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Environmental disturbances and system uncertainties are the main obstacles for wastewater treatment process of paper-making process. In this paper, a neural network based on on LSTM which is a neural network that can learn long-term dependencies is constructed to simulate the environment of the above process. A deep reinforcement learning method is then proposed to optimize the wastewater treatment process. With the proposed design, the control scheme could not only obtain a good performance of the control system, but also can enhance the robustness of the closed-loop system. Numerical simulations are given to demonstrate the effectiveness of the proposed method.

In this paper, a set of near space solar cell accurate test system is designed. The system includes high-altitude scientific balloon, solar tracking mechanism and solar cell high-precision acquisition equipment. This paper solved the problem of high accuracy acquisition of solar tracking control system for high-altitude balloon solar cell test in extreme environment, Continuous tracking and anti-shake and other key technical issues. The paper achieved near space I-V curve measurement for the first time, and the initial analysis of the system test results are reasonable, reliable and stable. The paper work provides a new idea and solution for our country near space solar cells accurate measurement.

In this paper, we address the event-triggered state estimation problem for a class of time-varying complex networks subject to multiplicative noises and stochastic coupling under uncertain occurrence probability. The stochastic coupling is modeled by introducing a set of Bernoulli distributed random variables, where the uncertainties of the occurrence probability is characterized. Moreover, the event-triggered mechanism is employed with hope to reduce the network burden and save energy consumption. The aim of the paper is to design the robust state estimator for addressed dynamics networks and derive an optimized upper bound of the estimation error covariance by properly choosing the estimator gain. Finally, simulations and comparisons are provided to verify the validity of the proposed robust state estimation method.

In practical applications of electromagnetic measurement while drilling (EM-MWD), the signal-to-noise ratio (SNR) of receiver cannot always meet the requirements of reliable communication conditions due to the earth-attenuation, interfering signal from well site, etc. Aimed to solve these problems, this paper presents a BCH encoder and decoder technology based on BPSK for EM-MWD. First, the paper studies the relationship between BCH encoding error performance and decoding method, source length and other factors through simulation; then, we obtain an optimal length of BCH code for EM-MWD through analyzing the bit error performance of hard-decision decoding and soft-decision decoding with different lengths of BCH code. The results show that the proposed algorithm can reduce the bit error rate at a lower SNR, achieving a reliable communication condition when the SNR of a received signal is reduced. It is demonstrated the effectiveness of the proposed BCH encoder and decoder algorithm based on BPSK for EM-MWD.

This paper is concerned with synchronization of congestion control models for underwater wireless sensor networks. Compared with some existing papers, the main contribution of this paper is that synchronization schemes for congestion control models are constructed and used to control the chaos in underwater wireless sensor networks. The conditions for positive solutions of congestion control models are derived. Some bounds of solutions of congestion control models are estimated. Moreover, two synchronization criteria are derived by using two different congestion controls. Furthermore, the conservative analysis of two synchronization criteria is achieved. Two examples are used to illustrate the effectiveness of synchronization control results.

Cooperative lifting by two cranes is widely used in the current construction site. Since the influence of changeable load in the cooperative lifting process, it is essential to take the load distribution into consideration while planning the lifting path to ensure the safety of the whole lifting process. However, most researches did not consider the reasonable actual load distribution or just used a certain load in the lift process. This paper analyzes the relationship between crane operations and the pose of the lifted object. Then based on this relationship, this paper gives the method calculating the distribution of actual load over each crane that the planning approach is based. This paper also gives calculation methods and constraints for the skew angle of each lifting rope. The results of simulation show the effectiveness of this calculation method.

In this paper, we propose an indoor robot autonomous navigation system. The robot firstly explores in an unknown environment, and then navigates autonomously by using the explored map. The robot is equipped a 2D laser scanner as the main sensor. The laser scanner is used for path planning and frontier-based exploration. A 2D global occupancy map is built for path planning, frontier-based exploration and multi-objective autonomous navigation. Laser scans are transmitted into Simultaneous Localization and Mapping (SLAM) process in the exploration phase. In indoor environment, the exploration efficiency is improved by merging a heuristic algorithm. By using multi-threading technology and a 3D perception approach proposed in this paper, the robot equipped with a low-cost RGBD sensor can detect all kinds of obstacles to achieve highly reliable navigation in complicated 3D environment. Meanwhile, we develop a multi-objective navigation application to make human-robot interaction more convenient and satisfy multi-task deployment. Our approaches are demonstrated by experimental results.

