scholarly journals A Runway Configuration Management Model with Marginally Decreasing Transition Capacities

2010 ◽  
Vol 2010 ◽  
pp. 1-21 ◽  
Author(s):  
Christopher Weld ◽  
Michael Duarte ◽  
Rex Kincaid
Keyword(s):  
Real World ◽  
Computational Cost ◽  
Configuration Management ◽  
Capacity Constraints ◽  
Management Model ◽  
New Model

The runway configuration management (RCM) problem governs what combinations of airport runways are in use at a given time, and to what capacity. Runway configurations (groupings of runways) operate under runway configuration capacity envelopes (RCCEs) which limit arrival and departure capacities. The RCCE identifies unique capacity constraints based on which tarmacs are used for arrivals, departures, or both, and their direction of travel. When switching between RCCEs, some decrement in arrival and departure capacities is incurred by the transition. A previous RCM model (Frankovich et al., 2009) accounted for this cost through a required period of inactivity. In this paper, we instead focus on the introduction and assessment of a model capable of marginally decreasing RCCE capacities during configuration transitions. A transition penalty matrix is introduced, specifying the relative costs (in terms of accepted arrival and departure capacities) for switching between RCCEs. The new model benefits from customizable transition penalties which more closely represent real-world conditions, at a reasonable computational cost.

Review of Network Flow Algorithms David P. Williamson

2021 ◽  
Vol 52 (1) ◽  
pp. 12-15
Author(s):  
S.V. Nagaraj
Keyword(s):  
Graduate Students ◽  
Real World ◽  
Network Flow ◽  
Network Flows ◽  
Optimization Problems ◽  
Capacity Constraints ◽  
Incoming Flow ◽  
Network Flow Problems ◽  
Flow Problems ◽  
Real World Problems

This book is on algorithms for network flows. Network flow problems are optimization problems where given a flow network, the aim is to construct a flow that respects the capacity constraints of the edges of the network, so that incoming flow equals the outgoing flow for all vertices of the network except designated vertices known as the source and the sink. Network flow algorithms solve many real-world problems. This book is intended to serve graduate students and as a reference. The book is also available in eBook (ISBN 9781316952894/US$ 32.00), and hardback (ISBN 9781107185890/US$99.99) formats. The book has a companion web site www.networkflowalgs.com where a pre-publication version of the book can be downloaded gratis.

Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Awder Mohammed Ahmed ◽  
◽  
Adnan Mohsin Abdulazeez ◽  
Keyword(s):  
Feature Selection ◽  
Swarm Intelligence ◽  
Real World ◽  
Computational Cost ◽  
Computing Methods ◽  
Open Problems ◽  
Label Data ◽  
Filter Methods ◽  
Wide Range ◽  
Classification Tasks

Multi-label classification addresses the issues that more than one class label assigns to each instance. Many real-world multi-label classification tasks are high-dimensional due to digital technologies, leading to reduced performance of traditional multi-label classifiers. Feature selection is a common and successful approach to tackling this problem by retaining relevant features and eliminating redundant ones to reduce dimensionality. There is several feature selection that is successfully applied in multi-label learning. Most of those features are wrapper methods that employ a multi-label classifier in their processes. They run a classifier in each step, which requires a high computational cost, and thus they suffer from scalability issues. Filter methods are introduced to evaluate the feature subsets using information-theoretic mechanisms instead of running classifiers to deal with this issue. Most of the existing researches and review papers dealing with feature selection in single-label data. While, recently multi-label classification has a wide range of real-world applications such as image classification, emotion analysis, text mining, and bioinformatics. Moreover, researchers have recently focused on applying swarm intelligence methods in selecting prominent features of multi-label data. To the best of our knowledge, there is no review paper that reviews swarm intelligence-based methods for multi-label feature selection. Thus, in this paper, we provide a comprehensive review of different swarm intelligence and evolutionary computing methods of feature selection presented for multi-label classification tasks. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods and categorize them based on different perspectives. We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically. We also introduce benchmarks, evaluation measures, and standard datasets to facilitate research in this field. Moreover, we performed some experiments to compare existing works, and at the end of this survey, some challenges, issues, and open problems of this field are introduced to be considered by researchers in the future.

Feedback-Driven Refinement of Mandarin Speech Recognition Result Based on Lattice Modification and Rescoring

2020 ◽  
pp. 1237-1247
Author(s):  
Xiangdong Wang ◽  
Yang Yang ◽  
Hong Liu ◽  
Yueliang Qian ◽  
Duan Jia
Keyword(s):  
Speech Recognition ◽  
Real World ◽  
Computational Cost ◽  
User Feedback ◽  
Recognition Result ◽  
Novel Method ◽  
Mandarin Speech Recognition ◽  
Recognition Errors ◽  
Dynamic Updating ◽  
Manual Correction

In real world applications of speech recognition, recognition errors are inevitable, and manual correction is necessary. This paper presents an approach for the refinement of Mandarin speech recognition result by exploiting user feedback. An interface incorporating character-based candidate lists and feedback-driven updating of the candidate lists is introduced. For dynamic updating of candidate lists, a novel method based on lattice modification and rescoring is proposed. By adding words with similar pronunciations to the candidates next to the corrected character into the lattice and then performing rescoring on the modified lattice, the proposed method can improve the accuracy of the candidate lists even if the correct characters are not in the original lattice, with much lower computational cost than that of the speech re-recognition methods. Experimental results show that the proposed method can reduce 24.03% of user inputs and improve average candidate rank by 25.31%.

