浅谈《红楼梦》人物命名艺术

《红楼梦》人物命名匠心独运 ,大有深意 ,其命名的方法有 :谐音法、取形法、取义法、生肖法、拆字法、关系法、随事法、别号法。

Seismic signal recognition using improved BP neural network and combined feature extraction method

Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.

Recognition of Finger Motion with sEMG and Gyrosensor Signals

A novel method to infer the finger flexing motions of various arm postures is proposed. From the gyroscope signal, the authors recognized forearm posture using K-means clustering method. Then finger motion inferred. For finger motion inference, Gaussian model of information entropy and maximum likelibood method was utilized. Experimentally it is obtained that the average recognition rate with the forearm posture inference is much higher than those without the inference by 30.7%.

A new image processing method for discriminating internal layers from radio echo sounding data of ice sheets via a combined robust principal component analysis and total variation approach

Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.

Acoustic emission characterization methods of damage modes identification on carbon fiber twill weave laminate

In order to get a deep understanding of composite failure mechanisms, the new advanced signal processing methodologies are established for the analysis of the large number of acoustic emission (AE) data obtained from the quasi-static tension test of carbon fiber twill weave composite. For this purpose, AE signals have been collected and post-processed for tension test, and are analyzed with three signal processing methods: Empirical Mode Decomposition (EMD), Hilbert-Huang Transform (HHT) and modified energy entropy algorithm. AE signals can be decomposed into a set of Intrinsic Mode Functions (IMF) components, results from this study reveal that the peak frequency of IMF components based on Fast Fourier Transform (FFT) corresponds to different damage mechanisms of composite. HHT of AE signals can clearly express the frequency distribution of IMF component in time-scale in different damage stages, and can calculate accurate instantaneous frequency for damage modes recognition. The energy entropy based on EMD is introduced to act as a new relevant descriptor of composite damage modes in order to improve the characterization and the discrimination of the damage mechanisms.

Detection and identification of S1 and S2 heart sounds using wavelet decomposition method

Phonocardiogram （PCG）, the digital recording of heart sounds is becoming increasingly popular as a primary detection system for diagnosing heart disorders and it is relatively inexpensive. Electrocardiogram （ECG） is used during the PCG in order to identify the systolic and diastolic parts manually. In this study a heart sound segmentation algorithm has been developed which separates the heart sound signal into these parts automa- tically. This study was carried out on 100 patients with normal and abnormal heart sounds. The algorithm uses discrete wavelet decomposition and reconstruction to pro- duce PCG intensity envelopes and separates that into four parts： the first heart sound, the systolic period, the second heart sound and the diastolic period. The performance of the algorithm has been evaluated using 14,000 cardiac periods from 100 digital PCG recordings, including normal and abnormal heart sounds. In tests, the algorithm was over93% correct in detecting the first and second heart sounds. The presented automatic seg- mentation Mgorithm using w^velet decomposition and reconstruction to select suitable frequency band for envelope calculations has been found to be effective to segment PCG signals into four parts without using an ECG.