by 81% with the introduction of Cu compared to that without the introduction of the transition metal. Based on the simulation results， electrolytic activation experiments are conducted to remove the passivated film from the substrate surface using electrolytic activation. The experimental results indicate that the interfacial bonding strength of the cast layer in the electrolytic activation zone is significantly higher than that in the non-activated zone. Simultaneously， a similar Cu transition layer experiment is conducted， and it demonstrates that the interface bonding strength of the cast layer is significantly improved after introducing the Cu transition layer. Based on the aforementioned simulation and experimental results， a micro inertial switch with a size of 23 × 20 mm and a total height of 900 μm is fabricated.

Notably， predictive control is an optimal control strategy that reduces the impact of system delays by predicting and adjusting the future behavior of a motor using mathematical models. System delays are nonlinear time-varying disturbances that are often difficult to accurately estimate and compensate for. Moreover， they degrade the position tracking performance of conventional position predictive control when applied to fast responding objects such as linear motors. This study analyzes the sources and effects of system delays in a permanent magnet synchronous linear motor. A multi-step virtual positive predictive control is proposed by introducing an active variable speed coefficient into the prediction model to reduce the system delay and speed up motor response. This addresses the observed decrease in motor speed with a decrease in the position prediction error during the tracking process of conventional predictive control， which often leads to the failure of rolling optimization， and realizes time delay compensation for high-performance systems. This study experimentally simulates a low-power permanent magnet linear synchronous motor for medical microscopes. The proposed method can accelerate the dynamic response of the motor and reduce the effect of system delays on the tracking of different types of curves such as triangular and sinusoidal. In particular， the position tracking accuracy is improved by approximately 20% over that of conventional position predictive control.

Generally， owing to variations in gravity， temperature， and external disturbances under differing conditions， the positions and orientations of primary mirrors of high-resolution large-aperture telescopes often change significantly in the free state； in this scenario， subsequent optical axes cannot be aligned with the primary mirrors， causing optical misalignment errors and degraded adaptive high-resolution imaging qualities， sometimes even leading to image fly off from the field of view. To eliminate these imaging errors resulting from variations in the positions and orientations of primary mirrors （POPMs）， this paper proposes a novel high-accuracy electrohydraulic control system for the POPM of a large telescope. For this， a mathematical model of the POPM is established for design and analysis for active control. First， a POPM resolving control model of an entire telescope is constructed， and the variation principle of the POPM is analyzed. Second， a five part muti-motor electrohydraulic control system is adopted to realize active control of the POPM. To guarantee control accuracy， we construct the electrohydraulic control system model of each part and use a multivariate linear fitting feed forward controller based on the position error resulting from a change in the telescope elevation； meanwhile， a linear active disturbance rejection controller is adopted for POPM control. Finally， experiments on large telescopes are performed. When the elevation of a 4 m telescope moves at a constant speed， the Z shift can be reduced from 91.5 μm to 0.5 μm， and the deflection shift can be controlled under 0.05 arcsec from 3 arcsec. Next， when the elevation of a 1.2 m telescope moves at a variable speed， the Z shift can be reduced from 5.04 μm to 0.2 μm， and the deflection shift can be controlled under 0.65 arcsec from 0.05 arcsec. Further， when multipoint force actuators are added to the primary mirror， the Z shift can be reduced from 12.2 μm to 2 μm， and the deflection shift can be controlled under 0.03 arcsec from 1 arcsec. This can effectively realize the optical axis stability of the primary mirror while guaranteeing the alignment of subsequent optical axes and high-resolution self-adaption image quality.

Piezoelectric actuators （PEAs） are smart drivers that are widely employed in precision instruments to achieve high-speed， high-precision positioning. However， the nonlinear properties of PEAs， such as creep and， particularly， hysteresis， seriously affect their control precision. This paper proposes a multiple delay-input Prandtl–Ishlinskii （MDPI） model to solve the offset and rate-dependent issues encountered during modeling. Notably， the MDPI model has a set of rate-dependent dynamic factors， and offset coefficients are added to improve the asymmetry of the model. Next， experimental data of 1 V sinusoidal signals ranging from 1 to 100 Hz are collected on the piezoelectric micro-motion platform， and the accuracy of the model is compared with that of rate-dependent and dynamic delay PI models. The experimental results indicate that the MDPI model describes the dynamic and hysteresis characteristics of PEAs more accurately than the other two dynamic PI models. For input signal frequencies of 50 and 100 Hz， the maximum absolute errors of the MDPI model are 0.0815 and 0.1429 μm， and the root mean square errors （RMSEs） are 0.009 5 and 0.011 9 μm， respectively. Compared with the RMSE accuracies of the other two models， that of the MDPI model is improved by 72.46% and 64.21%， respectively.

