Abstract: Human pose estimation and tracking are to accurately identify and locate the positions of human joints in the video. It is a computer vision task which is of great significance for human motion recognition, behavior understanding and scene analysis. There has been remarkable progress on human pose estimation in recent years. However, more researches are needed for human pose tracking especially for online tracking. In this paper, a framework, called PoseSRPN, is proposed for online single-person pose estimation and tracking. We use Siamese network attaching a pose estimation branch to incorporate Single-person Pose Tracking (SPT) and Visual Object Tracking (VOT) into one framework. The pose estimation branch has a simple network structure that replaces the complex upsampling and convolution network structure with deconvolution. By augmenting the loss of fully convolutional Siamese network with the pose estimation task, pose estimation and tracking can be trained in one stage. Once trained, PoseSRPN only relies on a single bounding box initialization and producing human joints location. The experimental results show that while maintaining the good accuracy of pose estimation on COCO and PoseTrack datasets, the proposed method achieves a speed of 59 frame/s, which is superior to other pose tracking frameworks.
Abstract: In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.
Abstract: Nowadays, Internet enables its users to share the information online and to interact with others. Facing with numerous information, these Internet users are confused and begin to rely on the opinion leaders’ recommendations. The online opinion leaders are the individuals who have professional knowledge, who utilize the online channels to spread word-of-mouth information and who can affect the attitudes or even the behavior of their followers to some degree. Because utilizing the online opinion leaders is seen as an important approach to affect the potential consumers, how to identify them has become one of the hottest topics in the related field. Hence, in this article, the concepts and characteristics are introduced, and the researches related to identifying opinion leaders are collected and divided into three categories. Finally, the implications for future studies are provided.
Abstract: A lattice network is a special type of network in
which all nodes have the same number of links, and its boundary
conditions are periodic. The most basic lattice network is the ring, a
one-dimensional network with periodic border conditions. In contrast,
the Cartesian product of d rings forms a d-dimensional lattice
network. An analytical expression currently exists for the clustering
coefficient in this type of network, but the theoretical value is valid
only up to certain connectivity value; in other words, the analytical
expression is incomplete. Here we obtain analytically the clustering
coefficient expression in d-dimensional lattice networks for any link
density. Our analytical results show that the clustering coefficient for
a lattice network with density of links that tend to 1, leads to the
value of the clustering coefficient of a fully connected network. We
developed a model on criminology in which the generalized clustering
coefficient expression is applied. The model states that delinquents
learn the know-how of crime business by sharing knowledge, directly
or indirectly, with their friends of the gang. This generalization shed
light on the network properties, which is important to develop new
models in different fields where network structure plays an important
role in the system dynamic, such as criminology, evolutionary game
theory, econophysics, among others.
Abstract: Superabsorbent polymers (SAPs) or hydrogels with three-dimensional hydrophilic network structure are high-performance water absorbent and retention materials. The in situ synthesis of metal nanoparticles within polymeric network as antibacterial agents for bio-applications is an approach that takes advantage of the existing free-space into networks, which not only acts as a template for nucleation of nanoparticles, but also provides long term stability and reduces their toxicity by delaying their oxidation and release. In this work, SAP/nanosilver nanocomposites were successfully developed by a unique green process at room temperature, which involves in situ formation of silver nanoparticles (AgNPs) within hydrogels as a template. The aim of this study is to investigate whether these AgNPs-loaded hydrogels are potential candidates for antimicrobial applications. Firstly, the superabsorbents were prepared through radical copolymerization via grafting and crosslinking of acrylamide (AAm) onto chitosan backbone (Cs) using potassium persulfate as initiator and N,N’-methylenebisacrylamide as the crosslinker. Then, they were hydrolyzed to achieve superabsorbents with ampholytic properties and uppermost swelling capacity. Lastly, the AgNPs were biosynthesized and entrapped into hydrogels through a simple, eco-friendly and cost-effective method using aqueous silver nitrate as a silver precursor and curcuma longa tuber-powder extracts as both reducing and stabilizing agent. The formed superabsorbents nanocomposites (Cs-g-PAAm)/AgNPs were characterized by X-ray Diffraction (XRD), UV-visible Spectroscopy, Attenuated Total reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR), Inductively Coupled Plasma (ICP), and Thermogravimetric Analysis (TGA). Microscopic surface structure analyzed by Transmission Electron Microscopy (TEM) has showed spherical shapes of AgNPs with size in the range of 3-15 nm. The extent of nanosilver loading was decreased by increasing Cs content into network. The silver-loaded hydrogel was thermally more stable than the unloaded dry hydrogel counterpart. The swelling equilibrium degree (Q) and centrifuge retention capacity (CRC) in deionized water were affected by both contents of Cs and the entrapped AgNPs. The nanosilver-embedded hydrogels exhibited antibacterial activity against Escherichia coli and Staphylococcus aureus bacteria. These comprehensive results suggest that the elaborated AgNPs-loaded nanomaterials could be used to produce valuable wound dressing.
Abstract: An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.
