Study on a piezoelectric micropump for the controlled drug delivery system

We present the design of a new controlled drug delivery system potential for in vitro injection of diabetics. The system incorporates some integrated circuit units and microelectromechanical system devices, such as micropump, microneedle array and microsensor. Its goal is to achieve safer and more effective drug delivery. Moreover, a valveless micropump excited by the piezoelectric actuator is designed for the drug delivery system, and a simple fabrication process is proposed. A dynamic model is developed for the valveless micropump based upon the mass conservation. To characterize the micropump, a complete electro-solid-fluid coupling model, including the diffuser/nozzle element and the piezoelectric actuator, is built using the ANSYS software. The simulation results show that the performance of micropump is in direct proportion to the stroke volume of the pump membrane and there is an optimal thickness of the piezoelectric membrane under the 500 V/mm electric field. Based on this simulation model, the effects of several important parameters such as excitation voltage, excitation frequency, pump membrane dimension, piezoelectric membrane dimension and mechanical properties on the characteristics of valveless micropump have been investigated.     

Minimizing maximum tardiness and delivery costs in a batched delivery system

High delivery costs usually urge manufacturers to dispatch their jobs in batches. However, dispatching the jobs in batches can have profound negative effects on important scheduling objective functions such as minimizing maximum tardiness. This paper considers a single machine scheduling problem with the aim of minimizing the maximum tardiness and delivery costs in a single-machine scheduling problem with batched delivery system. A mathematical model is developed for this problem which can serve to solve it with the help of a commercial solver. However, due to the fact that this model happens to be a mixed integer nonlinear programming model the solver cannot guarantee to reach the global solution. For this reason, a branch and bound algorithm (B&B) is presented to obtain the global solution. Besides, a heuristic algorithm for calculation of the initial upper bound is introduced. Computational results show that the algorithm can be beneficial for solving this problem, especially for large size instances.     

Solving the vehicle routing problem with drone for delivery services using an ant colony optimization algorithm

E-commerce and logistics companies are facing important challenges to satisfy the rapid growth of customer demands. Unmanned aerial vehicles such as drones are an emerging technology that are very useful to cope with rising customer expectations of fast, flexible, and reliable delivery services. Drones work in tandem with trucks to perform parcel delivery, which have proven to reduce costs, CO2 emissions, and delivery times. This research proposes a mixed integer programming formulation to address the Vehicle Routing Problem with Drone (VRPD) by assigning customers to drone-truck pairs, determining the number of dispatching drone-truck units, and obtaining optimal service routes while the fixed and travel costs of both vehicles are minimized. Given the NP-hard nature of the VRPD, an ant colony optimization (ACO) algorithm is elaborated to solve this problem. Two novel methods are proposed to investigate the efficiency of the drone-truck combination by allowing the drones to perform additional delivery services to only one feasible customer and also multiple feasible customers while the truck waits at a customer location. Experimental results show that the proposed ACO algorithm can effectively solve the VRDP for different size instances and different customer location distributions, and is successful in providing timely solutions for small test instances within 1% of the optimal solutions. Finally, experimentation also reveals that the ACO algorithm outperforms the classical VRP by obtaining cost-savings of over 30% for large instances.     

软件容错模型中反向与正向调度算法研究

为了保证实时系统在发生任务失效时仍旧能够正常地工作,通常采用软件容错模型来提高实时系统的容错能力。本文分析了软件容错模型中的BCE容错调度算法,针对该算法中的反向调度和正向调度两个过程,给出了RMB、DMB、FDMB、EDFB四种反向调度算法和RMF、EDFF两种正向调度算法,并指出了反向调度和正向调度相互协调的特性。将各种算法在BCE算法中进行模拟,结果表明,EDFF正向调度算法能够与四种反向调度算法更好地协调,从而获得比RMF正向调度算法更高的调度性能;模拟结果还表明,四种反向调 度算法在BCE算法中的性能相近。最后,本文得出了RMB（或DMB）反向调度算法与EDFF正向调度算法的组合较适用于软件容错模型的结论。     

