基于广义特征分析的模数统一测试故障隔离方法

在采用诊断专家系统的测试系统中,由于模拟和数字测试存在的差异,为了达到测试模数混合电路的目的,往往需采用不同的库结构,从而导致模数混合电路的故障诊断建库麻烦,运行繁琐。针对该问题,提出了一种基于广义特征分析的模数混合统一测试故障隔离方法。在介绍了模拟和数字电路测试的统一表述的基础上,介绍了该方法在模数混合电路单元故障诊断专家系统中的实际应用及效果。     

武器装备项目混合筛选方法探讨及应用

针对目前武器装备项目筛选方法主观性较强、科学性差的现状,提出了整合AHP多目标评价和0-1整数规划的混合筛选法。该方法在完善的评价指标体系下项目进行了综合评价,建立了基于0-1整数规划的优化选择模型,以被选项目的综合评价值之和作为衡量各种筛选目标函数下的筛选解优劣性的标准,拓展了综合评价的实际应用,便于进行定量的项目筛选。给出了进行筛选的方法步骤,通过应用到空军某次项目筛选,验证了该方法对筛选决策改进效果。     

A UNIFIED FRAMEWORK FOR SITE SELECTION AND BUSINESS FORECASTING USING ANP

1. Introduction This paper discusses the use of the Analytic Network Process (ANP), a multi-criteria approach for decision making that allows mixing tangible and intangible objectives (Sarkis and Sanadaraj 2002), to the selection of a host city for a large international professional conference. The motivation for this study was the decision faced by the Latin American Studies Association (LASA), the world’s largest professional association for Latin American studies. This association h…     

网络资源并行分配的多目标优化博弈量子方法

针对复杂网络资源分配问题的求解极其困难等问题,借鉴量子场理论,提出了用于解决网络资源并行分配的多目标优化博弈量子方法.通过建立网络资源并行分配的博弈量子场数学模型,刻画了复杂网络中实体之间的交互行为(如竞争、合作、单向行为等),描述了随局势的变化各个实体采取的博弈策略和自治行为,博弈量子场中所有量子按数学模型中定义的规则运动直至达到稳定状态.利用量子博弈理论,还进一步分析了合作、竞争状态的演化动力学.提出的网络资源并行分配的多目标优化博弈量子算法具有高度的并行性、交互性和动态性,证明了多目标优化博弈量子模型的收敛性及其解的唯一性,给出了在宽带网络的带宽分配问题中的应用实例.     

A TWO-PHASE APPROACH TO FUZZY SYSTEM IDENTIFICATION

A two-phase approach to fuzzy system identification is proposed. The first phase produces a baseline design to identify a prototype fuzzy system for a target system from a collection of input-output data pairs. It uses two easily implemented clustering techniques: the subtractive clustering method and the fuzzy c-means (FCM) clustering algorithm. The second phase (fine tuning) is executed to adjust the parameters identified in the baseline design. This phase uses the steepest descent and recursive least-squares estimation methods. The proposed approach is validated by applying it to both a function approximation type of problem and a classification type of problem. An analysis of the learning behavior of the proposed approach for the two test problems is conducted for further confirmation.     

A new approach to model financial markets

This paper deals with the problem of how to take full use of prices information to model financial markets. A range decomposition technique is proposed to decompose the returns into two components. It is proved theoretically that these two components are bi-directional Granger causality, which makes it convenient to establish a vector autoregressive model (VAR). Both simulations and empirical studies are performed, and the results are consistent with the theoretical ones. The range decomposition approach presented in this paper is more efficient in information employment and suggests a new framework to model financial markets.     

A proximal point algorithm for a system of generalized mixed variational inequalities

This paper introduces and considers a new system of generalized mixed variational inequalities in a Hilbert space,which includes many new and known systems of variational inequalities and generalized variational inequalities as special cases.By using the two concepts of 77-subdifferential andη-proximal mappings of a proper function,the authors try to demonstrate that the system of generalized mixed variational inequalities is equivalence with a fixed point problem.By applying the equivalence, a new and innovativeη-proximal point algorithm for finding approximate solutions of the system of generalized mixed variational inequalities will be suggested and analyzed.The authors also study the convergence analysis of the new iterative method under much weaker conditions.The results can be viewed as a refinement and improvement of the previously known results for variational inequalities.     

