Data-driven computing in elasticity via kernel regression

This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set.

DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

High Dimensional Dataset Compression Using Principal Components

Until recently, computational power was insufficient to diagonalize atmospheric datasets of order 108 - 109 elements. Eigenanalysis of tens of thousands of variables now can achieve massive data compression for spatial fields with strong correlation properties. Application of eigenanalysis to 26,394 variable dimensions, for three severe weather datasets (tornado, hail and wind) retains 9 - 11 principal components explaining 42% - 52% of the variability. Rotated principal components (RPCs) detect localized coherent data variance structures for each outbreak type and are related to standardized anomalies of the meteorological fields. Our analyses of the RPC loadings and scores show that these graphical displays can efficiently reduce and interpret large datasets. Data is analyzed 24 hours prior to severe weather as a forecasting aid. RPC loadings of sea-level pressure fields show different morphology loadings for each outbreak type. Analysis of low level moisture and temperature RPCs suggests moisture fields for hail and wind which are more related than for tornado outbreaks. Consequently, these patterns can identify precursors of severe weather and discriminate between tornadic and non-tornadic outbreaks.

Research on the big data feature mining technology based on the cloud computing

The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big data feature analysis and mining. The big data feature mining in the cloud computing environment is an effective method for the elficient application of the massive data in the information age. In the process of the big data mining, the method o f the big data feature mining based on the gradient sampling has the poor logicality. It only mines the big data features from a single-level perspective, which reduces the precision of the big data feature mining.

Novel region-based image compression method based on spiking cortical model

To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.

利用基于模糊划分的ISODATA模糊聚类方法识别电力系统同调机群

给出了一种利用基于模糊划分的迭代自组织数据分析技术(ISODATA)模糊聚类方法识别电力系统同调机群的算法。首先介绍了基于模糊划分的ISODATA模糊聚类方法的原理;然后在一些基本假设并对系统元件做了必要简化的基础上,形成了利用该方法识别同调机群的模糊集合;最后通过对新英格兰10机39节点系统的算例仿真验证了该方法的有效性。

COMPRESSIBLE FLOW SIMULATION AROUND AIRFOIL BASED ON LATTICE BOLTZMANN METHOD

The flow around airfoil NACA0012 enwrapped by the body-fitted grid is simulated by a coupled doubledistribution-function （DDF） lattice Boltzmann method （LBM） for the compressible Navier-Stokes equations. Firstly, the method is tested by simulating the low Reynolds number flow at Ma =0. 5,a=0. 0, Re=5 000. Then the simulation of flow around the airfoil is carried out at Ma：0. 5, 0. 85, 1.2; a=-0.05, 1.0, 0.0, respectively. And a better result is obtained by using a local refined grid. It reduces the error produced by the grid at Ma=0. 85. Though the inviscid boundary condition is used to avoid the problem of flow transition to turbulence at high Reynolds numbers, the pressure distribution obtained by the simulation agrees well with that of the experimental results. Thus, it proves the reliability of the method and shows its potential for the compressible flow simulation. The suecessful application to the flow around airfoil lays a foundation of the numerical simulation of turbulence.

Effect of Sample Disturbance on Unconfined Compression Strength of Natural Marine Clays

Quantitatively correcting the unconfined compressive strength for sample disturbance is an important research project in the practice of ocean engineering and geotechnical engineering. In this study, the specimens of undisturbed natural marine clay obtained from the same depth at the same site were deliberately disturbed to different levels. Then, the specimens with different extents of sample disturbance were trimmed for both oedometer tests and unconfined compression tests. The degree of sample disturbance SD is obtained from the oedometer test data. The relationship between the unconfined compressive strength q u and SD is studied for investigating the effect of sample disturbance on q u. It is found that the value of q u decreases linearly with the increase in SD. Then, a simple method of correcting q u for sample disturbance is proposed. Its validity is also verified through analysis of the existing published data.

