摩托车铝合金车轮地链式自动喷涂生产线简介

摩托车铝合金车轮因形状复杂、辐条间隙大、辐条两面及轮辋内均要求喷涂等特点,致使喷漆自动化较难实现。水平自旋式摩托车铝合金车轮静电自动涂装方式在实际应用中具有油漆利用率高,有害物质排放少,膜厚均匀一致,涂膜性能稳定等优点,大大降低了劳动强度,完全适合摩托车轮毂的喷涂,是很有推广潜力的环保型涂装生产线。     

基于信息衰减的无源干扰作战效能研究

针对箔条干扰云团的作战效能问题，从信息衰减的角度出发，建立了基于雷达最大作用距离的衰减模型和基于发现概率的效费比模型，通过仿真实例验证了箔条走廊密度对雷达最大作用距离的定量关系，提出了箔条走廊干扰取得最佳效费比的方法，结果表明，合理设计箔条走廊密度和抛撒箔条数量，可以取得最佳的效费比，对箔条的实际应用有很强的实用价值。     

浅谈渐开线花键的检测方法的应用

本文对渐开线花键的检测方法进行介绍分析,为渐开线花键的生产检验及零件验收提供理论依据和参考。     

图像边界检测区域对比度模糊增强算法在轮廓提取中的运用

分析了图像边界检测的区域对比度模糊增强算法的特点，通过实验验证了该算法的正确性和可行性，并指出了算法的一些不足之处和待改进的地方，提出了一种适用轮廓提取的新广义算子。     

From Twitter to detector: Real-time traffic incident detection using social media data

The effectiveness of traditional incident detection is often limited by sparse sensor coverage, and reporting incidents to emergency response systems is labor-intensive. We propose to mine tweet texts to extract incident information on both highways and arterials as an efficient and cost-effective alternative to existing data sources. This paper presents a methodology to crawl, process and filter tweets that are accessible by the public for free. Tweets are acquired from Twitter using the REST API in real time. The process of adaptive data acquisition establishes a dictionary of important keywords and their combinations that can imply traffic incidents (TI). A tweet is then mapped into a high dimensional binary vector in a feature space formed by the dictionary, and classified into either TI related or not. All the TI tweets are then geocoded to determine their locations, and further classified into one of the five incident categories.We apply the methodology in two regions, the Pittsburgh and Philadelphia Metropolitan Areas. Overall, mining tweets holds great potentials to complement existing traffic incident data in a very cheap way. A small sample of tweets acquired from the Twitter API cover most of the incidents reported in the existing data set, and additional incidents can be identified through analyzing tweets text. Twitter also provides ample additional information with a reasonable coverage on arterials. A tweet that is related to TI and geocodable accounts for approximately 5% of all the acquired tweets. Of those geocodable TI tweets, 60–70% are posted by influential users (IU), namely public Twitter accounts mostly owned by public agencies and media, while the rest is contributed by individual users. There is more incident information provided by Twitter on weekends than on weekdays. Within the same day, both individuals and IUs tend to report incidents more frequently during the day time than at night, especially during traffic peak hours. Individual tweets are more likely to report incidents near the center of a city, and the volume of information significantly decays outwards from the center.     

Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements

There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimeter/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks.     

Efficient multiple model particle filtering for joint traffic state estimation and incident detection

This article proposes an efficient multiple model particle filter (EMMPF) to solve the problems of traffic state estimation and incident detection, which requires significantly less computation time compared to existing multiple model nonlinear filters. To incorporate the on ramps and off ramps on the highway, junction solvers for a traffic flow model with incident dynamics are developed. The effectiveness of the proposed EMMPF is assessed using a benchmark hybrid state estimation problem, and using synthetic traffic data generated by a micro-simulation software. Then, the traffic estimation framework is implemented using field data collected on Interstate 880 in California. The results show the EMMPF is capable of estimating the traffic state and detecting incidents and requires an order of magnitude less computation time compared to existing algorithms, especially when the hybrid system has a large number of rare models.     

基于CAN总线的船舶自动气象站的研究

为提高船舶气象站的可集成性和维护性,本文构建基于CAN总线的船舶自动气象站。文章对系统总体结构、软硬件设计、传感器选型作了具体描述,以期对实现通用、可扩展的船舶自动气象站具有借鉴作用。