We consider a generalization of the proportionate flow shop problem with the makespan objective. Each job has a processing requirement and each machine has a characteristic value. In our case, we assume that the time ...
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We consider a generalization of the proportionate flow shop problem with the makespan objective. Each job has a processing requirement and each machine has a characteristic value. In our case, we assume that the time a job occupies a machine is equal to the processing requirement of the job plus a setup time that is equal to the characteristic value of that machine. In this paper, we consider permutation schedules and show that the problem is solvable in polynomial time when the number of machines is fixed.
The coming big data era brings data of unprecedented size and launches an innovation of learning algorithms in statistical and machine-learning communities. The classical kernel-based regularized least-squares (RLS) a...
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The coming big data era brings data of unprecedented size and launches an innovation of learning algorithms in statistical and machine-learning communities. The classical kernel-based regularized least-squares (RLS) algorithm is excluded in the innovation, due to its computational and storage bottlenecks. This article presents a scalable algorithm based on subsampling, called learning with selected features (LSF), to reduce the computational burden of RLS. Almost the optimal learning rate together with a sufficient condition on selecting kernels and centers to guarantee the optimality is derived. Our theoretical assertions are verified by numerical experiments, including toy simulations, UCI standard data experiments, and a real-world massive data application. The studies in this article show that LSF can reduce the computational burden of RLS without sacrificing its generalization ability very much.
In this paper, a new fast block-matching algorithm for video coding is presented. In the algorithm, the partial distance search (PDS) technique is performed in the wavelet domain to eliminate the undesired blocks. Sin...
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In this paper, a new fast block-matching algorithm for video coding is presented. In the algorithm, the partial distance search (PDS) technique is performed in the wavelet domain to eliminate the undesired blocks. Since the energy of blocks is concentrated in few coefficients in the wavelet domain, by starting the PDS at these coefficients, the algorithm can significantly reduce computational time for block matching. The PDS can be performed over the subspace in the wavelet domain to further lower the computational time at the expense of possible slight degradation in performance. In addition, the simple Haar wavelet can be used to remove the need for multiplication for blocking matching. Simulation results show that our algorithm is very effective for video coding where real-time processing is required.
This paper proposes an optimization strategy for searching moving targets' locations using cooperative unmanned aerial vehicles (UAVs) in an unknown environment. Such a strategy aims at reducing the overall search...
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This paper proposes an optimization strategy for searching moving targets' locations using cooperative unmanned aerial vehicles (UAVs) in an unknown environment. Such a strategy aims at reducing the overall search time and impact of uncertainties caused by the motion of targets, as well as improving the detection efficiency of UAVs. Specifically, we report, based on the UAV's scan of a location and taking into account (i) the detection and communication coverage limitations, and (ii) either a false alarm or inaccurate detection of the target, either the existence or the absence of the target. Moreover, leveraging a cooperative and competitive particle swarm optimization (PSO) algorithm, a decentralized target search model, relying on a real-time dynamic construction of cooperative UAV local sub-swarms (LoPSO), is proposed. Each sub-swarm strives to validate quickly the target location, updated based on the Bayesian theory. In such a strategy, each UAV operates in two flight modes, namely, either in swarm mode or in Greedy mode, and takes into consideration the received data from other UAVs to improve the overall environmental information. The simulation results revealed that the LoPSO outperforms other well-known searching methods of target methods for target search in unknown environments in terms of both performance and computational complexity.
The majority of algorithms developed for the narrowband direction of arrival (DOA) estimation problem rely on an eigenvalue decomposition (EVD) to determine both the number of signals and their respective DOAs. An alt...
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The majority of algorithms developed for the narrowband direction of arrival (DOA) estimation problem rely on an eigenvalue decomposition (EVD) to determine both the number of signals and their respective DOAs. An alternative algorithm is presented that solves both the DOA detection and estimation problems without resorting to an EVD. The algorithm is shown to have asymptotically equivalent performance to that of the (unconditional) maximum likelihood method, and hence it yields asymptotically minimum variance DOA estimates. The computational complexity required to update the DOA estimates in response to additional data from the array is investigated, and the algorithm is shown to be somewhat simpler than other methods with comparable performance. In addition, the asymptotic distribution of the algorithm's cost function is derived, and is shown to be composed of the sum of two differently scaled chi-squared random variables. A hypothesis test for determining the number of signals based on this result is then presented.
We have previously reported a number of tractable planning problems defined in the SAS(+) formalism. This article complements these results by providing a complete map over the complexity of SAS(+) planning under all ...
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We have previously reported a number of tractable planning problems defined in the SAS(+) formalism. This article complements these results by providing a complete map over the complexity of SAS(+) planning under all combinations of the previously considered restrictions. We analyze the complexity of both finding a minimal plan and finding any plan. In contrast to other complexity surveys of planning, we study not only the complexity of the decision problems but also the complexity of the generation problems. We prove that the SAS(+)-PUS problem is the maximal tractable problem under the restrictions we have considered if we want to generate minimal plans. If we are satisfied with any plan, then we can generalize further to the SAS(+)-US problem, which we prove to be the maximal tractable problem in this case.
