In [S1] Schoning proposed a simple yet efficient randomized algorithm for solving the k-SAT problem. In the case of 3-SAT, the algorithm has an expected running time of poly(n) center dot (4/3)(n) = 0(1.334(n)). In th...
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In [S1] Schoning proposed a simple yet efficient randomized algorithm for solving the k-SAT problem. In the case of 3-SAT, the algorithm has an expected running time of poly(n) center dot (4/3)(n) = 0(1.334(n)). In this paper we present randomized algorithms and show that one of them has 0(1.3302(n)) expected running time, improving Schoning's algorithm. (Note. At this point, the fastest randomized algorithm for 3-SAT is the one given by Iwama and Tamaki [IT] that runs in 0(1.324(n)).)
This paper considers interconnected nonlinear systems with on-line plugging/unplugging of subsystems that are modeled by state-varying and mode-varying switched systems. A modularized method is proposed to design each...
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This paper considers interconnected nonlinear systems with on-line plugging/unplugging of subsystems that are modeled by state-varying and mode-varying switched systems. A modularized method is proposed to design each subsystem's decentralized control law, which together with a new condition on time intervals of plugging/unplugging lead to the stability of the interconnected system. Three plug-and-play based fault tolerant control algorithms are further provided in the presence of coupling faults. The new result allows for the plugging/unplugging without reconfiguring any subsystem's control law and without taking any action on the faulty subsystems. An example of coupled pendulums is taken to illustrate the theoretical results. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Due to the importance of slack time utilization for power-aware scheduling algorithms, we propose a work-demand analysis method called parareclamation algorithm (PRA) to increase slack time utilization of the existing...
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Due to the importance of slack time utilization for power-aware scheduling algorithms, we propose a work-demand analysis method called parareclamation algorithm (PRA) to increase slack time utilization of the existing real-time DVS algorithms. PRA is an online scheduling for power-aware real-time tasks under rate-monotonic (RM) policy. It can be implemented and fully compatible with preemption-aware or transition-aware scheduling algorithms without increasing their computational complexities. The key technique of the heuristics method doubles the analytical interval and turns the deferrable workload out the potential slack time. Theoretical proofs show that PRA guarantees the task deadlines in a feasible RM schedule and takes linear time and space complexities. Experimental results indicate that the proposed method combining the preemption-aware methods seamlessly reduces the energy consumption by 14% on average over their original algorithms.
The number of 3D geometric models available in online repositories is growingdramatically. Examples include: the Protein Data Bank, which stores the 3D atomic coordinates for29,000 protein molecules; the National Desi...
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The number of 3D geometric models available in online repositories is growingdramatically. Examples include: the Protein Data Bank, which stores the 3D atomic coordinates for29,000 protein molecules; the National Design Repository, which stores 3D computer-aided design(CAD) models for tens of thousands of mechanical parts; and the Princeton Shape Database, whichstores polygonal surface models for 36,000 everyday objects crawled from the Web. Since graphicshardware is getting faster and 3D scanning hardware cheaper, there is every reason to believe thatdemand for and supply of 3D models will continue to increase into the future, leading to an onlineenvironment in which 3D models are as plentiful as images, videos, and audio files today.
An improved Bayesian fusion algorithm (BFA) is proposed for forecasting the blink number in a continuous video. It assumes that, at one prediction interval, the blink number is correlated with the blink numbers of onl...
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An improved Bayesian fusion algorithm (BFA) is proposed for forecasting the blink number in a continuous video. It assumes that, at one prediction interval, the blink number is correlated with the blink numbers of only a few previous intervals. With this assumption, the weights of the component predictors in the improved BFA are calculated according to their prediction performance only from a few intervals rather than from all intervals. Therefore, compared with the conventional BFA, the improved BFA is more sensitive to the disturbed condition of the component predictors for adjusting their weights more rapidly. To determine the most relevant intervals, the grey relation entropy-based analysis (GREBA) method is proposed, which can be used analyze the relevancy between the historical data flows of blink number and the data flow at the current interval. Three single predictors, that is, the autoregressive integrated moving average (ARIMA), radial basis function neural network (RBFNN), and Kalman filter (KF), are designed and incorporated linearly into the BFA. Experimental results demonstrate that the improved BFA obviously outperforms the conventional BFA in both accuracy and stability;also fatigue driving can be accurately warned against in advance based on the blink number forecasted by the improved BFA.