工程车辆燃油管理系统设计与实现

设计一种适用于工程车辆的燃油管理系统,讨论了如何利用高精度液位传感器实时获取车辆的燃油信息,并将该燃油信息与车辆实时工作状态信息对应起来。探讨了如何应用无线网络将车辆燃油及工况信息传送到服务器,再通过专门的油耗管理软件进行分析,生成各种形式的油耗与工况统计报表,以反映某一车辆在其各种工作状态下的燃油消耗状况,实现工程车辆燃油的信息化管理。     

Development of a decision support system for service delivery

Most traditional service delivery models were developed to solve single objective problems. While the disaster recovery task usually needed the consideration of multiple objectives (e.g. the total waiting time, the total weighted time of travelling, the fairness of resource distribution). Therefore, the traditional models can't completely support the disaster recovery task. In the real world, the assignments of service delivery are always performed by the vehicle dispatchers or truck drivers based on their experiences. However, the intuitive assignment methods are lacking a mathematic basis. They may be efficient but not necessarily effective. In order to provide an efficient and effective decision support system, this study has focused on the general expression of performances for service delivery and modifies the traditional delivery models by rule-inference techniques. The objective of this paper is to describe how a decision support system has been developed to achieve the performance requirement in emergency service delivery tasks, while traditional routing algorithms are modified and software techniques are utilized under a PC-based environment. Furthermore, some directions for future improvement are proposed.     

Greedy algorithms for a class of knapsack problems with binary weights

In this article we identify a class of two-dimensional knapsack problems with binary weights and related three-criteria unconstrained combinatorial optimization problems that can be solved in polynomial time by greedy algorithms. Starting from the knapsack problem with two equality constraints we show that this problem can be solved efficiently by using an appropriate partitioning of the items with respect to their binary weights. Based on the results for this problem we derive an algorithm for the three-criteria unconstrained combinatorial optimization problem with two binary objectives that explores the connectedness of the set of efficient knapsacks with respect to a combinatorial definition of adjacency. Furthermore, we prove that our approach is asymptotically optimal and provide extensive computational experiments that shows that we can solve the three-criteria problem with up to one million items in less than half an hour. Finally, we derive an efficient algorithm for the two-dimensional knapsack problems with binary constraints that only takes into account the results we obtained for the unconstrained three-criteria problem with binary weights.     

基于剩余装载能力的蚁群算法求解同时送取货车辆路径问题

建立了带车辆最大行程约束的同时送取货车辆路径问题的混合整数规划模型; 采用了基于排序的蚂蚁系统和最大最小蚂蚁系统的信息素更新策略; 设计了基于车辆剩余装载能力的启发信息策略, 可在满足车辆负载的限制下, 提高车辆的负载利用率; 并在改进阶段使用了节点交换的局部搜索策略,以提高算法收敛速度. 仿真结果表明本文算法能够在可接受的计算时间内得到满意解.     

On the k-NN performance in a challenging scenario of imbalance and overlapping

A two-class data set is said to be imbalanced when one (minority) class is heavily under-represented with respect to the other (majority) class. In the presence of a significant overlapping, the task of learning from imbalanced data can be a very difficult problem. Additionally, if the overall imbalance ratio is different from local imbalance ratios in overlap regions, the task can become in a major challenge. This paper explains the behaviour of the k-nearest neighbour (k-NN) rule when learning from such a complex scenario. This local model is compared to other machine learning algorithms, attending to how their behaviour depends on a number of data complexity features (global imbalance, size of overlap region, and its local imbalance). As a result, several conclusions useful for classifier design are inferred.     

A genetic algorithm for minimizing total tardiness/earliness of weighted jobs in a batched delivery system

This paper endeavors to solve a novel complex single-machine scheduling problem using two different approaches. One approach exploits mathematical modeling, and the other is based upon genetic algorithms. The problem involves earliness, tardiness, and inventory costs and considers a batched delivery system. The same conditions might apply to some real supply chains, in which delivery of products is conducted in a batched form and with some costs. In such delivery systems, the act of buffering the products can have both positive effects (i.e., decreasing the delivery costs and early jobs) and negative ones (i.e., increasing the number of tardy and holding costs). Accordingly, the proposed solution takes into account both effects and tries to find a trade-off between them to hold the total costs low. The suggestions are compared to existing solutions for older non-batched systems and have illustrated outperformance.     