Fast modeling of gravity gradients from topographic surface data using GPU parallel algorithm

The gravity gradient is a secondary derivative of gravity potential,containing more high-frequency information of Earth’s gravity field.Gravity gradient observation data require deducting its prior and intrinsic parts to obtain more variational information.A model generated from a topographic surface database is more appropriate to represent gradiometric effects derived from near-surface mass,as other kinds of data can hardly reach the spatial resolution requirement.The rectangle prism method,namely an analytic integration of Newtonian potential integrals,is a reliable and commonly used approach to modeling gravity gradient,whereas its computing efficiency is extremely low.A modified rectangle prism method and a graphical processing unit(GPU)parallel algorithm were proposed to speed up the modeling process.The modified method avoided massive redundant computations by deforming formulas according to the symmetries of prisms’integral regions,and the proposed algorithm parallelized this method’s computing process.The parallel algorithm was compared with a conventional serial algorithm using 100 elevation data in two topographic areas(rough and moderate terrain).Modeling differences between the two algorithms were less than 0.1 E,which is attributed to precision differences between single-precision and double-precision float numbers.The parallel algorithm showed computational efficiency approximately 200 times higher than the serial algorithm in experiments,demonstrating its effective speeding up in the modeling process.Further analysis indicates that both the modified method and computational parallelism through GPU contributed to the proposed algorithm’s performances in experiments.

Data-Driven Anomaly Diagnosis for Machining Processes

To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.

Object-Oriented Design for FDTD Visual Scientific Computing

A scheme for general purposed FDTD visual scientific computing software is introduced in this paper using object-oriented design (OOD) method. By abstracting the parameters of FDTD grids to an individual class and separating from the iteration procedure, the visual software can be adapted to more comprehensive computing problems. Real-time gray degree graphic and wave curve of the results can be achieved using DirectX technique. The special difference equation and data structure in dispersive medium are considered, and the peculiarity of parameters in perfectly matched layer are also discussed.

Application of the Hierarchical Functions Expansion Method for the Solution of the Two Dimensional Navier-Stokes Equations for Compressible Fluids in High Velocity

This work presents a new application for the Hierarchical Function Expansion Method for the solution of the Navier-Stokes equations for compressible fluids in two dimensions and in high velocity. This method is based on the finite elements method using the Petrov-Galerkin formulation, know as SUPG (Streamline Upwind Petrov-Galerkin), applied with the expansion of the variables into hierarchical functions. To test and validate the numerical method proposed as well as the computational program developed simulations are performed for some cases whose theoretical solutions are known. These cases are the following: continuity test, stability and convergence test, temperature step problem, and several oblique shocks. The objective of the last cases is basically to verify the capture of the shock wave by the method developed. The results obtained in the simulations with the proposed method were good both qualitatively and quantitatively when compared with the theoretical solutions. This allows concluding that the objectives of this work are reached.

Research of storage technique for C-Temporal Relation Data Model

As there is datum redundancy in tradition database and temporal database in existence and the quantities of temporal database are increasing fleetly. We put forward compress storage tactics for temporal datum which combine compress technology in existence in order to settle datum redundancy in the course of temporal datum storage and temporal datum of slow acting domain and momentary acting domain are accessed by using each from independence clock method and mutual clock method .We also bring forward strategy of gridding storage to resolve the problems of temporal datum rising rapidly.

Principle of MSD floating-point division based on Newton-Raphson method on ternary optical computer

The division operation is not frequent relatively in traditional applications, but it is increasingly indispensable and important in many modern applications. In this paper, the implementation of modified signed-digit （MSD） floating-point division using Newton-Raphson method on the system of ternary optical computer （TOC） is studied. Since the addition of MSD floating-point is carry-free and the digit width of the system of TOC is large, it is easy to deal with the enough wide data and transform the division operation into multiplication and addition operations. And using data scan and truncation the problem of digits expansion is effectively solved in the range of error limit. The division gets the good results and the efficiency is high. The instance of MSD floating-point division shows that the method is feasible.

基于视觉与加速度测量的结构动态位移融合估计

结构动态位移测量与精准估计对于结构安全运营和性态评估具有重要意义.基于计算机视觉的位移监测方法具有精度高、非接触式、成本低、设备安装简便等优点,在实际复杂工程环境中,设备难以架设,视觉测量方法较传统接触式位移监测方法具有明显的优势.图像分辨率和拍摄帧率等因素在一定程度上限制了视觉方法的使用.针对视觉位移测量技术高频振动识别精度低的问题,提出了一种基于视觉与加速度测量的结构动态位移重构方法,通过融合视觉低频与加速度高频振动响应信号,实现结构动态位移精准识别.首先,利用光流法从结构振动视频数据中提取结构位移响应,引入前后向误差与离群值过滤机制,提升特征点追踪精度,避免漂移问题.然后,利用逐次变分模态分解方法分别从视觉位移与加速度二次积分得到的位移信号中提取相应的本征模态函数(intrinsic mode function,IMF)分量.最后,基于互相关函数筛选机制,确定融合模态分量,融合基于视觉测量的低阶IMF与基于加速度测量的高阶IMF,重构结构位移响应.通过一个钢筋混凝土框架结构振动台试验,对提出的位移融合估计方法进行了试验验证.结果表明:与单一视觉测量方法相比,所提出的方法能够更为准确地估计结构动态位移,并且通过引入加速度测量中的动态位移分量,融合后的位移比基于视觉测量的结果具有更宽的频率范围.

