信息中心网络内缓存替换算法性能分析与优化

信息中心网络(information centric networking, ICN)是一类受到广泛关注的新型互联网体系结构.通过对网络内(in-network)缓存的充分利用,信息中心网络可以极大地增强内容分发效率.网络内缓存的管理机制一直是信息中心网络研究中的热点问题.分析了网络内缓存的基准缓存替换最近最少使用(least recently used, LRU)算法的性能,指出多跳LRU缓存中广泛存在的“缓存退化”问题：在首个缓存节点发生缺失的内容请求也很难被下游的缓存命中.针对这一问题,提出一种基于预过滤的O(1)复杂度的改进算法.在原有缓存前放置一个仅记录内容标识的预过滤缓存,完成对原始内容请求的整形,使得预处理后的请求流量可以更容易被后面几跳缓存命中.基于真实互联网拓扑的实验表明,在信息中心网络典型应用场景下,预过滤LRU的缓存命中率可以达到LRU的2～3倍.     

Performance evaluation of a mesh-evolving quality-of-service-aware multicast routing protocol for mobile ad hoc networks

The tremendous amount of multimedia applications running across the wireless communication medium makes quality of service (QoS) a fundamental requirement for mobile ad hoc networks. However, it is not easy to incorporate QoS into these networks. Moreover, the growing number of group-oriented applications also necessitates the efficient utilisation of network resources. The multicast model is a promising technique which can achieve this efficiency by facilitating the inherent broadcast capability of the wireless medium. The mesh-evolving ad hoc QoS multicast (MAQM) routing protocol is developed to address the resource efficiency and QoS problems with one, integrated solution. MAQM achieves multicast efficiency by tracking the availability of resources for each node within its neighbourhood. The QoS status is monitored continuously and announced periodically to the extent of QoS provision. Using these features, MAQM nodes can make their decisions on joining a new multicast session based on the sustainability of their perceived QoS. MAQM also evolves the initial multicast tree into a mesh during the course of an ongoing session to achieve a more robust network topology. Thus, MAQM integrates the concept of QoS-awareness into multicast routing in mobile ad hoc networks. Since ad hoc networks require the protocol control overhead to be as small as possible, we analyse the multicast session establishment process of MAQM to see its impact on the protocol performance in terms of system control overhead. We also evaluate the performance of MAQM through computer simulations using various qualitative and quantitative criteria. The simulation results validate our mathematical analysis of the control overhead and show that MAQM significantly improves multicast efficiency through its QoS-aware admission and routing decisions with an acceptably small overhead. Thus, MAQM shows that QoS is not only essential for, but also applicable to mobile ad hoc networks.     

Dynamic Prediction based Multi Queue (DPMQ) drop policy for probabilistic routing protocols of delay tolerant network

Two most important issues should be considered to achieve data delivery in DTN networking: routing protocols for the network and intelligent buffer management policy for everyone node in the network. The routing scheme decides which messages should be forwarded when nodes meet, and the buffer management policy determines which message is purged when the buffer overflows in a node. This study proposes a buffer management policy named as Dynamic Prediction based Multi Queue (DPMQ) for probabilistic routing protocols. It works by classification of local buffer into three queues of messages, which are DCTL, HPTL and LPTL. The simulation results have proven that the DPMQ performs well as compared to DLA, DOA, MOFO, LIFO, LEPR and LIFO in terms of reducing the message relay, message drop, hop counts average and overhead while rising in the delivery probability.     

A genetic fuzzy system based optimized zone based energy efficient routing protocol for mobile sensor networks (OZEEP)

Wireless sensor networks have become increasingly popular because of their ability to cater to multifaceted applications without much human intervention. However, because of their distributed deployment, these networks face certain challenges, namely, network coverage, continuous connectivity and bandwidth utilization. All of these correlated issues impact the network performance because they define the energy consumption model of the network and have therefore become a crucial subject of study. Well-managed energy usage of nodes can lead to an extended network lifetime. One way to achieve this is through clustering. Clustering of nodes minimizes the amount of data transmission, routing delay and redundant data in the network, thereby conserving network energy. In addition to these advantages, clustering also makes the network scalable for real world applications. However, clustering algorithms require careful planning and design so that balanced and uniformly distributed clusters are created in a way that the network lifetime is enhanced. In this work, we extend our previous algorithm, titled the zone-based energy efficient routing protocol for mobile sensor networks (ZEEP). The algorithm we propose optimizes the clustering and cluster head selection of ZEEP by using a genetic fuzzy system. The two-step clustering process of our algorithm uses a fuzzy inference system in the first step to select optimal nodes that can be a cluster head based on parameters such as energy, distance, density and mobility. In the second step, we use a genetic algorithm to make a final choice of cluster heads from the nominated candidates proposed by the fuzzy system so that the optimal solution generated is a uniformly distributed balanced set of clusters that aim at an enhanced network lifetime. We also study the impact and dominance of mobility with regard to the variables. However, before we arrived at a GFS-based solution, we also studied fuzzy-based clustering using different membership functions, and we present our understanding on the same. Simulations were carried out in MATLAB and ns2. The results obtained are compared with ZEEP.     

