Theoretical and experimental research on the in-plane comb-shaped capacitor for MEMS coriolis mass flow sensor

The Micro-Electro-Mechanical System Coriolis mass flow sensor uses a kind of in-plane comb-shaped capacitor to detect the vibration of tube containing the micro flow information. This paper takes the deflection of the micro tube caused by Coriolis force into account and models the in-plane comb-shaped capacitor of the sensor based on the electrostatic field method. Then the modulation and demodulation of the output capacitive signals are described in detail. The theoretical waveforms obtained by substituting the actual parameters into the models are consistent with the accepted conformal mapping method and match with the sampling signals, which attest the two models. According to the actual flow calibration experiment and the preliminary phase shift calculation, the measurement accuracy of the micro flow sensor reaches ±1.5 % with the repeatability of 0.75 % within 0–1.2 g/h flow range.     

Real-time transient stability status prediction using cost-sensitive extreme learning machine

Real-time transient stability status prediction (RTSSP) is very important to maintain the safety and stability of electrical power systems, where any unstable contingency will be likely to cause large-scale blackout. Most of machine learning methods used for RTSSP attempt to attain a low classification error, which implies that the misclassification costs of different categories are the same. However, misclassifying an unstable case as stable one usually leads to much higher costs than misclassifying a stable case as unstable one. In this paper, a new RTSSP method based on cost-sensitive extreme learning machine (CELM) is proposed, which recognizes the RTSSP as a cost-sensitive classification problem. The CELM is constructed pursuing the minimum misclassification costs, and its detailed implementation procedures for RSSTP are also researched in this work. The proposed method is implemented on the New England 39-bus electrical power system. Compared with three cost-blind methods (ELM, SVM and DT) and two cost-sensitive methods (cost-sensitive DT, cost-sensitive SVM), the simulation results have proved that the lower total misclassification costs and false dismissal rate with low computational complexity can be achieved by the proposed method, which meets the demands for the computation speed and the reliability of RTSSP.     

Relative attitude formation control of multi‐agent systems

This paper investigates the relative attitude formation control problem for a group of rigid‐body agents using relative attitude information on SO(3). On the basis of the gradient of a potential function, a family of distributed angular velocity control laws, which differ in the sense of a geodesic distance dependent function, is proposed. With directed and switching interaction topologies, the desired relative attitude formation is showed to be achieved asymptotically provided that the topology is jointly quasi‐strongly connected. Moreover, several sufficient conditions for the desired formation to be achieved exponentially and almost globally are given. Additionally, numerical examples are provided to illustrate the effectiveness of the proposed distributed control laws. Copyright © 2017 John Wiley & Sons, Ltd.     

Tamper detection and image recovery for BTC-compressed images

To ensure the integrity of images compressed using block truncation coding (BTC), a tamper detection and image recovery scheme is proposed in this paper. In this scheme, the size of the authentication data can be adaptively selected according to the user’s requirement. The authentication data is embedded in the value differences of the quantization levels in each BTC-compressed image block. Multiple copies of the recovery data are embedded into the bit maps of the smooth blocks. Experimental results show that the proposed scheme performs well in terms of detection precision and the embedded image quality. Meanwhile, the tampered areas can be roughly recovered by using the proposed scheme.     

Efficient image colorization based on seed pixel selection

Colorization is a technique to automatically produce color components for monochrome images and videos based on a few input colors. Generally, image colorization is initialized from a number of seed pixels whose colors are specified by users, and then the colors are gradually prorogating to the monochrome surroundings under a given optimization constraint. So, the performance of colorization is highly dependent on the selection of seed pixels. However, little attention has been paid to the selection of seed pixels, and how to improve the effectiveness of manual input remains a challenging task. To address this, an improved colorization method using seed pixel selection is proposed to assist the users in determining which pixels are highly required to be colorized for a high-quality colorized image. Specifically, the gray-scale image is first divided into non-overlapped blocks, and then, for each block, two pixels that approximate the average luminance of block are selected as the seeds. After the seed pixels are colored by users, an optimization that minimizes the difference between the seeds and their adjacent pixels is employed to propagate the colors to the other pixels. The experimental results demonstrate that, for a given amount of inputs, the proposed method can achieve a higher PSNR than the conventional colorization methods.     

Illumination robust single sample face recognition based on ESRC

Multimedia Tools and Applications - The extended sparse representation classifier (ESRC) is one of the state-of-the-art solutions for single sample face recognition, but it performs...     

An auto-encoder-based summarization algorithm for unstructured videos

Multimedia Tools and Applications - Video summarization is an effective way to quick view videos and relieve the pressure of videos storage. However the traditional algorithms are hardly adapted to...     

Multi-window based ensemble learning for classification of imbalanced streaming data

Imbalanced streaming data is commonly encountered in real-world data mining and machine learning applications, and has attracted much attention in recent years. Both imbalanced data and streaming data in practice are normally encountered together; however, little research work has been studied on the two types of data together. In this paper, we propose a multi-window based ensemble learning method for the classification of imbalanced streaming data. Three types of windows are defined to store the current batch of instances, the latest minority instances, and the ensemble classifier. The ensemble classifier consists of a set of latest sub-classifiers, and the instances employed to train each sub-classifier. All sub-classifiers are weighted prior to predicting the class labels of newly arriving instances, and new sub-classifiers are trained only when the precision is below a predefined threshold. Extensive experiments on synthetic datasets and real-world datasets demonstrate that the new approach can efficiently and effectively classify imbalanced streaming data, and generally outperforms existing approaches.     

Analysis and improvement of the quantum image matching

We investigate the quantum image matching algorithm proposed by Jiang et al. (Quantum Inf Process 15(9):3543–3572, 2016). Although the complexity of this algorithm is much better than the classical exhaustive algorithm, there may be an error in it: After matching the area between two images, only the pixel at the upper left corner of the matched area played part in following steps. That is to say, the paper only matched one pixel, instead of an area. If more than one pixels in the big image are the same as the one at the upper left corner of the small image, the algorithm will randomly measure one of them, which causes the error. In this paper, an improved version is presented which takes full advantage of the whole matched area to locate a small image in a big image. The theoretical analysis indicates that the network complexity is higher than the previous algorithm, but it is still far lower than the classical algorithm. Hence, this algorithm is still efficient.