上海市2011~2015年保健食品中铅、砷、汞污染 状况分析及评价

目的 采用新、旧两版GB 16740标准对上海市保健食品中铅、砷、汞残留量进行分析与评价, 了解重金属污染状况及评价标准改变带来的变化。方法 采用国家标准规定的检测方法, 对2011~2015年上海市收检的保健食品进行铅、砷和汞残留量测定, 并使用SPSS 19.0对测定结果进行统计分析。结果 上海市2011~2015年共检测保健食品7306份, 按旧版GB 16740评价, 总合格率为96.17%, 铅、砷和汞残留量总超标率分别为3.09%、1.46%和0.22%; 按新版GB 16740评价, 总合格率为98.06%, 铅、砷和汞残留量总超标率分别为1.28%、0.94%和0.22%, 超标样品中胶囊剂和茶剂居多。结论 上海市保健食品中铅、砷、汞污染水平总体较低, 新版的评价标准改变了原标准中根据剂型判断结果的模式, 使得结果评价更为合理。     

Weakly- and Semi-Supervised Fast Region-Based CNN for Object Detection

Learning an effective object detector with little supervision is an essential but challenging problem in computer vision applications. In this paper, we consider the problem of learning a deep convolutional neural network (CNN) based object detector using weakly-supervised and semi-supervised information in the framework of fast region-based CNN (Fast R-CNN). The target is to obtain an object detector as accurate as the fully-supervised Fast R-CNN, but it requires less image annotation effort. To solve this problem, we use weakly-supervised training images (i.e., only the image-level annotation is given) and a few proportions of fully-supervised training images (i.e., the bounding box level annotation is given), that is a weakly- and semi-supervised (WASS) object detection setting. The proposed solution is termed as WASS R-CNN, in which there are two main components. At first, a weakly-supervised R-CNN is firstly trained; after that semi-supervised data are used for finetuning the weakly-supervised detector. We perform object detection experiments on the PASCAL VOC 2007 dataset. The proposed WASS R-CNN achieves more than 85% of a fully-supervised Fast R-CNN’s performance (measured using mean average precision) with only 10% of fully-supervised annotations together with weak supervision for all training images. The results show that the proposed learning framework can significantly reduce the labeling efforts for obtaining reliable object detectors.

     

A New Calibration Method for a Directly Driven 3<Emphasis Type="Underline">P</Emphasis>RR Positioning System

Calibration is one of the most important works for the parallel manipulator. The manufacturing and assembling errors will modify the designed parameters of the parallel mechanism, leading to the positioning errors. Calibration is an effective method for improving the accuracy of the parallel mechanism. It is vital to identify the parameters and calibrate the system aiming at improving the positioning accuracy. In order to build an object stage of the micro/nano operation system, a 3 degree-of-freedoms (DOFS) parallel mechanism has been designed and constructed, with combination of legs of the PRR type (the underline of the P represent the actuated joint), P and R representing prismatic and revolute pairs respectively (3PRR). Due to the space constraint, this 3PRR mechanism is built without the end-effector feedback, and must be calibrated for high accuracy positioning. The error model of the 3PRR mechanism has been derived and analyzed, and the error distribution mappings of the 3PRR mechanism are obtained. The calibration method based on the error model is investigated. Since some parameters are difficult to be identified by using the decoupling error model, the assistant measurements are proposed and utilized to compensate for this calibration method. Numerical simulations and experiments are carried out. The simulation results show that it is not enough to calibrate this system by using the calibration method based on error model only, and the experimental results demonstrate that the combined assistant measurements will achieve a better effect for calibration.