IEEMARP- a novel energy efficient multipath routing protocol based on ant Colony optimization (ACO) for dynamic sensor networks

In the past few years, research and development in Wireless Sensor networks (WSNs) have gained momentum due to its numerous applications in agriculture, industrial manufacturing, military surveillance, environmental monitoring, consumer electronics, medical & healthcare, disaster recovery operations etc. Dynamic WSNs offer a robust blend of distributed sensing, computing and communication. Dynamic sensor networks are characterized by large scale deployment, dynamic and unstructured topology, power limitations, less memory and limited computational capabilities. Sensor nodes deployed in real-time environment’s for sensing data have power-limitations which hampers the overall performance of WSNs. So, the only obvious solution is to propose an energy efficient routing protocol to optimize WSN real-time performance. Different specialists have proposed various directing conventions for WSNs dependent on Fuzzy Logic, Genetic Algorithms, Meta-Heuristics, and other improvement strategies. However, every solution suggested till date has its advantages and limitations. In this paper, our primary objective is to utilize Swarm-Intelligence based approach i.e. “Ant Colony Optimization (ACO)”, for routing protocol development. Ant colony optimization (ACO) based approach gives optimal solution in terms of efficient routing path determination, energy efficiency and delivering high performance in terms of packet delivery and throughput. In this paper, we propose a novel energy efficient ACO based multipath routing protocol for WSN i.e. IEEMARP (Improvised Energy Efficient Multipath ACO based Routing Protocol). The proposed protocol works in three phases (Neighbor Discovery via Link Knowledge, Packet Transmission via exponentially weighted moving average method and ACKR packet delivery for assuring end-to-end delivery. To validate the performance of the protocol proposed, extensive simulations were conducted using NS-2.35-allinone simulator on diverse parameters like (PDR), throughput, routing overhead, energy consumption and end-to-end delay. In addition to this, the performance of protocol is compared with traditional routing protocols like Basic ACO, DSDV and DSR and other ACO based WSN protocols like ACEAMR, AntChain, EMCBR, IACR, AntHQSeN, FACOR and ANTALG. Simulation based results, clearly states that as compared to Basic ACO, DSDV and DSR, the performance of WSN network is improvised to around 10% in all performance metrics via IEEMARP routing protocol. And as compared to ACEAMR, AntChain, EMCBR and IACR, IEEMARP performs 20% better in overall functionality and almost 10–12% better as compared to AntHQSeN, FACOR, ANTLAG routing protocols in varied WSN scenarios. It is also observed that IEEMARP protocol is highly efficient in TCP packet transmission from source to destination node.

     

(OBIFS) isotropic image analysis for improving a predicting agent based systems

In this interdisciplinary study a novel hybrid forecasting system is presented, in which an isotropic buffer operator is applied for case-based creation within the structure of the organization-based multi-agent system. Commonly used as an image analysis technique by commercial Geographic Information Systems (GIS), the buffer operator in this particular system calculates the area of a forest fire for prediction and visualization tasks. The use of the buffer operator improves the quality of the data used by the system and in consequence the quality of the results obtained. The system has been successfully tested using real historical data on forest fires evolution, by generating accurate predictions.     

大型工程项目信息沟通系统（PICS）

大型工程项目由于建设周期长、参与单位多、技术工艺复杂及地理分布广等特点,在建设过程中普遍存在信息沟通的困难和紊乱。信息沟通问题不仅直接造成大量不必要的开支,同时也间接影响着工程建设的进度与质量。论文提出基于Internet的项目信息沟通系统（PICS）,其实质是利用Internet技术为项目参与各方营造业个信息沟通与协调合作的共享环境,在对大型工程建设传统信息沟通方式存在弊端进行深入分析的基础上,系统阐述了PICS的概念、虚拟组织环境下的信息沟通及具体技术框架。     

面向信息化战争整体需求的探索性分析方法

探索性分析方法是适应信息化战争需求发展起来的整体性分析方法。这种方法是一种“自顶向下”的过程,建立在对战争系统各方面要素进行采集、处理和归纳的基础上,通过对战争系统中存在着的大量不确定要素进行整体性的分析,得出综合性更强、更加符合信息化战争特点的分析结果。该文介绍了探索性分析的基本概念、方法和过程,研究了实现探索性分析的主要困难和相应的关键技术,并讨论了有关的问题。     

