The most economic setting for a uniformly shifting process

A method for finding the optimal setting for a uniformly-shifting process mean is developed and an algorithm for solution (suitable for microcomputer implementation) is presented. A determination of the number of parts to be made before resetting, and the level to which the process is to be reset is made given costs for improperly sized parts and for process resetting.     

Hybridization of genetic algorithm with immune system for optimization problems in structural engineering

Optimization is the task of getting the best solution among the feasible solutions. There are many methods available to obtain an optimized solution. Genetic algorithm (GA), which is a heuristic type of optimization method, is discussed in this paper. The focus of the paper is the use of GA for large dimensionality design problems, where computational efficiency is a major concern. The motivation of this paper is to hybridize GA with an immune system mechanism by avoiding the implementation of penalty constants, which are highly sensitive to the choice of algorithm parameters. The principal advantage of the immune system is in its seamless integration with GA-based search for optimal design. It is being hybridized with the immune system mechanism. The hybrid GA and immune system is applied for the design of the optimal mix of high-performance concrete (HPC), which is still based on trial mix and for which no rigorous mathematical approach is available. As such, to infer the values of strength and slump, a wavelet back propagation neural network or wavelet neural network is used for any HPC mix. It is necessary to minimize the cost of HPC/unit weight of HPC subjected to strength and slump constraints. The interwoven algorithm is also applied to obtain optimal sectional areas for minimum weight of space trusses subjected to static loading. Formian programming language is used for the generation of the space trusses, and Feast package is used for the static analysis of the trusses. In addition to the induction of immune system in the GA for constraint handling, it is being applied in this particular application for improving the search of GA in obtaining the best optimal solution. For obtaining the optimal sections of space trusses subjected to earthquake loading, SAP 90 package is used, and reliable results are obtained.     

An omni-directional mobile millimeter-sized microrobot with 3-mm electromagnetic micromotors for a micro-factory

This paper presents an omni-directional mobile microrobot for micro-assembly in a micro-factory. A novel structure is designed for omni-directional movement with three normal wheels. The millimeter-sized microrobot is actuated by four electromagnetic micromotors whose size is 3.1 mm × 3.1 mm × 1.4 mm. Three of the micromotors are for translation and the other one is for steering. The micromotor rotors are designed as the wheels to reduce the microrobot volume. A piezoelectric micro-gripper is fabricated for grasping micro-parts. The corresponding kinematics matrix is analyzed to prove the omni-directional mobility. A control system composed of two CCD cameras, a host computer and circuit board is designed. The macro camera is for a global view and the micro camera is for local supervision. Unique location methods are proposed for different scenarios. A microstep control approach for the micromotors is presented to satisfy the requirement of high positioning accuracy. The experiment demonstrates the mobility of the microrobot and the validity of the control system.     

Dynamic policy in linear models with rational expectations of future events: A computer package

A computer package is presented, called POREM, for policy optimisation of linear dynamic, continuous-time models with constant coefficients and rational expectations of future events, based on infinite horizons and quadratic preferences. It is possible to calculate cooperative, decentralised Nash and decentralised Stackelberg outcomes and for each outcome it is possible to allow for pre-commitment and for lack of pre-commitment vis-à-vis private sector agents. It is possible to allow for hierarchical games, that is to allow for a group of Stackelberg leaders and a group of Stackelberg followers. The input of the model is very user-friendly and can be done with the aid of mnemonics. The package is programmed in PORTRAN77 and a single-precision version is available for personal computers     

Solution-Domain-Decomposition Method for Heat Transfer Problem Using Parallel Distributed Computing

Solution-domain-decomposition (SDD) method is formulated for solving heat transfer problem and generalized for solving multi-domain problem. A generalized algorithm is suggested for parallel and distributing computation. Chebyshev expansion on the dependent variables is used for pseudospectral approximation of the governing equation in this study. Linear superposition principle is adapted to incorporate the interactions between the subdomains. By effective subdivision of computational domain, significant computational efficiency and computational memory savings are accomplished without losing spectral accuracy of the solution. Owing to independent characteristics of the subdomains. the scheme is well suited for multi-processor machines. Convergence study reveals that spectra! accuracy is still conserved for the multi-domain calculation. The calculation domain is divided up to 8 subdomains and calculation is distributed up to independent CPUs. Significant speed-up ratio is obtained by distributing the subtasks through the network.     

Automatic background music generation based on actors' mood and motions

Since adding background music and sound effects even to short animations is not simple, an automatic music generation system would help improve the total quality of computer generated animations. This paper describes a prototype system which automatically generates background music and sound effects for existing animations. The inputs to the system are music parameters (mood types and musical motifs) and motion parameters for individual scenes of an animation. Music is generated for each scene. The key for a scene is determined by considering the mood type and its degree, and the key of the previous scene. The melody for a scene is generated from the given motifs and the chord progression for the scene which is determined according to appropriate rules. The harmony accompaniment for a scene is selected based on the mood type. The rhythm accompaniment for a scene is selected based on the mood type and tempo. The sound effects for motions are determined according to the characteristics and intensity of the motions. Both the background music and sound effects are generated so that the transitions between scenes are smooth.     

