System reliability analysis for kinematic performance of planar mechanisms

Based on the reliability and mechanism kinematic accuracy theories, we propose a general methodology for system reliability analysis of kinematic performance of planar mechanisms. The loop closure equations are used to estimate the kinematic performance errors of planar mechanisms. Reliability and system reliability theories are introduced to develop the limit state functions （LSF） for failure of kinematic performance qualities. The statistical fourth moment method and the Edgeworth series technique are used on system reliability analysis for kinematic performance of planar mechanisms, which relax the restrictions of probability distribution of design variables. Finally, the practicality, efficiency and accuracy of the proposed method are demonstrated by numerical examples.     

TIFAflow: Enhancing Traffic Archiving System with Flow Granularity for Forensic Analysis in Network Security

The archiving of Internet traffic is an essential function for retrospective network event analysis and forensic computer communication. The state-of-the-art approach for network monitoring and analysis involves storage and analysis of network flow statistic. However, this approach loses much valuable information within the Internet traffic. With the advancement of commodity hardware, in particular the volume of storage devices and the speed of interconnect technologies used in network adapter cards and multi-core processors, it is now possible to capture 10 Gbps and beyond real-time network traffic using a commodity computer, such as n2disk. Also with the advancement of distributed file system (such as Hadoop, ZFS, etc.) and open cloud computing platform (such as OpenStack, CloudStack, and Eucalyptus, etc.), it is practical to store such large volume of traffic data and fully in-depth analyse the inside communication within an acceptable latency. In this paper, based on well- known TimeMachine, we present TIFAflow, the design and implementation of a novel system for archiving and querying network flows. Firstly, we enhance the traffic archiving system named TImemachine+FAstbit (TIFA) with flow granularity, i.e., supply the system with flow table and flow module. Secondly, based on real network traces, we conduct performance comparison experiments of TIFAflow with other implementations such as common database solution, TimeMachine and TIFA system. Finally, based on comparison results, we demonstrate that TIFAflow has a higher performance improvement in storing and querying performance than TimeMachine and TIFA, both in time and space metrics.     

A High Performance Multifrontal Code for Linear Solution of Structures Using Multi-Core Microprocessors

A multifrontal code is introduced for the efficient solution of the linear system of equations arising from the analysis of structures. The factorization phase is reduced into a series of interleaved element assembly and dense matrix operations for which the BLAS3 kernels are used. A similar approach is generalized for the forward and back substitution phases for the efficient solution of structures having multiple load conditions. The program performs all assembly and solution steps in parallel. Examples are presented which demonstrate the code’s performance on single and dual core processor computers.     

Performance Modeling and Approximate Analysis of Multiserver Multiqueue Systems with Poisson and Self-similar Arrivals

The problem of state space explosion is still ran outstanding challenge in Markovian performance analysis for multiserver multiqueue (MSMQ) systems. The system behavior of a MSMQ system is described using stochastic high-level Petri net (SHLPN) models, and an approximate performance analysis technique is proposed based on decomposition and refinement methods as well as iteration technique. A real MSMQ system, Web-server cluster, is investigated. The performance of an integrated scheme of request dispatching and scheduling is analyzed with both Poisson and self-similar request arrivals. The study shows that the approximate analysis technique significantly reduces the complexity of the model solution and is also efficient for accuracy of numerical results.     

Analysis of PR and near-PR cosine-modulated filter banks with odd and even numbers of channels

It is well known that M-channel filter banks, satisfying either the perfect-reconstruction (PR) or near-PR property, can be obtained by cosine modulation of a linear-phase prototype filter. In this work, we investigated the effects of choosing odd or even number of channels on the performance of both PR and near-PR cosine-modulated filter banks (CMFBs). Given the same m, a performance degradation happens in the PR case with odd numbered M. Contrarily, the choice of even or odd M has little effects on the whole system quality for near-PR one. In addition, we made some comparisons between the constrained optimization and the Parks-McClellan algorithm with cosine roll-off characteristic in designing near-PR CMFBs. Detailed analyses and numerical comparisons show that the PR and near-PR systems have their individual characteristics in terms of M and m selection. Studies here can provide useful and practical guidelines for choosing right filter systems.     

