Positive almost periodic solution for a class of Lasota–Wazewska model with multiple time-varying delays

In this paper, we study the existence and global exponential convergence of positive almost periodic solutions for the generalized Lasota–Wazewska model with multiple time-varying delays. Under proper conditions, we employ a novel proof to establish some criteria to ensure that all solutions of this model converge exponentially to a positive almost periodic solution. Moreover, we give an example to illustrate our main results.     

基于加密接入令牌的IGMP安全机制

在 IP组播中当前使用的组管理协议 IGMP不提供接入控制 ,任何端点用户可自由地加入组播组 ;此外 ,IGMP报文在传输时没有加密措施 ,无法保证其安全性 .因此 ,对 IGMP报文的认证和接入控制策略便成为亟待解决的重要课题 .在 IGMP报文后附加接入令牌的方法虽然可以解决 IGMP协议存在的一些安全问题 ,但其仍存在一些缺陷 .在此基础上 ,提出了一种加密接入令牌的方法 ,阐述了如何申请、产生、传输和使用令牌 .加密接入令牌可以安全的传输认证和接入控制信息 ,并且可以多次使用 ,不需要在用户和组播路由器之间建立安全关联 SA,提高了令牌的使用效率     

Convergence of non-autonomous discrete-time Hopfield model with delays

This paper is concerned with boundedness, convergence of solution of a class of non-autonomous discrete-time delayed Hopfield neural network model. Using the inequality technique, we obtain some sufficient conditions ensuring the boundedness of solutions of the discrete-time delayed Hopfield models in time-varying situation. Then, by exploring intrinsic features between non-autonomous system and its asymptotic equations, several novel sufficient conditions are established to ensure that all solutions of the networks converge to the solution of its asymptotic equations. Especially, for case of asymptotic autonomous system or asymptotic periodic system, we obtain some sufficient conditions ensuring all solutions of original system convergent to equilibrium or periodic solution of asymptotic system, respectively. An example is provided for demonstrating the effectiveness of the global stability conditions presented. Our results are not only presented in terms of system parameters and can be easily verified but also are less restrictive than previously known criteria.     

SAAMAN: Scalable Address Autoconfiguration in Mobile Ad Hoc Networks

Address autoconfiguration is one of the fundamental issues in mobile ad hoc networks (MANET). A node must need some form of identity before participating in any sort of communication. So each host in a MANET needs to be uniquely addressed so that the packets can be relayed hop-by-hop and delivered ultimately to the desired destination. Moreover, nodes in the MANET are free to move and organize themselves in an arbitrary fashion. Therefore any fixed infrastructure based solution for assigning identity (i.e. IP address) is not directly applicable to MANET. Under this infrastructureless and sporadic nature of the mobile nodes, several protocols of address autoconfiguration in the mobile ad hoc networks (MANET) have been proposed. Although some of these protocols perform decently in sparse and small networks, but exhibit poor performance (e.g., single point of failure, storage limitation, large protocol overhead and so on) when the network is either dense or very large. In this paper, we propose an efficient and scalable address autoconfiguration protocol that automatically configures a network by assigning unique IP addresses to all nodes with a very low overhead and minimal cost. Evenly distributed Duplicate-IP address Detection Servers are used to ensure the uniqueness of an IP address during IP address assignment session. In contrast to some other solutions, the proposed protocol does not exhibit any problems pertaining to leader election or centralized server-based solutions. Furthermore, grid based hierarchy is used for efficient geographic forwarding as well as for selecting Duplicate-IP address Detection Servers. Through simulation results we demonstrate scalability, robustness, low latency, fault tolerance and some other important aspects of our protocol.     

Physical-bound-preserving finite volume methods for the Nagumo equation on distorted meshes

In this paper, we present a boundedness preserving finite volume scheme for the Nagumo equation. In this method, we use the implicit Euler method for the time discretization, and construct a maximum-principle-preserving discrete normal flux for the diffusion term. For the nonlinear reaction term, we design a type of Picard iteration to ensure that at each iterative step it keeps physical boundedness. Moreover we prove that the numerical solution of the resulting scheme can preserve the bound of the solution for the Nagumo equation on distorted meshes. Some numerical results are presented to verify the theoretical analysis.     

Optimal policy for profit maximising in an EOQ model under non-linear holding cost and stock-dependent demand rate

In this article, we integrate a non-linear holding cost with a stock-dependent demand rate in a maximising profit per unit time model, extending several inventory models studied by other authors. After giving the mathematical formulation of the inventory system, we prove the existence and uniqueness of the optimal policy. Relying on this result, we can obtain the optimal solution using different numerical algorithms. Moreover, we provide a necessary and sufficient condition to determine whether a system is profitable, and we establish a rule to check when a given order quantity is the optimal lot size of the inventory model. The results are illustrated through numerical examples and the sensitivity of the optimal solution with respect to changes in some values of the parameters is assessed.     

