585 research outputs found
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment
Understanding mobile traffic patterns of large scale cellular towers in urban
environment is extremely valuable for Internet service providers, mobile users,
and government managers of modern metropolis. This paper aims at extracting and
modeling the traffic patterns of large scale towers deployed in a metropolitan
city. To achieve this goal, we need to address several challenges, including
lack of appropriate tools for processing large scale traffic measurement data,
unknown traffic patterns, as well as handling complicated factors of urban
ecology and human behaviors that affect traffic patterns. Our core contribution
is a powerful model which combines three dimensional information (time,
locations of towers, and traffic frequency spectrum) to extract and model the
traffic patterns of thousands of cellular towers. Our empirical analysis
reveals the following important observations. First, only five basic
time-domain traffic patterns exist among the 9,600 cellular towers. Second,
each of the extracted traffic pattern maps to one type of geographical
locations related to urban ecology, including residential area, business
district, transport, entertainment, and comprehensive area. Third, our
frequency-domain traffic spectrum analysis suggests that the traffic of any
tower among the 9,600 can be constructed using a linear combination of four
primary components corresponding to human activity behaviors. We believe that
the proposed traffic patterns extraction and modeling methodology, combined
with the empirical analysis on the mobile traffic, pave the way toward a deep
understanding of the traffic patterns of large scale cellular towers in modern
metropolis.Comment: To appear at IMC 201
Evaporation-triggered segregation of sessile binary droplets
Droplet evaporation of multicomponent droplets is essential for various
physiochemical applications, e.g. in inkjet printing, spray cooling and
microfabrication. In this work, we observe and study phase segregation of an
evaporating sessile binary droplet, consisting of a mixture of water and a
surfactant-like liquid (1,2-hexanediol). The phase segregation (i.e., demixing)
leads to a reduced water evaporation rate of the droplet and eventually the
evaporation process ceases due to shielding of the water by the non-volatile
1,2-hexanediol. Visualizations of the flow field by particle image velocimetry
and numerical simulations reveal that the timescale of water evaporation at the
droplet rim is faster than that of the Marangoni flow, which originates from
the surface tension difference between water and 1,2-hexanediol, eventually
leading to segregation
An observer-based type-3 fuzzy control for non-holonomic wheeled robots
Non-holonomic wheeled robots (NWR) comprise a type of robotic system; they use wheels
for movement and offer several advantages over other types. They are efficient, highly, and maneuverable, making them ideal for factory automation, logistics, transportation, and healthcare. The control of this type of robot is complicated, due to the complexity of modeling, asymmetrical non-holonomic constraints, and unknown perturbations in various applications. Therefore, in this study, a novel type-3 (T3) fuzzy logic system (FLS)-based controller is developed for NWRs. T3-FLSs are employed for modeling, and the modeling errors are considered in stability analysis based on the symmetric Lyapunov function. An observer is designed to detect the error, and its effect is eliminated by a developed terminal sliding mode controller (SMC). The designed technique is used to control a case-study NWR, and the results demonstrate the good accuracy of the developed scheme under non-holonomic constraints, unknown dynamics, and nonlinear disturbances
Characterizing the Influence of Graph Elements
Influence function, a method from robust statistics, measures the changes of
model parameters or some functions about model parameters concerning the
removal or modification of training instances. It is an efficient and useful
post-hoc method for studying the interpretability of machine learning models
without the need for expensive model re-training. Recently, graph convolution
networks (GCNs), which operate on graph data, have attracted a great deal of
attention. However, there is no preceding research on the influence functions
of GCNs to shed light on the effects of removing training nodes/edges from an
input graph. Since the nodes/edges in a graph are interdependent in GCNs, it is
challenging to derive influence functions for GCNs. To fill this gap, we
started with the simple graph convolution (SGC) model that operates on an
attributed graph and formulated an influence function to approximate the
changes in model parameters when a node or an edge is removed from an
attributed graph. Moreover, we theoretically analyzed the error bound of the
estimated influence of removing an edge. We experimentally validated the
accuracy and effectiveness of our influence estimation function. In addition,
we showed that the influence function of an SGC model could be used to estimate
the impact of removing training nodes/edges on the test performance of the SGC
without re-training the model. Finally, we demonstrated how to use influence
functions to guide the adversarial attacks on GCNs effectively
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