Pre‐necking and post‐necking relaxation and creep behavior of polycarbonate: A phenomenological study

Experiments have been performed to investigate the mechanical response of unfilled polycarbonate vis‐à‐vis the influence of prior deformation on stress relaxation and creep. Piecewise linear deformation histories, which involve strain‐controlled tensile loading of a specimen to a maximum load and partial unloading to a target strain/stress point as prologue to a relaxation test, have been shown to qualitatively influence the recorded stress‐time behavior. In particular, the stress magnitude during relaxation first increases and is then followed by a decrease. Analogously, in creep tests during unloading, the strain might decrease and then increase. Time characteristics for this U‐turn in the deformation response are influenced by the placement of the test. The influence of prior specimen conditioning on this phenomenon is investigated by comparing test data from virgin samples to that of specimens having high (～85%) inelastic strain from prior tensile elongation. Findings suggest that the observed persistence in the occurrence of this reversal effect for both types of specimens is evidence of the need to incorporate this behavior into the fold of material modeling. Additionally, this novel relaxation and creep behavior has been observed in other amorphous (poly(phenylene oxide)) and crystalline (high‐density polyethylene) polymers. Polym. Eng. Sci. 44:1783–1791, 2004. © 2004 Society of Plastics Engineers.     

A fast hybrid computer vision technique for real-time embedded bus passenger flow calculation through camera

Bus passenger flow calculation system is a critical part of the smart public transportation framework. Bus passenger flow information can help to make data statistics report of the passenger at a bus station which can be used by public transport operator to evaluate the quality of the transportation. Statistics report of crowded passengers in the bus station help managers to understand the bus transit operations, can provide the database for the intelligent transportation scheduling, help to provide more and better services for passengers, overall data statistics of passengers has important practical significance to improve public transport environment. This paper presents a passenger counting algorithm based on hybrid machine learning approach. In the first step, an advanced method is used to extract the Histogram of oriented gradients (HOG) feature of passenger’s heads. Classification of head features is done by using support vector machine (SVM) as a classifier for the liner model. Heads are detected successfully after performing all steps. In next step Kanade-Lucas-Tomasi (KLT) is used to reality head tracking, the multiple target tracking is achieved and the head motion trajectory of passenger target is captured stably. At last, the trajectory is analyzed and the automatic counting of bus passenger flow is realized. In the last step, the proposed algorithm is move to embedded system for practical implementation. In this paper, the algorithm intends to use ADSP-BF609 embedded platform for transplantation. The experimental results demonstrate that the statistical accuracy of the proposed algorithm is enhanced successfully; especially during the daytime with the good illustration, the effective counting of the passenger flow is achieved and the inward and outward passenger counting can be realized. In this paper three feature extraction models are used namely local binary patterns, histograms of oriented gradients and binarized statistical image in order to get accurate features. Furthermore, three common classification techniques including naïve bayes classifier, boosted tress and support vector machines are used for fine classification of extracted vectors obtained from different features extractors model. 94.50% accuracy is achieved when support vector machine (SVM) classifies the features extracted using Histogram of oriented gradients (HOG). SVM surpasses the accuracy obtained by Boosted tree namely 81.30% using Histogram of oriented gradients (HOG) features.