A Framework for Development the Open Systems Interconnection of Integrated Intelligent Knowledge for Management of Networks

2013 ◽  
Vol 411-414 ◽  
pp. 795-798 ◽  
Author(s):  
Yu Qing Shi ◽  
Yue Long Zhu
Keyword(s):  
Knowledge Management ◽  
Intelligent System ◽  
Open Systems ◽  
Management Model ◽  
New Model ◽  
Design And Implementation ◽  
Open Systems Interconnection ◽  
Intelligent Management

In this article, we present a new model for distributed intelligent management networks. This paper presents a approach for the design and implementation of a distributed intelligent system that is designed through the normalization of knowledge management. Our study focuses on a language for formalizing knowledge management descriptions and an intelligent framework and combining them with an existing Open Systems Interconnection (OSI) management model. Further, this work outlines the development of an example based on our proposed standard.

scholarly journals Real-time, whole-brain, temporally resolved pressure responses in translational head impact

2016 ◽  
Vol 6 (1) ◽  
pp. 20150091 ◽  
Author(s):  
Wei Zhao ◽  
Songbai Ji
Keyword(s):  
Brain Injury ◽  
Real Time ◽  
Real World ◽  
Risk Function ◽  
Computational Cost ◽  
Linear Acceleration ◽  
Tissue Level ◽  
Head Impact ◽  
Whole Brain ◽  
Brain Responses

Theoretical debate still exists on the role of linear acceleration ( a lin ) on the risk of brain injury. Recent injury metrics only consider head rotational acceleration ( a rot ) but not a lin , despite that real-world on-field head impacts suggesting a lin significantly improves a concussion risk function. These controversial findings suggest a practical challenge in integrating theory and real-world experiment. Focusing on tissue-level mechanical responses estimated from finite-element (FE) models of the human head, rather than impact kinematics alone, may help address this debate. However, the substantial computational cost incurred (runtime and hardware) poses a significant barrier for their practical use. In this study, we established a real-time technique to estimate whole-brain a lin -induced pressures. Three hydrostatic atlas pressures corresponding to translational impacts (referred to as ‘brain print’) along the three major axes were pre-computed. For an arbitrary a lin profile at any instance in time, the atlas pressures were linearly scaled and then superimposed to estimate whole-brain responses. Using 12 publically available, independently measured or reconstructed real-world a lin profiles representative of a range of impact/injury scenarios, the technique was successfully validated (except for one case with an extremely short impulse of approx. 1 ms). The computational cost to estimate whole-brain pressure responses for an entire a lin profile was less than 0.1 s on a laptop versus typically hours on a high-end multicore computer. These findings suggest the potential of the simple, yet effective technique to enable future studies to focus on tissue-level brain responses, rather than solely relying on global head impact kinematics that have plagued early and contemporary brain injury research to date.

scholarly journals Real-Valued GCS Classifier System

2007 ◽  
Vol 17 (4) ◽  
pp. 539-547 ◽  
Author(s):  
Łukasz Cielecki ◽  
Olgierd Unold
Keyword(s):  
Evolutionary Computation ◽  
Real World ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Context Free Grammar ◽  
New Model ◽  
Classifier System ◽  
Learning Classifier ◽  
Context Free ◽  
Real World Problems

Real-Valued GCS Classifier SystemLearning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify realvalued data. The approach applies the continous-valued context-free grammar-based system GCS. In order to handle data effectively, the terminal rules were replaced by the so-called environment probing rules. The rGCS model was tested on the checkerboard problem.

scholarly journals A NEW MODEL APPROXIMATING M/PH/1 QUEUING SYSTEMS DURING THE TRANSIENT PERIOD

2001 ◽  
Vol 6 (2) ◽  
pp. 199-209
Author(s):  
H. Gao ◽  
M. A. M. Lynch
Keyword(s):  
Diffusion Approximation ◽  
Computational Cost ◽  
Queuing Systems ◽  
Diffusion Approximations ◽  
New Model ◽  
Transient Period ◽  
Efficient Approximation ◽  
Vector Valued ◽  
Low Traffic

This paper presents an efficient approximation for M/PH/1 queuing systems based on the replacement of the majority of the vector valued state probabilities by a diffusion approximation. The strength of the new approximation is that it gives more accurate results than the current diffusion approximations at both high and low traffic intensities and at little extra computational cost. The accuracy of the new approximation during the transient is shown by comparing it numerically with solutions to the M/PH/1 system and current approaches based on the diffusion approximation.

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