Short-term prediction of traffic flow using a binary neural network

This paper introduces a binary neural network-based prediction algorithm incorporating both spatial and temporal characteristics into the prediction process. The algorithm is used to predict short-term traffic flow by combining information from multiple traffic sensors (spatial lag) and time series prediction (temporal lag). It extends previously developed Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour (k-NN) techniques. Our task was to produce a fast and accurate traffic flow predictor. The AURA k-NN predictor is comparable to other machine learning techniques with respect to recall accuracy but is able to train and predict rapidly. We incorporated consistency evaluations to determine whether the AURA k-NN has an ideal algorithmic configuration or an ideal data configuration or whether the settings needed to be varied for each data set. The results agree with previous research in that settings must be bespoke for each data set. This configuration process requires rapid and scalable learning to allow the predictor to be set-up for new data. The fast processing abilities of the AURA k-NN ensure this combinatorial optimisation will be computationally feasible for real-world applications. We intend to use the predictor to proactively manage traffic by predicting traffic volumes to anticipate traffic network problems.     

Vinyl bottle performance with the addition of vinyl bottle flake

The effect of adding recycled vinyl bottle flake to virgin bottle compound has been studied. Vinyl bottles contains 10 to 50% recycled vinyl bottle flake were produced on a Bekum extrusion blow molding machine. The performance properties of the vinyl bottles were evaluated, and the optimum level of recycled flake was determined.     

Moving people tracking with detection by latent semantic analysis for visual surveillance applications

The latent semantic analysis (LSA) has been widely used in the fields of computer vision and pattern recognition. Most of the existing works based on LSA focus on behavior recognition and motion classification. In the applications of visual surveillance, accurate tracking of the moving people in surveillance scenes, is regarded as one of the preliminary requirement for other tasks such as object recognition or segmentation. However, accurate tracking is extremely hard under challenging surveillance scenes where similarity among multiple objects or occlusion among multiple objects occurs. Usual temporal Markov chain based tracking algorithms suffer from the ‘tracking error accumulation problem’. The accumulated errors can finally make the tracking to drift from the target. To handle the problem of tracking drift, some authors have proposed the idea of using detection along with tracking as an effective solution. However, many of the critical issues still remain unsettled in these detection based tracking algorithms. In this paper, we propose a novel moving people tracking with detection based on (probabilistic) LSA. By employing a novel ‘twin-pipeline’ training framework to find the latent semantic topics of ‘moving people’, the proposed detection can effectively detect the interest points on moving people in different indoor and outdoor environments with camera motion. Since the detected interest points on different body parts can be used to locate the position of moving people more accurately, by combining the detection with incremental subspace learning based tracking, the proposed algorithms resolves the problem of tracking drift during each target appearance update process. In addition, due to the time independent processing mechanism of detection, the proposed method is also able to handle the error accumulation problem. The detection can calibrate the tracking errors during updating of each state of the tracking algorithm. Extensive, experiments on various surveillance environments using different benchmark datasets have proved the accuracy and robustness of the proposed tracking algorithm. Further, the experimental comparison results clearly show that the proposed tracking algorithm outperforms the well known tracking algorithms such as ISL, AMS and WSL algorithms. Furthermore, the speed performance of the proposed method is also satisfactory for realistic surveillance applications.     

An analysis of ball-and-race milling part II. The babcock E 1.7 mill

A mathematical model was developed for a ball-and-race mill based on specific rates of breakage and primary fragment distributions. The model includes internal classification of particles falling back into the race and external classification due to the built-on classifier. It was demonstrated that the normalized primary fragment distribution produced in a pilot-scale Babcock E-type mill of 17 in. race diameter was the same as in the Hardgrove laboratory test mill and that the specific rates of breakage varied with particle size in the same manner. Steady-state continuous tests on the pilot-scale mill showed that breakage rates depended on the rate of feed, since the mill pulled less power at low feed rates. This effect plus the residence time effect gave coarser product size distributions at low and high feed rates than at a medium feed rate. Model simulations based on parameters measured in the Hardgrove mill correctly predicted the product size distribution from the E-type mill.     

Thermal expansion of an SiC particle-reinforced aluminum composite

Using a push-rod dilatometer, we measured between 76 and 390 K the thermal expansion of a particle-reinforced-composite wrought plate obtained by powdermetallurgy methods. The particles, 30% by volume, consisted of monocrystals of -SiC with sizes near 10 m. The matrix consisted of a 6061 aluminum alloy with original particle sizes near 20 m. We found the perpendicular thermal expansivity, ₃, higher by 26% than the in-plane thermal expansivity, ₁-₂. These values differ from a rule-of-mixture prediction by –3 and –23%, respectively. All three _i, components lie outside the Rosen-Hashin bounds. Levin's isotropic model agrees within 10% with the ₁-₂-₃ average. Both the anisotropy and the bounds violation result from microstructural nonhomogeneity arising from processing. Rosen and Hashin's transverse-isotropicsymmetry relationships account approximately for these effects by introducing the anisotropic elastic constants. Using neutron diffraction, we determined that the SiC particles are textureless.Paper presented at the Tenth International Thermal Expansion Symposium, June 6–7, 1989, Boulder, Colorado, U.S.A.     

基于BP-GamysBoost的乳腺癌诊断模型

针对乳腺癌数据存在的不平衡性问题,对标准的Adaboost算法进行改进,即首先引入BP神经网络,然后融合模拟退火遗传算法（SA-GA）较强的全局寻优能力和较快的收敛速度,最后通过权重的合理分配,提出BP-GamysBoost算法。同时为验证所提出的新算法BP-GamysBoost的合理性,从UCI机器学习知识库中获取WBCD数据库,比较BP-GamysBoost算法模型与BP模型、BP-GA模型、BP-Adaboost模型的稳定性、准确率、漏诊率、灵敏度等性能指标。最终结果表明,BP-GamysBoost模型在乳腺癌数据库中运行良好,并优于其他3种算法模型。     

Large-scale information retrieval in software engineering - an experience report from industrial application

Software Engineering activities are information intensive. Research proposes Information Retrieval (IR) techniques to support engineers in their daily tasks, such as establishing and maintaining traceability links, fault identification, and software maintenance. We describe an engineering task, test case selection, and illustrate our problem analysis and solution discovery process. The objective of the study is to gain an understanding of to what extent IR techniques (one potential solution) can be applied to test case selection and provide decision support in a large-scale, industrial setting. We analyze, in the context of the studied company, how test case selection is performed and design a series of experiments evaluating the performance of different IR techniques. Each experiment provides lessons learned from implementation, execution, and results, feeding to its successor. The three experiments led to the following observations: 1) there is a lack of research on scalable parameter optimization of IR techniques for software engineering problems; 2) scaling IR techniques to industry data is challenging, in particular for latent semantic analysis; 3) the IR context poses constraints on the empirical evaluation of IR techniques, requiring more research on developing valid statistical approaches. We believe that our experiences in conducting a series of IR experiments with industry grade data are valuable for peer researchers so that they can avoid the pitfalls that we have encountered. Furthermore, we identified challenges that need to be addressed in order to bridge the gap between laboratory IR experiments and real applications of IR in the industry.