Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble ...
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Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble (CE) integrates several clustering algorithms such that the clustering results can be effectively improved. This work investigates similarity-based methods and proposes a new method called weight- incorporated similarity-based clustering ensemble (WSCE). Six classic data sets are used to test single clustering algorithms, similarity-based one, and the proposed one via simulation. The results prove the validity and performance advantage of the proposed method.
Context: the presence of several languages interacting each other within the same project is an almost universal feature in software development. Earlier work shows that this interaction might be source of problems. O...
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Active fault isolation of parametric faults in closed-loop MIMO systems are considered in this paper. The fault isolation consists of two steps. The first step is group-wise fault isolation. Here, a group of faults is...
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Active fault isolation of parametric faults in closed-loop MIMO systems are considered in this paper. The fault isolation consists of two steps. The first step is group-wise fault isolation. Here, a group of faults is isolated from other possible faults in the system. The group-wise fault isolation is based directly on the input/output signals applied for the fault detection. It is guaranteed that the fault group includes the fault that had occurred in the system. The second step is individual fault isolation in the fault group. Both types of isolation are obtained by applying dedicated auxiliary inputs and the associated residual outputs.
This paper describes a new method for how interfering MIMO links might transmit more effectively in a random access network. We assume perfect transmitter-side channel state information, zero-forcing pre-coding to gua...
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This paper describes a new method for how interfering MIMO links might transmit more effectively in a random access network. We assume perfect transmitter-side channel state information, zero-forcing pre-coding to guarantee no interference on links that have already won access, and zero-forcing receiver processing at the still-contending links to suppress interference from the links that have already won. We define an effectiveness metric, the Instantaneous Equivalent SNR Percentile (IESP), in which the Equivalent SNR is the SNR of a single-input-single-output (SISO) link that would have the same capacity of a MIMO link after its interference constraints have been met and the IESP is the percentile of the Equivalent SNR, assuming independent Rayleigh fading. We propose that fairness among heterogeneous contending links be realized by giving the shorter contention window to the link with the higher IESP based on its own distribution, enabling links with few antennas to compete with links that have many antennas. Through simulation of the sum capacity of the winning set of links, the proposed contention window design is shown to provide a higher sum capacity than contention based on equal-sized windows.
We propose a comprehensive framework to support the personalization and adaptivity of courses in e-learning environments where the traditional activity of individual study is augmented by social-collaborative and grou...
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Wireless rechargeable sensor network is a promising platform for long-term applications such as inventory management, supply chain monitoring and so on. For these applications, sensor localization is one of the most f...
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Wireless rechargeable sensor network is a promising platform for long-term applications such as inventory management, supply chain monitoring and so on. For these applications, sensor localization is one of the most fundamental challenges. Different from traditional sensor node, wireless rechargeable sensor has to be charged above a voltage level by the wireless charger in order to support its sensing, computation and communication operations. In this work, we consider the scenario where a mobile charger stops at different positions to charge sensors, and propose a novel localization design that utilizes the unique Time of Charge (TOC) sequences among wireless rechargeable sensors. Specifically, we introduce two efficient region dividing methods, Inter-node Division and Inter-area Division, to exploit TOC differences from both temporal and spatial dimensions to localize individual sensor nodes. To further optimize the system performance, we introduce both an optimal charger stop planning algorithm for single sensor case and a suboptimal charger stop planning algorithm for the generic multi-sensor scenario with a provable performance bound. We have extensively evaluated our design by both testbed experiments and large-scale simulations. The experiment and simulation results show that by as less as 5 stops, our design can achieve sub-meter accuracy and the performance is robust under various system conditions.
In this paper we study state-space realizations of Linear and Time-Invariant (LTI) systems. Motivated by biochemical reaction networks, Gonçalves and Warnick have recently introduced the notion of a Dynamical Str...
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Common practice in industrial design of discrete controllers as well as in most synthesis procedures advocated for discrete control in academia is to create the control logic and to transfer it into a PLC language bef...
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State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estim...
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State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.
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