This paper studies the optimal operating strategy of high-speed trains to reduce the total energy consumption in fixed time. Optimization of train running trajectory and efficient utilization of regenerative energy are two effective methods for energy-efficient operation. Based on the analysis of dynamic model of high-speed trains, this paper proposes a Genetic Algorithm-Particle Swarm Optimization(GA-PSO) fusion algorithm in the pursuit of a sequence of speed codes at each end of subsection, which shares the same geographical data, to minimize the energy consumption. First, we formulate a two-objective dynamic model with energy consumption and operating time. Second, we design a combined algorithm with real number encoding to obtain the optimal solution. Then, we conduct the proposed optimization method under an actual route condition to verify the effectiveness. The simulation results show that the proposed operating strategy saves 22.81% more than the minimum time strategy and 4.22% than the global optimization on energy consumption. Finally, the situation involving interval speed limit is simulated with proposed method. The results demonstrate that optimal train trajectory saves 6.76% more than the normal train control strategy.

To overcome the shortcomings of high cost, maintenance difficulties in traditional vehicle detection methods. This paper presents a novel vehicle detection method used in the military field. The anisotropic magneto resistive (AMR) sensor is used to detect geomagnetic disturbances in real time, meanwhile, the ultrasonic distance-measuring sensor is adopted to determine the position of the target vehicle and avoid miscalculation. To reduce detection delay, a scheme containing dual AMR sensors and wireless communication module for information transferring is used on this detection system. In order to verify the effectiveness of the detection system, a mathematical model of vehicle detection based on geomagnetic disturbance detection is established. According to the test results, the influence cased by different content of ferromagnetic material and detection distance is analyzed. Finally, the related experiments on the effects of different ferromagnetic contents on the geomagnetic disturbance at different detection distances are carried out. The experimental results verify the correctness of the mathematical model proposed in this paper, and further verify the effectiveness of the detection system. This detection system possesses many prominent advantages such as low -cost, long-life, good performance of real time, convenience.

In this paper, an improved adaptive filtered-x normalized least-mean-square (FxNLMS) algorithm is proposed to achieve active microvibration isolation. A developed magnetostrictive device is used as an isolator to mitigate the microvibration. Whereas, the magnetostrictive material characterizes hysteresis. Although the constitutive and Bouc- Wen model were used to compensate the hysteresis, they are non-analytical with certain approximation. Otherwise, the Preisach model, a popular operator-based model, was also used to compensate the hysteresis. However, due to the double integrals in this model, the hysteresis identification and inversion process is complicated. Instead, this paper applies a modified Prandtl- Ishlinskii (PI) model to estimate and invert hysteresis, which is more simple but effective. The inverse hysteresis is then applied to compensate the magnetostrictive- induced hysteresis with conventional FxNLMS algorithm. Experimental results demonstrate that the improved FxNLMS algorithm enhance the performance of the microvibration isolation effectively.

The affine formation maneuver control problem of a leader-follower type multi-agent systems with the directed interaction graphs is studied in this paper. This paper firstly gives and proves a sufficient and necessary condition of achieving the affine localizability. Then, under the $(d+1)$ -reachable condition of the given d-dimensional nominal formation with $d+1$ leaders, a formation of agents can be reshaped in arbitrary dimension by only controlling these leaders. In the sequel, a novel distributed control method for the followers with single-integrator dynamics is proposed to achieve the desired time-varying maneuvers, and the global stability is also proved. Corresponding simulations are carried out to verify the theoretical results, which show that these followers are tracking the time-varying references accurately and continuously.

This paper mainly talks about some equivalences of the optimal time control problems dominated by the impulsive ordinary differential equation. As the pulse has equipped with the quality of instantaneous effect, so, there is no fundamental difference between integral optimal control problems dominated by impulsive differential system and corresponding differential equation, which is also dominated by integral optimal control problems. And this is well proved by current studies, thus the instantaneous effect has been averaged. However, in terms of the optimal time control problems, the pulse has definite impacts on the optimal time because the optimal time is instantaneous value. Thus, one of the purposes, to study optimal time control problems dominated by the impulsive differential system, is to discover the influences of the pulse puts on the default properties of differential system from the aspect of optimal time control. In this paper, we offered a method to define optimal time and made a further study on the equivalence relation among optimal time control problems, optimal norm control problems and optimal control problems. During this process, we had finished some relevant targets which mentioned in this paper.

