Optimization of program recursive function calls analysis method

Static analysis can be classified into intra-procedural analysis and inter-procedural analysis-by-analysis objects. Inter-procedural analysis need to analyze the whole program adopting bottom-up parsing algorithms. Inter-procedural analysis can be converted to intra-procedural analysis by logging the context of follower’s function call in bottom-up analysis. However, the existence of recursive function calls in program increases the difficulty of inter-procedural analysis, which affects the detection accuracy of static analysis to some extent. An optimal method for analyzing the recursive function calls of program is presented in this paper. This method can lift the recursive function calls in function call graph of the program to be analyzed on the premise that vital function calls will be preserved. In this way, the false negative rate and false positive rate of program static analysis can be reduced and the accuracy of program security check can be improved.     

Appearance Harmonization for Single Image Shadow Removal

Shadow removal is a challenging problem and previous approaches often produce de‐shadowed regions that are visually inconsistent with the rest of the image. We propose an automatic shadow region harmonization approach that makes the appearance of a de‐shadowed region (produced using any previous technique) compatible with the rest of the image. We use a shadow‐guided patch‐based image synthesis approach that reconstructs the shadow region using patches sampled from non‐shadowed regions. This result is then refined based on the reconstruction confidence to handle unique textures. Qualitative comparisons over a wide range of images, and a quantitative evaluation on a benchmark dataset show that our technique significantly improves upon the state‐of‐the‐art.     

Single Criterion Net Flow Using Concordance and Discordance Relations

This article proposes a flow-based method using the single criterion net flow for pattern classification. Traditional single criterion net flow, generated on the basis of net partial concordance indices, measures the preference intensity for one pattern with respect to all others on this criterion. A concordance index measures the intensity of preference for one pattern over another pattern. Contrary to traditional flow-based methods, such as PROMETHEE, in which only the concordance relation is taken into account, the proposed method proposes a new definition of single criterion net flow using both concordance and discordance relations. This can balance pros and cons for the preference of one pattern over another pattern for a criterion. The final classification decision for a new pattern depends on its net flow. Criteria weights are determined using a genetic-algorithm-based approach. Empirical results involving bankruptcy prediction demonstrate that the proposed method performs well compared to other well-known classification methods.     

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.