A stochastic two-dimensional intelligent driver car-following model with vehicular dynamics

The law of vehicle movement has long been studied under the umbrella of microscopic traffic flow models,especially the car-following(CF)models.These models of the movement of vehicles serve as the backbone of traffic flow analysis,simulation,autonomous vehicle development,etc.Two-dimensional(2D)vehicular movement is basically stochastic and is the result of interactions between a driver's behavior and a vehicle's characteristics.Current microscopic models either neglect 2D noise,or overlook vehicle dynamics.The modeling capabilities,thus,are limited,so that stochastic lateral movement cannot be reproduced.The present research extends an intelligent driver model(IDM)by explicitly considering both vehicle dynamics and 2D noises to formulate a stochastic 2D IDM model,with vehicle dynamics based on the stochastic differential equation(SDE)theory.Control inputs from the vehicle include the steer rate and longitudinal acceleration,both of which are developed based on an idea from a traditional intelligent driver model.The stochastic stability condition is analyzed on the basis of Lyapunov theory.Numerical analysis is used to assess the two cases:(i)when a vehicle accelerates from a standstill and(ii)when a platoon of vehicles follow a leader with a stop-and-go speed profile,the formation of congestion and subsequent dispersion are simulated.The results show that the model can reproduce the stochastic 2D trajectories of the vehicle and the marginal distribution of lateral movement.The proposed model can be used in both a simulation platform and a behavioral analysis of a human driver in traffic flow.

Infrared drying kinetics and moisture diffusivity modeling of pork

This study investigated the drying kinetics of pork slice in infrared drying condition.Drying temperature,slice thickness and initial moisture content were selected as influencing factors on the drying characteristics and drying rate of pork slice.Drying curves obtained from the experimental data were fitted to semi theoretical and/or empirical thin layer drying models.The effects of drying temperature and slice thickness on the model constants were evaluated by the multiple regression method.All the models were compared according to three statistical indexes,i.e.,root mean square error,chi-square and modeling efficiency.The slice thickness,drying temperature and initial moisture content have significant influences on the effective diffusivity coefficient of pork.The results showed that the drying rate of pork slices increased with the increases of drying temperature and initial moisture content.The decreases of slice thickness also led to an increase of drying rate.The Henderson and Pabis model can best describe the drying curves of pork.

Improved method for prediction of milled rice moisture content based on Weibull distribution

The loss on drying method,which is regarded as the standard method of rice moisture content analysis,provides the most reliable results but is both labor intensive and time consuming.In order to improve the detection efficiency of the loss on drying method,this study investigated the drying characteristics of milled rice and developed an information fusion algorithm with which to predict milled rice moisture content based on the Weibull distribution and Levenberg-Marquardt(LM)algorithm.Application of the Weibull distribution model was investigated regarding its description of the drying kinetics of milled rice during infrared drying.An adaptive mechanism was applied to algorithm design,with the starting point of the estimation algorithm determined by calculating the drying rate at each measuring point,and the end-point distinguished using a two-level threshold algorithm.The calculated results were then compared with the measured data regarding the infrared drying of milled rice.For milled rice samples varying in moisture content from 14.44%-17.67%(dry basis),the relative error between predicted and observed values ranged 0.0037-0.0589,with a reduction in test time of 50.71%-67.87%.

A Feature Selection Method for Prediction Essential Protein

Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients（PCC）. The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.

Prediction and fusion algorithm for meat moisture content measurement based on loss-on-drying method

The loss-on-drying method has been widely used as a standard approach for measuring the moisture content of high-moisture materials such as solid and semi-solid foods.Loss-on-drying method provides reliable results,whilst usually labor-intensive and time-consuming.This paper presents a novel algorithm for predicting the moisture content of meats based on the loss-on drying method.The proposed approach developed a drying kinetics model of meats based on Fick’s Second Law and designed a prediction algorithm for meat moisture content using the least-squares method.The predicted results were compared with the official method recommended by the Association of Official Analytical Chemists(AOAC).When the moisture content of meat samples(beef and pork)was varied from 69.46%to 74.21%,the relative error of the meat moisture content(MMC)calculated by the proposed algorithm was 0.0017-0.0117,the absolute errors were less than 1%.The testing time was about 40.18%-56.87%less than the standard detection procedure.