The autonomous navigation problem of distributed satellite system is studied in this paper. State-dependent Riccati equation filter (SDREF) is introduced to the autonomous system which consists of two satellites and use relative position measurements. The difference between SDREF and extended Kalman filter (EKF) is compared in computation and filtering step. It is proved that when the SDREF satisfies certain conditions, it can drastically reduce the amount of computation compared to EKF. This paper also propose a modified state-dependent Riccati filter (MSDREF) based on system observable degree by adjusting the error covariance matrix in standard SDREF on line. Numerical simulation results illustrate that the MSDREF can significantly restrains the error augmentation phenomenon in standard SDREF when the system observability is weak.

Due to the high inertia and large time-delay characteristics of the process in controlling the boiler steam temperature, it is very challenging to design the boiler steam temperature control system. This paper proposes a two-degree-of-freedom analytical PID controller based on the internal model principle(IM-TDF-PID), to solve the problems of improving the stability, reducing overshoot and shortening the response time of boiler steam systems. The adjustment of two controller parameters provides the system with good tracking performance and interference rejection characteristics simultaneously. PD feedback is also used in the controller to improve the characteristics of large inertia of the steam. Finally, simulation results and robustness analysis of the IM-TDF-PID controller illustrate the effectiveness of reducing the time delay process when compared with conventional cascade PID controllers. The results show that the method presented in this paper can improve system control performance.

This paper proposes a data-driven control method of autonomous vehicle to deal with target-reaching and obstacle avoidance problem. Since unknown environment and modeling error bring uncertainties to control system, performance of accurate controller is bad. Controller needs to be designed online according to online and off-line data. Neural network possesses good nonlinear approximation and learning ability. So, a kind of recurrent fuzzy neural network is used and related learning method is designed to predict optimal control command. To improve robustness of control system, online tuning PID method is designed to obtain control compensation. By establishing constrained optimization strategy, suitable parameters of PID are got to guarantee optimization of control under disturbances. Experiment results prove that controller proposed in this paper is effective.

This paper design the load frequency controller based on model predictive control (MPC) for micro-grid with electrical vehicles. The MPC controller is designed by minimizing the objective function. Meanwhile, the limitations of the output power increment and the output power increment rate of each subsystem are also considered in this paper. The simulation results show that when the system is subjected to a load step fluctuation, the proposed MPC can reasonably allocate the output of each subsystem, and quickly eliminate the frequency fluctuation, while respecting the equivalent EVs and diesel power system constraints.

This paper studies product yields forecasting for fluid catalytic cracking unit (FCCU). Conventional product yields forecasting is usually based on mechanism model, which may ignore some significant factors due to manual approximations. Deep learning methods can extract features automatically based on data without prior knowledge. Considering bidirectional temporal features and spatial features of FCCU, deep bidirectional long-short-term memory (DBLSTM) network is proposed for product yields forecasting. The bidirectional structure can capture bidirectional temporal features of FCCU by considering previous information as well as future information over a period of time. Significant spatial features of sensors at each time step can be extracted automatically through a deep structure by stacking multiple bidirectional structures. Moreover, the deep bidirectional LSTM network can deal with long-term dependencies by integrating deep bidirectional structure with LSTM cell. To avoid overfitting, regularization adopted in this paper is dropout and early stopping. Efficacy of the DBLSTM approach is demonstrated by process data from an actual FCCU in China. Through the comparison of mean squared error on product yields forecasting, the DBLSTM approach is superior to traditional regression models and other recurrent models.

The research of miniaturized XRF/XRD analyzers are carried out in this paper, especially the qualitative analysis based on EDXRF, a miniaturized energy dispersive X-ray fluorescence spectrometry platform is designed and a qualitative elemental analysis software based on Android operating system is developed. The elemental qualitative analysis software can preprocess the X-ray feature spectrum by background eliminationsmoothing and energy coefficient calibration, after these pretreatments, the main elements of the sample can be analyzed. The experimental results show that the qualitative elemental analysis platform proposed in this paper can measure the elements of Al to Au in the periodic table, besides, the detection accuracy of multiple samples is higher than 97%. The mineral qualitative elemental analysis software proposed in this paper can be applied to any portable analysis platform based on Android operating system, moreover, it will spur advances in the development of the miniaturization and intelligent X-ray fluorescence spectrometer.