Design for availability: A holistic approach to create value for manufacturers and customers of capital goods

This research introduces a holistic framework called Design for Availability that uses the principles of Lean Sigma and Design for X to cost-effectively optimize the availability of capital goods(i.e., technical systems used in the production of end-products or -services such as medical systems, airplanes,and manufacturing equipment) throughout their entire lifetime.Manufacturers require such a framework because users of capital goods increasingly insist on high system availability levels against reduced lifetime costs.The Design for Availability framework allows manufacturers to determine the current status of system availability and associated lifetime costs,and to identify opportunities to create additional value for themselves and their customers.A case study at a global manufacturer of capital goods in the food processing industry illustrates how the framework can be used in practice and to what extent the manufacturer and customers may profit from applying Design for Availability.     

A novel approach to characterizing hesitations in intuitionistic fuzzy numbers

Building upon the concept of D_a operator introduced by Atanassov(1989),this article proposes an improved objective approach and a hybrid approach to operationalize D_a so that the hesitation in an intuitionistic fuzzy number(IFN) can be further refined and characterized.Numerical experiments are carried out to demonstrate the features and novelty of the proposed approach compared to existing methods in the literature.The aim is to furnish an effective way to refine hesitations in intuitionistic fuzzy assessments for more reliable and confident decision aids.     

A partial robust optimization approach to inventory management for the offline-to-online problem under different selling prices

This study examines an optimal inventory strategy when a retailer markets a product at different selling prices through a dual-channel supply chain, comprising an online channel and an offline channel. Using the operating pattern of the offline-to-online (O2O) business model, we develop a partial robust optimization (PRO) model. Then, we provide a closed-form solution when only the mean and standard deviation of the online channel demand distribution is known and the offline channel demand follows a uniform distribution (partial robust). Specifically, owing to the good structural properties of the solution, we obtain a heuristic ordering formula for the general distribution case (i.e., the offline channel demand follows a general distribution). In addition, a series of numerical experiments prove the rationality of our conjecture. Moreover, after comparing our solution with other possible policies, we conclude that the PRO approach improves the performance of incorporating the internet into an existing supply chain and, thus, is able to adjust the level of conservativeness of the solution. Finally, in a degenerated situation, we compare our PRO approach with a combination of information approach. The results show that the PRO approach has more “robust” performance. As a result, a reasonable trade-off between robustness and performance is achieved.     

A Markovian approach to determining process means with dual quality characteristics

This paper studies a production system where products are produced continuously and whose specification limits are specified for screening inspection. In this paper, we consider dual quality characteristics and different costs associated with each quality characteristic that falls below a lower specification limit or above an upper specification limit. Due to these different costs, the expected total profit will greatly depend on the process parameters, especially a process mean. This paper develops a Markovian-based model for determining the optimum process means with the consideration of dual quality characteristics in a single-stage system. The proposed model is then illustrated through a numerical example and sensitivity analysis is performed to validate the model. The results showed that the optimum process mean for both quality characteristics have a significant effect on the performance of the system. Since the literature survey shows that dealing with multi-quality characteristics is extremely limited, the proposed model, coupled with the Markovian approach, provides a unique contribution to this field.     

A situation calculus based approach to dynamic management of e-business services

In the era of e-businesses, the traditional business services are greatly challenged by the ever-increasing demands from customers with various backgrounds and personalities. Large numbers of new e-businesses are driven by the needs of customers. The existing dynamics inherent in the customer needs require the corresponding dynamic management of services. Attempting to respond to customers in a rapid and intelligent way, this paper proposes a situation calculus based approach for dynamically managing e-Business services in the ubiquitous environment. By employing the formalism of the situation calculus to enable intelligence and automation, the approach can implement. the functions of service automatic composition and model verification. These functions will improve the degree of customer-orientation and enable fast responsiveness in the emerging e-service systems.     

