一种改进的XML访问控制模型

在XML访问控制方面,改进传统的XML控制技术在现有模型中授权主体的定义范畴,同时将请求评估模块和决策执行模块显式分开,增加定制安全策略和安全规则的灵活性和通用性。     

Continuous collision detection for adaptive simulation of articulated bodies

We perform continuous collision detection (CCD) for articulated bodies where motion is governed by an adaptive dynamics simulation. Our algorithm is based on a novel hierarchical set of transforms that represent the kinematics of an articulated body recursively, as described by an assembly tree. The performance of our CCD algorithm significantly improves as the number of active degrees of freedom in the simulation decreases.     

激光扫描车身测量传感器靶标识别体系的研究

介绍了四光束激光扫描测量车身三维坐标的系统构成和基本原理,从系统PIN预处理输出的靶标回射信号的特点出发,为汽车车身测量传感器设计了基于条形码识别原理的合作靶标识别体系,最多可同时对27个测量点进行有效识别,确保传感器的精度要求和效率要求,达到了很好的实验效果。     

水下刚性壁装药爆炸气泡简化模型和数值仿真

为预测沉底装药水下爆炸产生气泡的最大半径和脉动周期,建立了水下刚性壁上的装药爆炸产生气泡的动力学模型.从Bernoulli方程和能量守恒两个角度推导出描述半球形气泡径向运动的流体动力学方程,通过能量法确定了方程的初始条件.对方程数值求解得到第一脉动周期内气泡半径和径向运动速度随时间变化的曲线.利用ANSYS-AUTODYN软件的高精度Euler-Godunov求解器和计算结果映射技术,建立了水下刚性壁上装药爆炸产生气泡运动的数值仿真模型.计算得到了气泡膨胀、收缩、上浮和脉动压力时程曲线及其几何形状变化规律.理论模型与仿真计算得到的气泡半径、脉动周期和径向运动速度均吻合,说明该理论模型满足工程所需精度要求.     

基于工作过程的“Flash动画”教学改革与实践

本文根据"Flash动画"课程的特点,结合学院的课程教学改革项目,从课程的教学理念、教学模式、教学体系和手段等方面着手,探讨了基于工作过程的教学改革与实践,以培养出符合新时代需要的创新人才。     

基于照片的人体尺寸信息自动提取技术

针对3D扫描仪成本高,传统手工软尺测量效率低的状况,在保证精度的前提下,尝试应用图像处理理论从满足一定条件的照片提取人体尺寸信息,为建立人体尺寸信息库提供一种信息采集的自动化方法.通过对实验结果的分析,本文验证了此方法的可行性,并提出未来的研究方向.     

智能公用室内照明系统的设计

分析比较了已有的几种智能照明系统方案,将检测单元阵列引入智能公用室内照明系统的设计中,从而实现了可精确定位使用者位置及检测此位置的自然光水平,进而实现照明灯的精确控制,实现了真正的智能照明。实际应用验证了本文所述方法的可行性,根据室内自然光及使用者情况自动进行智能照明控制可达到节能目的,且方便操作管理,具有很好的应用前景。     

Design of high-speed data transfer turbo code for body channel communication transceivers

In this work, a digital differential transmitter based on low-power wireless compensation transceiver for body channel communication (BCC) is proposed. Further, the proposed transceiver is composed of Touch Status Detection Unit (TSDU), Wireless Status Compensation Unit (WSCU), and a reconfigurable preamplifier. Initially, the human body channel environment for wireless communication is investigated based on properties from 1 to 100 MHz. Further, the turbo code-based encoding scheme is used to encode the data before transferring the data on the transmitter side. Also, the proposed error-correcting parallel turbo decoder using a modified step-by-step algorithm is presented. The turbo code-based decoding scheme is used to recover the error-free transmitted data at the receiver side. Results demonstrate that the proposed BCC transceiver is designed using 90 nm CMOS technology and it is observed that the proposed BCC transceiver has utilized an area of 600mm². Also, the maximum data rate achieved by a proposed BCC transceiver was 100 Mbps, and the overall transceiver power consumption is 0.42 mW, and energy for communication is 0.02 nj/b.     

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.     

Local calibration of rigid pavement performance models using resampling methods

The performance prediction models in the Pavement-ME design software are nationally calibrated using in-service pavement material properties, pavement structure, climate and truck loadings, and performance data obtained from the Long-Term Pavement Performance programme. The nationally calibrated models may not perform well if the inputs and performance data used to calibrate those do not represent the local design and construction practices. Therefore, before implementing the new M-E design procedure, each state highway agency (SHA) should evaluate how well the nationally calibrated performance models predict the measured field performance. The local calibrations of the Pavement-ME performance models are recommended to improve the performance prediction capabilities to reflect the unique conditions and design practices. During the local calibration process, the traditional calibration techniques (split sampling) may not necessarily provide adequate results when limited number of pavement sections are available. Consequently, there is a need to employ statistical and resampling methodologies that are more efficient and robust for model calibrations given the data related challenges encountered by SHAs. The main objectives of the paper are to demonstrate the local calibration of rigid pavement performance models and compare the calibration results based on different resampling techniques. The bootstrap is a non-parametric and robust resampling technique for estimating standard errors and confidence intervals of a statistic. The main advantage of bootstrapping is that model parameters estimation is possible without making distribution assumptions. This paper presents the use of bootstrapping and jackknifing to locally calibrate the transverse cracking and IRI performance models for newly constructed and rehabilitated rigid pavements. The results of the calibration show that the standard error of estimate and bias are lower compared to the traditional sampling methods. In addition, the validation statistics are similar to that of the locally calibrated model, especially for the IRI model, which indicates robustness of the local model coefficients.