An Automatic Pattern Recognition Methods for Power Network Fault Diagnosis

Wavelet neural networks (WNN) and support vector machine (SVM) are two advanced methods which are fit for classification. A comparative analysis of the two methods was conducted based on Chinese firms. The results show WNN has good classification effect. Wavelet decomposition has been demonstrated to be an effective tool for recognizing the firms' feature. Also the study applied SVM to the same estimation sample and test sample. The results show SVM is much superior to WNN for small sample learning.

A new forecasting method of wind speed based on wavelet analysis and support vector machine is proposed in this paper. By the method, firstly, the original wind speed sequences are decomposed into coarse components and detail components, and secondly, every wavelet components are separately forecasted with corresponding support vector machine models. Finally, the forecasting results of original wind speed series are achieved by using wavelet reconstruction. The experimental results by simulation prove that this method is capable of improving generalization performance and forecasting precision. The mean absolute percentage error (MAPE) reaches 8.21%.

In this paper, a new image denoising method based on wavelet analysis and support vector machine regression (SVR) is presented. The feasibility of image denoising via support vector regression is discussed and is demonstrated by an illustrative example which denoise a 1-dimension signal with Gauss KBF SVM. The wavelet theory is discussed and applied to construct the wavelet kernel, then the wavelet support vector machine (WSVM) is proposed. The result of experiment shows that the denoising method based on WSVM can reduce noise well, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than Gaussian KBF SVM and other traditional methods.

In this paper, the load prediction and early warning system of distribution network equipment is constructed by using remote monitoring technology, power network fault pattern recognition, high-speed system diagnosis technology and power network monitoring and evaluation system. Then, a heavy overload warning framework is established to screen out the components of the distribution network with short term heavy overload risk. The fuzzy monitoring method is used to control the overflow of abnormal data in power dispatching system. Secondly, this paper carries out load distribution prediction and determines the user load superposition result based on the user cluster analysis results. By analyzing the main functions and software and hardware of the intelligent scheduling visualization system, the visualization display based on SVG technology is realized. This paper analyzes the implementation scheme of the early warning system, the prediction of the power network state and the real-time monitoring method of the power network fault. It illustrates the experimental results of system operation.

Automatic classification of electrocardiogram (ECG) is importance in cardiac disease diagnosis. Support vector machine (SVM) has drawn more and more attention on pattern recognition, including ECG feature extraction and cardiac disease detection. The most prominent advantage of SVM can be represent as its excellent performance on simplification of inner product operation from high dimensional space to low dimensional space, avoiding calculations in high dimension space. In this study, a multi-classification method is proposed utilizing wavelet multi-resolution analysis (WMRA) and SVM. WMRA is applied to eliminate interference with frequency beyond the frequency interval of ECG signals (0.05∼100Hz). Meanwhile, WMRA provides detail coefficients and approximation coefficients of different decomposition levels, which are the input features fed into SVM for classification. After that, SVM is employed to recognize 6 types of cardiac beats from MIT-BIH arrhythmia database. Besides, different parameters C and γ are discussed and tested. Experimental results indicate that the classification performance gets better as C increases and γ decreases. When C and γ are set to be 1000 and 0.1 respectively, an overall classification accuracy, sensitivity and positive predictivity of 95.23%, 97.42% and 97.71% respectively are achieved.

Using information technology such as search engine for collecting and monitoring of network public opinion is a practical and effective method. This paper puts forward an improved algorithm of SVM classifier based on incremental learning online, and then implements a topic crawler system for network public opinion and to grab the public opinion.

The technology of microseismic monitoring, the first step of which is event recognition, provides an effective method for giving early warning of dynamic disasters in coal mines, especially mining water hazards, while signals with a low signal-to-noise ratio (SNR) usually cannot be recognized effectively by systematic methods. This paper proposes a wavelet scattering decomposition (WSD) transform and support vector machine (SVM) algorithm for discriminating events of microseismic signals with a low SNR. Firstly, a method of signal feature extraction based on WSD transform is presented by studying the matrix constructed by the scattering decomposition coefficients. Secondly, the microseismic events intelligent recognition model built by operating a WSD coefficients calculation for the acquired raw vibration signals, shaping a feature vector matrix of them, is outlined. Finally, a comparative analysis of the microseismic events and noise signals in the experiment verifies that the discriminative features of the two can accurately be expressed by using wavelet scattering coefficients. The artificial intelligence recognition model developed based on both SVM and WSD not only provides a fast method with a high classification accuracy rate, but it also fits the online feature extraction of microseismic monitoring signals. We establish that the proposed method improves the efficiency and the accuracy of microseismic signals processing for monitoring rock instability and seismicity.

We proposed a new method of aero-engine early fault intelligent diagnosis which combined with stochastic resonance, wavelet packet analysis and support vector machine. This method can effectively extract the early fault feature of aero-engine and it can fast identify the early faults. At first, we use the principle of stochastic resonance to zooms the early weak fault feature signals and amplify fault features. Then, we make use of multi-resolution analysis characteristic of wavelet packet to extract the early fault feature vectors. At last, the feather vector is inputted to a classifier which is constructed by support vector machines and carries on identification of the early faults. The results shown that its effect of classification identification is well and it is effective to identify early fault in strong noise.

