CloudRank-D: benchmarking and ranking cloud computing systems for data processing applications

With the explosive growth of information, more and more organizations are deploying private cloud systems or renting public cloud systems to process big data. However, there is no existing benchmark suite for evaluating cloud performance on the whole system level. To the best of our knowledge, this paper proposes the first benchmark suite CloudRank-D to benchmark and rank cloud computing systems that are shared for running big data applications. We analyze the limitations of previous metrics, e.g., floating point operations, for evaluating a cloud computing system, and propose two simple metrics: data processed per second and data processed per Joule as two complementary metrics for evaluating cloud computing systems. We detail the design of CloudRank-D that considers representative applications, diversity of data characteristics, and dynamic behaviors of both applications and system software platforms. Through experiments, we demonstrate the advantages of our proposed metrics. In several case studies, we evaluate two small-scale deployments of cloud computing systems using CloudRank-D.     

基于区域重组的异构FPGA工艺映射算法

传统异构FPGA工艺映射算法一般不打破实现专用功能和查找表功能的子网表之间的层次边界,因而缩小了映射的优化空间.为此提出一种利用区域重组打破单元间层次边界的异构FPGA工艺映射算法.首先利用贪心策略实现FPGA多单元的映射,即优先使用性能好的专用功能单元;然后利用标记锥实现子网表之间的区域重组,打破专用功能单元和查找表之间的层次边界,减小了映射结果的面积和延迟开销.实验结果表明,与公认的ABC中的工艺映射算法相比,该算法能平均减少逻辑单元面积12.2%,减少电路关键路径延时2.5%.     

用于麦克风阵列的阵元筛选方法研究

在使用TDOA算法进行被动声定向时,为精确获取麦克风阵列阵元之间接收声源信号的时间差,进而计算目标的方位角,麦克风阵元之间必须保持较高的相位一致性。而从论文与专利检索来看,目前还没有有效方法筛选出符合要求的麦克风阵元。针对此问题,本文基于麦克风阵列阵元相位一致性的指标,设计出麦克风阵元筛选系统,通过实测数据分析表明:一致性较高的麦克风更适合组建麦克风阵列。     

基于Linux VMware虚拟机的串口通信

嵌入式开发领域经常使用串口通信,但串口通信方式占用很多硬件资源,设备的利用率较低。文章研究了基于Linux VMware虚拟机进行串口通信的Use Physical Serial Port模式、Use Out File模式以及Use Named Pipe模式的功能和特点,给出了基于VMware构建串口通信环境的方法以及具体实现过程。     

Simple Question Answering over Knowledge Graph Enhanced by Question Pattern Classification

Knowledge and Information Systems - Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn...     

The parameterized level set method for structural topology optimization with shape sensitivity constraint factor

In recent years, the parameterized level set method (PLSM) has attracted widespread attention for its good stability, high efficiency and the smooth result of topology optimization compared with the conventional level set method. In the PLSM, the radial basis functions (RBFs) are often used to perform interpolation fitting for the conventional level set equation, thereby transforming the iteratively updating partial differential equation (PDE) into ordinary differential equations (ODEs). Hence, the RBFs play a key role in improving efficiency, accuracy and stability of the numerical computation in the PLSM for structural topology optimization, which can describe the structural topology and its change in the optimization process. In particular, the compactly supported radial basis function (CS-RBF) has been widely used in the PLSM for structural topology optimization because it enjoys considerable advantages. In this work, based on the CS-RBF, we propose a PLSM for structural topology optimization by adding the shape sensitivity constraint factor to control the step length in the iterations while updating the design variables with the method of moving asymptote (MMA). With the shape sensitivity constraint factor, the updating step length is changeable and controllable in the iterative process of MMA algorithm so as to increase the optimization speed. Therefore, the efficiency and stability of structural topology optimization can be improved by this method. The feasibility and effectiveness of this method are demonstrated by several typical numerical examples involving topology optimization of single-material and multi-material structures.

     

Modeling polypharmacy effects with heterogeneous signed graph convolutional networks

Applied Intelligence - Pharmaceutical drug combinations can effectively treat various medical conditions. However, some combinations can cause serious adverse drug reactions (ADR). Therefore,...     

Influential facilities placement over moving objects

Distributed and Parallel Databases - In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate...     

基于CPLD的某型导弹测角仪视频信号模拟

针对导弹检测系统的训练和计量,采用CPLD技术设计了某型导弹电视测角仪视频信号模拟系统;介绍了测角仪的视频显示方式和模拟信号产生原理,并给出了CPLD主要的内部逻辑电路和VHDL语言描述;将视频信号分解为同步信号、固定图像和可控实心圆,以行计数器和列计数器构成图像的时间基准,通过对行、列计数器的编码实现同步信号和固定图像,通过对可逆计数器及行、列计数器的运算实现可控实心圆,并对可逆计数器的数据进行编码构成变焦数据,最终精确地实现了测角仪视频信号及其变焦数据输出的模拟。     

基于强跟踪滤波器的开关变换器故障诊断

Buck变换器又称降压变换器、串联开关稳压电源、三端开关型降压稳压器。广泛用于计算机、家用电器、控制系统和通信系统中的电源几乎都是开关电源,具有超快速负载动态响应、高功率变换效率、高功率密度、低电压、大电流的特性.开关变换器作为它的主要电路,故障率较高,对它故障诊断研究是有意义的。基于强跟踪滤波理论,给出了一种开关变换器电路故障实时诊断的一种方法。该方法以建立一种开关变换器电路状态空间模型为基础,利用强跟踪滤波器对电路状态及元件参数进行估计,当元件参数发生软、硬型故障时,根据强跟踪滤波器对元件参数的跟踪结果及修正的Bayes分类算法,可实时诊断开关变换器电路中的元件故障。仿真结果证明了该方法的有效性。