基于边缘计算的高效低时延通信传输技术研究

边缘计算作为一种新型的计算架构,通过将计算和数据处理功能推向网络边缘,有效降低了数据传输时延和网络负载,成为解决高时延通信问题的关键技术之一。针对边缘计算环境下的通信传输优化问题展开研究,探讨基于数据压缩、缓存管理和动态流量调整等技术的应用与优化策略。

Mobile-Edge Computing Framework with Data Compression for Wireless Network in Energy Internet

Under the situations of energy dilemma, energy Internet has become one of the most important technologies in international academic and industrial areas. However, massive small data from users, which are too scattered and unsuitable for compression, can easily exhaust computational resources and lower random access possibility, thereby reducing system performance. Moreover, electric substations are sensitive to transmission latency of user data, such as controlling information. However, the traditional energy Internet usually could not meet requirements. Integrating mobile-edge computing makes energy Internet convenient for data acquisition,processing, management, and accessing. In this paper, we propose a novel framework for energy Internet to improve random access possibility and reduce transmission latency. This framework utilizes the local area network to collect data from users and makes conducting data compression for energy Internet possible. Simulation results show that this architecture can enhance random access possibility by a large margin and reduce transmission latency without extra energy consumption overhead.

高功率腔体滤波器的电磁-热-力多物理场智能代理模型及快速预测

电磁多物理场计算在模拟高功率微波器件方面至关重要。为了快速且精确地获取高功率腔体滤波器中由电磁引发的多物理场响应,本文构建了一种基于数据驱动的电磁多物理场代理模型。首先利用有限元方法建立腔体滤波器的多物理场响应数据库,随后基于该数据库构建数据驱动的人工神经网络代理模型。结果对比表明:该代理模型能够精确且快速地预测高功率腔体滤波器在不同输入功率下的S参数曲线,预测精度超过98%,预测时间少于0.2 s;相较于传统的电磁多物理场数值计算方法,计算速度提升了3个数量级以上。因此,本文代理模型的快速精确预测能力对新型高功率微波器件的稳定性分析、可靠性评估和优化设计具有重要的理论指导意义和实际工程应用价值。

智慧楼宇环境下基于数据压缩和改进灰狼算法的边缘计算卸载方法

智慧楼宇的边缘计算环境中,为了在资源受限且环境复杂的情况下最小化系统的整体时延和能耗,提出了一种结合数据压缩和改进灰狼算法(CLGWO)的边缘计算卸载方法。该方法首先采用差分字典编码压缩技术对数据进行压缩,预估数据压缩率及其产生的开销;随后引入莱维飞行算法和螺旋渐近狩猎方式对灰狼算法进行改进,以增强其全局搜索能力;最后结合预估压缩效果和改进灰狼算法求得最佳卸载方案。实验结果表明,CLGWO方法能够显著降低计算任务卸载的整体延迟和能耗,证实了该方法的有效性和可行性,为楼宇中边缘计算卸载问题提供了一种新的解决方案。

基于数据压缩的无人机边缘计算卸载优化

数据压缩技术通过压缩计算任务可以降低移动边缘计算(Mobile edge computing,MEC)网络中终端用户的卸载能耗。针对终端用户与基站之间的通信链路被障碍物阻挡对通信质量有影响问题,为满足应急通信和节能卸载需求,提出了一种无人机搭载中继设备和边缘服务器辅助MEC中基于数据压缩的任务卸载方案。考虑任务压缩比例、系统资源和无人机机载能量等约束条件,建立用户总能耗最小化问题。将该非凸优化问题建模成一个马尔可夫决策过程,使用深度强化学习中柔性演员?评论家算法求解。仿真结果表明,所提方案具有良好的收敛性,与基准算法相比,能耗降低了24.7%~42.2%。