考虑统计特性的OFDM分数维布朗信道模型

为了更准确地刻画多径信道,提出了任何采样率下的OFDM信道的分数维布朗模型。模型利用OFDM中具有导频符号的特点,考虑多径衰落具有服从Rice或Rayleigh分布的先验条件,从分形学角度出发分析无线信道的分形特性,建立模型。仿真结果表明,该模型比统计模型更准确地刻画多径信道。     

Reducing Brownian motion in an electrostatically tunable MEMS laser

Brownian motion of a tunable vertical-cavity surface-emitting lasers (VCSEL) movable microelectromechanical systems (MEMS) mirror is shown to broaden the spectral width of the laser emission line. A low-noise wavelength feedback system is presented and evaluated. This feedback system is employed to reduce measured linewidth from 1050 to 400 MHz fullwidth at half-maximum (FWHM), an improvement of 62%. The effects of device design parameters on closed-loop Brownian motion levels are analyzed. The potential increases in performance resulting from vacuum packaging and advanced controller design are discussed.     

Stochastic process-based degradation modeling and RUL prediction:from Brownian motion to fractional Brownian motion

Brownian motion(BM)has been widely used for degradation modeling and remaining useful life(RUL)prediction,but it is essentially Markovian.This implies that the ...     

城市空气污染脆弱性评价模型及实证研究

利用中国74个样本城市的微观监测数据,通过构建概率模型和分解模型,以三阶段可行广义最小二乘法估计的参数测度城市空气污染脆弱性.研究发现:超过80%的样本城市具有空气污染脆弱性,且呈地域性特征;脆弱性不仅在相邻两级之间转移,还存在跨级间突变.空气污染的差异性表现为近20%的样本城市中度及以上空气污染可能性较高, 46%的城市发生空气轻度污染是大概率事件,仅有12%的城市无污染脆弱性;而由于"污染避难所"效应的存在,空气污染"热点"城市趋向于欠发达地区.根据实证结果,对城市空气污染程度分类并提出了差异化的应对措施,以期为有关部门空气治理政策的制定提供科学依据,从而达到突出重点、分类指导、多管齐下、科学施策的目的.     

Learning probabilistic models for model checking: an evolutionary approach and an empirical study

Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically “learn” models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on the learned model be more accurate than the estimation we could have obtained by sampling many system executions within the same amount of time? Moreover, how well does learning scale to real-world applications? If the answer is negative, what are the potential methods to improve the efficiency of learning? In this work, we first investigate existing algorithms for learning probabilistic models for model checking and propose an evolution-based approach for better controlling the degree of generalization. Then, we present existing approaches to learn abstract models to improve the efficiency of learning for scalability reasons. Lastly, we conduct an empirical study in order to answer the above questions. Our findings include that the effectiveness of learning may sometimes be limited and it is worth investigating how abstraction should be done properly in order to learn abstract models.     

Breathing ontological knowledge into feature model synthesis: an empirical study

Feature Models (FMs) are a popular formalism for modeling and reasoning about the configurations of a software product line. As the manual construction of an FM is time-consuming and error-prone, management operations have been developed for reverse engineering, merging, slicing, or refactoring FMs from a set of configurations/dependencies. Yet the synthesis of meaningless ontological relations in the FM – as defined by its feature hierarchy and feature groups – may arise and cause severe difficulties when reading, maintaining or exploiting it. Numerous synthesis techniques and tools have been proposed, but only a few consider both configuration and ontological semantics of an FM. There are also few empirical studies investigating ontological aspects when synthesizing FMs. In this article, we define a generic, ontologic-aware synthesis procedure that computes the likely siblings or parent candidates for a given feature. We develop six heuristics for clustering and weighting the logical, syntactical and semantical relationships between feature names. We then perform an empirical evaluation on hundreds of FMs, coming from the SPLOT repository and Wikipedia. We provide evidence that a fully automated synthesis (i.e., without any user intervention) is likely to produce FMs far from the ground truths. As the role of the user is crucial, we empirically analyze the strengths and weaknesses of heuristics for computing ranking lists and different kinds of clusters. We show that a hybrid approach mixing logical and ontological techniques outperforms state-of-the-art solutions. We believe our approach, environment, and empirical results support researchers and practitioners working on reverse engineering and management of FMs.     

