Abstract:Under multipath interference, the transmission link of Internet of things is easily affected by multipath effect, which leads to poor channel equalization, and high bit error rate (BER) of Internet of things data forwarding. In order to improve the load balancing control ability of IOT link under multipath interference, This paper presents a load balance control model for Internet of things data forwarding based on fractional interval equalization and bit error rate feedback modulation technology. The model of Internet of things transmission link under multipath interference is constructed, and the multipath characteristics of Internet of things link are analyzed. The spread spectrum channel modulation method is used to filter the inter-symbol interference in the Internet of things link, the fractional interval equalization technique is used to design the channel equalization, and the adaptive channel bandwidth adjustment model is used to suppress the multipath of the Internet of things link. Combined with bit error rate feedback modulation and demodulation technology, load balancing control of Internet of things link is realized, which overcomes the influence of channel on multipath interference and improves load balance of Internet of things link. The simulation results show that the proposed algorithm is used for load balancing control in the Internet of things link. The code element fidelity of the data forwarding output is good, the bit error rate is low, and the link forwarding adaptive control performance in the multipath interference channel is better. The robustness and robustness of the Internet of things link are improved.

Abstract:The data rate is an important parameter in the tracking management of the electronic reconnaissance system. The size of the data rate determines the tracking accuracy, and also affects the system resource consumption. In order to balance the tracking accuracy and resource consumption, an adaptive data rate algorithm is needed. However, the current adaptive data rate algorithm of the active radar is not suitable for the electronic reconnaissance system because of the discontinuity of the radiation source in the time domain. In order to address these issues, the radiation source antenna scanning period as a constraint is introduced, and predictive covariance threshold algorithm is improved to be applied to electronic reconnaissance system. In the improved algorithm, the integer multiple of the scanning period of the radiation source antenna is used as the sampling interval, and the prediction covariance is obtained by the bearing-only tracking algorithm. When the prediction covariance is greater than the set threshold, the sampling is performed. The simulation results show that the method can adaptively get the sampling period according to the set tracking accuracy, save system resources.

Abstract:A method of fractional sliding mode variable structure control method based on neural network optimized by particle swarm optimization (PSO) is studied and applied to the path tracking of industrial robots. Firstly, the neural network optimized by particle swarm optimization is used to identify the system model of industrial robots, and the model most relevant to the resolution of system control parameters is trained. Then, the fractional order sliding mode variable structure controller is designed based on fractional order theory and sliding mode variable structure theory, which is applied to the path tracking of industrial robots as the main controller.Simulation and experimental results show that this method has good tracking performance, fast and robust.

Abstract:In order to improve the stability of permanent magnet synchronous motor control system, a novel current controller based on linear sliding mode active disturbance rejection is presented, the so-called linear sliding mode active disturbance rejection control is the combination of sliding mode control and active disturbance rejection control,which is based on the chattering phenomenon of sliding mode control and the disturbance of the system. Extended state observer is the core of active disturbance rejection control (ADRC),which can effectively suppress system disturbance and improve the dynamic and static performance of the system.The simulation model of permanent magnet synchronous motor (PMSM) sliding mode active disturbance rejection vector control is built.The simulation results show that compared with the traditional sliding mode control, this method improves the stability and disturbance problem of PMSM control system.

Abstract:In order to improve the flight stability, an anti-disturbance control algorithm for UAV based on multi-dimensional spectral peak combined weighted search is proposed. The spatial dynamics model of UAV flight is constructed in the velocity coordinate system, the body coordinate system and the ballistic coordinate system. The distributed source modeling method is used to analyze the beam directivity characteristics of UAV flight. The steady-state tracking error of UAV is controlled by the weighting coefficient of beamspace direction, and the small disturbance suppression is carried out by combining the width of the main lobe of the beam, and the joint estimation of UAV flight control parameters is realized by using the multi-dimensional spectral peak joint weighted search method. Error feedback and disturbance suppression are carried out according to parameter estimation results to improve flight stability control performance. The simulation results show that the proposed method has better anti-disturbance performance, higher accuracy of joint estimation of flight control parameters and better output stability.

