In this paper, we study data gathering in wireless sensor networks where intermediary nodes perform data aggregation with total fusion. We focus on gathering tree construction (from a general network topology) and tra...
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ISBN:
(纸本)9781467362337
In this paper, we study data gathering in wireless sensor networks where intermediary nodes perform data aggregation with total fusion. We focus on gathering tree construction (from a general network topology) and transmission scheduling in order to minimize gathering delay. We first propose an algorithm that calculates an optimal transmission schedule and the minimal delay, for any given gathering tree. Then, we prove a lower bound, in terms of the number of nodes in the network, on the optimal gathering delay for any graph. After analyzing the gathering trees formed by several popular tree constructing algorithms, we propose an algorithm that constructs the optimal gathering tree for a complete graph. We then conduct extensive simulations to show that the proposed algorithm is also a promising approximation algorithm for arbitrary graphs. We also propose an approximation algorithm that constructs a gathering tree that achieves a maximum-degree-optimal solution.
In this paper, we consider the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/...
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ISBN:
(纸本)9781479901777
In this paper, we consider the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/OFF modes to charge the TES and supply chilled water to the campus. A bilinear model is established to describe the system dynamics. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity costs. At each time step, the MPC problem is represented as a large-scale mixed integer nonlinear programming (MINLP) problem. We propose a heuristic algorithm to search for suboptimal solutions to the MINLP problem based on mixed integer linear programming (MILP), where the system dynamics is linearized along the simulated trajectories of the system. Simulation results show good performance and computational tractability of the proposed algorithm.
This paper presents a hybrid control approach for grasping objects by multiple agents without rebounding. When multiple agents grasp an object cooperatively, the motion of the agents is constrained due to the geometri...
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This paper presents a hybrid control approach for grasping objects by multiple agents without rebounding. When multiple agents grasp an object cooperatively, the motion of the agents is constrained due to the geometrical and frictional conditions at the contact points. In this paper, each agent acting on an object of interest is controlled by a hybrid controller which includes a position controller, a force controller, and some logic to coordinate grasping. The proposed approach provides a method to steer the agents to grasping positions on an object along appropriate directions and to asymptotically exert stabilizing forces at each contact point. The stability properties induced by the hybrid controller can be asserted using Lyapunov stability tools for hybrid systems. The set of allowed initial conditions guaranteed is characterized using sublevel sets of Lyapunov functions. The proposed algorithm is verified in simulations.
This paper focuses on the design of user selection algorithm for Unitary Beamforming (UBF) in multi-user multipleinput multiple-output (MIMO) systems. A long step user selection (LSUS) algorithm is proposed, and is ve...
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ISBN:
(纸本)9781467309899
This paper focuses on the design of user selection algorithm for Unitary Beamforming (UBF) in multi-user multipleinput multiple-output (MIMO) systems. A long step user selection (LSUS) algorithm is proposed, and is verified to be rather robust when combined with Givens rotation-based iterative optimization UBF. For most of existing user selection algorithms, only the best user is chosen at each selection, we regard the step length as one. However, because members of the optimal user set must both have high channel gains and low inter-user interference, if only one user is considered each step, the user that will be selected at next step can not be guaranteed to have both high channel gain and low inter-user interference with the just selected users. Therefore, an algorithm that two users are considered at each selection is proposed in this paper, the step length is regarded as two. It will in some extent help find the favorable user set and finally lead to a higher capacity. Moreover, Gram-Schmidt orthogonalization is used in our proposed algorithm to select the favorable users and at the same time generate the initial unitary beamforming matrix according to the channel state information (CSI). Two other beamforming schemes are simulated as references to demonstrate the good performance of the proposed algorithm even under low signal-to-noise-ratio (SNR) scenario.
Repeater jamming is a newly proposed jamming strategy much effective against linear frequency modulated (LFM) radars. In this paper, a general repeater jamming suppression algorithm is proposed based on Hilbert-Huang ...
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ISBN:
(纸本)9781509008643
Repeater jamming is a newly proposed jamming strategy much effective against linear frequency modulated (LFM) radars. In this paper, a general repeater jamming suppression algorithm is proposed based on Hilbert-Huang transform (HHT). Firstly, with the application of empirical mode decomposition (EMD), the time delay and duration of a repeater jamming can be calculated from the Hilbert-Huang spectrum. Then the repeater jamming is divided into several multi-components LFM segments and the other unknown parameters of each segment are estimated through a modified interpolation on Fourier coefficients (IFC) method. At last, the repeater jamming is suppressed by reconstruction and subtraction in a unified model. Monte-Carlo simulations demonstrate that the variances of the parameters estimation can attain the Cramér-Rao bounds (CRBs) asymptotically, and the proposed algorithm is verified having superior jamming suppression performance.
This paper proposed video stabilization techniques using undesired motion detection and alpha-trimming mean filter. The proposed method consists of detecting undesired motions step and filtering the undesired motions ...
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ISBN:
(纸本)9781509024018
This paper proposed video stabilization techniques using undesired motion detection and alpha-trimming mean filter. The proposed method consists of detecting undesired motions step and filtering the undesired motions step. The limitation on undesired motions is defined, using the local motion information. The alpha-trimming mean filter's alpha is controlled based on this limitation, so that regenerated video is controlled. The experimental results proved that the superior performance of the proposed algorithm.
Due to the rapid increase in images and image data, research examining the visual analysis of such unstructured data has recently come to be actively conducted. One of the representative image caption models the Dense...
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Due to the rapid increase in images and image data, research examining the visual analysis of such unstructured data has recently come to be actively conducted. One of the representative image caption models the DenseCap model extracts various regions in an image and generates region-level captions. However, since the existing DenseCap model does not consider priority for region captions, it is difficult to identify relatively significant region captions that best describe the image. There has also been a lack of research into captioning focusing on the core areas for story content, such as images in movies and dramas. In this study, we propose a new image captioning framework based on DenseCap that aims to promote the understanding of movies in particular. In addition, we design and implement a module for identifying characters so that the character information can be used in caption detection and caption improvement in core areas. We also propose a core area caption detection algorithm that considers the variables affecting the area caption importance. Finally, a performance evaluation is conducted to determine the accuracy of the character identification module, and the effectiveness of the proposed algorithm is demonstrated by visually comparing it with the existing DenseCap model.
Cell selection is an important issue in femtocell networks, which can balance the utilization of the whole network. In this paper, we investigate cell selection problem in a two-tier femtocell network that contains a ...
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ISBN:
(纸本)9781467359382
Cell selection is an important issue in femtocell networks, which can balance the utilization of the whole network. In this paper, we investigate cell selection problem in a two-tier femtocell network that contains a micro base station (MBS) and several femtocells with different access methods and coverage areas. We propose the evolutionary game model to describe the dynamics of the cell selection process and consider the evolutionary equilibrium as the solution. In order to achieve the evolutionary equilibrium, we introduce the reinforcement learning algorithm that can help distributed individual users make selection decisions independently. With their own knowledge of the past, the users can learn to achieve the evolutionary equilibrium without complete knowledge of other users. Finally, the performance of the evolutionary game and reinforcement learning algorithm is analyzed, and simulation results show the convergence and effectiveness of the proposed algorithm.
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