2,979 publications from this institution
Energy saving becomes a central issue in the design of wireless sensor network routing algorithms. In the wireless sensor networks (WSNs), when intra-network communication is ensured, the lifetime of node can be extended by reducing data transmission or data volume as much as possible. However, the problem is that energy of the nodes around the sink node becomes exhausted quickly due to excessive communication overhead. To handle this problem, in this study, we propose a routing algorithm based on the sink node path optimisation. The study uses the energy consumption model as a constraint, transforms the time optimisation problem into an optimisation model, optimises the sink node path with the aid of simulated annealing (SA) algorithm, and uses data fusion to reduce the intra-network redundant data in the time domain. The proposed algorithm innovatively self-adjusts the path of sink node that is optimised by SA using new fitness function. Comprehensive simulation results show that the proposed algorithm can reduce the node energy consumption of waiting of sink node at the address of sink node, balance the network load and improve survival time of WSNs by 30% in comparison with results produced with the state-of-the art algorithms REAC-IN and DALMDT.
We discuss a problem of rule-based fuzzy modeling of multiple-input single-output nonlinear relationships f: R/sub n//spl rarr/R. The model under investigation is viewed as a collection of conditional statements "if state /spl Omega/, then y=g/sub i/(x,at)", i=1,2,...N with /spl Omega//sub i/ being a fuzzy relation defined in the space of the input variables. In contrast to the commonly encountered identification approach, based exclusively upon discrete experimental data, the one proposed in this study is concerned with the rule-based modeling exploiting the available nonlinear input-output relationship. The main thrust is in the development of a relevant fuzzy partition of the input variables. We introduce and study criteria of separability and variability as the key means guiding a distribution and granularity of the linguistic labels forming the condition part of the local models.
Nowadays, multi-label classification methods are of growing interest. Due to the relationships among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem. This paper presents a novel multi-label classification framework based on the variable precision neighborhood rough sets, called Multi-Label classification using Rough Sets (MLRS) which considers the impact of correlation among the labels and the uncertainty that exists in the mapping between the feature space and label space. A series of experiments reported for seven multi-label datasets show that MLRS achieves promising performance when compared with some famous multi-label learning algorithms.
The paper introduces a neural network-based model of logical connectives. The basic processing unit consists of two types of generic OR and AND neurons structured into a three layer topology. Due to the functional integrity we will be referring to it as an OR/AND neuron. The specificity of the logical connectives is captured by the OR/AND neuron within its supervised learning. Further analysis of the connections of the neuron obtained in this way provides a better insight into the nature of the connectives applied in fuzzy sets by emphasizing their features of "locality" and interactivity. Afterward, we will study several architectures of neural networks comprising these neurons treated as their basic functional components. The numerical studies embrace both the structures formed by single OR/AND neurons and aimed at modeling logical connectives (including the Zimmermann-Zysno data set, 1980) and the networks representing various decision-making architectures. We will also propose a realization of a pseudo median filter in which the OR/AND neurons play an ultimate role.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