Abstract:It is of great significance to the reasonable power system planning and guarantee the safe operation of the power system that determine the distribution network planning. This paper puts forward the evaluation index system of power distribution network planning considering micro grid access. Distribution network planning scheme for the calculation of index weight through the improved AHP method, and the final plan evaluation using the weighted sum method. Collecting multiple expert opinion and according to the different structure of the expert experience weighted average of the judgment matrix and improving the consistency check method is the AHP improved thoughts.Finally, through the case analysis, using the proposed evaluation method and comprehensive score of 4 kinds of planning. Grading result is the plan 1, 2, 3, 4 respectively scored 0.285, 0.196, 0.307, 0.212. The evaluation result and expert evaluation result are consistent, and this method was verified in the scientific nature and rationality of evaluation on power distribution network planning.

Abstract:In industrial production, liquid level control system has been widely used, but for this complex control system with large delay and non-linearity, the traditional PID control method has the drawbacks of difficult parameter tuning and unsatisfactory control effect. Based on the research of traditional PID algorithm, fuzzy control algorithm and neural network algorithm, this paper puts forward a solution of applying fuzzy neural network PID algorithm to liquid level control system. Matlab is used to simulate the control of liquid level object, and A3000 water tank experimental platform is used to verify the simulation results. The results show that the liquid level control system based on the PID algorithm of the fuzzy neural network is superior to the traditional PID algorithm in adjusting time and overshoot, and has better control effect and anti-interference ability, which overcomes the shortcomings of the traditional PID algorithm.

Abstract:Ant colony algorithm is a kind of heuristic search algorithms. It has been widely used to solve complex combinatorial optimization problems. Basic ant colony algorithm has some disadvantages, such as slow convergence and premature stagnation. In order to overcome these problems, we propose an improved ant colony algorithm, which is based on search concentration and dynamic pheromone updating. Specifically, by introducing the “Search Concentration” factor in the selection strategy, the algorithm can adaptively adjust the range of cities selected by the ants. In addition, increments of pheromone are dynamically changed and a kind of pheromone rollback mechanism is used. As a result, the search time are shortened and the algorithm is more easy to jump out of the local extremum. Simulation experimental results show that the improved algorithm has a faster convergence speed, improves the global understanding, and effectively avoids the algorithm falling into local optimum.

Abstract:As an important living facility, electric power facility is of great significance to the real-time monitoring of its working current. Based on wireless sensor network technology, a wireless sensor network consisting of multiple current acquisition nodes and a master control module of integrated wireless communication technology is developed to monitor the current of distribution network. The current acquisition node using current transformer current signal acquisition node, and through the ZigBee technology will collect the results sent to the central processing unit coordinator of the unit, by the central processing unit to deal with the data after using GPRS technology sends the data to match the webmaster station server, at the same time using GSM sends the data to mobile terminals, such as mobile phone distribution network related staff. This paper designs and develops the hardware circuit and software program of the corresponding module. The test results show that the minimum measured current of the developed current monitoring system is 200 mA and the dynamic range is up to 24 dB.

Abstract:Aiming at solve the problem of the power quality influence by wind power integration, the integration characteristics and fault characteristics of wind power station is the thorough analysis, a novel method is proposed in this paper, the method makes combination with harmonic wavelet noise reduction and time-frequency joint analysis, and it is used for voltage fault signal feature extraction of wind power integration, this method has solved the problems that the traditional Fourier transform can′t process the fault testing signal with non-stationary character. The method makes full use of harmonic wavelet noise reduction technology for purification of non-stationary voltage fault test signal in strong noise, and then the time-frequency combined analysis is used to accurately locate the voltage fault of wind power integration, the method is able to provide necessary reference decision for fault crossing. Finally, the effectiveness of the method is verified by simulation and experiment.

Abstract:The characteristics of the concealed reception of the passive radar network make it have a advantage in the anti-jamming.This paper applies passive radar to ship formations. First, constructing a passive radar system with different receiving stations and different topological structures. Then, proposed p and q indicators, Evaluated the detection capability of the station topology from anti-interception capability, jamming exposed range of the system. At last, summarized the detection performance when using the particularly number of stations, gave advise on the number of station sites for warship formations.

