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In the adaptive optics system of astronomical observation, the deformable mirror and the tip/tilt mirror are coupled during the correction process. However, due to the limited sensitivity of the Shack-Hartmann wavefront sensor in detecting piston aberration, it becomes necessary to constrain piston aberration caused by the deformable mirror. This paper introduces a straightforward and efficient decoupling control algorithm that is based on mode-voltage constraints. The proposed algorithm utilizes the mode method to decompose the control voltage mode, constructs a transition matrix to avoid piston and tip/tilt aberrations, and combines this matrix with the response matrix of the deformable mirror to generate a new reconstruction matrix. The direct slope algorithm is used to obtain the mode coefficient of the control voltage, which is then followed by obtaining the constrained control voltage through the proportional-integral controller and transition matrix. Simulation results demonstrate that this algorithm outperforms both the traditional vector project-constraint algorithm and the projection-based decoupling algorithm. The experimental results demonstrate the effective suppression of coupling between the deformable mirror and tip/tilt mirror by this algorithm, leading to a higher Strehl Ratio and improved correction for first 44-order Zernike mode aberrations.

The low resolution and less feature information of small targets make it difficult to recognize and locate, which greatly hinders the improvement of object detection accuracy. In this paper, an object detection model (TDFP) based on CNN and transformer was established, which combines local and global context to establish the connection between features. In the proposed transformed dynamic feature pyramid network, a transformer module was designed to dynamically transform and fuse the multi-scale features generated by the backbone to generate a transformed feature pyramid with richer multi-scale features and context information. In this transformation process, gate block is used to dynamically select single-scale transformation or cross-scale transformation to achieve an optimal style of transformation and fusion of multi-scale features. The experimental results show that the model improves the small targets detection accuracy based on CNN and transformer. Based on the backbone ResNeXt-101, TDFP achieves 46.2% AP and 26.3% APS on MS COCO, and takes the amount of computation as a loss constraint to achieve a better balance between detection accuracy and computational complexity.

Session Initiation Protocol (SIP) is the most widely used signalling protocol for controlling communication, establishing, maintaining, and terminating sessions on the Internet. However, since sensitive information is often transmitted through open channels, a security authentication scheme is essential. Recently, Islam et al. proposed an authentication scheme for SIP, and argued that the scheme is immune to known attacks. However, we discover that their scheme fails to achieve user anonymity, and it cannot even resist impersonation attack. Therefore, this study proposes an enhanced mutual authentication scheme to eliminate the drawbacks of the scheme proposed by Islam et al. In addition, our proposed scheme is based on extended chaotic map, which avoids computationally expensive elliptic curve point multiplication. Then, we use Burrows-Abadi-Needham logic to prove that the proposed scheme achieves secure mutual authentication, and we use the Real-Or-Random model to analyze the formal security verification of the session key. Finally, we compare the performance and the security features of the proposed scheme with some existing schemes. Therefore, we provide better safety and efficiency than related schemes and the proposed scheme is suitable for SIP.

This paper considers the problem of uniformly asymptotic stability (UAS) for discrete-time switched linear time-varying (DSLTV) systems. Starting with discrete-time linear time-varying (DLTV) systems, some stability conditions are given by using function-dependent linear matrix inequalities (LMIs). Comparing with the existing results, the conditions obtained allow the norm and rate of variation of system matrix are unbounded. Furthermore, the obtained results are extended to study the UAS of DSLTV systems, the stability condition is given by combining the methods of function-dependent LMIs with average dwell time. Finally, some further discussions about the relaxation of conditions obtained are also proposed. Three numerical examples are given to illustrate the theoretical results.

With the development of medical multimedia analysis methods based on DBN, DBN models have gained the ability to surpass medical experts in the evaluation of multimedia in some clinical examinations. Firstly, based on the existing architecture of the Internet of Things, combined with the actual characteristics of the hospital, the medical multimedia data is accessed from the IoT support platform. Secondly, the medical multimedia data modeling and classification method based on DBN is studied and analyzed. Three network structure models, a deep belief network, a stacking automatic encoder and a convolutional neural network, were introduced and analyzed. The medical multimedia data classification modeling method based on DBN was proposed to further improve the accuracy of medical multimedia data classification. The experimental results show that compared with the traditional feature extraction based neural network classification method, the classification performance is better. Thirdly, the medical state assessment model is constructed based on the multivariate Gaussian distribution theory. To study how to use the multivariate Gaussian distribution theory to design an evaluation model that can evaluate the health status of users efficiently and accurately. Finally, using the MATLAB software platform, through the experiment and simulation of 40 groups of 8×8064-dimensional physiological big data of 32 volunteers, First, determine the optimal parameters of a set of health assessment models; then use the model to learn the characteristics of physiological parameters; finally, the state assessment model to obtain the health assessment results. The experimental results show that the feature learning model based on convolutional neural network theory can effectively extract the deep features of medical multimedia big data. The health state assessment model based on multivariate Gaussian distribution theory can effectively evaluate the health status of human body.

This paper is devoted to investigating the observer-based fault detection (FD) filters for nonlinear distributed processes described by hyperbolic partial differential equations (PDEs). To this end, the PDE systems are first approximated by the Takagi-Sugeno fuzzy models with spatiotemporal uncertainties. Then, the fuzzy FD filter is developed for the hyperbolic PDE systems to guarantee that the residual signal is robust against process inputs including disturbances. The dynamic threshold is designed to ensure the real-time detection of potential faults. It is worth mentioning that the distributed weighting factors are used to weigh the residual signal such that the overall fault detectability can be optimized.