Stability Analysis of Nonlinear Networked Control System with Integral Quadratic Constraints Performance in Takagi-Sugeno Fuzzy Model

This paper focuses on the stability analysis of nonlinear networked control system with integral quadratic constraints(IQC) performance, dynamic quantization, variable sampling intervals, and communication delays. By using input-delay and parallel distributed compensation(PDC) techniques, we establish the Takagi-Sugeno(T-S) fuzzy model for the system, in which the sampling period of the sampler and signal transmission delay are transformed to the refreshing interval of a zero-order holder(ZOH). By the appropriate Lyapunov-Krasovskii-based methods, a delay-dependent criterion is derived to ensure the asymptotic stability for the system with IQC performance via the H_∞ state feedback control. The efficiency of the method is illustrated on a simulation exampler.     

Predicting Firms＇ Financial Failure Using Financial Ratios： an Empirical Studies of Chinese Firms

The stock market has played an important role in today‘s China economy. Due to the fact that a number of the listed companies did not generate the return that the investors expected, the research on the causes of financial failure and predicting such financial failure becomes very important for future capital market development. The paper selected 43 ST(Special Treat) and 43 Non-ST companies as sample, using financial ratio and the factor analysis model to compare and analyse. This study especially analyses profitability, operation efficiency and development ability of firms and focuses on the design of an early warning system for possible financial failure of the listed companies by using factor analysis.     

A Mathematic Model of Gas-diffusion Electrodes in Contact with Liquid Electrolytes

A mathematic model is developed which is applied to analyze the main factors that affect electrode performance and to account for the process of reaction and mass transfer in gas-diffusion electrodes in contact with liquid electrolytes. Electrochemical Thiele modulus φ^2 and electrochemical effectiveness factor η are introduced to elucidate the effects of diffusion on electrochemical reaction and utilization of the gas-diffusion electrode. Profile of the reactant along axial direction is discussed, dependence of electrode potential V on current density J, are predicated by means of the newly developed mathematical model.     

High-temperature performance prediction of iron ore fines and the ore-blending programming problem in sintering

The high-temperature performance of iron ore fines is an important factor in optimizing ore blending in sintering. However, the application of linear regression analysis and the linear combination method in most other studies always leads to a large deviation from the desired results. In this study, the fuzzy membership functions of the assimilation ability temperature and the liquid fluidity were proposed based on the fuzzy mathematics theory to construct a model for predicting the high-temperature performance of mixed iron ore. Comparisons of the prediction model and experimental results were presented. The results illustrate that the prediction model is more accurate and effective than previously developed models. In addition, fuzzy constraints for the high-temperature performance of iron ore in this research make the results of ore blending more comparable. A solution for the quantitative calculation as well as the programming of fuzzy constraints is also introduced.     

Analysis of realizable rational decimated nonuniform filter banks with direct structure

In the design of nonuniform filter banks （NUFBs） with direct structure, the location of each analysis filter, corresponding to the sampling factor satisfying maximal decimation condition, should be set properly to avoid large aliasing. In this paper, a necessary and sufficient condition for the setting of the location of each analysis filter is derived. The NUFBs, we focus on, have rational decimation factors. Based on the derived condition, the frequency support of each analysis filter for the realizable NUFBs can be determined directly in such a way that the analysis filters can extract the corresponding bands of the input signal. This provides a guideline for the design of NUFBs with direct structure in choosing proper locations of analysis filters.     

Robust Adaptive Neural Control of a Class of MIMO Nonlinear Systems

In this paper we present a robust adaptive control for a class of uncertain continuous time multiple input multiple output (MIMO) nonlinear systems. Multiple multi-layer neural networks are employed to approximate the uncertainty of the nonlinear functions, and robustifying control terms are used to compensate for approximation errors. All parameter adaptive laws and robustifying control terms are derived based on Lyapunov stability analysis so that, under appropriate assumptions, semi-global stability of the closed-loop system is guaranteed, and the tracking error asymptotically converges to zero. Simulations performed on a two-link robot manipulator illustrate the approach and its performance.     

An optimization approach to the similarity criteria of flows and its application

In the present paper, we propose an optimization approach to investigate the similarity criteria of complex flows. With this approach, we may identify the dominant dimensionless variables governing complex flows by numerical sensitivity analysis. Firstly, we define the sensitivity factor and examine its dependence on the dimensionless variables. Then, we apply this approach to study the similarity criteria of porous media flow in a presumed oil reservoir. The similarity principle obtained from the numerical sensitivity analysis is in agreement with the theoretical law, thus demonstrating the feasibility of the proposed optimization approach. Further explanation is given by analyzing the deviation of pressure distribution in a model from its prototype. In addition, we examine the effects of flow parameter variation on the sensitivity factors and find that the dominant dimensionless variables may change from different sets of parameters.     