Abstract:The battery management system plays a very important role in pure electric vehicles for new energy vehicle. The distributed power battery management system is an integrated system with multi-module control. The complexity and hysteresis of CAN bus communication between each control module is aimed at this problem. It was tested by CANoe/MATLAB joint simulation method. Among them, the multi-module communication model of signal acquisition, SOC estimation and fault diagnosis is established in the MATLAB/Simulink simulation modeling environment. At the same time, the CAN network architecture of the distributed battery management system is established on the CANoe bus simulation platform, and the multi-module is verified. The real-time and accuracy of communication. The results show that the CANoe/MATLAB joint simulation method provides a convenient way to develop the communication strategy of the distributed battery management system.

Abstract:In order to meet the needs for the integration and miniaturization of CCD imaging circuit, a new high performance imaging circuit system is designed in this paper. Using FPGA as the core of the imaging circuit, configuring the A/D converter to realize analog to digital conversion, coding and synthesizing the converted image data and arranging the data transmission format, realizing the communication between the imaging circuit and the outside by the telemetry and telecontrol three wire interface, so as to receive the auxiliary data on the satellite and the adjustment instructions of the imaging parameters. The experimental results show that the imaging circuit software designed in this paper can meet the test requirements of video processing function of satellite camera. The imaging effect is clear, the hardware circuit is simplified and the software integration is improved. It has high engineering application value.

Abstract:In order to master the training state and the characteristics of fighting athletes, it need to obtain a lot of real-time data. By analyzing the characteristics of the process of fighting sports, a defensive and offensive model of sport response test and training is constructed, which is easy to monitor and collect the human fighting sports data, aiming at the special needs for the fast response and data collection of sport training. Based on this model, the basic requirements of fast response training and testing system are summed up, and a four-layer architecture design scheme is put forward by using software engineering technology, which is realized by computer hardware and software technology combined with real-time data acquisition and interface design. Finally, the practical verification is carried out by the application of the system, and the visual expression is given by the graph, and the system can meet the requirement of obtaining real-time data in fight sports training test.

Abstract:The thermal vacuum environment-simulating test provides a way of simulating surface heating load of aircrafts. In order to simulate the high vacuum and high heat, a set of thermal vacuum environment-simulating controlling and detecting system is established. The quartz radiation heating system is utilized as the heating source. High heat and temperature will damage the spacecraft and may even harm the safety of the spacecraft. Therefore, the covering methods of signal cables with thermal insulating materials are evaluated based on the thermal vacuum environment-simulating test. Experimental results indicate that the aluminized film outperforms alkali-free glass fiber in cable protection, which satisfies the thermal control requirements of spacecraft. Thus, this kind of test can be employed in demonstrating the thermal protection scheme within thermal environment.

Abstract:Temperature drift is an important factor affecting the accuracy of resonant sensors. Therefore, temperature compensation is essential in precision measurement. Then a new structure of resonant pressure sensor for temperature compensation is proposed.The overall structure includes three layers of silicon-glass-metal, thermal stress can be offset when the thermal expansion coefficients of these three materials satisfy a certain relationship. Moreover,a compensation beam is provided on the silicon substrate to further compensate the temperature drift of the working beam.In order to choose the suitable glass material,finite element method is used to study how the thermal expansion coefficient and thickness affect temperature sensitivity.The results show that the sensor has the lowest temperature sensitivity when using pyrex7740# glass with a thickness of 1.5 mm and the structure does compensate the temperature and improve the accuracy.

Abstract:For the traditional Brenner definition evaluation function, which is not ideal in the image evaluation with large amount of defocus and complex content, this paper proposes an improved Brenner definition evaluation function.On the basis of the Brenner evaluation function, this function calculates the image gradient by adding a set of vertical mask template and combining the horizontal direction template of Brenner operator itself.Finally, the effectiveness of the improved method is verified by comparing the different fuzzy degrees and the noisy images.Experimental results show that the improved method on the basis of the Brenner function improved the accuracy at the same time, the calculation time is better than other common gray evaluation function, effectively improve the traditional Brenner definition function evaluation effect.