Single-vendor multi-buyer discount pricing model under stochastic demand environment

In this paper, a single-vendor multi-buyer discount pricing model has been developed under stochastic demand information. The vendor offers multiple pricing schedules to encourage the buyers to adopt the global optimal policy instead of their individual optimal ordering policy. The global optimal solution ensures that each buyer is assigned to the best schedule with maximum benefit. The results show that coordination benefit increases with increase in the number of pricing schedules. However, the system performance deteriorates with increasing demand variability and service level. Hence, if the system contains high degree of uncertainty or each buyer sets her service level too high, coordination through discount policy may not be an efficient mechanism to enhance channel profitability.     

Text-independent speaker recognition based on the Hurst parameter and the multidimensional fractional Brownian motion model

In this paper, a text-independent automatic speaker recognition (ASkR) system is proposed-the SR/sub Hurst/-which employs a new speech feature and a new classifier. The statistical feature pH is a vector of Hurst (H) parameters obtained by applying a wavelet-based multidimensional estimator (M/spl I.bar/dim/spl I.bar/wavelets ) to the windowed short-time segments of speech. The proposed classifier for the speaker identification and verification tasks is based on the multidimensional fBm (fractional Brownian motion) model, denoted by M/spl I.bar/dim/spl I.bar/fBm. For a given sequence of input speech features, the speaker model is obtained from the sequence of vectors of H parameters, means, and variances of these features. The performance of the SR/sub Hurst/ was compared to those achieved with the Gaussian mixture models (GMMs), autoregressive vector (AR), and Bhattacharyya distance (dB) classifiers. The speech database-recorded from fixed and cellular phone channels-was uttered by 75 different speakers. The results have shown the superior performance of the M/spl I.bar/dim/spl I.bar/fBm classifier and that the pH feature aggregates new information on the speaker identity. In addition, the proposed classifier employs a much simpler modeling structure as compared to the GMM.     

Semimartingale approximation of fractional Brownian motion and its applications

The aim of this paper is to provide a semimartingale approximation of a fractional stochastic integration. This result leads us to approximate the fractional Black–Scholes model by a model driven by semimartingales, and a European option pricing formula is found.     

A model for implementing BPR based on strategic perspectives: an empirical study

Business process reengineering (BPR) efforts have been reported successful in many firms. However, the failure rates were as high as 70%. Various reasons for this have been given in past research. One major reason was that the efforts had not been connected to the corporate goals. In addition, the steps in implementing BPR in past discussions were, however, incomplete and the papers merely reported a part of the process. This study proposes an integrative approach based on a strategic perspective. Basically, this framework involves three steps that: (1) identify corporate strategies based on analysis of a strategic thrust/strategic target matrix; (2) select strategic paths for BPR based on analysis of a functional coupling framework with the IT application; (3) implement BPR based on analysis of project characteristics to decide on a suitable version of the composite methodology. Furthermore, this framework is illustrated by using an empirical study to demonstrate its application in some business units.     

Option pricing under the Merton model of the short rate

Previous option pricing research typically assumes that the risk-free rate or the short rate is constant during the life of the option. In this study, we incorporate the stochastic nature of the short rate in our option valuation model and derive explicit formulas for European call and put options on a stock when the short rate follows the Merton model. Using our option model as a benchmark, our numerical analysis indicates that, in general, the Black–Scholes model overvalues out-of-the-money calls, moderately overvalues at-the-money calls, and slightly overvalues in-the-money calls. Our analysis is directly extensible to American calls on non-dividend-paying stocks and to European puts by virtue of put-call parity.     