The de synchronization control of a network with two-compartment neurons is investigated in this paper. Neurons of the network are coupled with synaptic and embedded within a resistive array with external electric field. To close the loop of desynchronizing control, a feedback variable which can characterize the synchronous states of neurons is critical. But the only measurable variable of neurons in experiments is the membrane voltage. So in this paper we first analyze synchronization properties of the network to find out the key biomaker as the feedback variable, then we reconstruct this variable from the measurable membrane voltage based on unscented kalman filter. Numerical simulations show that the proposed methods can effectively mitigate the synchronous states of neurons, and have a better performance than the open-loop control.

A new wearable power assist device for lower back support is proposed in this paper. The waist power assist suit is designed to relieve lower back pain and it is equipped with four push rods to realize the three-dimensional rotation around the human waist joint. Mathematical model of the device including the kinematics and the dynamics is presented on the basis of the freedom analysis. A kind of lumbar rehabilitation movement is put forward. To ensure the coordination work of the four motors, an improved mean deviation coupling control strategy is proposed in this paper. To demonstrate the effectiveness of the proposed synchronous control strategy, simulation is carried out in Matlab/Simulink and the simulation results show better performances than the compared deviation coupling cooperative control.

This paper studied the robust $H\infty$ stochastic stability problem of nonlinear stochastic network control system containing time-varying delay. There are two kinds of time delays researched here. One is a constant delay in controlled object, another is a network-induced delay which is a variable time delay occurring when the sensors, controllers, and actuators of the network control system exchange data through the network. This paper concerned the parameter uncertainties and employed the improved free weighting matrix (IFWM) method to obtain the less conservative robust $H\infty$ stochastic stability criterion of the system in this paper. A numerical example is given to illustrate the suitability of the method proposed in this paper.

Vanadium Redox Battery (VRB) has the advantages of large capacity, long life, safety and environmentally friendly. This paper proposes a double closed-loop charge-discharge control strategy of VRB based on cerebellar model arithmetic computer (CMAC) to ensure safe charge-discharge and shorten the charge-discharge time. The proposed control strategy consists of two main control loops: an outer voltage loop and an inner current loop. The outer control loop is designed as a voltage loop with CMAC controller which uses kernel voltage for feedback, and the inner current loop adopts PI regulator with amplitude limiting. In this paper, we first build a mathematical model of VRB of 5 kW/30kW· h. Then, the proposed control strategy and conventional PID control strategy are respectively applied to VRB model and comparisons are made. Results show that under the proposed control strategy, charging time of VRB shortened by 3.03%, discharging time of VRB shortened by 9.37% compared with conventional PID control strategy, and the terminal voltage can be guaranteed in a safe range, thus realizing fast and safe charge-discharge of VRB.

This paper sums up four typical schemes of adaptive dynamic programming (ADP). The diagrams are provided and the algorithms of various schemes are described, which is convenient for comparison. Some schemes in this paper belong to the group of action-dependent (AD) adaptive critic designs, which features without a model network in the design. For simplicity of notation, we do not use the prefix AD. The learning process of ADP is accomplished by updating the weights of the networks. The weight updating processes of some networks in GDHP scheme are introduced.

Microgrid, as a buffer and link between renewable energy and Power Grid, represents the development trend of a power energy organizational structure in the future. Microgrid is to promote energy development and management changes in the important carrier, to facilitate energy-saving and emission reduction and to achieve sustainable energy development of great significance. This paper introduces the research status of the microgrid control strategy both at home and abroad, and proposes the future development direction of the microgrid control strategy. This paper first classifies the control strategy of micro power supply, and expounds the research status of three control strategies: V/f control, PQ control and droop control. Then, the overall control strategy of the microgrid is classified. The research status of the four control strategies, namely peer control, master-slave control, hierarchical control and decentralized control is described respectively. Finally, the advantages and disadvantages of various control strategies of the microgrid are elaborated.