Abstract:At present, the calculation of the external surface settlement of the deep foundation pit is based on the premise that the soil is an ideal undisturbed soil. This does not match the actual project. The external surface settlement of deep foundation pits cannot be well characterized. Aiming at this problem, an improved calculation method for ground settlement of deep foundation pit support is proposed. Firstly, based on the theory of damage soil mechanics, a soil damage model is established. Then, using the soil evolution and constitutive relation under the damage condition, the calculation model of ground settlement supported by the foundation pit is constructed. The experimental results show that the model can well describe the settlement process of the supporting ground surface in deep foundation pit engineering, which is consistent with the engineering practice monitoring results. The experimental results also prove that soil damage and surface overload have a great impact on the foundation pit support.

Abstract:Handwritten digit recognition has important application value in today′s society, and has broad application prospects in finance, social networking, education, communication and other fields. Handwritten digit recognition is a branch of optical character recognition technology. The common methods are identified by BP neural network, but there are three defects in BP neural network, such as local minimum, slow learning speed, and structure selection, and the optimization algorithm is used to optimize its structure. In this paper, genetic algorithm is used to optimize the initial threshold, initial weight and structure of BP neural network to overcome its shortcomings. The study of handwritten digital recognition as an object is carried out. The results of Matlab simulation show that the BP neural network optimized by genetic algorithm has the advantages of higher recognition accuracy, stronger generalization ability, faster convergence speed and stronger practicability, which provides a good theoretical basis for handwritten digital recognition.

Abstract:Temperature is an important factor affecting the life and safety performance of power batteries. Under complex driving conditions, temperature prediction control of vehicle power batteries is very important. Aiming at the problem of the accuracy measurement of the temperature measurement of the pure electric vehicle power battery, the temperature data of the vehicle under the typical driving conditions of FTP75 and UDDS are collected respectively. Based on the thermodynamic model of the power battery, the parameter model based generalization is adopted. The predictive control algorithm establishes the vehicle model of pure electric vehicle and the accurate prediction model of power battery temperature, and builds a test bench for power battery temperature measurement. The test results show that the model predicts the temperature in two typical driving conditions of vehicle FTP75 and UDDS. The error with the actual temperature is very small, and the generalized predictive control algorithm model satisfies the target demand for accurate prediction for the temperature of vehicle power battery.

Abstract:Load forecasting is the basis of safe operation of power system. Due to the randomness and volatility of residential electricity load, it may affect the normal operation and maintenance of power system. Therefore, accurate prediction of residential power load provides favorable guidance for real-time dispatching of power grid. In this paper, an ultra-short-term prediction method for residential electricity load based on long-short-time memory-type cyclic neural network is proposed. The “memory” feature of this method is used to mine the correlation characteristics between load data, and a resident based on long-short-term memory network is established. The ultra-short-term prediction model of electric load is compared with the simulation results of the double-layer feedforward neural network model. The prediction results based on the long-short-time memory network are more accurate, and the validity of the model is verified.

Abstract:State of health (SOH) is an important indicator of good battery performanceNew energy vehicle. Aiming at the target requirement of accurately estimating the health status of 18650 lithium battery, based on the mathematical model of lithium battery cell, the equivalent circuit model is used to analyze the factors affecting the health status of lithium battery, and the universal nonlinear battery equivalent circuit model is adopted. Gneral nonlinear model (GNL) and extended Kalman filter algorithm, the SOH estimation model of lithium battery was built in AMEsim simulation environment, and the charge and discharge cycle experiment of 18650 lithium battery was carried out. The collected data set was imported into the data module of AMEsim estimation model. The simulation results show that the SOH estimation error is less than 8%, and the established lithium battery SOH estimation model meets the target demand for High estimation accuracy and fast response.

Abstract:A new damage identification method was proposed to identify common damages of rotor blades. Firstly, the method performed a variational mode decomposition (VMD) on the vibration signals collected by wireless measurement and control system, obtaining a series of principal modes in different frequency bands. Then the cross-sample entropy (CSE) was calculated between different sensors in the same frequency band. Finally, the entropy values, which were damage features, were taken input to a SVM optimized by the empire competition algorithm （ECA）. By analyzing the experimental data, the results show that the feature based on cross-sample entropy has a high degree of discrimination. Damages with different positions and different sizes were identified using this method, and the total identification accuracy is 98.67%, which proves the effectiveness of the proposed damage identification method.

