817 research outputs found
Many Server Scaling of the N-System Under FCFS-ALIS
The N-System with independent Poisson arrivals and exponential
server-dependent service times under first come first served and assign to
longest idle server policy has explicit steady state distribution. We scale the
arrival and the number of servers simultaneously, and obtain the fluid and
central limit approximation for the steady state. This is the first step
towards exploring the many server scaling limit behavior of general parallel
service systems
Function and design of simulation system for the workload distribution among storage blocks in a container terminal yard
#mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks
We study how users of multiple online social networks (OSNs) employ and share
information by studying a common user pool that use six OSNs - Flickr, Google+,
Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical
signature of users' sharing behaviour, showing how they exhibit distinct
behaviorial patterns on different networks. We also examine cross-sharing
(i.e., the act of user broadcasting their activity to multiple OSNs
near-simultaneously), a previously-unstudied behaviour and demonstrate how
certain OSNs play the roles of originating source and destination sinks.Comment: IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, 2015. This is the pre-peer reviewed version and the
final version is available at
http://wing.comp.nus.edu.sg/publications/2015/lim-et-al-15.pd
Getting Anxious while Retouching Photos? Exploring the Relationships between Affordances of Photo Retouching Applications and Appearance Anxiety
Urban Freight Management with Stochastic Time-Dependent Travel Times and Application to Large-Scale Transportation Networks
This paper addressed the vehicle routing problem (VRP) in large-scale urban transportation networks with stochastic time-dependent (STD) travel times. The subproblem which is how to find the optimal path connecting any pair of customer nodes in a STD network was solved through a robust approach without requiring the probability distributions of link travel times. Based on that, the proposed STD-VRP model can be converted into solving a normal time-dependent VRP (TD-VRP), and algorithms for such TD-VRPs can also be introduced to obtain the solution. Numerical experiments were conducted to address STD-VRPTW of practical sizes on a real world urban network, demonstrated here on the road network of Shenzhen, China. The stochastic time-dependent link travel times of the network were calibrated by historical floating car data. A route construction algorithm was applied to solve the STD problem in 4 delivery scenarios efficiently. The computational results showed that the proposed STD-VRPTW model can improve the level of customer service by satisfying the time-window constraint under any circumstances. The improvement can be very significant especially for large-scale network delivery tasks with no more increase in cost and environmental impacts
Existence and multiplicity of positive solutions to a fourth-order impulsive integral boundary value problem with deviating argument
Research and Application on Traffic Safety Education to Migrant Workers
AbstractThere are more and more migrant workers in the cities, which brings higher risks in urban transportation. This paper discusses what proper traffic safety education is for migrant workers. It describes the traffic safety awareness, knowledge and behavior of migrant workers by focus group and observation. With such kinds of survey, this paper analyzes the population characteristics and travel behavior of migrant workers, finds the cause of their travel behavior and what content and form of traffic safety education they need. Then these findings are put in practice. A traffic safety education activity for migrant workers is held in Shandong Province, which works well. The final results show that migrant workers have weak awareness of traffic safety, less knowledge of transportation and many bad travel behaviors. Migrant workers prefer education with real stories and pictures
Particle swarm optimization in constrained maximum likelihood estimation a case study
The aim of paper is to apply two types of particle swarm optimization, global
best andlocal best PSO to a constrained maximum likelihood estimation problem
in pseudotime anal-ysis, a sub-field in bioinformatics. The results have shown
that particle swarm optimizationis extremely useful and efficient when the
optimization problem is non-differentiable and non-convex so that analytical
solution can not be derived and gradient-based methods can not beapplied.Comment: 11 pages, 7 figure
A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning
The selective fixed-filter active noise control (SFANC) method selecting the
best pre-trained control filters for various types of noise can achieve a fast
response time. However, it may lead to large steady-state errors due to
inaccurate filter selection and the lack of adaptability. In comparison, the
filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower
steady-state errors through adaptive optimization. Nonetheless, its slow
convergence has a detrimental effect on dynamic noise attenuation. Therefore,
this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive
algorithm's slow convergence and provide a better noise reduction level than
the SFANC method. A lightweight one-dimensional convolutional neural network
(1D CNN) is designed to automatically select the most suitable pre-trained
control filter for each frame of the primary noise. Meanwhile, the FxNLMS
algorithm continues to update the coefficients of the chosen pre-trained
control filter at the sampling rate. Owing to the effective combination of the
two algorithms, experimental results show that the hybrid SFANC-FxNLMS
algorithm can achieve a rapid response time, a low noise reduction error, and a
high degree of robustness
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