The limit theorem for dependent random variables with applications to autoregression models

This paper studies the autoregression models of order one, in a general time series setting that allows for weakly dependent innovations. Let {X _t } be a linear process defined by X _t = Σ _k=0^∞ψ _k ɛ _t−k , where {ψ _k , k ≥ 0} is a sequence of real numbers and {ɛ _k , k = 0, ±1, ±2, …} is a sequence of random variables. Two results are proved in this paper. In the first result, assuming that {ɛ _k , k ≥ 1} is a sequence of asymptotically linear negative quadrant dependent (ALNQD) random variables, the authors find the limiting distributions of the least squares estimator and the associated regression t statistic. It is interesting that the limiting distributions are similar to the one found in earlier work under the assumption of i.i.d. innovations. In the second result the authors prove that the least squares estimator is not a strong consistency estimator of the autoregressive parameter α when {ɛ _k , k ≥ 1} is a sequence of negatively associated (NA) random variables, and ψ ₀ = 1, ψ _k = 0, k ≥ 1.     

A new statistical approach for the analysis of uncertain systems

This paper addresses the issues of conservativeness and computational complexity of probabilistic robustness analysis. The authors solve both issues by defining a new sampling strategy and robustness measure. The new measure is shown to be much less conservative than the existing one. The new sampling strategy enables the definition of efficient hierarchical sample reuse algorithms that reduce significantly the computational complexity and make it independent of the dimension of the uncertainty space. Moreover, the authors show that there exists a one to one correspondence between the new and the existing robustness measures and provide a computationally simple algorithm to derive one from the other. This research is supported in part by grants from NASA (NCC5-573), LEQSF (NASA /LEQSF(2001-04)-01), the NNSFC Young Investigator Award for Overseas Collaborative Research (60328304) and a NNSFC grant (10377004).     

A relative error estimation approach for multiplicative single index model

As an alternative to absolute error methods, such as the least square and least absolute deviation estimations, a product relative error estimation is proposed for a multiplicative single index regression model. Regression coefficients in the model are estimated via a two-stage procedure and their statistical properties such as consistency and normality are studied. Numerical studies including simulation and a body fat example show that the proposed method performs well.     

A hybrid approach for minimizing makespan in permutation flowshop scheduling

This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan (total completion time). To solve scheduling problems, a combination of Decision Tree (DT) and Scatter Search (SS) algorithms are used. Initially, the DT is used to generate a seed solution which is then given input to the SS to obtain optimal / near optimal solutions of makespan. The DT used the entropy function to convert the given problem into a tree structured format / set of rules. The SS provides an extensive investigation of the search space through diversification. The advantages of both DT and SS are used to form a hybrid approach. The proposed algorithm is tested with various benchmark datasets available for flowshop scheduling. The statistical results prove that the proposed method is competent and efficient for solving flowshop problems.     

面向复杂场景真实感绘制的并行简化方案

在大规模复杂场景的真实感绘制中,如何在简化效果与简化速度之间进行折衷一直是各种简化算法所面临的重要技术难题.基于快速局域网链接的多主机系统,提出了一种面向复杂场景真实感绘制的并行简化方案.该方案根据各绘制通道的实时负载情况,对共享简化池中的硬件资源进行自动分配,在一定程度上实现了动态负载平衡.同时,在每个绘制通道内部,对应已经集成的各种简化算法,基于"简化率/简化时间"历史数据进行动态调度,以实现最优"简化率/简化时间".最后,使用火电厂模型在实际环境下验证了算法的有效性,取得了较好的并行简化效果.     

A novel approach for unlabeled samples in radiation source identification

Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learn...     

Novel approach to GPS/SINS integration for IMU alignment

Strapdown inertial navigation system(SINS) requires an initialization process that establishes the relationship between the body frame and the local geographic reference.This process,called alignment,is generally used to estimate the initial attitude angles.This is possible because an accurate determination of the inertial measurement unit(IMU) motion is available based on the measurement obtained from global position system(GPS).But the update frequency of GPS is much lower than SINS.Due to the non-synchronous data streams from GPS and SINS,the initial attitude angles may not be computed accurately enough.In addition,the estimated initial attitude angles may have relatively large uncertainties that can affect the accuracy of other navigation parameters.This paper presents an effective approach of matching the velocities which are provided by GPS and SINS.In this approach,a digital high-pass filter,which implements a pre-filtering scheme of the measured signal,is used to filter the Schuler cycle of discrete velocity difference between the SINS and GPS.Simulation results show that this approach improves the accuracy greatly and makes the convergence time satisfy the required accuracy.