Abstract:The maritime FBB station is mainly responsible for the data transmission and emergency communication of ships. To solve the problem that the ship-borne maritime FBB station is unable to track target maritime satellite after system booting, detailed troubleshooting steps are illuminated after introducing the components and working principle of FBB station. After analyzing the system self-checking report and booting sequence, output signals of each modules of FBB antenna are measured and analyzed, and the breakdown chip of antenna is located finally. By comparing with the historical data, effects on FBB antenna of external factors such as working environment, vibration and shelters are analyzed, and methods to prevent system failures such as temperature and humidity control, anti-oxidation treatment are introduced. Preventive maintenance methods such as developing maritime FBB station surveillance software is proposed, which is very valuable for analysis and troubleshooting on the same type of equipment.

Abstract:In order to realize the fault diagnosis of the tennessee eastman (TE) process, the fault diagnosis method of the deep belief network (DBN) is improved. The traditional DBN will generate redundant features in the training process, weaken the feature extraction ability of the network, improve the DBN to add the penalty regular term in the likelihood function of the unsupervised learning phase, obtain the sparse distribution of the DBN training set through the sparse constraint, and then use the Laplace function. The distribution guides the sparse state of the DBN node, and uses the positional parameters in the Laplace function to control the sparse strength, so that the unlabeled data features can be more intuitively represented. Finally, the improved DBN and traditional DBN and BP neural network simulation results are compared. The experimental results show that the improved DBN is superior to the traditional DBN and BP neural network in fault diagnosis, achieving the best diagnostic accuracy and high Theoretical research value.

Abstract:The safety and economy of power grid operation are affected by load forecasting accuracy in the intelligent distribution network environment. It reduces the convergence speed and prediction accuracy of the algorithm, which randomly access the input neurons, neurons in hidden layer and output neurons between the weights and thresholds in BP algorithm. In order to obtain the optimal model of the network, this paper uses AFSA algorithm for the initial weights and threshold of BP algorithm for global optimization. The AFSA-BP short-term load forecasting model is established, based on the analysis of the power system load characteristics. In order to verify the accuracy of the algorithm, BP, LS-SVM, AFSA-BP algorithm is used to power load simulation, respectively. The RMSE value caculated by AFSA-BP, BP and LS-SVM algorithm are 0.0862, 0.2558 and 0.1522 respectively, which verifies that the AFSA-BP algorithm is suitable for short-term power load forecasting.

Abstract:A fault nature identification method for single-phase adaptive reclosure on UHAVC transmission lines with shunt reactors is proposed by considering bandpass characteristics of the voltage transformer and power frequency component in the power system. By using the ATP-EMTP software the arc model is built. Then, the attenuation factor is extracted by Prony algorithmand the attenuation factor ratio in fixed time window is calculated with Prony algorithm. Finally, the identification strategy which can distinguish the permanent fault and intermittent fault is put forward by comparing the difference of attenuation ratio factor. A lot of calculation results and simulation results show that wherever the fault location is, the transient fault and permanent fault can be correctly and effectively identified.

Abstract:Aiming at the problem of poor fault diagnosis of airborne electronic equipment under noise interference conditions, an improved self-organizing feature mapping (SOM) network algorithm is proposed based on SOM network. Based on the standard SOM network, the algorithm introduces the filtering algorithm for primary noise reduction, then performs threshold learning, redefines the neighborhood function and learning rate, and finally uses the fault evaluation index as the benchmark to isolate the fault. Under the condition of Gaussian white noise, the fault data of the front-end receiver of a certain aircraft is taken as an example to establish a diagnostic model. The classification and isolation of fault modes are obtained through simulation tests such as clustering and network training. At the same time, the effectiveness, accuracy and robustness of SOM neural network in fault diagnosis under Gaussian white noise interference conditions are verified by comparison with other methods.