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This paper proposes a reverse procedure detection method for space target streaks based on motion parameter estimation. According to the phase shift characteristics of the spectrum, the interframe phase difference is used to obtain the displacement vectors of the target streak and image background, and then the morphological parameters of the target streak are estimated. In addition, a streak window is designed on the basis of the parameters, utilizing the local grayscale correlation in two image frames to carry out target searching and positioning with the interval of the target displacement vector. Images from different scenes are used to evaluate the adaptability of our method, and the experimental results suggest that the expected targets can be detected successfully with subpixel precision in all telescope operating modes. The proposed frequency-domain reverse procedure detection method avoids the problem of fake target confirmation caused by blind detection in traditional methods and overcomes the interference produced when the target streak passes through a bright star. Moreover, first target tracking and then positioning, which cannot be performed with time-domain methods, can be achieved. Our proposed method is attractive for multiple applications, such as space target cataloging, early warning and orbit prediction.

The polarization states of scattered photons can be used to map or image the anisotropic features of a nanostructure. However, the scattering strength depends heavily on the refractivity contrast in the near field under measurement, which limits the imaging sensitivity for viral particles which have little refractivity contrast with their nano-ambientes. In this paper, we show the photon scattering signal strength can be magnified by introducing a more abrupt change of refractivity at the virus particle using antibody-conjugated gold nanoparticles (AuNPs), allowing the presence of such viruses to be detected. Using two different deep learning methods to minimize scattering noise, the photon states scattering signal of a AuNPs ligated virus is enhanced significantly compared to that of a bare virus particle. This is confirmed by Finite Difference Time Domain (FDTD) numerical simulations. The sensitivity of the polarization state scattering spectra from a virus-gold particle doublet is 5.4 times higher than that of a conventional microscope image.

In the AC/DC hybrid microgrids equipped with power electronic transformer (PET), different AC grids and DC grids are connected through multiport PET to operate the entire network in a secure and economic manner. This paper proposes a decentralized optimal power flow model, considering the multiport coordinated control strategy of PET, for running autonomous AC/DC hybrid microgrids. To optimize the power flow in the network, the steady state model of PET is established, including voltage source converter power injection model and Kriging model. By introducing a secondary droop control layer can realize the autonomous operation of AC/DC hybrid microgrids and increase the utilization efficiency of distributed generations. Then using the analytical target cascading approach, each AC/DC sub-microgrid is regarded as different autonomous subjects. The distributed PET is as power coupling node and information interaction node to decompose the optimal problem of AC/DC hybrid microgrids equipped with PET which is solved in parallel. The obtained subproblems for these grid levels are solved with a nested solution process. The simulation results verify the correctness and effectiveness of the proposed model in this paper.

To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads, a two-stage distributionally robust optimization model is proposed for the active distribution network (ADN) optimization problem considering the uncertainties of the source and load in this paper. By establishing an ambiguity set to capture the uncertainties of the photovoltaic (PV) power, wind power and load, the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables. The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set. The first-stage expected cost is obtained based on the predicted value of the uncertainty variable. The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision. The generalized linear decision rule approximates the two-stage optimization model, and the affine function is introduced to provide a closer approximation to the second-stage optimization model. Finally, the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods, stochastic programming, and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.

In modern societies, people are more likely to be exposed to technological devices that emit extreme low frequency (ELF

The cable joint is a very complex and sensitive part of the electrical cable. Defects in the cable joint and accessories cause deterioration and degradation of the insulation system because of improper performance, production, assembly, and bad installation. The defects in the cable joints may lead significantly to an increase in the temperature distribution. Some specific features and factors represent the cable's working state and the cable joint's temperature behavior. However, the power cable system strength relies on different characteristics such as a cavity, permittivity, size & shape, and cable position. The main purpose of this paper is to determine the temperature distribution behavior of the cable joint effectively and to resolve the shortcoming of the conventional thermal simulation model, based on the concept of heat transfer model and finite element method (FEM) by using COMSOL Software. First, an equivalent model of 11kV cable joint with XLPE and EPDM insulation layers has been designed. Second, the typical artificial particles, including air particles, semiconductor particles, metal particles, knife cut defect, and needle defect, were added to the cable's insulation. Third, the cable joint simulation with each typical particle and defect has been carried out to examine the temperature distribution behavior. The temperature distribution of each particle has been identified individually. The simulation result determines that each particle and defect's temperature distribution behavior has a different response, and the temperature effect change as the particles and defect change.