Cyber-Physical Systems (CPS) is mostly deployed in security-critical applications where their failures can cause serious consequences, and therefore it is critical to evaluate its availability. In this paper, an architecture model of CPS is established from the perspective of object-oriented system. The system is a unified whole formed by various independent objects (including sensors, controllers and actuators) through communication connection. Then the paper presents the Object-oriented Timed Petri Net to model the system. The modeling method can be used to describe the whole system and the characteristics of the object. At the same time, the availability analysis of the system is carried out by using the mathematical analysis method and simulation tool of Petri net. Finally, a concrete case is given to verify the feasibility of the modeling method in CPS availability analysis.

In this paper, an extremum seeking control with the sliding mode method for a motor driving system with deadzone nonlinearity is presented. The proposed scheme can cope with tracking control problem for the motor driving system with high relative degree. A novel cost function is designed to not only track the reference signal but also compensate for the nonlinearity. The simple form of the designed scheme makes it practical. Stability analysis proves that the present scheme can converge to a neighborhood relating to the cost function. The scheme in this paper can also ensure a satisfactory tracking performance with varying system parameters. Simulation results demonstrate the effectiveness of the proposed scheme.

In recent research, the macroscopic fundamental diagram (MFD) has been proved to be a powerful tool for large urban network modelling and control. This paper proposes a novel concept of queue length based MFD (QMFD) considering the fact that queue length is usually regarded as an important index to evaluate the efficiency of intersections. Compared to traditional MFD, the QMFD can reflect the traffic status more intuitively and can be understood more easily by transportation managers and residents. However, the queue length of some links may not be obtained directly in real situations if no fixed detector is available. To solve this problem, this paper proposes a floating car data (FCD) based method to estimate the QMFD. Firstly, a new queue length estimation method is developed by using BP neural network with different floating car percentage. Secondly, based on the estimated queue length, QMFD is calculated by fitting the relationship between the average queue length and other macroscopic traffic parameters such as average flow at intersections. Finally, the proposed method is verified by the traffic data provided by traffic simulation software VISSIM with the real road networks of Beijing's Second Ring Road. The simulation results demonstrate the effectiveness of the proposed queue length estimation method, and also reveal the existence of QMFD.

The paper deals with the maximum power point tracking (MPPT) problem in large-scale wind turbine systems. The inertia of the large-scale wind turbine is very big and the optimal torque control (OTC) only considers the steady-state operating point of the system, leading to a long tracking time. For the existing sliding mode control (SMC) based on OTC, the MPPT efficiency is improved at the cost of the fluctuation of generator torque. In this paper, a sliding mode controller with variable parameter is proposed for improving MPPT efficiency. Compared with the existing control strategies, the performance of the proposed method is validated by using a FAST (Fatigue, Aerodynamic, Structure, and Turbulence) model based on the 5MW wind turbine. Simulation results clearly show that a more rapid speed response could be obtained through the proposed method, while the power capture efficiency is increased and the fluctuation of generation torque is decreased.

In this paper, a fault detection scheme based on the data-driven K-gap metric is proposed. Using the I/O data collected from the process, a computation identification of the K-gap metric between the fault free system and the fault system is derived, based on which a fault detection threshold is set with the consideration of system disturbance. A case study on a ship propulsion system is presented at the end of the paper and demonstrate the effectiveness of the proposed results.

This paper addresses the Kalman filter divergence problem for Global Navigation Satellite System (GNSS)in Precise Point Positioning (PPP). Continuous improvement of precision of GNSS PPP makes it widely used in various fields, however the Kalman filter divergence resolutions for PPP signal attenuating and satellite missing condition are still insufficient. Kalman filter is a recursive, linear unbiased, minimum variance method. Compared with the standard Kalman filter, the dynamic Kalman filter with attenuation factor in this paper makes up for the Kalman filter divergence problem. The effectiveness of dynamic Kalman Filter is demonstrated via matlab simulation.

The wind field plays an important role in air-ground guided weapons. In this paper, we design a wind correction algorithm for WCMD based on the integrated navigation information. Firstly, the WCMD 6DOF simulation model is constructed in MATLAB / simulink environment, and the wind speed and wind direction in the horizontal plane are estimated by vector triangle method. The corresponding proportional navigation law is designed, which modifies the longitudinal and lateral deviation based on the estimated wind speed. Finally, the wind correction simulation of WCMD is performed, and the results show that the wind correction algorithm designed in this paper can ensure the submunitions landing accuracy.

