嵌入式系统扩展网卡的实现

智能电子设备具备多个以太网通信网卡是电力二次设备的标准配置。针对LPC4078芯片只有一个原生网卡的限制,提出基于BCM53101交换芯片扩展多个网卡的技术方案,利用BCM53101的VLAN和TAG技术特性,实现了多个独立网卡扩展,满足了智能电子设备多个网卡应用需求。该方案能降低硬件的设计难度和成本,具有较好的工程应用价值。

Proactive traffic responsive control based on state-space neural network and extended Kalman filter

The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.

Wireless location algorithm using digital broadcasting signals based on neural network

In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.

An attitude calculation algorithm based on WNN-EKF

In the strapdown inertial navigation system,the attitude information is obtained through an inertial measurement unit(IMU)device,which mainly includes a triaxial gyroscope,a triaxial accelerometer and a triaxial magnetometer.However,IMU sensors have system noise and drift errors,and these errors can accumulate over time,which makes it difficult to control the attitude accuracy.In order to solve the problems of gyro drift over time and random errors generated by the surrounding environment,this paper presents an attitude calculation algorithm based on wavelet neural network-extended Kalman filter(WNN-EKF).The wavelet neural network(WNN)is used to optimize the model and compensate the extended Kalman filter’s own model error.Through the semi-physical simulation experiment,the results show that the algorithm improves the accuracy of attitude calculation and enhances the self-adaptability to the environment.

Single-Phase Inverter Synchronized to the Grid by Linear Kalman Filter in Microgrids

This paper presents the analysis and implementation of a synchronizer to the grid using a linear Kalman filter. The synchronizer is used in a single-phase inverter, which is applied in an environment of microgrids. The inverter converts the energy that comes from renewable energy sources （photovoltaic, wind, fuel cell, etc.）. The main objective of obtaining the phase of the grid is to achieve a power factor close to unity in the inverter. For this reason it is vital that the phase difference between the synchronizer and the grid zero. To obtain synchronizer algorithm using LKF （linear Kalman filter） is necessary to know the EKF （extended Kalman filter）. This allows to analyze the operation of the filter, which allows to reach reduce linear Kalman filter or also known as simplified Kalman filter. It is necessary to generate an orthogonal signal in order to obtain a stationary reference frame from a single-phase grid because the linear Kalman filter works with a stationary reference frame. Orthogonal signal is created with an all-pass filter.

A Neural Network based Method for Detection of Weak Underwater Signals

Detection of weak underwater signals is an area of general interest in marine engineering.A weak signal detection scheme was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function (RBF) neural networks and an extended Kalman filter (EKF).In this method chaos theory was used to model background noise.Noise was predicted by phase space reconstruction techniques and RBF neural networks in a synergistic manner.In the absence of a signal, prediction error stayed low and became relatively large when the input contained a signal.EKF was used to improve the convergence rate of the RBF neural network.Application of the scheme to different experimental data sets showed that the algorithm can detect signals hidden in strong noise even when the signal-to-noise ratio (SNR) is less than -40d B.