Design and Development of the DTC Induction Motor Drive for Electric Vehicle

The design and development of the traction controller for electric vehicle is introduced, which is based on the induction motor. This drive is developed by using a digital signal processor at low cost and carried out with the module design concept of both software and hardware. Nevertheless, a scheme of the sensorless direct torque control is based on the developed hardware, of which the feasibility is tested by a trial program. Additionally, both the interface function of the drive hardware and the feasibility of its software are proved to be good by the trail programs. A test motor can run about 18?r/min by a variable frequency program with the space vector pulse width modulation technology, of which the torque is visible pulsatile. In this presentation, based on the theoretical approach, the sensorless torque control is to be studied and applied to electric vehicles, of which the quick, smooth and stable torque response is emphasized because it quite benefits improving the drive performance of electric vehicles.

一汽人生活的“保障”

作为经济发展支柱产业的汽车行业,其后勤服务则贯穿于企业的生产、经营和市场的拓展之中,并成为管理的重要环节,发挥着不可替代的调节作用。 作为全国特大型国有企业的一汽集团公司,穿越50年的风雨历程,已形成企业内部全功能社会服务职能管理系统。享有中国汽车工业摇篮盛誉的一汽集团,其后勤服务工作始终依托主体的开创、振兴和发展,为民族汽车工业驶过昨天奔向明天做出应有的贡献。

Research on Software Radio Fuze

The functions and characteristics of software radio are discussed. Using techniques and method of software radio, the concept and advantages of a new kind of radio fuze, software radio fuze, are analysed. Several kinds of hardware platform structures of the software radio fuze are studied and the key techniques are analysed. The software radio fuze will become the most promising radio fuze techniques in 21st century.

Signal and Data Processing of Television Based on Multistatic Radar Systems

A television based multistatic radar system is described. The commercial television transmitter is used as the illuminator in the multistatic radar system. The reflected commercial television signals are measured by an array of sensors. A data processing scheme is developed that adapts to the poor signal processing ability. The innovation is focused on the construction of the observation space, which could reduce the non linearity error. The new method leads to better system stability than the traditional one. Monte Carlo simulation is utilized and compared with the traditional method.

EEG processing and its application in brain-computer interface

Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI). The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI. First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed. Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton's rings are introduced. Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated. As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.

BOOTSTRAPPING FOR EXTRACTING RELATIONS FROM LARGE CORPORA

A new approach of relation extraction is described in this paper. It adopts a bootstrap- ping model with a novel iteration strategy, which generates more precise examples of specific relation. Compared with previous methods, the proposed method has three main advantages: first, it needs less manual intervention; second, more abundant and reasonable information are introduced to represent a relation pattern; third, it reduces the risk of circular dependency occurrence in bootstrapping. Scalable evaluation methodology and metrics are developed for our task with comparable techniques over TianWang 100G corpus. The experimental results show that it can get 90% precision and have excellent expansibility.

Study of CMOS integrated signal processing circuit in capacitive sensors

A CMOS integrated signal processing circuit based on capacitance resonance principle whose structure is simple in capacitive sensors is designed. The waveform of output voltage is improved by choosing bootstrap reference current mirror with initiate circuit, CMOS analogy switch and positive feedback of double-stage inverter in the circuit. Output voltage of this circuit is a symmetric square wave signal. The variation of sensitive capacitance, which is part of the capacitive sensors, can be denoted by the change of output vohage＇s frequency. The whole circuit is designed with 1.5 μm P-weU CMOS process and simulated by PSpice software. Output frequency varies from 261.05 kHz to 47. 93 kHz if capacitance varies in the range of 1PF - 15PF. And the variation of frequency can be easily detected using counter or SCU.

