Vehicle speed prediction is quite essential for many intelligent vehicular and transportation applications. Accurate on-road vehicle speed prediction is challenging because individual vehicle speed ...is affected by many factors related to driver–vehicle–road–traffic system, e.g. the traffic conditions, vehicle type, and driver's behavior, in either a deterministic or stochastic way. Also machine learning makes vehicle speed predictions more accessible by exploring the potential relationship between the vehicle speed and its main factors based on the historical driving data in the context of vehicular networks. This study proposes a novel data-driven vehicle speed prediction method based on back propagation-long short-term memory (BP-LSTM) algorithms for long-term individual vehicle speed prediction along the planned route. Also Pearson correlation coefficient is adopted to analyse the correlation of driver–vehicle–road–traffic historical characteristic parameters for the enhancement of the model's computing efficiency. Finally, a real natural driving data in Nanjing is used to evaluate the prediction performance with a result that the proposed vehicle speed prediction method outperforms other ones in terms of prediction accuracy. Moreover, based on the predicted vehicle speed, this work studies and analyses its effectiveness in two scenarios of energy consumption prediction and travel time prediction.
To mitigate the threat to power system caused by ramp events – large wind power fluctuation, this study proposes an advanced ramp prediction approach based on event detection framework. This approach ...contains two successive stages of work, including wind power forecasting and ramp detection. Considering high-performance ramp prediction requires long-term and accurate wind power prediction results; this study also proposes a hybrid prediction model at the first stage. By using wind power curve to reflect the physic mechanism of wind power generation, data from numerical weather prediction system could be used to realise long-term trend prediction. Then, a multivariate model is built with a data-mining algorithm to correct system errors of the primary prediction, which is addressed to improve long-term prediction performance. At the second stage, a modified swinging door algorithm is applied for ramp detection. Performance of both the proposed long-term wind power prediction and the corresponding ramp prediction are computed and compared with conventional models on an actual wind dataset. Comprehensive results validated the feasibility and superiority of the proposed ramp prediction approach.
This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks and BG events. The surveyed models are classified into three categories, and they are ...presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.
This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks, and BG events. The surveyed models are classified into three categories, and they are presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.
Ensemble flood forecasting: A review Cloke, H.L.; Pappenberger, F.
Journal of hydrology (Amsterdam),
09/2009, Volume:
375, Issue:
3
Journal Article
Peer reviewed
Operational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to ...drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble flood forecasting’ and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the ‘added value’ of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.
Traditional time series prediction methods are unable to handle the complex nonlinear relationship of a large data set. Most of the existing techniques are unable to manage multiple dimensions of a ...data set, due to which the computational complexity escalates with the increasing size of a data set. Many machine learning (ML) methods are unable to handle known unknown predictions. This paper presents a new forecasting method in the neural network structure based on the induced ordered weighted average (IOWA) weighted average (WA) and fuzzy time series. The proposed model is more efficient than existing complexity handling fuzzy time series prediction methods and other traditional time series prediction methods. The proposed model can accommodate the IOWA operator, weighted average, and relevance degree of each concept in a particular problem for a fuzzy nonlinear prediction. The contribution of this paper is twofold. First, it contributes to theory by proposing a new IOWAWA layer in the neural network to handle complex nonlinear prediction for a large data set. The second contribution is the application of the approach to predict nonlinear stock market data. The robustness of the approach is tested using Australian Securities Exchange (ASX) stock data by considering a case study of the housing and property sector. We further compare the prediction accuracy of the approach with sixteen existing methods. The experimental results demonstrate that the proposed model outperforms existing methods.
Knowledge of protein structure can be used to predict the phenotypic consequence of a missense variant. Since structural coverage of the human proteome can be roughly tripled to over 50% of the ...residues if homology-predicted structures are included in addition to experimentally determined coordinates, it is important to assess the reliability of using predicted models when analyzing missense variants. Accordingly, we assess whether a missense variant is structurally damaging by using experimental and predicted structures. We considered 606 experimental structures and show that 40% of the 1965 disease-associated missense variants analyzed have a structurally damaging change in the mutant structure. Only 11% of the 2134 neutral variants are structurally damaging. Importantly, similar results are obtained when 1052 structures predicted using Phyre2 algorithm were used, even when the model shares low (<40%) sequence identity to the template. Thus, structure-based analysis of the effects of missense variants can be effectively applied to homology models. Our in-house pipeline, Missense3D, for structurally assessing missense variants was made available at http://www.sbg.bio.ic.ac.uk/~missense3d
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•Interpretation of genotype–phenotype relationship is a major challenge in modern genetics.•Structural analysis can differentiate neutral versus pathogenic missense variants.•Three-dimensional protein models are as effective as experimental structures to infer the pathogenic effect of a variant.•Structural analysis should be routinely implemented in the pipeline for variant effect prediction.•A server Missense3D is available to the community to perform the structural analysis on uploaded coordinates and variants.
The aim of this study was to present the factors affecting prediction of the functioning and locomotion of children with cerebral palsy. Cerebral palsy is one of the most common causes of disability ...among children. When predicting the future of a child, its functioning mode and locomotion, many factors should be considered, i.e. the degree of brain injury, the moment of implementing therapy, cognitive abilities of the child, level of mental retardation, epileptic seizures, hearing and vision impairment, etc. The goal of the therapy and its effectiveness depends on the appropriate assessment, and this determines the prediction of the patient’s future. The Gross Motor Function Classification System (GMFCS) can be a useful tool in predicting a child’s functional performance.
The aim of this study was to present the factors affecting prediction of the functioning and locomotion of children with cerebral palsy. Cerebral palsy is one of the most common causes of disability ...among children. When predicting the future of a child, its functioning mode and locomotion, many factors should be considered, i.e. the degree of brain injury, the moment of implementing therapy, cognitive abilities of the child, level of mental retardation, epileptic seizures, hearing and vision impairment, etc. The goal of the therapy and its effectiveness depends on the appropriate assessment, and this determines the prediction of the patient’s future. The Gross Motor Function Classification System (GMFCS) can be a useful tool in predicting a child’s functional performance.
•An adaptive time-resolution method is proposed to improve the accuracy of USTWPP.•The HPE is defined to reveal the real-time fluctuation characteristics of wind power.•Fluctuation regularities are ...mined and grouped to formulate adaptive adjustment rules.•The proposed method can cope with the instantaneous USTWPP with violent fluctuations.
Accurate wind power prediction (WPP) plays an important role in the secure operation and dispatch of power systems. This paper proposes an adaptive time-resolution method to improve the accuracy of ultra-short-term wind power prediction (USTWPP). Firstly, the hidden prediction error (HPE) with its fluctuation magnitude and rate indicators is defined to reveal the fluctuation characteristics of real-time wind power. Then, the adjustment time of time-resolution can be dynamically determined by evaluating the fluctuation magnitudes, and the adjustment rules are formulated by mining the regularities of fluctuation rate of historical wind power data and establishing the interval grouping optimization model. Finally, by coupling the adjustment time and rules into the prediction model of back propagation neural network (BPNN), the rolling prediction with adaptive adjustment of time-resolution is achieved. Extensive tests have not only demonstrated the validity of the proposed method, but also confirmed its capability to cope with the USTWPP especially under situations of extremely violent fluctuations of wind power.
Driven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance ...prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction.
To explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist's real-value distance prediction is 0.896 Å
when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å
of DeepDist's multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist's multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment.
DeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone.
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