Gingivitis is usually detected by a series of oral examinations. In this process, the dental record plays a very important role. However, it often takes a lot of physical and mental effort to accurately detect gingivitis in a large number of dental records. Therefore, it is of great significance to study the classification technology of gingivitis. In this study, a new gingivitis classification method based on wavelet entropy and support vector machine is proposed to help diagnose gingivitis. The feature of the image is extracted by wavelet entropy, and then the image is classified by support vector machine. The experimental results show that the average sensitivity, specificity, precision and accuracy of this method are 75.17%, 75.29%, 75.35% and 75.24% respectively, which are superior to the other three methods This method is proved to be effective in the classification of gingivitis.

An original dual-class voting mechanism was put forward as the final decision method for brain computer interface, which is based on wavelet packet decomposition (WPD) to extract the brainwave features from EEG signals, and support vector machines (SVMs) to classify five mental tasks. Moreover, several preprocessing methods were applied efficiently. Segmentation along the time axis for increasing the correct classification rate, and nonlinear as well as linear normalization for emphasizing the important information in small magnitude and optimizing data distribution. Further, an especial grouping method was proposed to realize optimizing parameters automatically. Approximately, 95% of correct classification rate is obtained based on the proposed method, which is higher than the conventional.

With the imminent completion of the Human Genome Project and the fast increase of many complete genomes of prokaryotes and eukaryotes, the task of organizing and understanding the generated sequence and structural data becomes more and more pressing and demands better and efficient analysis algorithms. Computational gene identification is an issue of obvious importance as a tool of identifying biologically relevant features that often cannot be found by traditional sequence database searching technique. It bases on the accurate gene structure and developing accurate splice sites identification algorithms plays an important role in the identification of gene structure in eukaryote organisms. In this paper, we proposed a novel approach and developed an accurate splice sites identification algorithm based on discrete wavelet transform learned by support vector machine, which can achieve a superior performance for the prediction of splice sites, without any explicit use of local features. What's more we bring forward a new coding method for DNA sequence, so that the sequence data can be dealt with as image processing. The results show that our splice sites identification method is much better than or similar to other methods that are developed based on explicit statistical features, despite that it is so easy and simple. So this method could be applied in other signal detection problems for a try.

Objective To investigate the feasibility of speech recognition technologies including double threshold method, Mel frequency cepstral coefficient (MFCC) and vector quantization (VQ) in automatic recognition of cough sounds. Methods Five healthy adults and 15 patients with cough were recruited for recording of both non- cough and cough sounds in a quiet environment. The records were randomized into training and testing samples. The training samples were used to generate the code book for cough recognition software, which was then used to recognize and analyze the testing samples automatically.The sensitivity and specificity of recognition were calculated by comparison with outcomes from human ear recognition. The recognition time in two approaches was recorded. Results Two hundred cough and 200 noncough sound samples were used to generate the code book for cough recognition software, while 375 cough sound samples and 125 non-cough sound samples were used as the testing samples. The recognition time of the testing samples needed was 33 minutes and 18 seconds by human ear recognition vs 1 minute and 35seconds by code book-based automatic recognition. In addition, the sensitivity and specificity in code bookbased automatic recognition of the cough sound were 98.93% and 100% respectively. Conclusion The double threshold method based on VQ and MFCC appears feasible in automatic recognition of cough sounds. Key words: Cough; Speech recognition software; Pattern recognition, automatic; Sensitivity and specificity; Human ear recognition

Short-Term Load Forecasting Based on Gaussian Wavelet SVM

Power system networks are one of the most widely used methods in the real world for transferring large amounts of electrical energy from one location to another. At present, High Voltage Direct Current Transmission is preferred for long distances over hundreds of miles due to minimal power loss and transmission cost of transmission.Due to an increase in power demand, integration of renewable sources to minimise the voltage fluctuations and compensate for power loss is necessary. This is a mandatory requirement to produce sophisticated protection methods for mainly smart systems under various balanced and unbalanced fault conditions. The system protection scheme must respond as quickly as possible to protect the connected devices in a smart environment. The network must be monitored and protected under various weather conditions as well as electrical parametric problems. The proposed research work is carried on the basis of physical monitoring with the aid of the Internet-of-Things and electrical parameters calibrated with the help of wavelet analysis. A wavelet is a mathematical tool to investigate the behaviour of transient signals at different frequencies, which provides important information related to the detailed analysis of faults in power networks. The major goals of this research are to analyse faults using detailed coefficients of current signals through the bior-1.5 mother wavelet for fault identification and artificial neural network analysis for fault localization. This proposed approach furnishes an IoT supervised Photovoltaic - High Voltage Direct Current (HVDC) combined wide area power network security scheme using wavelet detailed coefficients under various types of faults with Fault-Inception-Angles.

In this paper, a new recognition algorithm based on curvelet moment and support vector machine(SVM) is proposed for chip defect recognition. The proposed recognition method is implemented through a reference comparison method. First the defect regions of chips are extracted through preprocessing, and then the curvelet moment feature of the defect region is computed as the input of SVM classifier, the output of the trained SVM classifier is the result of defect recognition. The algorithm combines the good properties of curvelet moment and SVM classifier, the former can provide multi-scale, local details and orientation information of the defect region, and the latter is suitable to solve the small samples, nonlinear and high dimensions pattern recognition problem. Experimental results show that the algorithm has higher recognition rate compared with PCA based method and can solve the complex defects recognition problem effectively.