DATA BYPASSING ARCHITECTURE AND CIRCUIT DESIGN FOR 32-BIT DIGITAL SIGNAL PROCESSOR

This paper presents a design method of ByPassing Unit(BPU) in 32-bit Digital Signal Processor(DSP)-MD32. MD32 is realized in 0,18μm technology, 1.8V and 200 MHz working clock. It focuses on the Reduced Instruction Set Computer(RISC) architecture and DSP computation capability thoroughly, extends DSP with various addressing modes in a customized DSP pipeline stage architecture. The paper also discusses the architecture and circuit design of bypassing logic to fit MD32 architecture. The parallel execution of BPU with instruction decode in architecture level is applied to reduce time delay. The optimization of circuit that serial select with priority is analyzed in detail, and the result shows that about half of time delay is reduced after this optimization. Examples show that BPU is useful for improving the DSP's performance.The forwarding logic in MD32 realizes 8 data channels feedback and meets the working clock limit.

Independent component analysis approach for fault diagnosis of condenser system in thermal power plant

A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.

PROJECTED GRADIENT DESCENT BASED ON SOFT THRESHOLDING IN MATRIX COMPLETION

Matrix completion is the extension of compressed sensing.In compressed sensing,we solve the underdetermined equations using sparsity prior of the unknown signals.However,in matrix completion,we solve the underdetermined equations based on sparsity prior in singular values set of the unknown matrix,which also calls low-rank prior of the unknown matrix.This paper firstly introduces basic concept of matrix completion,analyses the matrix suitably used in matrix completion,and shows that such matrix should satisfy two conditions:low rank and incoherence property.Then the paper provides three reconstruction algorithms commonly used in matrix completion:singular value thresholding algorithm,singular value projection,and atomic decomposition for minimum rank approximation,puts forward their shortcoming to know the rank of original matrix.The Projected Gradient Descent based on Soft Thresholding(STPGD),proposed in this paper predicts the rank of unknown matrix using soft thresholding,and iteratives based on projected gradient descent,thus it could estimate the rank of unknown matrix exactly with low computational complexity,this is verified by numerical experiments.We also analyze the convergence and computational complexity of the STPGD algorithm,point out this algorithm is guaranteed to converge,and analyse the number of iterations needed to reach reconstruction error.Compared the computational complexity of the STPGD algorithm to other algorithms,we draw the conclusion that the STPGD algorithm not only reduces the computational complexity,but also improves the precision of the reconstruction solution.

Software Radio: A Review of Design Considerations and Digital Hardware Choices

This paper reviews the requirements for Software Defi ned Radio (SDR) systems for high-speed wireless applications and compares how well the different technology choices available-from ASICs, FPGAs to digital signal processors (DSPs) and general purpose processors (GPPs) - meet them.

Local Measurement of Gas-Liquid Bubbly Flow with a Double-Sensor Probe

A double-sensor probe was used to measure local interfacial parameters of a gas-liquid bubbly flow in a horizontal tube. The parameters included void fraction, interfacial concentration, bubble size distribution, bubble frequency and bubble interface velocity. The authors paid special attention to the probe design and construction for minimizing measurement errors. Measures were also taken in the design of sensor ends for preventing corrosions in the flow. This is an effort to improve the current double-sensor probe technique to meet the ever-increasing needs to local parameter measurements in gas-liquid two-phase flows.

Supervised Kernel Uncorrelated Discriminant Neighborhood Preserving Projections

To separate each pattern class more strongly and deal with nonlinear ease, a new nonlinear manifold learning algorithm named supervised kernel uneorrelated diseriminant neighborhood preserving projections （SKUDNPP） is proposed. The algorithm utilizes supervised weight and kernel technique which makes the algorithm cope with classifying and nonlinear problems competently. The within-class geometric structure is preserved, while maximizing the between-class distance. And the features extracted are statistically uneorrelated by introducing an uneorrelated constraint. Experiment results on millimeter wave （MMW） radar target recognition show that the method can give competitive results in comparison with current papular algorithms.

A frame synchronization solution for orthogonal frequency division multiplexing system

To improve frame synchronization precision, a scheme named training symbol correlation （TSC） is presented for orthogonal frequency division multiplexing （ OFDM ） system. Based on the solution from Schmidl and Cox, a timing metric related to TSC scheme is put forward and examined. The specific method to select a threshold value provides more precise detection results, which can be shown by performance comparison between the two schemes through Monte Carlo simulation. Taking IEEE 802.1 la WLAN standard as an example, the proposed approach is superior to the most popular Schmidl scheme in terms of BER.

