Multi-parameter Fusion Research Articles

Research on Distribution Network Fault Recognition Method Based on Time-Frequency Characteristics of Fault Waveforms

Accurate recognition of distribution line fault types can provide directional guidance for line operation and maintenance personnel. Based on the analysis of time-frequency features of fault waveform, a recognized method of distribution line fault type was proposed in this paper. Through modeling and theoretical analysis of waveforms of different fault types, characteristic parameters, which could characterize waveforms of different fault types from three aspects, time domain, frequency domain, and electric arc, were put forward. Calculation formula for extracting characteristic parameters according to fault waveform data was proposed, recognition logic was established by taking multi-parameter fusion as a basis, and then,automatic recognition of distribution line fault types caused by different factors was realized through detection and classification of characteristic parameters of input waveform data. Finally, 136 groups of field fault waveform data provided by the Electric Power Research Institute were used to do closed-loop control and verification of the algorithm, and results indicated that recognition success rate reached 90%, which verified the feasibility of using time-frequency characteristics of fault waveform to realize recognition of distribution line fault types.

Highly maneuvering target tracking using multi-parameter fusion singer model

An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.

Continuous blood pressure estimation based on multiple parameters from eletrocardiogram and photoplethysmogram by Back-propagation neural network

The cuff-less continuous blood pressure monitoring provides reliable and invaluable information about the individuals’ health condition. Conventional sphygmomanometer with a cuff measures only the value of the blood pressure intermittently and the measurement process is sometimes inconvenient. In this work, a systematic approach with multi-parameter fusion has been proposed to estimate the non-invasive beat-to-beat systolic and diastolic blood pressure with high accuracy. The methods involve real-time monitoring of the electrocardiogram (ECG) and photoplethysmogram (PPG), and extracting the R peak from the ECG and relevant feature parameters from the synchronous PPG. Also, it covers the creation of the topological model of back-propagation neural network that has fifteen neurons in the input layer, ten neurons in the single interlayer, and two neurons in the output layer, where all the neurons are fully connected. As for the results, the proposed method was validated on the volunteers. The reference blood pressure (BP) is from Finometer (MIDI, Finapres Medical System, Netherlands). The results showed that the mean±S.D. for the estimated systolic BP (SBP) and diastolic BP (DBP) with the proposed method against reference were −0.41±2.02mmHg and 0.46±2.21mmHg, respectively. Thus, the continuous blood pressure algorithm based on Back-Propagation neural network provides a continuous BP with a high accuracy.

Multi-parameter fusion applied to road traffic condition forecasting

In view of the chaotic characteristic in road traffic flow and the actual traffic condition that cannot be comprehensively reflected by single traffic parameter, a fusion algorithm of multi-parameters for traffic condition forecasting, with the consideration of the relationship between multiple parameters, is proposed. This algorithm is based on the reconstruction of phase space. According to Bayesian estimation theory, the multiple traffic parameters are optimally fused into phase points in the same phase space. Accordingly, the phase space information increases and the phase points are closer to dynamical behavior of the traffic system. On the basis, by using the multi-parameter chaos prediction method, the tendency of dynamic systems from different aspects is described, with reference to the method of predicting single parameter chaotic time series. The experimental results confirm that more features of real traffic condition are reflected by fusing multiple traffic parameters. The multi-parameters forecasting algorithm reduces the prediction error and improves the equalizer coefficients compared with the results generated from single parameter prediction. That is to say, the prediction method used in this paper is effective and accurate for predicting traffic condition based on multiple traffic parameters.

Precision Generating Cutting Research of Rectangle Spline Based on Parameters Superposition

Aiming at the precision generating cutting problem of rectangle spline shaft, the multi-parameter fusion method to modified cutting edge of rectangle spline hob is presented. The method based on the analysis of workpiece machining errors to establish a fusion model about the rectangle spline hob cutting parameters and the rectangle spline shaft deformation quantity, the model can indicate the connection between rectangle spling hob theoretics profile and actual cutting profile, therefore, the rectangle spline hob’s profile is modified and to simulate by polynomials. The simulation experiment indicates that the method exact approximation the hob’s modified profile. Compare with the methods of the double-circular-arc and the least square fitting, the multi-parameter fusion method can enhance both design precision and design efficiency.

Multi-parameter fusion algorithm for auto focus

A multi-parameter fusion algorithm (MPFA) for auto-focus (AF) is discussed. The image sharpness evaluation algorithm (ISEA) and zoom tracking method (ZTM) are combined for AF. The zoom motor position (z) and background complexity (c) are regarded as the main parameters of this algorithm. A priority table depending on z and c is proposed. Modified ISEA or ZTM is adopted according to the priority table value. The hardware implementation of the MPFA on Texas Instruments' Davinci digital signal processor is also provided. Results show that the proposed scheme provides faster focusing compared with the conventional approaches.