和田绿洲蒸发能力预测的多因素GM(1,3)模型

在灰关联分析的基础上，找出了影响和田绿洲蒸发能力变化的主要影响因子为平均气温与相对湿度。根据灰色GM（1，N）建模原理，对和田绿洲蒸发能力建立了GM（1，3）预测模型，经检验模型预测结果较好，同时也说明了GM（1，N）模型在中长期水文预报中的适用性。     

Potential of 5,10,15,20-Tetrakis(3′,5′-di-tertbutylphenyl)porphyrinatocopper(II) for a multifunctional sensor

An organic compound 5,10,15,20-Tetrakis(3′,5′-di-tertbutylphenyl)porphyrinatocopper(II) (TDTPPCu) is synthesized and studied as an active material for multifunctional capacitive sensor. The capacitance of the device as a function of illumination, humidity and temperature has been investigated. It is observed that the capacitance increases by 4.7 times from the dark condition under an illumination of 3850 lx. The capacitance is also changed 9.5 times with the increase in relative humidity (RH) from 30% to 95%. No change in capacitance appeared below critical temperature 120 °C. Based on the experimental results for the multifunctional sensor a mathematical model has been developed. The model is mainly based on the assumption that the capacitive response of the sensor is associated with dielectric polarization. The sensors are simulated using this model. The simulated results match well with experimental results.     

An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs

This paper concerns with the Job Shop Scheduling Problem (JSSP) considering the transportation times of the jobs from one machine to another. The goal of a basic JSSP is to determine starting and ending times for each job in which the objective function can be optimized. In here, several Automated Guided Vehicles (AGVs) have been employed to transfer the jobs between machines and warehouse located at the production environment. Unlike the advantages of implemented automatic transportation system, if they are not controlled along the routes, it is possible that the production system encounters breakdown. Therefore, the Conflict-Free Routing Problem (CFRP) for AGVs is considered as well as the basic JSSP. Hence, we proposed a mathematical model which is composed of JSSP and CFRP, simultaneously and since the problem under study is NP-hard, a two stage Ant Colony Algorithm (ACA) is also proposed. The objective function is to minimize the total completion time (make-span). Eventually, in order to show the model and algorithm’s efficiency, the computational results of 13 test problems and sensitivity analysis are exhibited. The obtained results show that ACA is an efficient meta-heuristic for this problem, especially for the large-sized problems. In addition, the optimal number of both AGVs and rail-ways in the production environment is determined by economic analysis.     

Poly(3-hexylthiophene) and hexafluoro-2-propanol-substituted polysiloxane based OFETs as a sensor for explosive vapor detection

The organic field effect transistors (OFETs) with regioregular poly 3-hexylthiophene (rr-P3HT) and hexafluoro-2-propanol-substituted polysiloxane (SXFA) as an organic layer, have been used for detection of explosive vapors with excellent sensitivity of less than 70 ppt for 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX) and less than 100 ppt for 2,4,6-trinitrotoluene (TNT). The sensor response (% change in saturation current) was found to be 125 ± 10% for TNT and 90 ± 10% for RDX. It was also observed that the incorporation of Cu^II tetraphenylporphyrin (CuTPP) into rr-P3HT/SXFA matrix resulted in an improved selectivity for the vapors of nitro based analytes (TNT, RDX and DNB) as compared to the vapors of non explosive oxidizing agents such as nitrobenzene (NB), benzoquinone (BQ) and benzophenone (BP). This is attributed to the increased binding of the vapors containing nitro compound to the thin films due to the presence of CuTTP. Spin coated thin films were further characterized by Atomic Force Microscopy (AFM) and Electrostatic Force Microscopy (EFM).     

数据库技术在模糊聚类预测中的应用研究

将数据库技术应用于模糊聚类预测中，用ＦＯＸＰＲＯ开发了模糊聚类预测软件，通过对上海水泥厂提供的实测数据的实旆计算，取得了令人满意的结果，文中提供的方法具有普遍的意义和广阔的应用前景，作为数据库技术原应用开辟了新领域。     

The digital storage oscilloscope (DSO) as a cost-effective solution to measurements in digital circuits

A logic analyser may not be the best tool for all design and test needs. Chris Crook looks at the advantages of a digital oscilloscope     

Reverse logistics network design for a biogas plant: An approach based on MILP optimization and Analytical Hierarchical Process (AHP)

Among sustainable energy production processes, methanation (anaerobic co-digestion) has a high potential to valorize organic residual waste by exploiting its energetic capacities in the form of biogas. Nevertheless, at the early stage of the project, decisions must be made concerning the network used to supply biomass to the anaerobic co-digestion facility. However, these decisions involve complex hierarchical processes, taking into account the best compromise to be found among diverse factors and actors (economic, social, environmental, etc.). In this article a systematic approach integrating Mixed Integer Linear Programming (MILP) optimization and Analytical Hierarchical Process is proposed. It will allow project managers to evaluate possible scenarios for the implementation of an anaerobic co-digestion logistics network in order to facilitate the integration of the preferences of the stakeholders involved in the project. The approach proposed is then illustrated by the design of a municipal biogas facility in Nancy, France.     