This paper establishes an approximate optimal critic learning algorithm based on single-network adaptive dynamic programming (ADP) aiming at solving for continuous-time 2-player zero-sum games(ZSG). However, the situation where the accurate dynamics is influenced by disturbance will occur from time to time. Because neural network(NN) is used in this paper, we have to face the approximation error, which will disturb the control. In order to surmount this problem, we use online data to calculate the weights of NN, and design robust controller to stabilize the disturbed nonlinear system. In other way, we used policy iteration and integral reinforcement learning to settle the Hamilton-Jacobi-Isaacs equation. And through the leastsquares method, the NN weights are solved. Based on the theoretical analysis, this algorithm is a derivation from Gauss-Newton method, which can solve an optimization problem without disturbance. Thus it will converge to the optimal value. Because large quantities of online data are used, the process will accurately converge optimal control. Simulation results can verify that it's realizable to deal with disturbed nonlinear ZSG.

Reasonable task scheduling is a long-standing challenge in cloud computing. Scheduling process of cloud computing has the characteristics of dynamic nature, meanwhile the constraint of the target function from a single aspect cannot meet the needs of users. According to the above problem, a multi-objective task scheduling GA-DE algorithm based on Genetic Algorithm (GA) and Differential Evolution (DE) is proposed in this paper, in which total time, cost and virtual machine load balancing three aspects are taken into account simultaneously. In the phase of population initialization and crossover, diversity of the initial population is increased by introducing individual different factors, which can prevent cross-operation of similar individuals and satisfy laws of inbreeding of natural relatives. The introduction of the DE in the GA mutation stage can not only give full play to the advantages of the global search ability of GA but also accelerate the algorithm produce optimal solution by utilizing advantage of local search ability and fast convergence speed of DE. The algorithm proposed in this paper is compared with GA and DE by cloud computing simulation experiments on CloudSim platform. Experimental result shows that this algorithm can optimize both GA and DE in terms of quality of service and virtual machine load balancing under the same conditions, which is proved to be an efficient task scheduling algorithm in cloud computing environment.

This paper introduces a kind of zero energy consumption LED intelligent lighting system based on the technology of PoE. The system is a new kind of energy-saving innovative LED intelligent lighting control system based on PoE technology, and the only power source is solor energy. It transfers DC power and data on Ethernet at the same time, reduces the data network failure caused by the power adapter failure and promotes the application of the low power network equipment. By sensing the human activity in the current room and lighting intensity, the system determines the indoor lighting requirements automatically to achieve the intelligent control of LED lighting. This paper describes the system architecture based on mesh topology structure, presents the hardware circuit of the key equipment in the system, analyses the experimental process and gives the experimental results. This design enbodies the zero energy and intelligent lighting.

This paper is concerned with the resilient containment control for discrete-time multi-agent systems subject to packet dropouts. Based on relative measurement outputs, the purpose of this paper is to design an output-feedback controller such that all followers converge into the convex hull spanned by leaders in mean-square sense. First, with the help of the property of the Laplacian matrix, the containment control problem is transformed to an easy to analyze form. Then, sufficient conditions with the form of matrix inequalities are derived to guarantee the desired containment in mean-square sense. Further, by introducing a free matrix combined with an orthogonal basis of the null space of matrix, the controller gain can be obtained by solving a set of linear matrix inequalities. Finally, an example is exploited to illustrate the effectiveness of the established results.

The microphone array speech enhancement algorithm (MASEA), the minimum mean square error algorithm for short-time logarithmic spectrum estimation based on voice activity detection (VAD-LSA-MMSE), and the Wiener filtering algorithm based on voice activity detection (VAD-Wiener) are currently the three most commonly used speech enhancement algorithms. Among them, the MASEA algorithm has some disadvantages such as poor noise reduction effect. VAD-LSA-MMSE algorithm has some disadvantages of relying on high SNR and introducing music noise, thereby reducing the intelligibility. The VAD-Wiener algorithm has some disadvantages such as higher SNR requirements. Aiming at the shortcomings of these three speech enhancement algorithms, based on the VAD algorithm and the MASEA algorithm, this paper proposed a new speech enhancement algorithm by combining the characteristics of Wiener filtering algorithm and LSA-MMSE algorithm. The new speech enhancement algorithm is a VAD-based microphone array speech enhancement algorithm (VAD-MASEA). VAD-MASEA is better than the other three algorithms in noise reduction, speech enhancement and voice intelligibility, and has the characteristics of adapting to a lower SNR environment. This paper used MATLAB to carry out experimental research, including the new algorithm and the three existing algorithms were simulated and compared the signal waveforms of the four algorithms. Experimental results show that the proposed VAD-MASEA algorithm overcomes the high SNR requirement and can be used in low SNR environments and obtain highly intelligible enhanced signals.