On scheduling transactions in a grid processing system considering load through Ant Colony Optimization

Load balanced transaction scheduling problem is an important issue in distributed computing environments including grid system. This problem is known to be NP-hard and can be solved by using heuristic as well as any meta-heuristic method. We ponder over the problem of the load balanced transaction scheduling in a grid processing system by using an Ant Colony Optimization for load balancing. The problem that we consider is to achieve good execution characteristics for a given set of transactions that has to be completed within their given deadline. We propose a transaction processing algorithm based on Ant Colony Optimization (ACO) for load balanced transaction scheduling. We modify two meta-heuristic along with ACO and three heuristic scheduling algorithms for the purpose of comparison with our proposed algorithm. The results of the comparison show that the proposed algorithm provides better results for the load balanced transaction scheduling in the grid processing system.     

On the minimum of a positive polynomial over the standard simplex

We present a new positive lower bound for the minimum value taken by a polynomial P$P$ with integer coefficients in k$k$ variables over the standard simplex of R^k${\mathbb{R}}^k$, assuming that P$P$ is positive on the simplex. This bound depends only on the number of variables k$k$, the degree d$d$ and the bitsize τ$\tau$ of the coefficients of P$P$ and improves all the previous bounds for arbitrary polynomials which are positive over the simplex.     

跨层穿梭车双提升机系统料箱拣选任务调度

为提高跨层穿梭车系统料箱拣选出库效率,降低任务出库超时率,建立了跨层穿梭车双提升机系统出库任务调度数学模型,并将任务出库期限引入调度策略。在此基础上,使用蚁群-粒子群双层智能优化算法对模型进行了求解,引入随机变异对粒子群算法进行改进,提出使用置换复杂度对粒子变异程度进行控制,避免算法早熟收敛。利用MATLAB进行过程仿真,获得各调度方案的出库总时间和任务超时信息。通过实验证明该策略能更好地适应电商环境下复杂的出库任务调度要求,得到更为合理的任务调度方案。     

On bounds for the existence of a bistable controller

By we shall denote the set of polynomials nonzero on the closed disk {z: |z|r}. The problem to be studied is given as follows.Avoidance Problem. Given polynomials A and B, nonnegative integers n, m, and positive numbers r₁, r₂, r₃, determine if there exist , with degrees n and m, respectively, such that AP+BQ is in .It is shown that the Avoidance Problem for r₁=r₂=r₃=1 is equivalent to the well-known Bistable Stabilization Problem for a large class of control systems.Also, a general class of optimization problems for solving the Avoidance Problem is introduced. The class of optimization problems includes problems with sets more general than disks . In addition, an algorithm is proposed for solving these optimization problems, and numerical experience is reported.As an application, bistable controllers for the plant are shown to exist for values of β as low as 0.0246, thus improving on previously reported results by Blondel et al. for this difficult case.Furthermore, it is shown how in some cases it is possible to find optimal controllers by using a combination of numerical and algebraic techniques, and a variant of the algorithm already introduced. As an example, a low order controller is found for for the case when .This method could be applied to other problems where the plant depends on a parameter.     

Assessment of gradient-based iterative learning controllers using a multivariable test facility with varying interaction

A multiple input, multiple output (MIMO) experimental test facility has been developed for the evaluation, benchmarking and comparison of iterative learning control (ILC) strategies. The system addresses the distinct lack of experimental studies for the multivariable case and enables controller performance and robustness to be rigorously investigated over a broad range of operating conditions. The electromechanical facility is multi-configurable with up to 3 inputs and permits both exogenous disturbance injection and a variable level of coupling to be applied between input and output pairs. To confirm its suitability for evaluation and comparison of ILC, theoretical results are derived for two popular forms of gradient-type ILC algorithm, linking interaction with fundamental performance limitations. The test facility is then used to establish how well theoretical predictions match experimental results. The analysis is then extended to provide solutions to address this performance degradation, and these are again confirmed using the test facility.     