Graphical tools for production planning in small medium industries (SMIs) based on pinch analysis

Small medium industries (SMIs) routinely face supply variations in their production cycle. Such firms are typically characterised by limited resources and insufficiency of funds. Thus, SMIs need simple solutions to cope with the production planning issues. Pinch analysis has been proven as a strategy for planning of efficient use of scarce resources. Recently, it has been extended to various production planning problems. In this paper, a simple novel graphical approach is proposed to address two common production planning problems in SMI's, which are warehouse space allocation and production capacity planning. Two industrial case studies are shown in this paper to illustrate the proposed approach.     

An analytics appliance for identifying (near) optimal over-the-counter medicine products as health indicators for influenza surveillance

In the era of “Big Data”, a challenge is how to optimize our use of huge volumes of data. In this paper, we address this challenge in the context of a public health surveillance system which identifies disease outbreaks using individual and population health indicators. Our goal is to automate and improve the accuracy of the selection process of the health indicators, a process which is data-intensive and computationally expensive. The health indicators selection process traditionally has been carried out manually by public health experts in collaboration with health data providers. In particular, we present an approach for identifying sets of over-the-counter (OTC) medicine products whose aggregate sales correlate optimally with aggregate counts of emergency department (ED) visits. Towards this goal, we propose an OTC Analytics Appliance which utilizes a distributed search engine to efficiently generate time series of time-stamped records and supports “plug-and-play” search and correlation functionalities. Using the OTC Analytics Appliance with the Pearson correlation coefficient function, we evaluate Brute-force search, Greedy search, and Knapsack search for their ability to select the optimal or suboptimal set of OTC products automatically. Our results show that greedy search is the most preferable, producing a set of OTC products whose sales that correlate optimally or near optimally to ED visits, while achieving acceptable search times with large datasets. Also, our evaluations show that our approach using the greedy search can be potentially used to efficiently identify different optimal OTC medicine products for detection of different types of disease outbreaks.     

Using the analytic network process (ANP) in a SWOT analysis - A case study for a textile firm

Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis does not provide an analytical means to determine the importance of the identified factors or the ability to assess decision alternatives according to these factors. Although the analysis successfully pinpoints the factors, individual factors are usually described briefly and very generally. For this reason, SWOT analysis possesses deficiencies in the measurement and evaluation steps. Although the analytic hierarchy process (AHP) technique removes these deficiencies, it does not allow for measurement of the possible dependencies among the factors. The AHP method assumes that the factors presented in the hierarchical structure are independent; however, this assumption may be inappropriate in light of certain internal and external environmental effects. Therefore, it is necessary to employ a form of SWOT analysis that measures and takes into account the possible dependency among the factors. This paper demonstrates a process for quantitative SWOT analysis that can be performed even when there is dependence among strategic factors. The proposed algorithm uses the analytic network process (ANP), which allows measurement of the dependency among the strategic factors, as well as AHP, which is based on the independence between the factors. Dependency among the SWOT factors is observed to effect the strategic and sub-factor weights, as well as to change the strategy priorities.     

A new family of metrics for compact, convex (fuzzy) sets based on a generalized concept of mid and spread

One of the most important aspects of the (statistical) analysis of imprecise data is the usage of a suitable distance on the family of all compact, convex fuzzy sets, which is not too hard to calculate and which reflects the intuitive meaning of fuzzy sets. On the basis of expressing the metric of Bertoluzza et al. [C. Bertoluzza, N. Corral, A. Salas, On a new class of distances between fuzzy numbers, Mathware Soft Comput. 2 (1995) 71-84] in terms of the mid points and spreads of the corresponding intervals we construct new families of metrics on the family of all d-dimensional compact convex sets as well as on the family of all d-dimensional compact convex fuzzy sets. It is shown that these metrics not only fulfill many good properties, but also that they are easy to calculate and easy to manage for statistical purposes, and therefore, useful from the practical point of view.     

基于小波-神经网络的数字电路I_(DDT)故障诊断

利用小波变换与神经网络相结合的方法,采用“能量-故障”特征提取方法和BP算法,提出了一种基于小波分析和神经网络的数字电路瞬态电流I_DDT故障诊断方法。该方法首先采样电源到地的瞬态电流I_DDT,然后通过小波分析提取电路的故障特征向量,最后输入到神经网络进行故障诊断。经过计算机软件对故障进行仿真,结果表明使用小波-神经网络的数字电路I_DDT方法行之有效。