To increase the radiometric calibration accuracy of the Hyperspectral Infrared Atmospheric Sounder （HIRAS） onboard FY-3D， the phase-correction module used in the data preprocessing of HIRAS is improved. Phase correction is one of the basic preprocessing steps and is used to determine the zero optical path difference position （ZPD） of the interferogram. The ZPD is the center of the Fourier transform and is also the premise of the Fourier transform； thus， it has an important influence on the inversion spectrum. However， the current phase-correction method used in industry can only accurate ZPD to the integer sampling point. This paper presents the use of the instrument phase method to compare the spectral phases of earth observation， black-body observation， and deep-space observation and extract the linear phase component； thus， the accuracy of the ZPD reaches the subsampling level. A comparison between HIRAS and JPSS-1/CrIS indicates that the improved phase-correction method reduces the mean deviations of the three bands by approximately 0.1， 0.4， and 0.8 K and reduces the standard deviations of the three bands by approximately 0.06， 0.2， and 1.5 K， respectively. Additionally， the dependence of the deviation on the target temperature is reduced. The improved phase-correction method compensates for the shortcomings of the original phase-correction module and effectively reduces the radiation uncertainty of HIRAS.

Submarine cables are key carriers of electrical power and communication signals in submarine cable infrastructure， and the monitoring of such cables is essential to ensure the safety and stable operation of submarine cable infrastructure. In this study， experimental research on the detection of submarine cable disturbances was conducted according to the requirements of submarine cable monitoring. First， a submarine cable disturbance detection experimental system based on distributed optical fiber sensing technology was built and coupled to the dark fiber of the seafloor observation network in the South China Sea. Experiments were performed， and the results indicated that submarine cable disturbances induced by spade beating， vehicles， ocean waves， and typhoons could be detected by the proposed system. Following this， a submarine cable disturbance localization method based on the bidirectional long short-term memory neural network in terms of the submarine cable Rayleigh backscattering （RBS） amplitude signal features was developed， and its input features included the spectral entropy and instantaneous frequency of the submarine cable fiber RBS amplitude signal. These submarine cable fiber RBS amplitude signal samples were used to test the detection accuracy of the proposed method， and the results indicated that the average detection accuracy of disturbance and non-disturbance signals along the submarine cable was >99.6%. Thus， disturbances along submarine cables can be effectively detected with the combination of the proposed submarine cable disturbance detection experimental system and the proposed disturbance localization method.

To solve the problems of the small spot of graded-index lenses and the significant coupling loss and short effective alignment time of the current direct alignment-type off-axis fiber optic rotary joint， this study devised a large-spot fiber collimator made of a double-lens collimator and thermal expansion core fiber. Based on the periodic variation law of the overlapping area when the exit spot rotates relatively， the change in the coupling loss when there are radial and angular errors between the collimator was analyzed. The theoretical effective alignment time of the off-axis fiber optic rotary joint under a 3-dB coupling loss was calculated from the exit spot size of the rotor ring and the fiber collimator. An optical fiber coupling experimental platform was set up to test the coupling performance of the off-axis fiber optic rotary joint and measure the effective alignment time. The experimental results indicate that a larger mode field diameter （MFD） of the single-mode fiber used in the fiber collimator corresponds to a smaller coupling loss. In the test， a large MFD of 28 µm yields the minimum loss of 0.22 dB. At a speed of 60 r/min， the effective alignment times of the four MFD fiber collimators were obtained via photoelectric conversion and function fitting. For the MFD of 17 μm， the maximum effective alignment time is 7.05 ms， which is close to the theoretical value of 7.1672 ms. Finally， fiber Bragg grating sensing was applied to achieve contactless transmission of optical signals. This design provides theoretical and technical support for the large-spot off-axis fiber optic rotary joint and its application in optical fiber sensing.

In this study， a technique for the synthesis of multi-core optical fiber connectors used in optical network systems is developed. In addition， systems that can measure the insertion and return losses of multi-core optical fiber movable connectors are constructed using an optical continuous wave reflectometer and optical low-coherence reflectometer， respectively， to evaluate the connection effect. Three quality control requirements-accurate core alignment， physical contact， and non-relative rotation-that must be satisfied during the preparation and docking of multi-core optical fiber connectors are proposed after a detailed analysis of the effects of external factors and end-face quality on the insertion and return losses of multi-core optical fiber connectors. A multi-core optical fiber connection with a typical LC-type interface is prepared. A three-dimensional （3D） profiler is used to record the 3D characteristics （vertex offset， curvature radius， optical fiber concavity， and convexity） of the multi-core optical fiber connector， as well as the roughness of its end face. Using the constructed measurement systems， an average low insertion loss of 0.089 dB and average high return loss of 52.31 dB are achieved for all fiber core channels in the prepared four-core fiber connector. Thus， the proposed synthesis method and performance evaluation scheme may facilitate the practical applications of multi-core fiber-optic movable connectors.