Surgery is the best treatment for liver canner, segmentation of livers is one of the most crucial steps in surgery. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. Automatic segmentation of livers remains intractable owing to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The paper proposes a new liver segmentation architecture named DU-Net, which is composed of cascade U-Net based on deep separable convolution and deconvolution. We make use of the Dice loss function for training. Finally, we evaluate the proposed DU-Net on liver segmentation task under the dataset provided by the public 3D-IRCADb (3D Image Reconstruction for Comparison of Algorithm Database).The experimental result demonstrates that the proposed network can outperform the U-Net, FCN and RV-FCN methods in term of the performance measures, such as Dice score, VOE, and RVD.

Regarding to the phenomenon that the throttle valve plate was impacted and failed during the test of the damping valve, the reason was analyzed through the water hammer theory, and the physical model of damping valve was built. On this basis, the equation of valve opening varying with time was derived, and the mathematical model of damping valve non-transient opening and closing water hammer was created through feature line method. The influence of the structural parameters of the orifice at the throttle plate on the transient impact on the damper valve was analyzed by programming, and it was concluded that increasing the area of the orifice can reduce the impact of the valve. The conclusions can be used as design reference for damping valves, so as to avoid the recurrence of valve fracture.

The solid phase diffusion (SPD) time constant (SPD-TC) is an important parameter of the fractional-order model (FOM) of the lithium-ion battery. The estimation of SPD-TC is one of significant problem for the battery aging analysis. Both a three-electrode pouch cell with a copper micro-reference electrode and the electrochemical impedance spectroscopy (EIS) are employed to identify the SPD-TC recent years but the method is hardly used in the field of engineering applications. This paper proposes a method to estimate the SPD-TC based on time-domain data of two-electrode battery and a back propagation neural-network (BPNN). First, the FOM of the battery is adopted to generate samples to train a BPNN. Then, the terminal voltage of a ternary lithium-ion battery, measured by a step current discharge experiment, is used as the input of the trained BPNN, and SPD-TCs of positive and negative electrodes are estimated. Finally, the error is analyzed according to the EIS of the positive and negative electrodes.

The modeling of continuum robot is one of the difficulties in this direction due to the large amount of computation, variable coupling and strong nonlinearity. Starting from the model control commonly used in traditional industrial manipulator, this paper analyzes the difficulties of modeling continuum robot, and discusses how to weaken the dependence on model in the control of continuum robot from three aspects of reducing operation, weakening description and bionic motion. Among them, we take the designed 10 DOF elephant trunk robot as the research object, and verify the proposed methods respectively. The purpose of this paper is to provide some ideas and directions for model-weak control and even model-free control of continuum robot.

In this paper, mathematical modeling of a certain type of 6×4 mining dump truck hydraulic balanced oil-gas suspension is carried out, and the elastic characteristic calculation formula of the double-air chamber balanced suspension is derived, and used as the input of the Adams/view multi-body dynamic model. The simulation calculation of crossing trench conditions, analysis of balance suspension cylinder stroke changes and force conditions, verifies the accuracy of the model through actual vehicle tests, and lays the foundation for the determination of suspension system design parameters and the prediction of crossing performance capacity.

The main results achieved by exploiting quartz enhanced photoacoustic spectroscopy (QEPAS) sensors for real world applications will be here reviewed. The obtained results clearly demonstrate the capability of QEPAS-based sensors for in-situ and real time operations. Outdoor monitoring of a CO sensor for several days will be discussed. Results achieved by implementing an SF 6 sensor in an industrial environment for leak detection in mechatronic systems will be also described. Finally, the monitoring of CH 4 around a landfill with a QEPAS mobile system will be reported. All these achievements lead to the first commercialization of QEPAS sensing modules.

By using a three-dimensional (3D) radio-frequency based sensor, which is called Walabot, and machine learning (ML) algorithm, this paper presents a contactless way to generate lithium-ion battery face images for battery voltage classification. First, Walabot was applied to sampling images, which can reflect inside physic structure of lithium-ion batteries (LIBs). Second, these images were preprocessed by data enhancement or wavelet transform. Finally, these preprocessed images were set as inputs of a convolutional neural network (CNN). After images network training, the CNN can be applied to validating test images in different voltage values. Experiment results of five LIBs illustrate that the proposed contactless battery face imaging method provides a totally new way to conduct voltage classification for LIBs.

This article presents a new evacuation model of the tier building. Compared with others, this model has multi-input source and multi-destination. It is based on graph theory and applied to many kinds of accidents. Besides, the model is solved by modified PSO owning quicker solving speed. On the basis of results, we implement linear programming to discern potential bottlenecks that may reduce evacuation efficiency.

The handheld stabilization system is a strong-coupling, nonlinear and time-varying system and its model parameters are not accurate enough. Under the control of traditional control methods, the anti-interference performance of the three-axis handheld stabilizer is often insufficient. In order to solve this problem a control strategy based on the active disturbance rejection control has been proposed. The results of simulation and experiment show that the active disturbance rejection controller has the characteristics of high robustness, low model dependence and simple operation. It can control system stability in a high speed when parameters of the system changed rapidly.