An alternative use of the Riccati recursion for efficient optimization

In optimization routines used for on-line Model Predictive Control (MPC), linear systems of equations are solved in each iteration. This is true both for Active Set (AS) solvers as well as for Interior Point (IP) solvers, and for linear MPC as well as for nonlinear MPC and hybrid MPC. The main computational effort is spent while solving these linear systems of equations, and hence, it is of great interest to solve them efficiently. In high performance solvers for MPC, this is performed using Riccati recursions or generic sparsity exploiting algorithms. To be able to get this performance gain, the problem has to be formulated in a sparse way which introduces more variables. The alternative is to use a smaller formulation where the objective function Hessian is dense. In this work, it is shown that it is possible to exploit the structure also when using the dense formulation. More specifically, it is shown that it is possible to efficiently compute a standard Cholesky factorization for the dense formulation. This results in a computational complexity that grows quadratically in the prediction horizon length instead of cubically as for the generic Cholesky factorization.     

An execution profiler for modular programs

In modular programs, groups of routines constitute conceptual abstractions. A method for providing execution profiles for such programs is presented. The central idea is that the execution time for a routine is charged to the routines that call it. The implementation of this method by a profiler called gprof is described. The techniques used to gather the necessary information about the timing and structure of the program are given, as is the processing used to propagate routine execution times along arcs of the call graph of the program. The method for displaying the profile to the user is discussed. Experience using the profiles for hand-tuning large programs is summarized. Additional uses for the profiles are suggested.     

Discrete-time spare ordering policy with randomized lead times and discounting

The paper considers a generalized discrete‐time order‐replacement model for a single unit system, which is subject to random failure when in operation. Two types of discrete randomized lead times are considered for a spare unit; one is for regular (preventive) order and another is for expedited (emergency) order. The model is formulated based on the discounted cost criterion. The underlying two‐dimensional optimization problem is reduced to a simple one‐dimensional one and then the optimal ordering policy for the spare unit is characterized under two extreme conditions: (i) unlimited inventory time and (ii) zero inventory time for the spare unit. A numerical example is used to determine the optimal spare‐ordering policy numerically and to examine the sensitivity of the model parameters.     

Theories for mechanical proofs of imperative programs

For convenient application of a first-order theorem prover to verification of imperative programs, it is important to encapsulate the operational semantics in generic theories. The possibility to do so is illustrated by two theories for the Boyer-Moore theorem prover Nqthm.The first theory is an Nqthm version of the classical while-theorem. Here the main interest is to show how one can use Nqthm's facilities to constrain and to functionally instantiate for the development and application of a generic theory. The theory is illustrated by a linear search program.The second theory is a finitary approach to progress for shared-memory concurrent programs. It is illustrated by Peterson's algorithm for mutual exclusion of two processes. The proof of progress for Peterson's algorithm is new. The assertion of bounded fairness is slightly stronger than the conventional notion of weak fairness. This new concept may have other applications.     

Optimal robust classifiers

Qualitatively, a filter is said to be “robust” if its performance degradation is acceptable for distributions close to the one for which it is optimal, that is, the one for which it has been designed. This paper adapts the signal-processing theory of optimal robust filters to classifiers. The distribution (class conditional distributions) to which the classifier is to be applied is parameterized by a state vector and the principle issue is to choose a design state that is optimal in comparison to all other states relative to some measure of robustness. A minimax robust classifier is one whose worst performance over all states is better than the worst performances of the other classifiers (defined at the other states). A Bayesian robust classifier is one whose expected performance is better than the expected performances of the other classifiers. The state corresponding to the Bayesian robust classifier is called the maximally robust state. Minimax robust classifiers tend to give too much weight to states for which classification is very difficult and therefore our effort is focused on Bayesian robust classifiers. Whereas the signal-processing theory of robust filtering concentrates on design with full distributional knowledge and a fixed number of observation variables (features), design via training from sample data and feature selection are so important for classification that robustness optimality must be considered from these perspectives—in particular, for small samples. In this context, for a given sample size, we will be concerned with the maximally robust state-feature pair. All definitions are independent of the classification rule; however, applications are only considered for linear and quadratic discriminant analysis, for which there are parametric forms for the optimal discriminants.     

Finite horizon planning for a production system with permitted shortage and fixed-interval deliveries

Raw material ordering policy and the manufacturing batch size for frequent deliveries of finished goods for a finite horizon plays a significant role in managing the supply chain logistics economically. This research develops an ordering policy for raw materials and determines an economic batch size for a product in a manufacturing system that supplies finished products to customers for a finite planning horizon. Fixed quantities of finished products are delivered to customers frequently at a fixed interval of time. In this model, an optimal multi-ordering policy for procurement of raw materials and production cycle time for a two-stage production and supply system is developed to minimize the total cost incurred due to raw materials and finished goods inventories. The problem is then extended to compensate for the lost sales of finished products. A closed-form solution to the problem is obtained for the minimal total cost. A lower bound on the optimal solution is also developed for problem with lost sale. It is shown that the solution and the lower bound are consistently tight.     