This paper proposes a multi-task detection system for garbage sorting based on high-order fusion of convolutional feature hierarchical representation. This system achieves the identification and localization of small target objects in complex background before using manipulator to automatically grab the sorting objects. The object recognition in complex background is a key problem which the system is trying to solve. The existing convolutional neural networks based object detection algorithms are usually designed for large target objects, lack of positioning ability for small targets, and cannot estimate the target pose changes at the same time. Aiming at the above problems, this paper, from the perspective of Hyper-Column, introduces the high order feature, which has been used in image classification into object detection. For the sake of retaining the position information, this paper employs a second order feature schemes in detection. The results of bottle sorting experiment in the garbage show that the algorithm and manipulator control method of the proposed system can efficiently achieve the garbage sorting.

The model of the carrier-based UCAV is the basis for the design of the flight control system, which requires that its accuracy must conform to the design requirements. The nonlinear model of the carrier-based UCAV is difficult to get accurately. The aerodynamic layout of the carrier-based UCAV in this paper is used normal pneumatic layout design, with V wing and low-wing used small angle of sweepback and high aspect ratio. A complete nonlinear model is built through aerodynamic data fitting in this paper. It is necessary to model and simulate the control system of the aero-surfaces, on account of the V wing is controlled by the elevator and the rudder. The control system of the aero-surfaces is distinguished from the previous control system of the carrier aircraft, it is difficult to use the traditional trim method. A new trim method is established on the balance of aerodynamic force and torque. In the simulated of the carrier-based UCAV model, it's verified the accuracy by the responses to elevator and throttle step command. The model of the carrier-based UCAV provides an accurate model for the future simulation research which is established in this paper.

Rotary modulation and control algorithms are the key technologies of the rotating strapdown inertial navigation system. The complex rotation scheme earns limited accuracy, while causing larger size, lower reliability and higher additional cost. This paper analyzed the sources of inertial sensors' errors, compared similarities and differences, and evaluated the performance of all kinds of single-axis rotation modulation schemes. Initial alignment requires transposition in high accuracy and the rotary motor needs to stay a steady pace in actual practice under multiple interferences, so this paper designed a dual closed-loop controller for all concerned, with an ordinary PID in the position loop and ADRC controller in the speed loop, ensuring the modulation runs smoothly. The simulation results show that the system has some good characteristics such as fast response, no overshoot and good robustness. Compared with the original PID control, the new control algorithm helps rotary motor improve its anti-jamming performance.

This paper deals with the reconstruction of probabilistic logical networks. Using semi-tensor product of matrix, probabilistic logical network is converted into algebraic form. In this paper, we propose an efficient algorithm for reconstruct the original multi-valued logical network from its probabilistic matrix. It was pointed out that the reconstruction of the network is not unique. Also, an illustrative example is given to testify the algorithm.

With the rapid development of data analysis technology, the implementation of smart grid construction, the mass installation and use of smart sensing devices, the combination of data analysis technology and smart grid is an inevitable trend. The power data will bring great benefits to power companies. Among them, the topology estimation of distribution network is the cornerstone of smart grid data analysis technology. This paper firstly introduces the voltage deviation analysis of the users on the distribution network, and uses the relationship to conduct a series of topology analysis of the distribution network, such as topology verification, phase identification and topology estimation. The data used in this paper is the actual electricity data of a city, and the result of topology verification is validated through actual investigation.

In this paper, a fully distributed adaptive method is proposed to solve the simultaneous attack problem with multiple missiles against maneuvering targets. When the target acceleration is bounded, the relative distance and relative velocity between the missiles and target are taken as the state variables. The communication graph and missile-target dynamics are decoupled by neighborhood information and adaptive coupling gain coefficient. Without the access to global information, the designed fully distributed adaptive guidance law can realize cooperative and simultaneous attack against maneuvering targets. The fully distributed adaptive cooperative guidance law proposed in this paper can be extended to general linear systems and can be applied to a variety of cooperative attack scenarios. Numerical examples are given to demonstrate the effectiveness of the proposed control method.