Impact of climate model uncertainties on socioeconomics: A case study with a medium mitigation scenario

Carbon dioxide (CO₂) emissions are strongly associated with economy. The amount of CO₂ that human society can emit in order to achieve a climate target depends on physical and biogeochemical properties in the climate system; these vary among climate models or earth system models (ESMs). Thus, uncertainties in such models, the spread remained when we both consider the range of existing models and observational data for key variables, can affect analysis of future global economy. In this study, using a computable general equilibrium model, we analyze the impacts on socioeconomics under a medium climate mitigation scenario by following three emission pathways considering uncertainties in existing ESMs (the lower and upper bounds as well as the mean). The results indicate that the impacts are larger in the lower bound case, despite the fact that economic and energy demands will increase continuously. In a comparison between the upper and lower bound cases, the carbon price of the latter case is approximately three times higher than that of the former case in 2100. Consequently, primary/final energy demand in the lower bound case becomes 1.0%/14% lower, and more renewables and carbon capture and storage are required to be used. Furthermore, the gross domestic product in the lower bound case is 4.1% smaller. Thus, within the scenario, the socioeconomic impacts caused by ESM uncertainties are not insignificant, but are smaller than the differences in annual and cumulative emissions.     

Performance Comparison of Mirrored Disk Scheduling Methods with a Shared Non-Volatile Cache

Mirrored disks or RAID1 is a popular disk array paradigm, which in addition to fault-tolerance, doubles the data access bandwidth. This is important in view of rapidly increasing disk capacities and the slow improvement in disk access time. Caching of dirty data blocks in a non-volatile storage (NVS) cache allows the destaging of dirty blocks to be deferrable, so as to improve the response time of read requests by giving them a higher priority than write requests. Destaging of dirty blocks in batches to take advantage of disk geometry entails in lowered disk utilization due to writes and improved performance for reads. Polyzois et al. [12] propose a scheduling policy for mirrored disks equipped with an NVS cache, so that one disk processes read requests, while the other disk is processing a write batch according to the CSCAN policy. We propose an improved scheduling policy as follows: (i) eliminating the forced idleness caused by the batch processing paradigm for write requests, i.e., allowing write requests to be processed individually; (ii) using SATF or even an exhaustive search, to reduce destaging time compared to CSCAN; (iii) introducing a threshold for the number of read requests, which when exceeded defers the destaging of dirty blocks. We compare these two scheduling policies with each other and also against prioritizing the processing of reads versus writes: (i) the head-of-the-line (HOL) priority queueing discipline, (ii) SATF with conditional priorities. It follows from simulation results that the new method outperforms Polyzois' method, which is even outperformed by the HOL priority policy. SATF with conditional priorities slightly outperforms the proposed method from the viewpoint of its throughput and response time, but is susceptible to more variability in response time. Recommended by: Ahmed Elmagarmid     

实时系统调度算法综述

在多道程序环境下,主存中有多个进程,其数目往往多于处理机数目。操作系统通过处理机调度程序,按照某种调度算法动态地把处理机分配给就绪队列中的一个进程,使之执行。处理机是重要的计算机资源,提高处理机的利用率及改善系统性能（吞吐量、响应时间）,很大程度上取决于处理机调度性能的好坏,因而操作系统的调度算法是非常重要的。通过研究基本的操作系统作业（进程）调度算法,详尽分析和对比这些调度算法的优势和劣势。最后对新兴的实时系统研究现状进行介绍和展望,为以后实时系统调度算法研究提供了有效的参考价值。     

混合型实时容错调度算法的设计和性能分析

以往文献中研究的实时容错调度算法都只能调度单一的具有容错需求的任务.该文建立了一个混合型实时容错调度模型,提出一种静态实时容错调度算法.该算法能同时调度具有容错需求的实时任务和无容错需求的实时任务.该文还提出了一个求解最小处理机个数的算法,用于对静态实时容错调度算法的性能进行模拟分析.为了提高静态调度算法的调度性能,提出了一种动态调度算法.最后,通过模拟实验分析了静态和动态调度算法的性能.实验表明,调度算法的性能与实时任务的个数、任务的计算时间、周期和处理机个数等系统参数相关.