Scheduling Delays in Synchronous Transportation Networks

1976 ◽  
Vol 98 (2) ◽  
pp. 173-179
Author(s):  
A. L. Kornhauser ◽  
P. J. McEvaddy
Keyword(s):  
Network Management ◽  
Transportation Networks ◽  
Routing And Scheduling ◽  
Minimum Number ◽  
Management Policies ◽  
Improved Performance

This paper investigates the performance of proposed network management policies responsible for routing and scheduling vehicles in automated transportation networks Performance is quantified in probabilistic terms of expected delays inherent to the scheduling process. Improved performance resulting from extensions of synchronous management to include considerations of multiple feasible routes and scheduled slot slipping is quantified and compared to quasi-synchronous management. This comparison suggests a bound on the minimum number of feasible routes and slot slipping capability for synchronous management to rival quasi-synchronous. The implications of network management on queue formation in stations and, consequently, on station design and layout are presented. It is found that synchronous-type management policies require parallel or lateral berthing in stations, whereas quasi-synchronous management imposes no such restriction on station design.

Home Network Management Policies: Putting the User in the Loop

2012 ◽  
Author(s):  
Dimosthenis Pediaditakis ◽  
Anandha Gopalan ◽  
Naranker Dulay ◽  
Morris Sloman ◽  
Tom Lodge
Keyword(s):  
Network Management ◽  
Home Network ◽  
Management Policies

scholarly journals NORA: An Approach for Transforming Network Management Policies into Automated Planning Problems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1790
Author(s):  
Angela Rodriguez-Vivas ◽  
Oscar Mauricio Caicedo ◽  
Armando Ordoñez ◽  
Jéferson Campos Nobre ◽  
Lisandro Zambenedetti Granville
Keyword(s):  
Natural Language ◽  
Network Management ◽  
Language Processing ◽  
Automated Planning ◽  
Management Control ◽  
Management Scenarios ◽  
Autonomic Management ◽  
Management Policies ◽  
Network Status ◽  
Network Administrators

Realizing autonomic management control loops is pivotal for achieving self-driving networks. Some studies have recently evidence the feasibility of using Automated Planning (AP) to carry out these loops. However, in practice, the use of AP is complicated since network administrators, who are non-experts in Artificial Intelligence, need to define network management policies as AP-goals and combine them with the network status and network management tasks to obtain AP-problems. AP planners use these problems to build up autonomic solutions formed by primitive tasks that modify the initial network state to achieve management goals. Although recent approaches have investigated transforming network management policies expressed in specific languages into low-level configuration rules, transforming these policies expressed in natural language into AP-goals and, subsequently, build up AP-based autonomic management loops remains unexplored. This paper introduces a novel approach, called NORA, to automatically generate AP-problems by translating Goal Policies expressed in natural language into AP-goals and combining them with both the network status and the network management tasks. NORA uses Natural Language Processing as the translation technique and templates as the combination technique to avoid network administrators to learn policy languages or AP-notations. We used a dataset containing Goal Policies to evaluate the NORA’s prototype. The results show that NORA achieves high precision and spends a short-time on generating AP-problems, which evinces NORA aids to overcome barriers to using AP in autonomic network management scenarios.

scholarly journals Combining diverse neural nets

1997 ◽  
Vol 12 (3) ◽  
pp. 231-247 ◽  
Author(s):  
AMANDA J. C. SHARKEY ◽  
NOEL E. SHARKEY
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Neural Nets ◽  
Neural Net ◽  
Neural Computing ◽  
Minimum Number ◽  
Software And Hardware ◽  
Improved Performance ◽  
Four Levels

An appropriate use of neural computing techniques is to apply them to problems such as condition monitoring, fault diagnosis, control and sensing, where conventional solutions can be hard to obtain. However, when neural computing techniques are used, it is important that they are employed so as to maximise their performance, and improve their reliability. Their performance is typically assessed in terms of their ability to generalise to a previously unseen test set, although unless the training set is very carefully chosen, 100% accuracy is rarely achieved. Improved performance can result when sets of neural nets are combined in ensembles and ensembles can be viewed as an example of the reliability through redundancy approach that is recommended for conventional software and hardware in safety-critical or safety-related applications. Although there has been recent interest in the use of neural net ensembles, such techniques have yet to be applied to the tasks of condition monitoring and fault diagnosis. In this paper, we focus on the benefits of techniques which promote diversity amongst the members of an ensemble, such that there is a minimum number of coincident failures. The concept of ensemble diversity is considered in some detail, and a hierarchy of four levels of diversity is presented. This hierarchy is then used in the description of the application of ensemble-based techniques to the case study of fault diagnosis of a diesel engine.

Neuro-dynamic programming for designing water reservoir network management policies

2007 ◽  
Vol 15 (8) ◽  
pp. 1031-1038 ◽  
Author(s):  
A. Castelletti ◽  
D. de Rigo ◽  
A.E. Rizzoli ◽  
R. Soncini-Sessa ◽  
E. Weber
Keyword(s):  
Dynamic Programming ◽  
Network Management ◽  
Water Reservoir ◽  
Management Policies

scholarly journals Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments

2019 ◽  
Vol 11 (2) ◽  
pp. 37 ◽  
Author(s):  
Adamantia Stamou ◽  
Grigorios Kakkavas ◽  
Konstantinos Tsitseklis ◽  
Vasileios Karyotis ◽  
Symeon Papavassiliou
Keyword(s):  
Network Management ◽  
Software Defined Radio ◽  
Network Function Virtualization ◽  
Cross Layer ◽  
Allocation Mechanism ◽  
Network Function ◽  
Cross Layer Optimization ◽  
Significant Research ◽  
Improved Performance ◽  
Eu Project

The demand for Autonomic Network Management (ANM) and optimization is as intense as ever, even though significant research has been devoted towards this direction. This paper addresses such need in Software Defined (SDR) based Cognitive Radio Networks (CRNs). We propose a new framework for ANM and network reconfiguration combining Software Defined Networks (SDN) with SDR via Network Function Virtualization (NFV) enabled Virtual Utility Functions (VUFs). This is the first approach combining ANM with SDR and SDN via NFV, demonstrating how these state-of-the-art technologies can be effectively combined to achieve reconfiguration flexibility, improved performance and efficient use of available resources. In order to show the feasibility of the proposed framework, we implemented its main functionalities in a cross-layer resource allocation mechanism for CRNs over real SDR testbeds provided by the Orchestration and Reconfiguration Control Architecture (ORCA) EU project. We demonstrate the efficacy of our framework, and based on the obtained results, we identify aspects that can be further investigated for improving the applicability and increasing performance of our broader framework.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

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