Experimental study on fitness of one-piece casting osseointegrated implant frameworks with relation to fixture abutment positions

An experiment on fitness of one-piece casting osseointegrated implant frameworks was performed among three fixture abument positions where other conditions were controlled. The result revealed that there was statistically significant differences in dimensional change among the three fixture abument positions. In terms of the relation between abument position and fitness, the more backward the abument position was, the worse the osseointegrated framework fitness was. The frameworks showed deformations in "arch form" and "warp" in three dimensions.     

CHV190系列变频器在永磁同步电机驱动矿井提升机中的应用

本文分析了矿井提升机的运行特点,详细阐述了英威腾CHV190系列起重提升专用变频器用于永磁同步电动机驱动的节能型矿井提升机的选型配置原则,最后介绍了应用现场的调试步骤及注意事项。     

Issues and solution on distortion drift in reversible video data hiding

Different from reversible image data hiding, most reversible video data hiding schemes have the particular problem that the distortion due to hidden data will spread and accumulate. In this paper, the problem of distortion drift caused by reversible data hiding in compressed video is analyzed, and a lossless drift compensation scheme is proposed to restrain the distortion for the first time. In order to ensure the reversibility, drift compensation signals are merged in the quantized DCT (Discrete Cosine Transform) coefficients of P-frames and the corresponding recovery mechanism is presented as well. Experimental results demonstrate that the proposed lossless drift compensation scheme significantly improves the video quality, and the original compressed video can be recovered exactly after the hidden data and compensation signals are removed. In addition, the proposed scheme does not depend on specific reversible data hiding method.     

一种基于可信度矩阵的多传感器目标识别方法

为了解决多传感器目标识别中不同等级信息源数据的融合问题,在研究DSmT证据理论的基础上,引入证据可信度矩阵.依据可信度矩阵对证据进行转化,使之可以用传统的方法进行证据融合.将这种方法应用到等级不同的多传感器目标识别中,可以解决传统证据理论只能进行相同等级传感器目标识别的难题.仿真实验表明,该方法提高了目标识别的准确性和有效性.     

A belief-rule-based inventory control method under nonstationary and uncertain demand

This paper is devoted to investigating inventory control problems under nonstationary and uncertain demand. A belief-rule-based inventory control (BRB-IC) method is developed, which can be applied in situations where demand and demand-forecast-error (DFE) do not follow certain stochastic distribution and forecasting demand is given in single-point or interval styles. The method can assist decision-making through a belief-rule structure that can be constructed, initialized and adjusted using both manager’s knowledge and operational data. An extended optimal base stock (EOBS) policy is proved for initializing the belief-rule-base (BRB), and a BRB-IC inference approach with interval inputs is proposed. A numerical example and a case study are examined to demonstrate potential applications of the BRB-IC method. These studies show that the belief-rule-based expert system is flexible and valid for inventory control. The case study also shows that the BRB-IC method can compensate DFE by training BRB using historical demand data for generating reliable ordering policy.     

汽车电动助力转向器的控制器研制

研究了日本N4样机的电动助力转向系统的电机电流-输入扭矩特性.采用ATmega16L单片机为处理器,以直流有刷电机为执行器,应用PID补偿算法,设计了电动助力转向控制器.在试验台上进行了电机电流-输入扭矩特性试验,试验结果表明,该控制器的助力曲线接近日本N4样机,能取得比较满意的助力效果.     

Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction

The successful launch of the Landsat 8 satellite continues the Earth observation of the Landsat series, which has been taking place for nearly 40 years. With the increase in the band number and the improved spectral range compared with the previous Landsat imagery, it will be possible to expand the application of the new Landsat 8 imagery. The purpose of this study is to explore water extraction based on the new Landsat 8 Operational Land Imager (OLI) imagery. According to the specific inland water conditions (clear water, turbid water, and eutrophic water), a number of highly adaptable water indices are assessed for water extraction using Landsat OLI imagery. The results show that clear water is the easiest to extract among the different types of waterbodies, with the highest average accuracy of 97%. The highest-accuracy methods are the automated water extraction index for shadow pixels (AWEI_sh), the normalized difference water index using bands 4 and 7 (NDWI₄₇), and the normalized difference water index using bands 3 and 7 (NDWI₃₇), with accuracies of 98.55%, 95.50%, and 96.61%, corresponding to clear water, turbid water, and eutrophic water, respectively. Through the analysis of the different methods for optimal band selection, the seventh band OLI7 (shortwave infrared 2, SWIR-2) of Landsat OLI shows the best performance in water identification. When applying the water indices to water extraction, Otsu’s algorithm has been used to automatically select the water threshold. Using extensive experiments with Otsu’s algorithm and a manual method, it was found that Otsu’s algorithm can replace manual selection and has the ability to select an accurate threshold for water extraction.