The article discusses an article published in the journal which discusses computer patches that use evolutionary computation to find and repair computer bugs. The author discusses how search-based optimization can all...
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The article discusses an article published in the journal which discusses computer patches that use evolutionary computation to find and repair computer bugs. The author discusses how search-based optimization can allow software programmers to locate computer bugs using optimization algorithms, a process known as search based software engineering (SBSE).
Reliability of cyberphysical system (CPS) components substitution is an important issue for CPS troubleshooting and system upgrading. In this paper, decision problem of components substitution is regarded as decision ...
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Reliability of cyberphysical system (CPS) components substitution is an important issue for CPS troubleshooting and system upgrading. In this paper, decision problem of components substitution is regarded as decision problem of services substitution through a service-oriented architecture of CPS. Further, a reliability assurance method for CPS service substitution is proposed, which comprises two parts. The first one is a qualitative judgment method for CPS service substitution according to the relationship between service compatibility and substitution based on time-space p-calculus with time and space operators. The other one is consisted of substitution processes from above judgment results based on service management theory. Finally, a case study is performed to show how to apply this method to ensure CPS components reliable substitution. The experimental result shows that this method is reasonable and feasible.
Shows that high-quality personal computers (PC) compiler implementation combined with the language features of the Cobol are improving the PC environment for Cobol. Cobol compiler performance; Cobol compilation speeds...
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Shows that high-quality personal computers (PC) compiler implementation combined with the language features of the Cobol are improving the PC environment for Cobol. Cobol compiler performance; Cobol compilation speeds; debugging of Cobol programs for eventual uploading to a mainframe.
Axle box bearings are the most critical mechanical components of railway vehicles. Condition monitoring is of great benefit to ensure the healthy status of bearings in the railway train. In this paper, a novel fault d...
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Axle box bearings are the most critical mechanical components of railway vehicles. Condition monitoring is of great benefit to ensure the healthy status of bearings in the railway train. In this paper, a novel fault diagnosis model for axle box bearing based on symmetric alpha-stable distribution feature extraction and least squares support vector machines (LS-SVM) using vibration signals is proposed which is conducted in three main steps. Firstly, fast nonlocal means is used for denoising and ensemble empirical mode decomposition is applied to extract fault feature information. Then a new statistical method of feature extraction, symmetric alpha-stable distribution, is employed to obtain representative features from intrinsic mode functions. Additionally, the hybrid fault feature sets are input into LS-SVM to identify the fault type. To enhance the performance of LS-SVM in the case of small-scale samples, Morlet wavelet kernel function is combined with LS-SVM for the classification of fault type and fault severity and the particle swarm optimization is used for the optimization of LS-WSVM parameters. Finally, the experimental results demonstrate that the proposed approach performs more effectively and robustly than the other methods in small-scale samples for fault detection and classification of railway vehicle bearings.
The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel...
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The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM(PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.
Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this ...
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Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.
In wireless networks, wireless sniffers are distributed in a region to monitor the activities of users. It can be applied for fault diagnosis, resource management, and critical path analysis. Due to hardware limitatio...
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In wireless networks, wireless sniffers are distributed in a region to monitor the activities of users. It can be applied for fault diagnosis, resource management, and critical path analysis. Due to hardware limitations, wireless sniffers typically can only collect information on one channel at a time. Therefore, it is a key topic to optimize the channel selection for sniffers to maximize the information collected, so as to maximize the Quality of Monitoring (QoM) for wireless networks. In this paper, a Multiple-Quantum-Immune-Clone-Algorithm- (MQICA-) based solution was proposed to achieve the optimal channel allocation. The extensive simulations demonstrate that MQICA outperforms the related algorithms evidently with higher monitoring quality, lower computation complexity, and faster convergence. The practical experiment also shows the feasibility of this algorithm.
作者：
Yangjia LiMingsheng Ying[]State Key Laboratory of Intelligent Technology and Systems
Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 China and Centre for Quantum Computation and Intelligent Systems (QCIS) Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia
Since observation on a quantum system may cause the system state collapse, it is usually hard to find a way to monitor a quantum process, which is a quantum system that continuously evolves. We propose a protocol that...
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Since observation on a quantum system may cause the system state collapse, it is usually hard to find a way to monitor a quantum process, which is a quantum system that continuously evolves. We propose a protocol that can debug a quantum process by monitoring, but not disturb the evolution of the system. This protocol consists of an error detector and a debugging strategy. The detector is a projection operator that is orthogonal to the anticipated system state at a sequence of time points, and the strategy is used to specify these time points. As an example, we show how to debug the computational process of quantum search using this protocol. By applying the Skolem-Mahler-Lech theorem in algebraic number theory, we find an algorithm to construct all of the debugging protocols for quantum processes of time-independent Hamiltonians.
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