基于模糊推理的碟式太阳能跟踪系统改进研究

在碟式太阳能系统的长期实验数据中发现,太阳辐照度较低时系统集热效率非常低。为使碟式太阳能系统始终运行在高集热效率状态,提出模糊推理方法改进跟踪系统的启停判定条件,并根据集热器出口气流温度的影响因素建立模糊推理系统。仿真结果表明模糊系统在已知太阳辐照度、辐照变化度和空气流量的前提下,可智能控制系统在集热效率高时运行,集热效率低时待机,证明了该方法的可行性和有效性。     

A new approach to contour error control in high speed machining

High speed machining technology attempts to maximize productivity through the use of high spindle speeds and axis traverse rates. The technology is dependent upon the development of suitable mechanical hardware, electrical drives and associated control software to ensure that all components are used to maximum advantage. The role of the control software is particularly demanding since one needs to maximize traverse rates while providing the necessary accuracy, and indeed providing a margin of safety to deal with unexpected changes in process, or system parameters. There have been relatively few improvements in commercial CAD or CAM systems that would help machine tool users to take maximum advantage of high speed machining; rather the majority of the approaches have been undertaken at the machine tool controller level. This paper uses circular interpolation and corner tracking to compare several such control techniques, (Cross Coupled Control (CCC), Zero Phase Error Tracking Control (ZPETC), and Realtime Frequency Modulated Interpolation (FMI)), each of which have been proposed in the literature order to improve machining accuracy. None of these approaches are found to be universally successful when used alone and the authors, in this paper, examine the use of these systems in combination. Particular attention is focused upon an extension of a simplified version of cross coupled control together with Frequency Modulated Interpolation. It is shown that the combined system performs extremely well, and is easily actuated at high frequencies with conventional hardware. A custom built high speed x-y table is used to confirm system performance with multiple constraints present.     

Affine hull based target representation for visual tracking

Handling appearance variations is a challenging issue in visual tracking. Existing appearance models are usually built upon a linear combination of templates. With such kind of representation, accurate visual tracking is not desirable when heavy appearance variations are in presence. Under the framework of particle filtering, we propose a novel target representation for tracking. Namely, the target candidates are represented by affine combinations of a template set, which leads to better capability in describing unseen target appearances. Additionally, in order to adapt this representation to dynamic contexts across a video sequence, a novel template update scheme is presented. Different from conventional approaches, the scheme considers both the importance of one template to a target candidate in the current frame and the recentness of the template that is kept in the template set. Comprehensive experiments show that the proposed algorithm achieves superior performances in comparison with state-of-the-art works.     

Background and foreground interaction: Influence of complementary colors on the search task

Labels are indispensable visual (communication) elements that completely deliver the geospatial message of maps. The objective of this study is to investigate the impact of complementary colors between the map's background and text on the readability of cartographic texts and thus on the efficiency of the map user's search task. This is compared with the use of the “traditional” black labels on the corresponding colored backgrounds. Furthermore, a number of user characteristics, such as gender and expertise, are taken into account as well. The users' eye movements were registered to study their attentive behavior during the visual search task. In addition to the complement of the color's hue, the analyses were based on the difference in luminance, which could also affect the labels' readability. The difference between the black and colored label design was significantly different versus the eye‐tracking metrics. A correlation was found between the color difference and reaction time measurement and between the luminance difference and fixation duration. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 437–445, 2015     

Boost converter with adaptive reference tracking control for dimmable white LED drivers

An LED driver consisting of dimmable current regulators and a boost converter with adaptive reference tracking control (ARTC) is proposed. The ARTC is realized with only one integrator and one subtractor to detect the voltage variation among the current regulators and then to generate an adaptive reference tracking voltage. Based on the reference tracking voltage, the driving voltage of the boost converter is dynamically adjusted, so as to not only maintain a minimum voltage on the current regulator but also keep the required LED-string current. A prototype control circuit including ARTC and PWM control fabricated with TSMC 0.35-μm 2P4M CMOS process validates the proposed technique. The measurement results show that the driving voltage of the boost converter is automatically regulated to compensate for the voltage alteration on the current regulators. Furthermore, the overall efficiency of the LED driver with ARTC is 6.1% better than that of the LED driver without ARTC.     

Self‐similarity analysis of vehicle driver's electrodermal activity

This paper characterizes stress levels via a self‐similarity analysis of the electrodermal activity (EDA) collected in a real‐world driving context. To characterize the EDA richness over scales, the fractional Brownian motion (FBM) process and its corresponding exponent H, estimated via a wavelet‐based approach, are used. Specifically, an automatic scale range selection is proposed in order to detect the linearity in a log scale diagram. The procedure is applied to the EDA signals, from the open database drivedb, originally captured on the foot and the hand of the drivers during a real‐world driving experiment, designed to evoke different levels of arousal and stress. The estimated Hurst exponent H offers a distinction in stress levels when driving in highway versus city, with a reference to restful state of minimal stress level. Specifically, the estimated H values tend to decrease when the driving environmental complexity increases. In addition, the estimated H values on the foot EDA signals allow a better characterization of the driving task than that of hand EDA. The self‐similarity analysis was applied to various physiological signals in literature but not to the EDA so far, a signal which was found to correlate most with human affect. The proposed analysis could be useful in real‐time monitoring of stress levels in urban driving spaces, among other applications.     

A novel robot co-worker system for paint factories without the need of existing robotic infrastructure

This paper presents a human–robot co-working system to be applied to industrial tasks such as the production line of a paint factory. The aim is to optimize the picking task with respect to manual operation in a paint factory. The use of an agile autonomous robot co-worker reduces the time in the picking process of materials, and the reduction of the exposure time to raw materials of the worker improves the human safety. Moreover, the process supervision is also improved thanks to a better traceability of the whole process. The whole system consists of a manufacturing process management system, an autonomous navigation system, and a people detection and tracking system. The localization module does not require the installation of reflectors or visual markers for robot operation, significantly simplifying the system deployment in a factory. The robot is able to respond to changing environmental conditions such as people, moving forklifts or unmapped static obstacles like pallets or boxes. The system is not tied to specific manufacturing orders. It is fully integrated with the manufacturing process management system and it can process all possible orders as long as their components are placed into the warehouse. Real experiments to validate the system have been performed in a paint factory by a real holonomic platform and a worker. The results are promising from the evaluation of performance indicators such as exposure time of the worker to raw materials, automation of the process, robust and safe navigation, and the assessment of the end-user.     

基于滑模变结构控制的路径跟踪研究

为解决锅炉水冷壁磨损检测机器人的路径跟踪问题,提出了一种基于指数趋近律的滑模变结构控制的机器人路径跟踪方法。在水冷壁磨损检测机器人运动模型的基础上,进行路径跟踪误差分析,设计一种基于指数趋近律的滑模变结构控制器,再利用Lyapunov定理验证其收敛性,最后通过MATLAB软件模拟仿真,仿真结果表明该控制器可以克服误差,使位姿误差收敛至零。     

基于深度学习的视频跟踪研究进展综述

近年来深度学习迅猛发展，颠覆了语音识别、图像分类、文本理解等领域的算法设计思路。深度学习因其具备强大的特征提取能力，在图像识别领域的成绩尤为突出。然而深度学习与视频监控领域的结合并不多，由于深度模型具有多层网络结构，算法复杂度大，训练和更新模型时比较耗时，很难满足实时性要求。回顾了深度学习的发展史，介绍了最近10年来国内外深度学习主要模型，论述了基于深度学习的目标跟踪算法，指出了各算法的优缺点，最后对当前该领域存在的问题和发展前景进行了总结和展望。