This paper presents a robust image hashing algorithm that exploits low-rank decomposition and path integral local binary pattern (pi-LBP), referred to LRPL hashing. The proposed algorithm generates a compact binary sequence from a low-rank component of the normalized image as the hash code. Considering the excellent texture structure description ability of pi-LBP features, the new hashing algorithm extracts a feature vector from a low-rank feature matrix with pi-LBP. The pi-LBP feature vector is then encrypted by using the logistic map to produce the final hash sequence. The Hamming distance between the hash sequences is employed to authenticate the tested image. The experimental results demonstrate that the proposed LRPL hashing method has better robustness and discrimination compared with existing hashing algorithms.

Finite-time terminal sliding mode control of a time-delay fractional-order hydraulic turbine regulating system (HTRS) is studied. First, an improved Adams-Bashforth-Moulton algorithm is introduced to solve the fractional-order nonlinear system with a time delay. Then, given the unique advantage of fractional calculus and the great influence that time delay has on system stability, a time-delay fractional-order HTRS is introduced. Moreover, by means of a frequency distribution model, the transformation of the fractional-order HTRS is realized. To stabilize the system under the influence of a random disturbance, a novel terminal sliding surface and a controller are proposed, and the detailed mathematical deduction for system stability is given. Finally, the simulation results, compared with traditional proportional-integral-derivative control and the conventional sliding mode control in the existing literature, demonstrate the validity and significant advantages of the proposed finite-time control scheme.

In order to investigate the electromechanical coupling characteristics of the integrated electric drive system (IEDS) that consists of a surface-mounted permanent magnet synchronous motor (SPMSM) and a helical gear reducer, a transient dynamic model including the motor model and the driveline dynamic model is established. Based on this model, the interaction between the mechanical system and the electrical system of IEDS for electric vehicle is simulated and analyzed. To suppress the dynamic load of IEDS mechanical components caused by the sudden step electromagnetic torque during rapid acceleration, an active damping control strategy (ADCS) is proposed. With the effect of ADCS, the maximum mesh force and displacement of IEDS gear pair have reduced by 23.26% and 26.96% respectively, the maximum dynamic load of motor shaft and IEDS output shaft have reduced by 28.75% and 29.05% respectively. The simulation results indicate that ADCS could effectively suppress the driveline torsional vibration during rapid acceleration.

With the development of information technology, the informationization of the medical industry is also constantly developing rapidly, and medical data is growing exponentially. In the context of “Big Data +”, people began to study the application of data visualization to medical data. Data visualization can make full use of the human sensory vision system to guide users through data analysis and present information hidden behind the data in an intuitive and easy-to-use manner. This paper first introduces the workflow of DBN, a deep learning algorithm, and summarizes the computational characteristics of the algorithm. The classification function is translated into an assembler using an instruction set-based assembly language, and the program is evaluated for performance. Secondly, based on the Hadoop ecosystem, this paper analyzes the BDMISS system for big data medical information resource sharing. Based on the system's requirements and functional positioning, from the medical information collection and sharing, data mining and knowledge management level, the big data medical service system is constructed. Based on the semantic network and ontology theory, big data mining technology and the design of “medical cloud”, the resource sharing mechanism is analyzed. Based on the Spring MVC framework, using Echarts, HCharts and other data visualization technology, according to the design of specific modules, the visualization and display of medical data is realized, which has certain promotion effect on the research and development of medical big data visualization analysis.

Both the model-based and data-driven techniques for fault detection have their merits and drawbacks. The fault detection systems are usually laid out separately with the health monitoring systems in practice. In this paper, the well-established observer-based residual generator is formulated to construct multiple evaluation functions which are employed as the classification features of the support vector machine (SVM) for fault detection. It can be regarded as a tentative approach to combine the model-based and data-driven methods to enhance the fault detection performance. The standard SVM is modified for fault detection to achieve the quantitative tradeoff between false alarm rate and fault detection rate. In Addition, this paper also provides a unified framework for fault detection and health monitoring based on the SVM. Simulations on the ship propulsion system show the effectiveness of the proposed method.

In this paper, a new non-stationary Vehicle-to-Vehicle (V2V) channel model is proposed. It could generate more smooth fading phase between the adjacent channel states and guarantee more accurate Doppler frequency, which is a great improvement comparing with those of the existing non-stationary geometry-based stochastic models (GBSMs) for V2V channels. Meanwhile, the spatial-temporal correlation function (STCF) as well as a temporal correlation function (TCF) and a spatial correlation function (SCF) are derived in details based on the power angle spectrums of both the mobile transmitter (MT) and mobile receiver (MR) following the Von Mises Fisher (VMF) distribution. Simulation results have demonstrated that the time-variant correlation properties of our proposed channel model have an excellent agreement with the theoretical results, which verifies the correctness of theoretical derivations and simulations. Finally, the TCF and stationary interval of the proposed model are verified by the measured results.

Online estimation of the state of power (SoP) of lithium-ion batteries is crucial for both battery management system and energy management system in electric vehicles. In this paper, the approach of online estimating the SoP is investigated with a concern of the impact of the imprecise state of charge (SoC). First, the characteristics of lithium batteries under different state of health (SoH) conditions are experimented based on a typical vehicle driving cycle; then the SOP estimation algorithm using genetic algorithm (GA) is proposed to deal with the long time-scale estimation for power management application, on top of that, the sensitivity coefficient (δ) of the SoP estimation to the SoC precision is analyzed and the correlations of δ with the varying SoH, estimation time-scale are established. Finally, the presented algorithm is evaluated by a simulation study. The proposed GA-based estimation method can improve the SoP estimation accuracy by up to 7.2% in certain cases compared with the traditional Taylor method.