Efficient Timing and Frequency Offset Estimation Scheme for OFDM Systems

A new training symbol weighted by pseudo-noise(PN) sequence is designed and an efficient timing and fre-quency offset estimation scheme for orthogonal frequency division multiplexing(OFDM) systems is proposed. The timing synchronization is accomplished by using the piecewise symmetric conjugate of the primitive training symbol and the good autocorrelation of PN weighted factor. The frequency synchronization is finished by utilizing the training symbol whose PN weighted factor is removed after the timing synchronization. Compared with conventional schemes, the pro-posed scheme can achieve a smaller mean square error and provide a wider frequency acquisition range.

Position Sensorless Control for Permanent Magnet Synchronous Motor Using Sliding Mode Observer

An approach of position sensorless control for permanent magnet synchronous motor （ PMSM ） is put forward based on a sliding mode observer. The mathematical model of PMSM in a stationary αβ reference frame is adopted, and the system is controlled by the digital signal processor （ DSP; TMS320LF2407 according to the control achieve closed loop operation of the motor, the stator theory of sliding mode observer. In order to magnetic field should be vertical with the rotor magnetic field and be synchronous with rotor rotating, so the position and speed of PMSM is estimated in real time and the estimated position is modified continuously. The simulation results indicate that the proposed observer has high precision is more robust to the parametric variation and load in estimation of PMSM position and speed, and torque disturbance.

Detection of Partial Discharge in Power Transformers Using Rogowski Coil and Multiresolution Analysis

Partial discharge detection in power transformers is discussed using a new approach that exploit the broad band of the Rogowski coils and the potential of two signal processing tools： discrete wavelet transform and empirical mode decomposition. Detecting and analyzing incipient activities of partial discharge can provide useful information to diagnostics and prognostics about transformer insulation. So, partial discharge signals embedded in the electric current at ground conductor are measured using the Rogowski coil. These signals are submitted to noise suppression and the partial discharges waveforms are extracted through different ways： using discrete wavelet transform and using empirical mode decomposition. The comparison of these two methods show that the extraction with discrete wavelet transform results in a faster and simpler algorithm than the empirical mode decomposition. But this one produces more precise waveforms due its adaptive characteristic.

THE DESIGN OF VMEBUS BRIDGE CONTROLLER WITH SHARC BUS

Targeting at the high expense and inflexibility to realize VMEbus bridge controller by professional Integrated Circuit (IC), this paper presents a scheme of adopting CPLD/FPGA (Complicated Programmable Logic Device/Field Programmable Gate Array) to design bridge controller between VMEbus and local bus. SHARC DSP (Digital Signal Processor) bus is an example. It has functions of nearly entire master/slave interface of VMEbus, and can act as DMA (Direct Memory Access) controller and perform block transfer in DMA or master processor initiative way without length limit. External circuit of the design is very simple. In comparison with special ICs, it has high performance to price ratio and can be easily applied to local buses of other processors with quite a little modification.

Influence of DSP controller on performance of a permanent magnet brushless AC drive in flux-weakening mode

The flux-weakening performance of a permanent magnet brushless AC drive was investigated using both floating-point and fixed-point DSP controllers. A significant current oscillation was observed when the drive was operated at high-speed in the flux-weakening mode with the fixed-point DSP. The investigation showed that this was due to the on-line compensation of the winding resistance voltage drop and quantisation errors associated with the fixed-point architecture of the DSP. A simple look-up table scheme is proposed to eliminate the oscillation and to achieve extended flux-weakening capability.

Online electrophysiological spike discrimination with wavelet-fractal signatures

Objective: To study extracellular multi-neuron activity in the nervous system based on wavelet-fractal technique. Methods: The wavelet transform was employed to decompose the original signal and obtain 4 sub-patterns. The dividing fractal dimensions of these sub-patterns were computed. A knn-classier was used to classify feature vectors. Results: Not all the elements in feature vector DimDC were very powerful for this pattern recognition problem through the empirical study of noise signals. The most effective feature vector was defined as DimDC= (d3:d4) above. Conclusion:Wavelet fractal algorithm has high accuracy and provides a powerful tool for clinical application.