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Remote Sensing And Image Interpretation

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Citizen Science: A Study of People, Expertise and Sustainable Development

A review of deep learning for renewable energy forecasting

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach

Time series forecasting is important in many aspects of our lives, since it can be used to deal with the uncertainty to further support the decision making. Despite many advanced methodologies have been proposed, modelling the underlying relationship between past and future conditions is still a challenge. In this research, we develop a novel time series forecasting model which can effectively predict future conditions in a timely fashion. A time series has nonlinear and nonstationary characteristics which make prediction using statistical or computational intelligent methods a difficult task. Therefore, a hybrid deep learning and empirical mode decomposition model, namely EMD–SAE, for multistep ahead forecasting is proposed to predict the traffic flow and random time series. The performances of the proposed model are compared and discussed. This paper shows the potential of hybridizing the deep learning and empirical mode decomposition to the ordinary time series forecasting approach, and the experimental results suggest that the proposed EMD–SAE is reliable, suitable and a promising method for time series forecasting.

Hybrid deep learning and empirical mode decomposition model for time series applications

Traffic flow forecasting is a useful technology applied to solve traffic congestion problems and to improve transportation mobility. Neural networks related approaches have been applied to develop traffic forecasting models for more than two decades. Since neural networks are sensitivity in parameters selection, selecting appropriate modeling configuration is essential to improve the accuracy and efficiency of traffic flow prediction. However, this is usually conducted by the trial-and-error method, which is very time consuming while involving too many design factors. Therefore, this paper utilizes a robust and systematic optimization approach, the Taguchi method, for obtaining the optimized configuration of the proposed exponential smoothing and extreme learning machine forecasting model. The developed model is applied to real-world data collected from freeways and highways in the United Kingdom and is compared with three existing forecasting models. The results indicate that the Taguchi method is efficient and capable for the forecasting model design and the proposed model with the optimized configuration has superior performance in traffic flow forecasting with approximate 91% and 88% accuracy rate in freeway and highway in both peak and nonpeak traffic periods.

Optimized Configuration of Exponential Smoothing and Extreme Learning Machine for Traffic Flow Forecasting

Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods

We utilised data mining techniques in cancer staging diagnosis.We found the correlation between pathology report and clinical information.Many interesting rules have been generated and evaluated.The evaluation results demonstrated the availability of the proposed framework. Lung cancer is one of the leading cancers for both genders all over the world. It is the most common cause of cancer death and almost reaches 20% of the total. The incidence of lung cancer has significantly increased from the early 19th century. In this manuscript we have discussed various data mining techniques that have been utilised for cancer diagnosis. The lung cancer pathologic staging is set based on the pathology report to describe the size and/or the extent of the original tumour and whether the cancer has spread (metastasis). Being aware of the lung cancer pathologic staging is important because it can be used to estimate a patient's prognosis and also can help physicians plan a suitable treatment. A sample of tissue from the patient's lung is required in order to complete the pathology report for the lung cancer pathologic staging diagnosis. In this procedure, a surgery biopsy is necessary but it may put the patient's health in jeopardy. Therefore, this study focuses on taking the clinical information which can be obtained without surgery to replace the pathology report. The data mining techniques are used to find the correlation between the clinical information and the pathology report in order to support lung cancer pathologic staging diagnosis.

Hao-Fan Yang

Papers

Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach

Hybrid deep learning and empirical mode decomposition model for time series applications

Optimized Configuration of Exponential Smoothing and Extreme Learning Machine for Traffic Flow Forecasting

Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods

Data mining in lung cancer pathologic staging diagnosis