Recent theory and experiments offer a new solution regarding how infant learners may break into word learning by using cross-situational statistics to find the underlying word referent mappings. Computational models d...
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Recent theory and experiments offer a new solution regarding how infant learners may break into word learning by using cross-situational statistics to find the underlying word referent mappings. Computational models demonstrate the in-principle plausibility of this statistical learning solution and experimental evidence shows that infants can aggregate and make statistically appropriate decisions from word referent co-occurrence data. We review these contributions and then identify the gaps in current knowledge that prevent a confident conclusion about whether cross-situational learning is the mechanism through which infants break into word learning. We propose an agenda to address that gap that focuses on detailing the statistics in the learning environment and the cognitive processes that make use of those statistics.
A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal ...
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A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 x 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.
Ontology gives us a reliable group of concepts and the relations between concepts in an IOT system. It does not only save words of format but also accurately transfers semantic data between human users and the compute...
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Ontology gives us a reliable group of concepts and the relations between concepts in an IOT system. It does not only save words of format but also accurately transfers semantic data between human users and the computers. Hence, the usefulness of resources in IOT system depends on whether the domain ontology can be constructed effectively and correctly. In this paper we propose an automated method to construct the IOT ontology. First, we explain the necessity of introducing ontology automatic construction in IOT system and summarize the major challenges in existing approaches. Secondly, we introduced the existing ontology construction methods and summarize their issues. Thirdly, we give a framework of our ontology construction and research the key algorithms in detail: (1) knowledge-tuple extraction algorithm which contains contextual information;(2) concept semantic similarity algorithm which is based on the structure of tuple;(3) knowledge-tuple extraction model which is based on the structured information. Then we build a prototype and evaluate the ontology. Finally, we make conclusions and suggest directions for future research.
Detection of faulty nodes and network energy saving have become the hottest research topics. Furthermore, current fault detection algorithms always pursue high detection performance but neglect energy consumption. In ...
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Detection of faulty nodes and network energy saving have become the hottest research topics. Furthermore, current fault detection algorithms always pursue high detection performance but neglect energy consumption. In order to obtain good fault detection performance and save the network power, this paper proposes a low energy consumption distributed fault detection algorithm (LEDFD), which takes full advantage of temporally correlated and spatially correlated characteristics of the sensor nodes. LEDFD utilizes the temporally correlated information to examine some faulty nodes and then utilizes the spatially correlated information to examine the nodes that have not been detected as faulty through exchanging information among neighbor nodes to determine those nodes' state. Because LEDFD takes the data produced by nodes themselves to detect certain types of faults, which means nodes need not exchange information with their neighbor nodes during the entire detection process, the energy consumption of networks is efficiently reduced. Experimental results show that the algorithm has good performance and low energy consumption compared with current algorithms.
This paper deals with asymptotic rejection of a multi-sinusoidal signal for linear single-input single-output stable systems with unknown structure. An adaptive orthogonal signals generator is used to both reconstruct...
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This paper deals with asymptotic rejection of a multi-sinusoidal signal for linear single-input single-output stable systems with unknown structure. An adaptive orthogonal signals generator is used to both reconstruct the disturbance and cancel its effect on the system output. An interesting feature is that the disturbance is removed by the generated internal signals with no additional dynamics in the cancellation algorithm. A fractional-order controller is designed which guarantees the closed-loop stability of the system if the location of the plant frequency response at the estimated frequencies lies in a half-plane passing through the origin of the complex plane, i.e. no information about the order of the system to be controlled, the relative degree, the nature of its poles and zeros, is required. The case of multi-sinusoidal disturbance is also analyzed. Simulations and comparisons with existing approaches are presented that highlight the performances of the proposed method. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
In recent years, fractal is widely used everywhere and escape time algorithm (ETA) became the most useful fractal creating method. However, ETA performs not so well because it needs huge computations. So, in this pape...
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In recent years, fractal is widely used everywhere and escape time algorithm (ETA) became the most useful fractal creating method. However, ETA performs not so well because it needs huge computations. So, in this paper, we first present an improved fractal creating algorithm by symmetrical radius of k-M set. Meanwhile, we use distributed cooperative method to improve classic ETA into parallel system, which is called distributed cooperative ETA (DCETA). Secondly, we present the proof of fractal property in k-M set f(c) (z) = z(k) + c with exponent k (k < 0), which concludes its threshold and symmetrical property. Finally, computational result shows correctness of the novel DCETA, which shows better computational effectiveness and lower waste.
In recent years, barrier coverage problem in directional sensor networks has been an interesting research issue. Most of the existing solutions to this problem aim to find as many barrier sets as possible to enhance c...
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In recent years, barrier coverage problem in directional sensor networks has been an interesting research issue. Most of the existing solutions to this problem aim to find as many barrier sets as possible to enhance coverage for the target area, which did not consider the power conservation. In this paper, we address the efficient sensor deployment (ESD) problem and energy-efficient barrier coverage (EEBC) problem for directional sensor networks. First, we describe a deployment model for the distribution of sensor locations to analyze whether a target area can be barrier covered. By this model, we examine the relationship between the probability of barrier coverage and network deployment parameters. Moreover, we model the EEBC as an optimization problem. An efficient scheduling algorithm is proposed to prolong the network lifetime when the target area is barrier covered. Simulation results are presented to demonstrate the performance of this algorithm.
We describe two implementations of the optimal error correction algorithm known as the maximum likelihood decoder (MLD) for the two-dimensional surface code with a noiseless syndrome extraction. First, we show how to ...
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We describe two implementations of the optimal error correction algorithm known as the maximum likelihood decoder (MLD) for the two-dimensional surface code with a noiseless syndrome extraction. First, we show how to implement MLD exactly in time O(n2), where n is the number of code qubits. Our implementation uses a reduction from MLD to simulation of matchgate quantum circuits. This reduction however requires a special noise model with independent bit-flip and phase-flip errors. Secondly, we show how to implement MLD approximately for more general noise models using matrix product states (MPS). Our implementation has running time O(nχ3), where χ is a parameter that controls the approximation precision. The key step of our algorithm, borrowed from the density matrix renormalization-group method, is a subroutine for contracting a tensor network on the two-dimensional grid. The subroutine uses MPS with a bond dimension χ to approximate the sequence of tensors arising in the course of contraction. We benchmark the MPS-based decoder against the standard minimum weight matching decoder observing a significant reduction of the logical error probability for χ≥4.
The area of three-dimensional (3D) underwater wireless sensor networks (UWSNs) has attracted significant attention recently due to its applications in detecting and observing phenomena that cannot be adequately observ...
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The area of three-dimensional (3D) underwater wireless sensor networks (UWSNs) has attracted significant attention recently due to its applications in detecting and observing phenomena that cannot be adequately observed by means of two-dimensional UWSNs. However, designing routing protocols for 3D UWSNs is a challenging task due to stringent constraints imposed by acoustic communications and high energy consumption in acoustic modems. In this paper, we present an ultrasonic frog calling algorithm (UFCA) that aims to achieve energy-efficient routing under harsh underwater conditions of UWSNs. In UFCA, the process of selecting relay nodes to forward the data packet is similar to that of calling behavior of ultrasonic frog for mating. We define the gravity function to represent the attractiveness from one sensor node to another. In order to save energy, different sensor nodes adopt different transmission radius and the values can be tuned dynamically according to their residual energy. Moreover, the sensor nodes that own less energy or locate in worse places choose to enter sleep mode for the purpose of saving energy. Simulation results show the performance improvement in metrics of packet delivery ratio, energy consumption, throughput, and end-to-end delay as compared to existing state-of-the-art routing protocols.
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