Robust identification of process models from plant data

A precursor to any advanced control solution is the step of obtaining an accurate model of the process. Suitable models can be obtained from phenomenological reasoning, analysis of plant data or a combination of both. Here, we will focus on the problem of estimating (or calibrating) models from plant data. A key goal is to achieve robust identification. By robust we mean that small errors in the hypotheses should lead to small errors in the estimated models. We argue that, in some circumstances, it is essential that special precautions, including discarding some part of the data, be taken to ensure that robustness is preserved. We present several practical case studies to illustrate the results.     

Almost periodic solution to Cohen–Grossberg-type BAM networks with distributed delays

In this paper, a class of Cohen–Grossberg-type bi-directional associative memory (BAM) neural networks with distributed delays is discussed. Based on inequality analysis method and combining the exponential dichotomy with fixed point theorem, some novel sufficient conditions are obtained to ensure the existence and globally exponential stability of almost periodic solution to this system. Moreover, an example is given to demonstrate the feasibility of our results.     

A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery

Vehicle routing problem (VRP) is an important and well-known combinatorial optimization problem encountered in many transport logistics and distribution systems. The VRP has several variants depending on tasks performed and on some restrictions, such as time windows, multiple vehicles, backhauls, simultaneous delivery and pick-up, etc. In this paper, we consider vehicle routing problem with simultaneous pickup and delivery (VRPSPD). The VRPSPD deals with optimally integrating goods distribution and collection when there are no precedence restrictions on the order in which the operations must be performed. Since the VRPSPD is an NP-hard problem, we present a heuristic solution approach based on particle swarm optimization (PSO) in which a local search is performed by variable neighborhood descent algorithm (VND). Moreover, it implements an annealing-like strategy to preserve the swarm diversity. The effectiveness of the proposed PSO is investigated by an experiment conducted on benchmark problem instances available in the literature. The computational results indicate that the proposed algorithm competes with the heuristic approaches in the literature and improves several best known solutions.     

Optimal Admission Control of Discrete Event Systems with Real-Time Constraints

The problem of optimally controlling the processing rate of tasks in Discrete Event Systems with hard real-time constraints has been addressed in prior work under the assumption that a feasible solution exists. Since this cannot generally be the case, we introduce in this paper an admission control scheme in which some tasks are removed with the objective of maximizing the number of remaining tasks which are all guaranteed feasibility. We derive several optimality properties based on which we develop a computationally efficient algorithm for solving this admission control problem under certain conditions. Moreover, when no future task information is available, we derive necessary and sufficient conditions under which idling is optimal and define a metric for evaluating when and how long it is optimal to idle. Numerical examples are included to illustrate our results.     

A novel neural network for variational inequalities with linear and nonlinear constraints

Variational inequality is a uniform approach for many important optimization and equilibrium problems. Based on the sufficient and necessary conditions of the solution, this paper presents a novel neural network model for solving variational inequalities with linear and nonlinear constraints. Three sufficient conditions are provided to ensure that the proposed network with an asymmetric mapping is stable in the sense of Lyapunov and converges to an exact solution of the original problem. Meanwhile, the proposed network with a gradient mapping is also proved to be stable in the sense of Lyapunov and to have a finite-time convergence under some mild conditions by using a new energy function. Compared with the existing neural networks, the new model can be applied to solve some nonmonotone problems, has no adjustable parameter, and has lower complexity. Thus, the structure of the proposed network is very simple. Since the proposed network can be used to solve a broad class of optimization problems, it has great application potential. The validity and transient behavior of the proposed neural network are demonstrated by several numerical examples.     

A tabu search approach for the livestock collection problem

We present a problem dealing with transportation of live animals to slaughterhouses. The problem is taken from the Norwegian meat industry, and may be viewed as a vehicle routing problem extended with inventory constraints to ensure a smooth production flow at the slaughterhouse. In addition, several constraints to ensure animal welfare have to be met. These include duration limits for how long animals can stay on the vehicle as well as rules for mixing different animal categories. In this paper, we show that this real-world problem can be solved heuristically, even if it is large both in size and complexity. A tabu search based solution method for the problem is presented, and results from computational testing are given, including comparisons with manual solutions from today's planning system.     

基于PAM和均匀设计的并行粒子群优化算法

聚类技术是数据挖掘中的一个重要方法,PAM（Partitioning Around Medoids）是基于分区的聚类算法的一种,它试图将[n]个数据对象分成[k]个部分。在并行粒子群PSO（Particle Swarm Optimization）算法中,需要划分整个种群为几个相互不重叠的子种群。因此,引入PAM来划分整个种群。通过聚类,相同子种群的粒子相对集中,从而能够较容易地相互学习。这使得有限的时间能够花费在最有效的搜索上,以便提高算法的搜索效率。为了均匀地探测整个解空间,引入均匀设计来产生初始种群,使种群成员均匀地分散在可行解空间中。进化过程中,均匀设计也被引入来替换种群中的较差个体。提出基于PAM和均匀设计的并行粒子群算法,它结合并充分利用了二者的优点。对几个测试问题的实验结果证明,提出的算法比传统的并行粒子群算法具有更高的性能和更好的收敛准确性。     

