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Objective: The use of multiple-stage cycloconverters is suitable for variable-frequency speed control of high-voltage, large-capacity motors, for which it is sometimes necessary to establish separate controls for variable-frequency operation and the start-up phase. This approach effectively limits the starting current of the large-capacity motor by switching the phase relationship of the triple circuit’s output voltages to control the overall output voltage. Methods: We analyze a triple circuit using voltage vector analysis and simulation analysis with MATLAB modeling. Results: We find that our triple soft-start control strategy can be applied to large-capacity motors, as this strategy results in a pure sine wave output voltage and current, and maintains balance in the three-phase voltage during startup. Conclusion: In order to further decrease these types of effects, we will conduct more research on a cycloconverter that has six stages of the converter circuit, rather than the three stages mentioned before. Furthermore, the sextuple cycloconverter, which can be used to practically drive high capacity pumps or fans, will be developed by combining the “phase hopping” method and the soft-start method mentioned in this paper.

The global motion vectors estimation is the most critical step for eliminating undesirable disturbances in unsafe video. In this paper, we proposed a novel global motion estimation approach based on improved K-means clustering algorithm to acquire trustworthy sequences. Firstly, the speeded up robust feature algorithm is employed to match feature points between two adjacent frames, and then we calculate the motion vectors of these matching points. Secondly, to remove the local motion vectors and reduce redundancy from the motion vectors, an improved K-means clustering algorithm is proposed. Thirdly, by using matching points from richest cluster, global motion vectors are calculated by homography transformation. The experimental simulation results demonstrate that the proposed method can obtain significantly higher computational efficiency and superior video security performance than traditional approaches.

Rapeseed pests will result in a rapeseed production reduction. The accurate identification of rapeseed pests is the foundation for the optimal opportunity for treatment and the use of pesticide pertinently. Manual recognition is labour-intensive and strong subjective. This paper propsed a image recognition method of rapeseed pests based on the color characteristics. The GrabCut algorithm is adopted to segment the foreground from the image of the pets. The noise with small area is filtered out. The benchmark images is obtained from the minimum enclosing rectangle of the rapeseed pests. Two types of color feature description of pests is adopt, one is the three order color moments of the normalized H/S channel; the other is the cross matching index calculated by the reverse projection of the color histogram. A multi-dimensional vector, which is used to train the random forest classifier, is extracted from the color feature of the benchmark image. The recognition results can be obtained by inputing the color features of the image to be detected to the random forest classifier and training.The experiment showed that the proposed method may identify five kinds of rapeseed accurately such as erythema, cabbage caterpillar, colaphellus bowringii baly, flea beetle and aphid with the recognition rate of 96%.

One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW). Effective methods for suppressing BW include the wavelet-based (WT) and the mathematical morphological filtering-based (MMF) algorithms. However, the T waveform distortions introduced by the WT and the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WT to overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinical BW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with other state-of-the-art methods commonly used in the literature. The results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG.

T-wave alternans (TWA) is hypothesized to be related with patients at an increased risk for ventricular arrhythmias. A novel TWA assessment method based on least squares curve fitting techniques (LSCF) is presented in this paper. Real clinical ECG recordings and simulated ECG signals containing additive TWAs and noise are used to test the efficiency of the LSCF. The results are also compared with those by the Modified Moving Average (MMA) algorithm. The experimental results demonstrate that the LSCF is efficiency and promising in detecting and measuring TWAs.

Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.

A vehicular ad hoc network (VANET) is a specific type of mobile ad hoc networks (MANETs); it can provide direct or multi-hop vehicle-to-vehicle (V2V), vehicle-to-roadside (V2R), vehicle-to-pedestrian (V2P), and vehicle-to-internet (V2I) communications based on the pre-existing road layouts. The emerging and promising VANET technologies have drawn tremendous attention from the government, academics, and industry over the past few years and have been increasingly available for a large number of cutting edge applications that can be classified into road safety, traffic efficiency, and infotainment categories. Due to the unique characteristics of VANETs, such as high mobility with an organized but constrained pattern, and diverse radio propagation conditions, the conventional researches dedicated for general MANETs cannot be directly applied to VANETs. This paper presents an analytical framework to investigate the minimum node degree of k-connected VANETs, with a homogeneous range assignment in highway scenarios. We simulate the mobility patterns with realistic vehicular traces, model the network topology as a two-path fading geometric random graph, and conduct extensive experiments on the derived analytical results. Through a combination of mathematical modeling and simulations, we derive a probabilistic bound for the minimum node degree of a homogeneous vehicular ad hoc network in highway scenarios. The analytical framework is useful in the study of connectivity and estimation of performance in one-dimensional vehicular ad hoc networks.

A new algorithm for single hidden layer feedforward neural networks SLFN, Orthogonal Basis Extreme Learning OBEL algorithm, is proposed and the algorithm derivation is given in the paper. The algorithm can decide both the NNs parameters and the neuron number of hidden layers during training while providing extreme fast learning speed. It will provide a practical way to develop NNs. The simulation results of function approximation showed that the algorithm is effective and feasible with good accuracy and adaptability.