面向连续查询的敏感语义位置隐私保护方案

针对连续查询位置服务中构造匿名区域未考虑语义位置信息导致敏感隐私泄露问题，通过设计[(K,θ)]-隐私模型，提出一种路网环境下面向连续查询的敏感语义位置隐私保护方案。该方案利用Voronoi图将城市路网预先划分为独立的Voronoi单元，依据用户的移动路径和移动速度，选择具有相似特性的其他[K-1]个用户，构建匿名用户集；利用匿名用户集用户设定的敏感语义位置类型和语义安全阈值，以及用户所处语义位置的Voronoi单元，构建满足[(K,θ)]-隐私模型的语义安全匿名区域，可以同时防止连续查询追踪攻击和语义推断攻击。实验结果表明，与SCPA算法相比，该方案在隐私保护程度上提升约15%，系统开销上降低约20%。     

基于虚拟现实的复杂场景加载优化算法

目前大规模场景模型生成的需求量日益增加, 现提出了一种基于区域的自动 LOD (Levels Of Details)构建算法. 该算法基于动态网格简化算法, 在游戏设计制作的过程中, 游戏开发人员会经常需要对美工部门人员提供的模型进行优化. 为了可以使模型的面数得到简化, 并且不改变模型的外观. 当今流行的 LOD技术非常擅于处理这种情况, 判断模型与摄像机的距离如果超过一定范围之后, 自动调取不同层次的模型, 当模型距离摄像机很远的情况下使用面数低的模型替换高模, 这样可以提高帧率并且降低摄像机前的三角面以及顶点数从而减少渲染压力. 一般模型的简化分为静态和动态模型的简化.大部分情况下, 程序员会让美工部门提供几套不同面数的模型或者通过模型简化工具对高模进行减面并存成多个Mesh, 并在程序运行的时候, 根据模型与摄像机的距离远近动态的替换Mesh. 这是一种静态的方法.这里将尝试使用一种动态的网格简化和LOD技术相结合的方法. 这种新型算法的大大的简化了操作流程, 美术人员只需要提供一个模型, 程序员可以使用这种方法生成量级不同的低模, 根据摄像机与模型的远近自动的调取不同精度的模型.     

基于VR技术的全息化教学应用研究

目前,虚拟现实技术已经大量运用于教育领域,对于基于VR技术的全息化教学资源的建设与应用研究受到了广大学者的关注,如何运用好这一热点技术是当今教育领域的热门话题。首先阐述了全息化教学的资源应用现状和主要作用,然后介绍了全息化教学在典型专业教学中的应用,最后对全息化教学资源建设与开展提出了合理化建议。     

Differences between oculomotor and perceptual artifacts for temporally limited head mounted displays

We used perceptual and oculomotor measures to understand the negative impacts of low (phantom array) and high (motion blur) duty cycles with a high‐speed, AR‐likehead‐mounted display prototype. We observed large intersubject variability for the detection of phantom array artifacts but a highly consistent and systematic effect on saccadic eye movement targeting during low duty cycle presentations. This adverse effect on saccade endpoints was also related to an increased error rate in a perceptual discrimination task, showing a direct effect of display duty cycle on the perceptual quality. For high duty cycles, the probability of detecting motion blur increased during head movements, and this effect was elevated at lower refresh rates. We did not find an impact of the temporal display characteristics on compensatory eye movements during head motion (e.g., VOR). Together, our results allow us to quantify the tradeoff of different negative spatiotemporal impacts of user movements and make subsequent recommendations for optimized temporal HMD parameters.     

Smart control of the assembly process with a fuzzy control system in the context of Industry 4.0

Assembly line balancing is important for the efficiency of the assembly process, however, a wide range of disruptions can break the current workload balance. Some researchers explored the task assignment plan for the assembly line balancing problem with the assumption that the assembly process is smooth with no disruption. Other researchers considered the impacts of disruptions, but they only explored the task re-assignment solutions for the assembly line re-balancing problem with the assumption that the re-balancing decision has been made already. There is limited literature exploring on-line adjustment solutions (layout adjustment and production rate adjustment) for an assembly line in a dynamic environment. This is because real-time monitoring of an assembly process was impossible in the past, and it is difficult to incorporate uncertainty factors into the balancing process because of the randomness and non-linearity of these factors. However, Industry 4.0 breaks the information barriers between different parts of an assembly line, since smart, connected products, which are enabled by advanced information and communication technology, can intelligently interact and communicate with each other and collect, process and produce information. Smart control of an assembly line becomes possible with the large amounts of real-time production data in the era of Industry 4.0, but there is little literature considering this new context. In this study, a fuzzy control system is developed to analyze the real-time information of an assembly line, with two types of fuzzy controllers in the fuzzy system. Type 1 fuzzy controller is used to determine whether the assembly line should be re-balanced to satisfy the demand, and type 2 fuzzy controller is used to adjust the production rate of each workstation in time to eliminate blockage and starvation, and increase the utilization of machines. Compared with three assembly lines without the proposed fuzzy control system, the assembly line with the fuzzy control system performs better, in terms of blockage ratio, starvation ratio and buffer level. Additionally, with the improvement of information transparency, the performance of an assembly line will be better. The research findings shed light on the smart control of the assembly process, and provide insights into the impacts of Industry 4.0 on assembly line balancing.     