For the 400Hz intermediate frequency inverter power supply, the fundamental frequency is high, and the carrier wave ratio in large-capacity intermediate frequency inverter power supply is relatively small. The traditional control method is difficult to suppress harmonics and it is difficult to realize tracking without steady-state error. In this paper, a composite control method with repetitive control and proportional resonant control is proposed, and the feasibility of the scheme is verified by theoretical analysis and simulation. Finally, an experiment on a 90kW principle prototype shows that the control scheme not only can suppress the voltage waveform distortion of the intermediate frequency inverter power supply, so that the three-phase output voltage achieve THD < 1.5% when the inverter power supply at 90KW , also can guarantee good steady-state and dynamic performance.

The energy crisis has promoted the development of solar photovoltaic power generation systems, but during the operation of solar panels, there will be hidden troubles such as ground fault, line-to-line fault, open-circuit fault, short-circuits fault and the hot spots. This will cause serious obstacles to the power generation of photovoltaic systems. Therefore, the immediate diagnosis and elimination of the fault of the photovoltaic system is the guarantee for the stable operation of the photovoltaic system. To address these issues, this paper makes contribution in the following Three aspects: (1) Building a 4×3 PV array model based on the key points and model parameters extracted from PV array by using Matlab, an efficient feature vector of five dimensions is proposed as the input of the fault diagnosis model; (2) The probabilistic neural network (PNN) is proposed as the fault classification tools, and achieving a good classification effect by using the simulated data after normalization to classify; (3) Performing the field test and inputting the experimental data into PNN for classification, with an accuracy of 97%. Both the simulation and experimental results show that the PNN can achieve high accuracy classification, provide a more favorable premise basis for the intelligent classification of faults in photovoltaic arrays.

With DSP56F8037 of Freescale Ltd. as the central controller, this paper has developed a smart ultrasound conductivity meter system with no pain, minimally invasive, needle-free injection, targeted drug delivery and less side effects. The intelligent ultrasonic conductivity meter system consists of a hardware device, a communication subsystem, a mobile APP subsystem, a website subsystem and a central database subsystem. Specifically, a protection module has also been designed into the system circuit to guarantee the safety and the stability of the system operation. This new medical instrument improves some deficiencies in current treatment technology, and it not only has a good curative effect, but also provides a whole new form of treatment. Furthermore, the interactions among the ultrasound conductivity meter, the communication subsystem and the central database subsystem makes the telemedicine function into reality.

The planning and locating of charging stations is of great significance for the development of EV. The existed methods are often carried out by solving an optimal model, which requires a large amount of user data to simulate user behavior. Considering that a large number of planning schemes are available, this paper proposes a construction planning method of EV charging station based on the conditional Generative Adversarial Networks, aiming to find a quick and convenient planning method and thus reduce the demand for user’s data under the condition of ensuring the rationality of the results. The pix2pix problem was solved by cGANs, and the network structure was adjusted reasonably to avoid over-fitting. The construction diagram of the charging station was captured for training, and the network achieved good results in both the training set and the test set.

For the AC/DC hybrid distribution network with power electronic transformer (PET), a two-stage robust optimization model is established to minimize the operation cost under the uncertainties of renewable energy and load in this paper. The first-stage of the model considers the control constraints and coordinated control of each controllable unit to obtain the scheduling cost of each unit; the second-stage of the model considers the deviations of predicted values and actual values in the dispatching operation, introducing the penalty of the abandoned wind, discarding light and losing load. Introducing the uncertainty adjustment parameters to adjust the conservativeness of the robust optimization solution. Using the column-and-constraint generation (C&CG) algorithm to solve two-stage robust optimization problem, decomposing the original problem into a master-problem and a sub-problem with mixed integer linear features. Finally, through the analysis of the improved IEEE 33-bus system, the simulation results verify that the proposed scheduling strategy can effectively cope with the uncertainty of renewable energy, and improve the capacity of distributed power supply.

This paper proposes a stochastic multi-time scale scheduling model based on scenario analysis for AC / DC distribution network. Taking into account the correlation of errors at each point on the time axis, the multi-scenario technique based on Markov chain is used to simulate the uncertainty and volatility over time of wind power, photovoltaic and load; reducing to get typical scenes through the fuzzy C-means clustering idea. A multi-time scale scheduling strategy is proposed: uncertainty is calculated on a long-time scale, the output of the connecting line and the output of flexible DC devices are optimized with the goal of minimizing the total cost of the power grid; the short-time scale adjusts the output of adjustable unit on the basis of long-time scale. The example verifies that the proposed scheduling strategy can realize the power support between networks and help to deal with the uncertain fluctuations of intermittent energy sources.