A rule based fuzzy controller is presented for attitude stabilization of a small satellite in low earth orbit. Active control is done by using 3-axis magneto-torquing. A prefered torque vector can not always be genera...
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A rule based fuzzy controller is presented for attitude stabilization of a small satellite in low earth orbit. Active control is done by using 3-axis magneto-torquing. A prefered torque vector can not always be generated from the magnetic coils in certain regions of the orbit, due to an unfavourable direction of the geomagnetic field vector. The main objective of an acceptable control algorithm will then be to optimize the control effort: maximize the desired influence on the controlled axis and minimize the undesired disturbances to the other axes. It was found that a fuzzy controller can achieve these goals in a computationally effective way, compared to other more involved MIMO algorithms, while performing just as well or better under similar conditions. This fuzzy controller solves the control constraint problem by chosing the best magneto- torquer, the polarity and switching instances.
A vehicle stability control approach for four-wheel independently actuated (FWIA) electric vehicles is presented. The proposed control method consists of a higher-level controller and a lower-level controller. An adap...
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A vehicle stability control approach for four-wheel independently actuated (FWIA) electric vehicles is presented. The proposed control method consists of a higher-level controller and a lower-level controller. An adaptive control-based higher-level controller is designed to yield the vehicle virtual control efforts to track the desired vehicle motions due to the possible modeling inaccuracies and parametric uncertainties. The lower-level controller considering tire force saturation is given to allocate the required control efforts to the four in-wheel motors for providing the desired tire forces. An analytic method is given to distribute the high-level control efforts, without using the numerical-optimization-based control allocation algorithms. Simulations based on a high-fidelity, CarSim, and full-vehicle model show the effectiveness of the control approach.
In this article we discuss the design and implementation of a real-time parallel anomaly detection system. The key idea is to use multiple existing anomaly detection algorithms in parallel on thousands of network traf...
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In this article we discuss the design and implementation of a real-time parallel anomaly detection system. The key idea is to use multiple existing anomaly detection algorithms in parallel on thousands of network traffic subclasses, which not only enables us to detect hidden anomalies but also to increase the accuracy of the system. The main challenge then is the management and aggregation of the vast amount of data generated. We propose a novel aggregation process that uses the internal continuous anomaly metrics used by the algorithms to output a single system-wide anomaly metric. The evaluation on real-world attack traces shows a lower false positive rate and false negative rate than any individual anomaly detection algorithm.
In this paper, we address energy-aware online scheduling of jobs with resource contention. We propose an optimization model and present new approach to resource allocation with job concentration taking into account ty...
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In this paper, we address energy-aware online scheduling of jobs with resource contention. We propose an optimization model and present new approach to resource allocation with job concentration taking into account types of applications and heterogeneous workloads that could include CPU-intensive, disk-intensive, I/O-intensive, memory-intensive, network-intensive, and other applications. When jobs of one type are allocated to the same resource, they may create a bottleneck and resource contention either in CPU, memory, disk or network. It may result in degradation of the system performance and increasing energy consumption. We focus on energy characteristics of applications, and show that an intelligent allocation strategy can further improve energy consumption compared with traditional approaches. We propose heterogeneous job consolidation algorithms and validate them by conducting a performance evaluation study using the Cloud Sim toolkit under different scenarios and real data. We analyze several scheduling algorithms depending on the type and amount of information they require.
This paper develops new methods for vision-based satellite attitude control aimed at space-based optical tracking of resident space objects (RSOs). An Earth-orbiting chaser satellite equipped with either one or two bo...
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This paper develops new methods for vision-based satellite attitude control aimed at space-based optical tracking of resident space objects (RSOs). An Earth-orbiting chaser satellite equipped with either one or two body-fixed cameras can successfully track an RSO provided that the target is kept within the camera field of view. Because the cameras are body fixed, the attitude of the satellite needs to be controlled to maintain target lock. Novel vision-based control algorithms are developed to align the chaser camera's optical axis with the chaser-target line of sight. Two control architectures are presented for the cases of monocular and stereoscopic vision. In the case of the monocular architecture, relative chaser-target acceleration information is not available. Moreover, in both cases unknown perturbations can impair the tracking performance. To increase the tracking algorithm's robustness to these effects, a variable structure attitude control technique is employed. The stability of the developed control laws are substantiated based on Lyapunov's direct method and demonstrated using Monte Carlo simulations. The results clearly show that using stereoscopic vision yields faster target tracking, increased robustness to noise and field-of-view limits, and reduced fuel consumption compared with monocular vision-based attitude tracking.
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