Application of adaptive filtering by selecting the parameter weight factor in precise kinematic GPS positioning

A new method of adaptive filtering by selecting the parameter weight factor is introduced in this paper. Based on the theoretical analysis and research, this method is firstly used for precise kinematic GPS positioning with single frequency double-differenced (DD) carrier phase observations. Two numerical examples are given to illustrate the feasibility and validity of the method. The preliminary results show that precise positioning results can be obtained by using only the GPS single frequency DD carrier phase measurements for short baselines with the new method.     

On Peer-Assisted Data Dissemination in Data Center Networks: Analysis and Implementation简

Data Center Networks （DCNs） are the fundamental infrastructure for cloud computing. Driven by the massive parallel computing tasks in cloud computing, one-to-many data dissemination becomes one of the most important traffic patterns in DCNs. Many architectures and protocols are proposed to meet this demand. However, these proposals either require complicated configurations on switches and servers, or cannot deliver an optimal performance. In this paper, we propose the peer-assisted data dissemination for DCNs. This approach utilizes the rich physical connections with high bandwidths and mutli-path connections, to facilitate efficient one-to-many data dissemination. We prove that an optimal P2P data dissemination schedule exists for FatTree, a specially- designed DCN architecture. We then present a theoretical analysis of this algorithm in the general multi-rooted tree topology, a widely-used DCN architecture. Additionally, we explore the performance of an intuitive line structure for data dissemination. Our analysis and experimental results prove that this simple structure is able to produce a comparable performance to the optimal algorithm. Since DCN applications heavily rely on virtualization to achieve optimal resource sharing, we present a general implementation method for the proposed algorithms, which aims to mitigate the impact of the potentially-high churn rate of the virtual machines.     

A Study of the Driving Force Model Revealing Changes in Land Utilization Level Based on 3S Technologies——The Example of Yuanmou, Yunnan, China

This paper introduced the theory and approaches of building driving force models revealing the changes in land utilization level by integrating RS, GPS, and GIS technologies based on the example of Yuanmou County of Yunnan Province. We first created the land utilization type database, natural driving forces for land utilization database, and human driving forces for land utilization database. Then we obtained the dependent and the independent variables of changes in land utilization level by exploring various data. Lastly we screened major factors affecting changes in land utilization level by using the powerful spatial correlation analysis and main component analysis module of GIS and obtained a multivariable linear regression model of the changes in land utilization level by using GIS spatial regression analysis module.     

Design and implementation of prototype system for online handwritten Uyghur character recognition

Based on the analysis of the unique shapes and writing styles of Uyghur characters,we design a framework for prototype character recognition system and carry out a systematic theoretical and experimental research on its modules.In the preprocessing procedure,we use the linear and nonlinear normalization based on dot density method.Both structural and statistical features are extracted due to the fact that there are some very similar characters in Uyghur literature.In clustering analysis,we adopt the dynamic clustering algorithm based on the minimum spanning tree(MST),and use the k-nearest neighbor matching classification as classifier.The testing results of prototype system show that the recognition rates for characters of the four different types(independent,suffix,intermediate,and initial type) are 74.67%,70.42%,63.33%,and 72.02%,respectively;the recognition rates for the case of five candidates for those characters are 94.34%,94.19%,93.15%,and 95.86%,respectively.The ideas and methods used in this paper have some commonality and usefulness for the recognition of other characters that belong to Altaic languages family.     

Cache performance optimization of irregular sparse matrix multiplication on modern multi-core CPU and GPU

This paper focuses on how to optimize the cache performance of sparse matrix-matrix multiplication （SpGEMM）.It classifies the cache misses into two categories：one is caused by the irregular distribution pattern of the multiplier-matrix,and the other is caused by the multiplicand.For each of them,the paper puts forward an optimization method respectively.The first hash based method removes cache misses of the 1 st category effectively,and improves the performance by a factor of 6 on an Intel 8-core CPU for the best cases.For cache misses of the 2nd category,it proposes a new cache replacement algorithm,which achieves a cache hit rate much higher than other historical knowledge based algorithms,and the algorithm is applicable on CELL and GPU.To further verify the effectiveness of our methods,we implement our algorithm on GPU,and the performance perfectly scales with the size of on-chip storage.     

Simulation of g factor for size optimization and coupling in phased array coil design

The effect of geometric parameters of the coil element in an array on signal-to-noise ratio (SNR) and g factor of parallel imaging are analyzed, and the optimal parameters for the maximal SNR and minimal g factor are also given in this paper. In addition, the effects of coupling on SNR and g factor have been analyzed. The test results indicate that g factor for parallel imaging is greatly increased and SNR is decreased for coupling. According to the optimization, a six-channel parallel coil has been designed for torso imaging.