An extension of an over-dispersion test for count data

While over-dispersion in capture-recapture studies is well known to lead to poor estimation of population size, current diagnostic tools to detect the presence of heterogeneity have not been specifically developed for capture-recapture studies. To address this, a simple and efficient method of testing for over-dispersion in zero-truncated count data is developed and evaluated. The proposed method generalizes an over-dispersion test previously suggested for un-truncated count data and may also be used for testing residual over-dispersion in zero-inflation data. Simulations suggest that the asymptotic distribution of the test statistic is standard normal and that this approximation is also reasonable for small sample sizes. The method is also shown to be more efficient than an existing test for over-dispersion adapted for the capture-recapture setting. Studies with zero-truncated and zero-inflated count data are used to illustrate the test procedures.     

移动式起重机吊装路径规划仿真平台设计

针对移动式起重机吊装路径规划的过程、结果难以直观显示,算法性能测评困难等问题,设计并实现了一款面向移动式起重机吊装路径规划的仿真平台。给出平台的系统框架,具体介绍路径规划、可视化的设计,用一个实例验证其可用性和有效性。实例表明,平台可实时显示搜索树(图)的生长过程、规划所得的路径及其吊装过程,同时可进行算法性能的自动测试,有助于吊装路径规划算法的研究。     

Supply chain network design using an integrated neuro-fuzzy and MILP approach: A comparative design study

In this study, an integrated supply chain (SC) design model is developed and a SC network design case is examined for a reputable multinational company in alcohol free beverage sector. Here, a three echelon SC network is considered under demand uncertainty and the proposed integrated neuro-fuzzy and mixed integer linear programming (MILP) approach is applied to this network to realize the design effectively. Matlab 7.0 is used for neuro-fuzzy demand forecasting and, the MILP model is solved using Lingo 10.0. Then Matlab 7.0 is used for artificial neural network (ANN) simulation to supply a comparative study and to show the applicability and efficiency of ANN simulation for this type of problem. By evaluating the output data, the SC network for this case is designed, and the optimal product flow between the factories, warehouses and distributors are calculated. Also it is proved that the ANN simulation can be used instead of analytical computations because of ensuring a simplified representation for this method and time saving.     

主蒸汽母管压力自动控制及其实施方案

针对主蒸汽母管压力自动控制难以实现的问题,设计了一种独立的母管压力控制系统,规划了它与各台锅炉DCS之间的信息交换方案,提出了调压锅炉在线识别及蒸汽母管总负荷给定的计算方法;应用等微增率准则进行锅炉负荷的优化分配,得到一种能兼顾控制品质和机组经济性的母管压力自动控制方案。应用结果表明,该方案易于实现,且有很高的控制精度和很好的节煤效果。     

Ⅱ-VI族半导体的掺杂与补偿

采用离散变分局域密度泛函（ＤＶＬＤＦ）方法及团簇模型研究了ＩＩＶＩ族半导体中的掺杂及补偿问题。研究了ＺｎＳｅ中的Ｎ，Ｐ，Ａｓ，Ｆ，Ｃｌ，Ｂｒ等杂质不同的结构和电子性质。发现ＮＳｅ在ＺｎＳｅ中没有ＪａｈｎＴｅｌｅｒ形变，而ＰＳｅ和ＡｓＳｅ存在这一形变，从而判定Ｎ是其中最有效的ｐ型掺杂剂。对于ｎ型的ＺｎＳｅ，研究发现Ｃｌ是最有效的施主杂质。根据对缺陷复合体的计算，发现ＮＳｅＺｎＶＳｅ及诸如ＮＳｅＺｎｉｎｔ的多Ｎ集团对于ｐ型ＺｎＳｅ的补偿有着重要作用。研究表明，对于ＺｎＴｅ来说，Ｃｌ不是有效的施主杂质，而Ｎ是有效的受主杂质。     

基于能量异构的无线传感器网络分布式成簇算法

能量异构是无线传感器网络中普遍存在的现象.为了解决分簇算法在能量异构网络中节点能量负载不平衡的问题,提出了一种适用于能量异构环境的无线传感器网络分布式成簇算法.算法基于簇的本地信息给出了评估节点当前能量水平的相对估计因子,用以调整节点在簇头轮转过程中出任簇头的概率及其轮转周期,从而使当前高能量节点获得更多担任簇头的机会,实现整个网络能量均衡消耗,最大限度地延长网络生命周期.实验仿真结果表明,在能量异构网络环境中,新算法表现出很好的适应性和可扩展性,其能量有效性得到了很大的提高,节点能量负载更加均衡.     

分布式远程教育资源网的设计及通信机制

远程教育是互联网中的重要应用,分布式的资源组织模式与大范围的教育受众覆盖是部署实施的关键。该文通过分布式资源网以实现资源共享,给出资源发布的通信过程和方法,研究了提高资源同步效率的端系统组播机制,设计了兼容IPv4和IPv6的 IPDVB传输系统,用来满足向卫星接收终端用户递送资源的需求。