1,154 research outputs found
Local Edge Betweenness based Label Propagation for Community Detection in Complex Networks
Nowadays, identification and detection community structures in complex
networks is an important factor in extracting useful information from networks.
Label propagation algorithm with near linear-time complexity is one of the most
popular methods for detecting community structures, yet its uncertainty and
randomness is a defective factor. Merging LPA with other community detection
metrics would improve its accuracy and reduce instability of LPA. Considering
this point, in this paper we tried to use edge betweenness centrality to
improve LPA performance. On the other hand, calculating edge betweenness
centrality is expensive, so as an alternative metric, we try to use local edge
betweenness and present LPA-LEB (Label Propagation Algorithm Local Edge
Betweenness). Experimental results on both real-world and benchmark networks
show that LPA-LEB possesses higher accuracy and stability than LPA when
detecting community structures in networks.Comment: 6 page
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks
Short-term traffic flow prediction is one of the crucial issues in
intelligent transportation system, which is an important part of smart cities.
Accurate predictions can enable both the drivers and the passengers to make
better decisions about their travel route, departure time and travel origin
selection, which can be helpful in traffic management. Multiple models and
algorithms based on time series prediction and machine learning were applied to
this issue and achieved acceptable results. Recently, the availability of
sufficient data and computational power, motivates us to improve the prediction
accuracy via deep-learning approaches. Recurrent neural networks have become
one of the most popular methods for time series forecasting, however, due to
the variety of these networks, the question that which type is the most
appropriate one for this task remains unsolved. In this paper, we use three
kinds of recurrent neural networks including simple RNN units, GRU and LSTM
neural network to predict short-term traffic flow. The dataset from TAP30
Corporation is used for building the models and comparing RNNs with several
well-known models, such as DEMA, LASSO and XGBoost. The results show that all
three types of RNNs outperform the others, however, more simple RNNs such as
simple recurrent units and GRU perform work better than LSTM in terms of
accuracy and training time.Comment: arXiv admin note: text overlap with arXiv:1706.06279,
arXiv:1804.04176 by other author
Free singularity path planning of hybrid parallel robot
This paper presents a singularity-free path planning approach for a hybrid parallel robot. The hybrid robot is composed of two well-known parallel robots, a hexapod and a tripod, that are serially connected. In this paper a methodology is developed to avoid singularity configurations of the hybrid parallel robot. Nominal polynomial paths are used for motion of end effector, and the strokes of each actuator is calculated by using the developed inverse kinematic. A MATLAB program has been developed to generate the designed paths, and several poses have been tested in a CAD model of the hybrid parallel robot to validate the feasibility of the path planning approach
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