Aiming at the problems of slow recognition, low efficiency and degree of automation in handwritten letter recognition system at present, a handwritten letter recognition system based on extreme learning machine is designed in this paper. The system is implemented by mixed programming with MATLAB and visual studio, it can reads, normalize, binarize and extract the handwritten letter images. The real-time interactive recognition of handwritten letters can be realized on the basis of training the simple pictures by using the identification model of the extreme learning machine algorithm. The experimental results show that the handwriting recognition system based on extreme learning machine designed in this paper can recognize 98.82% of handwritten letters and greatly reduce learning and testing time. Compared with BP neural network and other recognition algorithms, its training times have been reduced by hundreds or even thousands of times. At the same time, there is no manual intervention in the entire learning and testing process, which improves the automation of handwriting recognition.

With the continuous growth of China's power grid scale and load, more and more attention is paid to the security and stability of regional power grid. Since the proportion of power generation, transmission and substation planning in some areas is not coordinated, grid structure is not reasonable, this will lead to the frequent fault of safety and stability of the power grid, so some measures must be taken to optimize the transformation. In this paper, it use operation of the power grid in northern Chenggu area as the research object, which belong to Hanzhong, Shaanxi Province. Based on the analysis of the general methods and the steps of regional power network calculation, combined with the characteristics of the grid structure in the area, this paper analyses the typical operation mode of the regional power grid, based on the analysis results, the reactive power optimization is optimized. Aiming at the stability problem of regional power network, and according to different operation mode, this paper puts forward the corresponding measures to improve the level of power grid stability.

This paper establishes the evaluation index system of regulatory environment for overseas electricity market and puts forward the method of combination weighting - improved portfolio weighting method based on gray relational analysis. Based on this, a model for assessing the regulatory environment of overseas electricity market has been established. Regulatory environment of four electricity market including Greece, Italy, Sweden and Portugal was assessed. The results show that the combination weighting method proposed in this paper can effectively reduce the heterogeneity of experts' weight judgment and can better highlight the differences between the evaluation results of different countries.

Based on the product research of foreign advanced and the relevant standards of the development requirements and product assessment and verification, this paper develops an engine reliability test control system. Based on motor gasoline engine as the research object, this paper builds a more accurate mathematical model of Simulink and designs RBF-PID controller, and control the engine model, finally, the simulation shows that designed controller has good control effect, fast response speed, and strong adaptive ability, and achieves satisfactory results.

This paper investigates the finite-time parallel simultaneous stabilization problem for two nonlinear descriptor systems. Firstly, we provide a sufficient condition which can guarantee to impulse-free of descriptor system. Then, by constructing a suitable Lyapunov function and designing an effective output feedback controller, we combine two nonlinear descriptor systems to generate an augmented nonlinear descriptor system such that the system can be proved the finite-time stabilization. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach and the simulation results are showed in this paper.

Washout filter algorithm is one of the key factors that affect the fidelity of the crane simulator. Because algorithm structure and filter parameters are fixed in classical washout algorithm, a washout algorithm based on fuzzy control is presented in this paper. The paper realizes the real-time adjustment of washout filter parameters by means of the fuzzy control characteristics, and illustrates the realization process and optimization process of the algorithm. Finally, the simulation results show that the longitudinal motion and the pitching motion of the simulator are simulated by Matlab software, the paper also verifies the effectiveness and feasibility of the washout algorithm based on fuzzy control by comparing with the classical washout algorithm and genetic algorithm.

In this paper, we study the cascading state-space decomposition problem for Boolean control networks by nested method. Firstly, based on the algebraic forms of BCNs, we give several algebraic criterions for the cascading state-space decomposable of BCNs. Secondly, we propose the concepts of multi-layer nested block matrix, and obtain two sufficient and necessary conditions for the cascading state-space decomposition of BCNs. Furthermore, a method is given to design controllers. Finally, an example is given to display the effectiveness of the method provided in this paper.

In this paper, the consensus problem is investigated for a general protocol of second-order multi-agent systems with diverse time delays. Different from the existing results, in this paper different consensus dynamics including linear, periodic and positive exponential dynamics can be realized by selecting different gains. Through matrix theory and frequency domain analysis, a sufficient consensus condition is obtained. Simulation results illustrate the correctness of the results.