A free boundary approach and keller's box scheme for BVPs on infinite intervals

A free boundary approach for the numerical solution of boundary value problems (BVPs) governed by a third-order differential equation and defined on infinite intervals was proposed recently [SIAM J. Numer. Anal., 33 (1996), pp. 1473–1483]. In that approach, the free boundary (that can be considered as the truncated boundary) is unknown and has to be found as part of the solution. This eliminates the uncertainty related to the choice of the truncated boundary in the classical treatment of BVPs defined on infinite intervals. In this article, we investigate some open questions related to the free boundary approach. We recall the extension of that approach to problems governed by a system of first-order differential equations, and for the solution of the related free boundary problem we consider now the reliable Keller's box difference scheme. Moreover, by solving a challenging test problem of interest in foundation engineering, we verify that the proposed approach is applicable to problems where none of the solution components is a monotone function.     

基于自组织映射的遗传聚类算法

遗传聚类算法往往需要较大的种群规模才能得到最优解,导致收敛速度慢,针对这一问题,本文提出一种基于自组织映射的超启发遗传聚类算法。首先利用自组织映射把数据空间转换到特征空间,再在特征空间里利用遗传算法进行搜索,然后进行反映射,即把聚类结果在数据空间里表现,从而得到一组解,同时利用K-means算法在数据空间里进行粗聚类,获得另一组解,最后比较2组解的聚类结果,相同的样本保留,不同的再次聚类,进而有效地保证了最优解的获得。计算机仿真实验验证了所提算法在种群规模较小的情况下,可以获得较高的准确率。      

煤矿安全监控分站远程升级方案

针对煤矿井下安全监控分站升级和维护难的问题,提出了一种基于LPC2114处理器的煤矿安全监控分站远程升级方案;阐述了IAP技术原理,介绍了远程升级方案的思路、实现方法以及设计时应注意的关键问题。该方案一方面可保障升级数据包的正确传送,另一方面可保证在升级中断时自动运行升级前的程序,从而保证了升级过程的安全性。大量测试表明,在低速率条件下,该方案可安全稳定地实现煤矿安全监控分站的远程升级。     

Towards a construction of an intelligent business process based on cloud services and driven by degree of similarity and QoS

Knowledge and competence management in companies focus on resources allocation to business activities, knowledge management approaches promise a positive impact on cost structures. Moreover, with cloud computing and multi-agent systems, we can have a positive impact on business process construction. In fact, cloud computing gives us the ability to perceive resources such as unlimited computer resources. Meanwhile, multi-agent systems provide some degree of independence to achieve specific goals. Nowadays, on the web we can find a significant number of services that provide the same functionality. Hence, business process construction by reusing these services has become a challenging problem. The solution proposed in this paper, can be considerate as an enterprise solution to construct a business process using some competence and knowledge management aspects. Furthermore, the solution is based on cloud computing and agents to discover similar services and classify services according to their similarity degrees and their quality of services.     

Ciphertext-policy attribute-based encryption supporting policy-hiding and cloud auditing in smart health

Smart Health, with its flexibility and efficiency, has been widely deployed, especially during the COVID-19 pandemic. However, privacy protection mechanisms for Smart Health are not yet well established and still present a number of security issues. Ciphertext-Policy Attribute-Based Encryption (CP-ABE), is identified as the furthest potential approach for constructing privacy-preserving Smart Health. However, traditional CP-ABE is facing some new challenges. On the one hand, access policy is not encrypted, and the identity information of the user could be exposed. On the other hand, Smart Health Records (SHRs) are outsourced to the Cloud Service Providers (CSP) and may be at risk of being tampered with. In this article, we have built a CP-ABE solution (PHCA) that supports policy-hiding and cloud auditing to ensure privacy security for smart health, in which the decryption cost is constant. To ensure data integrity, we securely introduce an effective third-party auditor. In addition, we design and implement safe and effective outsourcing decryption algorithms, which significantly low the decryption costs for users. Performance comparisons and security analysis demonstrate that our solutions function effectively.     

A Lagrangian heuristic for sprint planning in agile software development

Agile methods for software development promote iterative design and implementation. Most of them divide a project into functionalities, called user stories; at each iteration, often called a sprint, a subset of user stories are developed. The sprint planning phase is critical to ensure the project success, but it is also a difficult problem because several factors impact on the optimality of a sprint plan, e.g., the estimated complexity, business value, and affinity of the user stories to be included in each sprint. In this paper we present an approach for sprint planning based on an integer linear programming model. Given the estimates made by the project team and a set of development constraints, the optimal solution of the model is a sprint plan that maximizes the business value perceived by users. Solving to optimality the model by a general-purpose MIP solver, such as IBM Ilog Cplex, takes time and for some instances even finding a feasible solution requires too large computing times for an operational use. For this reason we propose an effective Lagrangian heuristic based on a relaxation of the proposed model and some greedy and exchange algorithms. Computational results on both real and synthetic projects show the effectiveness of the proposed approach.