基于Petri网的售货机物流配送建模研究

利用Petri网技术构建了无线自动售货机和传统自动售货机的物流配送模型,分别对这两种情况下的配送利用HPSIM仿真软件建模进行时效分析与比较。结果表明,利用无线网络的自动售货机的配送时效小于传统方式,提升了物流配送效率。     

Batch process modelling with mixtures of Gaussian processes

In recent years Gaussian processes have attracted a significant amount of interest with the particular focus being that of process modelling. This has primarily been a consequence of their good predictive performance and inherent analytical properties. Gaussian processes are a member of the family of non-parametric Bayesian regression models and can be derived from the perspective of neural networks. Their behaviour is controlled through the structure of the covariance function. However, when applied to batch processes, whose data exhibits different variance structures throughout the duration of the batch, a single Gaussian process may not be appropriate for the accurate modelling of its behaviour. Furthermore there are issues with respect to the computational costs of Gaussian processes. The implementation of a Gaussian process model requires the repeated computation of a matrix inverse whose order is the cubic of the number of training data points. This renders the algorithm impractical when dealing with large data sets. To address these two issues, a mixture model of Gaussian processes is proposed. The resulting prediction is attained as a weighted sum of the outputs from each Gaussian process component, with the weights determined by a Gaussian kernel gating network. The model is implemented through a Bayesian approach utilising Markov chain Monte Carlo algorithms. The proposed methodology is applied to data from a bench-mark batch simulation polymerization process, methyl methacrylate (MMA), and the results are compared with those from a single Gaussian process to illustrate the advantages of the proposed mixture model approach.     

IPv6下的ICMPv6重定向欺骗与NS/NA欺骗研究

阐述了IPv6下网络协议欺骗的一般原理,详细分析了ICMPv6协议及其存在的漏洞.对于ICMPv6重定向及NS/NA欺骗的原理进行了深入的讨论,并提出了ICMPv6重定向欺骗和NS/NA欺骗的具体实现方法.     

Vibration analysis of cable-driven parallel manipulators

A cable-driven parallel manipulator is a manipulator whose end-effector is driven by a number of parallel cables instead of rigid links. Since cables always have more flexibility than rigid links, a cable manipulator bears a concern of possible vibration. Thus, investigation of vibration of cable manipulators caused by cable flexibility is important for applications requiring high system stiffness or bandwidth. This paper provides a vibration analysis of general 6-DOF cable-driven parallel manipulators. Based on the analysis of the natural frequencies of the multibody system, the study demonstrates that a cable manipulator can be designed stiff enough for special applications like the cable-manipulator based hardware-in-the-loop simulation of contact dynamics. Moreover, under an excitation, a cable may vibrate not only in its axial direction, but also in its transversal direction. The paper also analyzes the vibration of cable manipulators caused by cable flexibilities in both axial and transversal directions. It is shown that the vibration of a cable manipulator due to the transversal vibration of cables can be ignored comparing to that due to the axial flexibility of cables.     

基于多信息融合技术的智能汽车防盗报警系统研究与设计

为完善汽车的防盗系统,提出研制的智能汽车防盗报警器是在ARM嵌入式系统的基础上,利用Dempster--Sharer证据理论融合密码识别、加速度传感、GPS全球定位信息,判断车辆的安全状况,再将车况信息通过GPRS无线通信网络发送到车主手机。实验室测试表明,系统实现了监控基本功能并满足实时性,增强了汽车安全性和智能报警性。     

GPU加速的圆弧轨迹Katsevich锥束CT重建算法

通过改进GPU的渲染到纹理策略加速锥束反投影计算,提高了圆弧轨迹Katsevich锥束CT重建算法的执行效率.在CPU中完成投影数据的滤波处理,并将其重排为四通道浮点纹理载人GPU内存;绑定2个浮点输出纹理到帧缓冲对象的不同绑定点,用于将渲染结果输出到纹理,并同时完成反投影数据的累加计算;将矩形网格编码为四叉树结构用于加速物体切片的绘制.实验结果表明,文中算法在较大锥角时仍能重建出高质量图像,并获得接近于实时的重建速度.     

嵌入式系统的安全技术研究

随着嵌入式系统的网络化发展,嵌入式系统的安全将成为嵌入式系统必须考虑的问题。嵌入式系统的资源有限性使它的安全问题比一般的桌面系统更加复杂。因此,如何构建一个安全的嵌入式系统是个很值得研究的问题。同时,嵌入式系统的安全不是安全嵌入式系统的附加功能,安全嵌入式系统要在整个设计过程中考虑安全因素,从而决定采用哪种安全技术。从安全的整体性出发,研究了嵌入式系统的各种安全技术,总结归纳出几种安全增强方式,并指出其相应的应用特点,为如何建立嵌入式系统的安全提供依据。     

A Dilute Fluorinated Phosphate Electrolyte Enables 4.9 V-Class Potassium Ion Full Batteries

Potassium ion batteries using graphite anode and high-voltage cathodes are considered to be optimizing candidates for large-scale energy storage. However, the lack of suitable electrolytes significantly hinders the development of high-voltage potassium ion batteries. Herein, a dilute (0.8 m ) fluorinated phosphate electrolyte is proposed, which exhibits extraordinary compatibility with both graphite anode and high-voltage cathodes. The phosphate solvent, tris(2,2,2-trifluoroethyl) phosphate (TFP), has weak solvating ability, which not only allows the formation of robust anion-derived solid electrolyte interphase on graphite anode but also effectively suppresses the corrosion of Al current collector at high voltage. Meanwhile, the high oxidative stability of fluorinated TFP solvent enables stable ultrahigh-voltage (4.95 V) cycling of a potassium vanadium fluorophosphate (KVPO₄F) cathode. Using TFP-based electrolyte, the 4.9 V-class potassium ion full cell based on graphite anode and KVPO₄F cathode shows rather remarkable cycling performance with a high capacity retention of 87.2% after 200 cycles. This study provides a route to develop dilute electrolytes for high-voltage potassium ion batteries, by utilizing solvents with both weak solvating ability and high oxidative stability.     

Face anti-spoofing based on multi-modal and multi-scale features fusion

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network (CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion ( MMFF) is proposed. Specifically, first residual network ( Resnet )-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network (FPN), finally squeeze-and-excitation fusion ( SEF) module and self-attention network ( SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.     

Estimating potential disaster waste generation for pre-disaster waste management

The paper discusses development of a method for estimating disaster waste that can be potentially generated by a natural disaster in the future for pre-disaster waste management. In particular, this research focuses on micro-disaster waste originating from household consumer durables. We documented the number of household consumer durables and built a mass per unit database of major consumer durables using web-based and statistical surveys. We also estimated present and future figures of disaster waste that can be generated in the study area. The estimated total amount of disaster waste that could be generated in 2015 was 24.1 kt (18.1–29.8 kt) and 108 kt (81.8–133 kt) for Kobe City and Ise-Shima region, respectively. The total quantities of TV sets, air conditioners, refrigerators, and washing machines generated in Kobe and Ise-Shima will range between 10.9 and 22.8 kt (247,000–545,000 units) in 2015, and 10.7 kt to 22.8 kt (249,000–550,000 units) in 2035. The quantity estimated for 2015 is equal to 61 % of the annual processing capacity of Plant A, Japan’s leading home appliances recycling plant. Finally, we discussed the contribution of the estimation results and geographic information systems in future recycling planning.