High-order time stepping scheme for pricing American option under Bates model

Pricing of European and American options under Bates model give rise to a partial integro-differential equation. In this paper a strongly stable fourth-order implicit predictor–corrector time stepping method based on exponential time differencing) is proposed for solving such problems. We provide stability, and convergence of the proposed method, and study the impact of the jump intensity, penalty and other parameters on convergence and solution accuracy. The American option constraint is enforced by using a penalty method. Spatial derivatives are approximated using second-order finite central differences which leads to block tridiagonal systems. The integral term is evaluated using simple quadrature where the non-locality of the jump term in such models leads to dense matrix. We treat the approximated integral term and nonlinear penalty term explicitly in time. Numerical experiments are demonstrated by discussing the efficiency, accuracy and reliability of the proposed method.     

Improving multiple-password recall: an empirical study

As one of the most common authentication methods, passwords help secure information by granting access only to authorized parties. To be effective, passwords should be strong, secret, and memorable. While password strength can be enforced by automated information technology policies, users frequently jeopardize secrecy to improve memorability. The password memorability problem is exacerbated by the number of different passwords a user is required to remember. While short-term memory theories have been applied to individual-password management problems, the relationship between memory and the multiple-password problem has not been examined. This paper treats the multiple-password management crisis as a search and retrieval problem involving human beings’ long-term memory. We propose that interference between different passwords is one of the major challenges to multiple-password recall and that interference alleviation methods can significantly improve multiple-password recall. A lab experiment was conducted to examine the effectiveness of two interference alleviation methods: the list reduction method and the unique identifier method. While both methods improve multiple-password recall performance, the list reduction method leads to statistically significant improvement. The results demonstrate the potential merit of practices targeting multiple-password interference. By introducing long-term memory theory to multiple-password memorability issues, this study presents implications benefiting users and serves as the potential starting point for future research.     

Software quality practices — an empirical study

Concern about the quality of software is widespread. Reliable data about current practice are hard to get as no one likes to admit that they are producing poor software. In a recent survey, Wilson has taken an approach to obtaining data about the practice of software quality assurance among companies in Australia. The same technique has been used here to investigate practice among companies in Europe and America. The survey ams to investigate quality systems used by the organizations, and the application of these systems by software fevelopers. In this paper they are compared with the Australian experience and the results of the survey shown. The company's demographic data and overall response pattern from this survey are compared with Wilson's. Patterns in software quality assurance programs of organizations are suggested by comparing the management's perspective with that of the developers. The most popular software development techniques identified by the developers are prioritized in the order of their popularity by using a weighting scheme and their implications are discussed.     

A study of the Brownian motion of the non-spherical microparticles on fluctuating lattice Boltzmann method

In this paper, the fluctuating lattice Boltzmann (FLB) model is used to simulate the Brownian motion of spherical and non-spherical particles directly on 3D. The FLB model is validated by comparing the simulation results with theoretical and experimental results. And then the Brownian motion characteristics of different shapes of non-spherical particles are analyzed, which includes some ellipsoidal and cylindrical particles with different aspect ratios. It is found that, although the ellipsoidal and cylindrical particles are anisotropic, they still obey the energy equalization theorem. The velocity and angular velocity autocorrelation functions of the particles still have the “long-time tail” effect. Moreover, the velocity and angular velocity autocorrelation functions are independent of the particle shape, and the diffusion coefficient is insensitive to the shape under a low aspect ratio. This work is a fundamental study for the further research of the motion of microparticles based on Brownian motion.     

现代制造单元员工知识能力模型及实证研究

以人为中心的制造单元是现代制造环境下的先进生产方式,针对员工知识能力在现代制造单元制造过程中的重要作用,对制造单元员工知识能力的构成要素、要素间相互作用关系及结构特征进行了分析和研究,构建了包含基础知识能力、技术知识能力和知识表达能力的员工知识能力结构模型。采用结构方程模型对员工知识能力与制造单元生产绩效之间的影响关系和影响程度进行了实证研究,得出基础知识能力、技术知识能力和知识表达能力均对生产绩效具有正向影响作用,其中基础知识能力和技术知识能力的影响程度较大。对制造企业进行员工知识能力测评和生产绩效提高提供了一定的参考价值和科学依据。