More and more users are attracted by P2P networks characterized by decentralization, autonomy and anonymity. However, users' unconstrained behavior makes it necessary to use a trust model when establishing trust r...
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More and more users are attracted by P2P networks characterized by decentralization, autonomy and anonymity. However, users' unconstrained behavior makes it necessary to use a trust model when establishing trust relationships between peers. Most existing trust models are based on recommendations, which, however, suffer from the shortcomings of slow convergence and high complexity of trust computations, as well as huge overhead of network traffic. Inspired by the establishment of trust relationships in human society, a guarantee-based trust model, GeTrust, is proposed for Chord-based P2P networks. A service peer needs to choose its guarantee peer(s) for the service it is going to provide, and they are both required to pledge reputation mortgages for the service. The request peer makes evaluations on all the candidates of service peer by referring their service reputations and their guarantee peers' reputations, and selects the one with highest evaluation to be its service provider. In order to enhance GeTrust's availability and prevent malicious behavior, we also present incentive mechanism and anonymous reputation management strategy. Simulation results show that GeTrust is effective and efficient in terms of improving successful transaction rate, resisting complex attacks, reducing network overhead and lowering computational complexity.
We propose a centralized radix-2 multistage decision fusion strategy comprising simple AND and OR rules for cooperative spectrum sensing in cognitive sensor networks. Earlier works on centralized decision fusion show ...
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We propose a centralized radix-2 multistage decision fusion strategy comprising simple AND and OR rules for cooperative spectrum sensing in cognitive sensor networks. Earlier works on centralized decision fusion show the half-voting and majority rules to be optimum in many spectrum sensing scenarios in terms of minimizing the decision error (or equivalently maximizing the probability of correct decision). We consider a commonly occurring case in spectrum sensing in which the detection probability of a cognitive radio enabled sensor node is greater than its false-alarm probability. For this case, we consider five scenarios and demonstrate that the proposed method either performs better than half-voting and majority rules or exhibits a comparable performance. In this context, we also establish a criterion to make a choice between the AND and OR rules and compute the optimum number of nodes participating in cooperative spectrum sensing for these rules to maximize the correct decision probability. (c) 2015 Elsevier Inc. All rights reserved.
The concept of the sharing economy has attracted wide attention due to its huge impact on transforming traditional industries. Online car-hailing, combining the sharing economy and ICT technologies, shapes a new lands...
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The concept of the sharing economy has attracted wide attention due to its huge impact on transforming traditional industries. Online car-hailing, combining the sharing economy and ICT technologies, shapes a new landscape, which can greatly shorten the traveler's waiting time and reduce the empty run rate of cars (such as Uber and DiDi). However, a few comprehensive studies have been conducted on the sustainable development of online car-hailing considering both the user experience and the operational cost. To address this issue, this paper systematically studies the influencing factors, their relations, and their impacts on online car-hailing in an empirical way. First, an index system in four key aspects, namely service, price, safety, and traveling time, is established to evaluate the user experience. Second, the Bayesian network theory is employed to model the complexity of each factor and the extent of its influence on the online car-hailing system with expert scores. The two most important influence paths affecting the passengers' choices of online car-hailing are determined. Third, we further construct an investment allocation model with the aim of minimizing the economic cost of the online car-hailing system while maintaining the system performance, considering the limiting factors, such as the complexity and cost. Finally, we perform a simulation experiment, which generates some practical suggestions for improving the online car-hailing system.
In this paper, the received signal in a range cell is modelled as a multi-component linear frequency-modulated (LFM) signal after range compression and motion compensation, and a new method based on segment integratio...
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In this paper, the received signal in a range cell is modelled as a multi-component linear frequency-modulated (LFM) signal after range compression and motion compensation, and a new method based on segment integration and Lv's transform (LVT) is introduced for parameter estimation of LFM signals over long observation interval. In this method, the LFM signals are firstly divided into segments and fast Fourier transform (FFT) is then applied within each signal segment. After that, the same frequency resolution bins of each segment are selected to construct new series and inter-segment LVT is implemented to obtain the parameter estimates. The criteria to choose the number of segments, output signal-to-noise ratio, computational complexity and memory cost are analysed in detail for this new approach. This method is fast and able to obtain the accurate parameter estimates by using the complex multiplications and FFT. Comparisons with other popular methods, LVT, maximum-likelihood estimation and fractional Fourier transform are performed. Experimental results demonstrate the proposed method is capable of obtaining the accurate parameter estimates with low computational burden and storage memory, making it suitable to be applied in memory-limited and real-time processing systems.
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