360 research outputs found
Subjective Image Quality Assessment:A Pre-Assessment on Visual Distortion of Medical Images by Clinicians and Radiologists
Computer-Aided Atrial Fibrillation Diagnosis System with the Naive Bayesian Network:Based on the Analysis of 2016 Actual Cases of Electrocardiography Signals
Semantic Role Labeling with Associated Memory Network
Semantic role labeling (SRL) is a task to recognize all the
predicate-argument pairs of a sentence, which has been in a performance
improvement bottleneck after a series of latest works were presented. This
paper proposes a novel syntax-agnostic SRL model enhanced by the proposed
associated memory network (AMN), which makes use of inter-sentence attention of
label-known associated sentences as a kind of memory to further enhance
dependency-based SRL. In detail, we use sentences and their labels from train
dataset as an associated memory cue to help label the target sentence.
Furthermore, we compare several associated sentences selecting strategies and
label merging methods in AMN to find and utilize the label of associated
sentences while attending them. By leveraging the attentive memory from known
training data, Our full model reaches state-of-the-art on CoNLL-2009 benchmark
datasets for syntax-agnostic setting, showing a new effective research line of
SRL enhancement other than exploiting external resources such as well
pre-trained language models.Comment: Published at NAACL 2019; This is camera Ready version; Code is
available at https://github.com/Frozenmad/AMN_SR
Research on Medication Rules of Chronic Gastritis and Allergic Rhinitis Based on the Complex System Entropy Clustering Method
Optimal Power Flow in Hybrid AC and Multi-terminal HVDC Networks with Offshore Wind Farm Integration Based on Semidefinite Programming
Multi-terminal high voltage direct current (MTHVDC) technology is a promising
technology for the offshore wind farm integration, which requires the new
control and operation scheme. Therefore, the optimal power flow problem for
this system is important to achieve the optimal economic operation. In this
paper, convex relaxation model based on semidefinite programming for the
MT-HVDC system considering DC/DC converters is proposed to solve the optimal
power flow problem. A hybrid AC and MT-HVDC system for offshore wind farm
integration is used for the test. The simulation results validate the
effectiveness of the proposed model and guarantee that the global optimum
solution is achieved.Comment: Accepted in IEEE/PES ISGT Asia 2019 conference (May, 2019), Chengdu,
Chin
Island area, not isolation, drives taxonomic, phylogenetic and functional diversity of ants on land-bridge islands
Chaotic Phase-Coded Waveforms with Space-Time Complementary Coding for MIMO Radar Applications
A framework for designing orthogonal chaotic phase-coded waveforms with space-time complementary coding (STCC) is proposed for multiple-input multiple-output (MIMO) radar applications. The phase-coded waveform set to be transmitted is generated with an arbitrary family size and an arbitrary code length by using chaotic sequences. Due to the properties of chaos, this chaotic waveform set has many advantages in performance, such as anti-interference and low probability of intercept. However, it cannot be directly exploited due to the high range sidelobes, mutual interferences, and Doppler intolerance. In order to widely implement it in practice, we optimize the chaotic phase-coded waveform set from two aspects. Firstly, the autocorrelation property of the waveform is improved by transmitting complementary chaotic phase-coded waveforms, and an adaptive clonal selection algorithm is utilized to optimize a pair of complementary chaotic phase-coded pulses. Secondly, the crosscorrelation among different waveforms is eliminated by implementing space-time coding into the complementary pulses. Moreover, to enhance the detection ability for moving targets in MIMO radars, a method of weighting different pulses by a null space vector is utilized at the receiver to compensate the interpulse Doppler phase shift and accumulate different pulses coherently. Simulation results demonstrate the efficiency of our proposed method
Chaotic Phase-Coded Waveforms with Space-Time Complementary Coding for MIMO Radar Applications
A framework for designing orthogonal chaotic phase-coded waveforms with space-time complementary coding (STCC) is proposed for multiple-input multiple-output (MIMO) radar applications. The phase-coded waveform set to be transmitted is generated with an arbitrary family size and an arbitrary code length by using chaotic sequences. Due to the properties of chaos, this chaotic waveform set has many advantages in performance, such as anti-interference and low probability of intercept. However, it cannot be directly exploited due to the high range sidelobes, mutual interferences, and Doppler intolerance. In order to widely implement it in practice, we optimize the chaotic phase-coded waveform set from two aspects. Firstly, the autocorrelation property of the waveform is improved by transmitting complementary chaotic phase-coded waveforms, and an adaptive clonal selection algorithm is utilized to optimize a pair of complementary chaotic phase-coded pulses. Secondly, the crosscorrelation among different waveforms is eliminated by implementing space-time coding into the complementary pulses. Moreover, to enhance the detection ability for moving targets in MIMO radars, a method of weighting different pulses by a null space vector is utilized at the receiver to compensate the interpulse Doppler phase shift and accumulate different pulses coherently. Simulation results demonstrate the efficiency of our proposed method
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