纵轴式掘进机截割头载荷影响因素的仿真分析

针对现有掘进机截割头载荷特性研究方法采用单一影响因素不能全面反映截割头载荷及其波动变化规律的问题,通过分析截割头瞬时载荷,确定了纵轴式掘进机在水平截割工况下截割头载荷的主要影响因素有截割岩石特性、截割头掏槽深度、截割头吃刀深度、截割头转速和截割臂摆速。针对某纵轴式掘进机水平截割工况,采用Matlab对影响截割头载荷的多种因素进行仿真分析,得到了各向载荷及其波动随各因素的变化规律:截割头载荷随着岩壁普氏系数的增大而增加,其中横向阻力增加尤为明显,横向阻力波动程度高于其他方向载荷,且随着岩壁普氏系数的增大呈减小趋势;随着截割头掏槽深度的增加,截割头各向载荷近似呈线性增加,其中升力增加幅度最大,各向载荷波动则随着截割头掏槽深度的增大而减小;随着吃刀深度的增加,截割头载荷总体呈增大趋势,载荷波动程度则随之减小;在截割头转速一定的情况下,截割头载荷均随着截割臂摆速的增加而增大,在同一摆速下,截割头载荷随着截割头转速的减小而增大,横向阻力波动明显高于升力和推进阻力波动,横向阻力和推进阻力波动按截割头载荷规律变化,升力波动则与之相反。截割头载荷波动变化规律与截割头载荷变化规律不尽一致,有时甚至相互冲突。因此,掘进机作业过程中应合理选择截割头掏槽深度、吃刀深度等操作参数和截割头转速、截割臂摆速等运动参数,使各参数相互匹配,以减小掘进机振动,延长使用寿命。     

基于并行多通道卷积长短时记忆网络的轴承寿命预测方法

在预测轴承剩余使用寿命时，数据间的时序特性是一个可以利用的重要隐藏信息。为了更好地提取具有时序信息的特征用于预测，提出了一种基于并行多通道卷积长短时记忆网络（PMCCNN-LSTM）的剩余使用寿命预测模型。该模型主要由两部分组成：前端为并行多通道卷积网络（PMCCNN），提取信号特征，挖掘数据的时序特性，并采用逐层训练和微调的方式提升参数的收敛性；后端为长短时记忆（LSTM）网络，基于特征进行剩余使用寿命预测，并采用加权平均的方法对预测结果进行平滑处理。在一个轴承加速寿命实验的公开数据集上使用留一法验证了该模型的准确性，实验结果表明：所提模型的平均误差与最大误差分别比传统的卷积神经网络（CNN）低23.38%和15.84%，比传统的LSTM低24.14%和19.01%，比卷积长短时记忆网络（CNN-LSTM）低30.32%和23.09%。     

高强不锈钢绞线网与ECC黏结-滑移关系模型

高强不锈钢绞线网与ECC的黏结是二者协同工作的基础，且黏结 滑移关系模型是其黏结性能的综合反映，故通过对17组51个高强不锈钢绞网增强ECC薄板试件进行单边拉拔试验，研究横向钢绞线间距、纵向钢绞线直径和相对锚固长度等因素对钢绞线网在ECC中黏结性能的影响规律。试验结果表明，横向钢绞线的设置可使黏结破坏由脆性破坏转变为延性破坏；高强不锈钢绞线网与ECC的黏结滑移曲线可分为5个阶段，分别为上升段、微降段、延性强化段、下降段和残余段。基于试验结果，对钢绞线网在ECC中的黏结破坏特征和黏结 滑移机理进行分析，在相关黏结-滑移关系模型的基础上，提出钢绞线网与ECC的黏结 滑移关系模型，并进行模型参数分析。所提模型及模型参数计算公式与试验结果吻合良好，能较好地反映钢绞线网与ECC的界面黏结滑移特征。     

The alginate fermentation strain Pantoea sp. F16-PCAi-T3P21 and ethanol production

Bio-ethanol production from algae is a promising way to help solve the energy problem. Alginate is a major component of algae, but it cannot be utilized by existing ethanol fermentation microorganisms. In order to improve the utilization rate of brown algae, high alginate fermentation strains should be obtained. In this research, strains for algae fermentation were got from several experiments. The ethanol yield of strain A was the highest, which was 0.095 g/g (ethanol to alginate). The identification of strain A was carried out and it was 99% identical to Pantoea sp. F16-PCAi-T3P21. Fermentation experiments with different substrates were carried out, such as laminaran, mannitol, L. japonica and acid hydrolysate of L. japonica, and the ethanol yield rate of L. japonica acid hydrolysate was the highest, which reached 0.17 g/g ethanol to L. japonica. It showed that strain A can converse alginate to ethanol in a relatively high yield rate, and might be a promising strain with L. japonica as the substrate, we believe more research should be carried out on this strain.