The Experts below are selected from a list of 136092 Experts worldwide ranked by ideXlab platform
Yilu Liu - One of the best experts on this subject based on the ideXlab platform.
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Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach
Energies, 2018Co-Authors: Qiaomu Zhu, Jinfu Chen, Lin Zhu, Xianzhong Duan, Yilu LiuAbstract:Wind speed prediction with spatio–Temporal Correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–Temporal Correlation. This paper proposes a model for wind speed prediction with spatio–Temporal Correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the Temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and Temporal Correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–Temporal Correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–Temporal Correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.
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wind speed prediction with spatio Temporal Correlation a deep learning approach
Energies, 2018Co-Authors: Qiaomu Zhu, Jinfu Chen, Lin Zhu, Xianzhong Duan, Yilu LiuAbstract:Wind speed prediction with spatio–Temporal Correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–Temporal Correlation. This paper proposes a model for wind speed prediction with spatio–Temporal Correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the Temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and Temporal Correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–Temporal Correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–Temporal Correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.
Hiroshi Saito - One of the best experts on this subject based on the ideXlab platform.
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Spatio-Temporal Correlation of Interference in MANET Under Spatially Correlated Shadowing Environment
IEEE Transactions on Mobile Computing, 2020Co-Authors: Tatsuaki Kimura, Hiroshi SaitoAbstract:Correlation of interference affects spatio-Temporal aspects of various wireless mobile systems, such as retransmission, multiple antennas and cooperative relaying. In this paper, we study the spatial and Temporal Correlation of interference in mobile ad-hoc networks under a correlated shadowing environment. By modeling the node locations as a Poisson point process with an i.i.d. mobility model and considering Gudmundson (1991)' s spatially correlated shadowing model, we theoretically analyze the relationship between the Correlation distance of log-normal shadowing and the spatial and Temporal Correlation coefficients of interference. Since the exact expressions of the Correlation coefficients are intractable, we obtain their simple asymptotic expressions as the variance of log-normal shadowing increases. We found in our numerical examples that the asymptotic expansions can be used as tight approximate formulas and useful for modeling general wireless systems under spatially correlated shadowing.
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WiOpt - Temporal Correlation of interference under spatially correlated shadowing
2018 16th International Symposium on Modeling and Optimization in Mobile Ad Hoc and Wireless Networks (WiOpt), 2018Co-Authors: Tatsuaki Kimura, Hiroshi SaitoAbstract:In this paper, we study the Temporal Correlation of interference in mobile ad-hoc networks under a correlated shadowing environment. By modeling the node locations as a 1-D Poisson point process with an i.i.d. mobility model and considering spatially correlated shadowing that depends on the distance between nodes, we derive a simple asymptotic expression of the Temporal Correlation coefficient of interference as the variance of log-normal shadowing increases. This shows a readable relationship between the Correlation distance of lognormal shadowing and the Temporal Correlation of interference and thus can be useful for modeling general wireless systems with spatially correlated shadowing.
Qiaomu Zhu - One of the best experts on this subject based on the ideXlab platform.
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Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach
Energies, 2018Co-Authors: Qiaomu Zhu, Jinfu Chen, Lin Zhu, Xianzhong Duan, Yilu LiuAbstract:Wind speed prediction with spatio–Temporal Correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–Temporal Correlation. This paper proposes a model for wind speed prediction with spatio–Temporal Correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the Temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and Temporal Correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–Temporal Correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–Temporal Correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.
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wind speed prediction with spatio Temporal Correlation a deep learning approach
Energies, 2018Co-Authors: Qiaomu Zhu, Jinfu Chen, Lin Zhu, Xianzhong Duan, Yilu LiuAbstract:Wind speed prediction with spatio–Temporal Correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–Temporal Correlation. This paper proposes a model for wind speed prediction with spatio–Temporal Correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the Temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and Temporal Correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–Temporal Correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–Temporal Correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.
Kuldip K. Paliwal - One of the best experts on this subject based on the ideXlab platform.
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ICASSP (2) - Use of Temporal Correlation between successive frames in a hidden Markov model based speech recognizer
IEEE International Conference on Acoustics Speech and Signal Processing, 1993Co-Authors: Kuldip K. PaliwalAbstract:The Temporal Correlation between successive frames is incorporated in an HMM (hidden Markov model) based speech recognizer. This is done by making the probability of the current observation vector dependent on the previous observation vectors. Preliminary results show that this approach provides significant improvement in recognition performance (with the use of Temporal Correlation between two successive frames alone). >
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use of Temporal Correlation between successive frames in a hidden markov model based speech recognizer
International Conference on Acoustics Speech and Signal Processing, 1993Co-Authors: Kuldip K. PaliwalAbstract:The Temporal Correlation between successive frames is incorporated in an HMM (hidden Markov model) based speech recognizer. This is done by making the probability of the current observation vector dependent on the previous observation vectors. Preliminary results show that this approach provides significant improvement in recognition performance (with the use of Temporal Correlation between two successive frames alone). >
Chengxiang Wang - One of the best experts on this subject based on the ideXlab platform.
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a novel 3d non stationary vehicle to vehicle channel model and its spatial Temporal Correlation properties
IEEE Access, 2018Co-Authors: Qiuming Zhu, Ying Yang, Xiaomin Chen, Yi Tan, Chengxiang WangAbstract:In this paper, a new non-stationary Vehicle-to-Vehicle (V2V) channel model is proposed. It could generate more smooth fading phase between the adjacent channel states and guarantee more accurate Doppler frequency, which is a great improvement comparing with those of the existing non-stationary geometry-based stochastic models (GBSMs) for V2V channels. Meanwhile, the spatial–Temporal Correlation function (STCF) as well as a Temporal Correlation function (TCF) and a spatial Correlation function (SCF) are derived in details based on the power angle spectrums of both the mobile transmitter (MT) and mobile receiver (MR) following the Von Mises Fisher (VMF) distribution. Simulation results have demonstrated that the time-variant Correlation properties of our proposed channel model have an excellent agreement with the theoretical results, which verifies the correctness of theoretical derivations and simulations. Finally, the TCF and stationary interval of the proposed model are verified by the measured results.
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spatial Temporal Correlation properties of the 3gpp spatial channel model and the kronecker mimo channel model
Eurasip Journal on Wireless Communications and Networking, 2007Co-Authors: Chengxiang Wang, Xuemin Hong, Hanguang Wu, Wen XuAbstract:The performance of multiple-input multiple-output (MIMO) systems is greatly influenced by the spatial-Temporal Correlation properties of the underlying MIMO channels. This paper investigates the spatial-Temporal Correlation characteristics of the spatial channel model (SCM) in the Third Generation Partnership Project (3GPP) and the Kronecker-based stochastic model (KBSM) at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-Temporal separability at all the three levels. The spatial-Temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial Correlation is related to the joint distribution of the angle of arrival (AoA) and angle of departure (AoD). The KBSM with the Gaussian-shaped power azimuth spectrum (PAS) is found to fit best the 3GPP SCM in terms of the spatial Correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-Temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.
