Detection of sudden pedestrian crossings for driving assistance systems

In this paper, we study the problem of detecting sudden pedestrian crossings to assist drivers in avoiding accidents. This application has two major requirements: to detect crossing pedestrians as early as possible just as they enter the view of the car-mounted camera and to maintain a false alarm rate as low as possible for practical purposes. Although many current sliding-window-based approaches using various features and classification algorithms have been proposed for image-/video-based pedestrian detection, their performance in terms of accuracy and processing speed falls far short of practical application requirements. To address this problem, we propose a three-level coarse-to-fine video-based framework that detects partially visible pedestrians just as they enter the camera view, with low false alarm rate and high speed. The framework is tested on a new collection of high-resolution videos captured from a moving vehicle and yields a performance better than that of state-of-the-art pedestrian detection while running at a frame rate of 55 fps.     

Stock trading strategy creation using GP on GPU

This paper investigates the speed improvements available when using a graphics processing unit (GPU) for evaluation of individuals in a genetic programming (GP) environment. An existing GP system is modified to enable parallel evaluation of individuals on a GPU device. Several issues related to implementing GP on GPU are discussed, including how to perform tree-based GP on a device without recursion support, as well as the effect that proper memory layout can have on speed increases when using CUDA-enabled nVidia GPU devices. The specific GP implementation is designed to evolve stock trading strategies using technical analysis indicators. The second goal of this research is to investigate the possible improvement in performance when training individuals on a larger number of stocks and training days. This increased training size (nearly 100,000 training points) is enabled due to the speedups realized by GPU evaluation. Several different scenarios were used to test various speed optimizations of GP evaluation on the GPU device, with a peak speedup factor of over 600 (when compared to sequential evaluation on a 2.4 GHz CPU). Also, it is found that increasing the number of stocks and the length of the training period can result in higher out-of-training testing profitability.     

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.     

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。     

Runout effects in milling: Surface finish, surface location error, and stability

This paper investigates the effect of milling cutter teeth runout on surface topography, surface location error, and stability in end milling. Runout remains an important issue in machining because commercially-available cutter bodies often exhibit significant variation in the teeth/insert radial locations; therefore, the chip load on the individual cutting teeth varies periodically. This varying chip load influences the machining process and can lead to premature failure of the cutting edges. The effect of runout on cutting force and surface finish for proportional and non-proportional tooth spacing is isolated here by completing experiments on a precision milling machine with 0.1 μm positioning repeatability and 0.02 μm spindle error motion. Experimental tests are completed with different amounts of radial runout and the results are compared with a comprehensive time-domain simulation. After verification, the simulation is used to explore the relationships between runout, surface finish, stability, and surface location error. A new instability that occurs when harmonics of the runout frequency coincide with the dominant system natural frequency is identified.     

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.