Abstract: Makishima and Mackenzie model was used to
simulation of acoustic properties (longitudinal and shear ultrasonic
wave velocities, elastic moduli theoretically for many tellurite and
borate glasses. The model was proposed mainly depending on the
values of the experimentally measured density, which are obtained
before. In this search work, we are trying to obtain the values of
densities of amorphous glasses (as the density depends on the
geometry of the network structure of these glasses). In addition, the
problem of simulating the slope of linear regression between the
experimentally determined bulk modulus and the product of packing
density and experimental Young's modulus, were solved in this
search work. The results showed good agreement between the
experimentally measured values of densities and both ultrasonic wave
velocities, and those theoretically determined.
Abstract: This paper presents an evolutionary algorithm for
solving multi-objective optimization problems-based artificial neural
network (ANN). The multi-objective evolutionary algorithm used in
this study is genetic algorithm while ANN used is radial basis
function network (RBFN). The proposed algorithm named memetic
elitist Pareto non-dominated sorting genetic algorithm-based RBFN
(MEPGAN). The proposed algorithm is implemented on medical
diseases problems. The experimental results indicate that the
proposed algorithm is viable, and provides an effective means to
design multi-objective RBFNs with good generalization capability
and compact network structure. This study shows that MEPGAN
generates RBFNs coming with an appropriate balance between
accuracy and simplicity, comparing to the other algorithms found in
literature.
Abstract: Pioneer networked systems assume that connections are reliable, and a faulty operation will be considered in case of losing a connection. Transient connections are typical of mobile devices. Areas of application of data sharing system such as these, lead to the conclusion that network connections may not always be reliable, and that the conventional approaches can be improved. Nigerian commercial banking industry is a critical system whose operation is increasingly becoming dependent on information technology (IT) driven information system. The proposed solution to this problem makes use of a hierarchically clustered network structure which we selected to reflect (as much as possible) the typical organizational structure of the Nigerian commercial banks. Representative transactions such as data updates and replication of the results of such updates were used to simulate the proposed model to show its applicability.
Abstract: The aim of this paper is to evaluate the performance of the faculties of Islamic Azad University of Zahedan Branch based on two-component (teaching and research) decision making units (DMUs) in data envelopment analysis (DEA). Nowadays it is obvious that most of the systems as DMUs do not act as a simple inputoutput structure. Instead, if they have been studied more delicately, they include network structure. University is such a network in which different sections i.e. teaching, research, students and office work as a parallel structure. They consume some inputs of university commonly and some others individually. Then, they produce both dependent and independent outputs. These DMUs are called two-component DMUs
with network structure. In this paper, performance of the faculties of Zahedan branch is calculated by using relative efficiency model and also, a formula to compute relative efficiencies teaching and research components based on DEA are offered.
Abstract: This study uses a simulation to establish a realistic
environment for laboratory research on Accountable Care
Organizations. We study network attributes in order to gain insights
regarding healthcare providers- conduct and performance. Our
findings indicate how network structure creates significant
differences in organizational performance. We demonstrate how
healthcare providers positioning themselves at the central, pivotal
point of the network while maintaining their alliances with their
partners produce better outcomes.
Abstract: Inferring the network structure from time series data
is a hard problem, especially if the time series is short and noisy.
DNA microarray is a technology allowing to monitor the mRNA
concentration of thousands of genes simultaneously that produces
data of these characteristics. In this study we try to investigate the
influence of the experimental design on the quality of the result.
More precisely, we investigate the influence of two different types of
random single gene perturbations on the inference of genetic networks
from time series data. To obtain an objective quality measure for
this influence we simulate gene expression values with a biologically
plausible model of a known network structure. Within this framework
we study the influence of single gene knock-outs in opposite to
linearly controlled expression for single genes on the quality of the
infered network structure.
Abstract: The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: The existence of many biological systems,
especially human societies, is based on cooperative behavior
[1, 2]. If natural selection favors selfish individuals, then what
mechanism is at work that we see so many cooperative
behaviors? One answer is the effect of network structure. On a
graph, cooperators can evolve by forming network bunches
[2, 3, 4]. In a research, Ohtsuki et al used the idea of iterated
prisoners- dilemma on a graph to model an evolutionary
game. They showed that the average number of neighbors
plays an important role in determining whether cooperation is
the ESS of the system or not [3]. In this paper, we are going to
study the dynamics of evolution of cooperation in a social
network. We show that during evolution, the ratio of
cooperators among individuals with fewer neighbors to
cooperators among other individuals is greater than unity. The
extent to which the fitness function depends on the payoff of
the game determines this ratio.
Abstract: Nowadays due to globalization of economy and
competition environment, innovation and technology plays key role
at creation of wealth and economic growth of countries. In fact
prompt growth of practical and technologic knowledge may results in
social benefits for countries when changes into effective innovation.
Considering the importance of innovation for the development of
countries, this study addresses the radical technological innovation
introduced by nanopapers at different stages of producing paper
including stock preparation, using authorized additives, fillers and
pigments, using retention, calender, stages of producing conductive
paper, porous nanopaper and Layer by layer self-assembly. Research
results show that in coming years the jungle related products will lose
considerable portion of their market share, unless embracing radical
innovation. Although incremental innovations can make this industry
still competitive in mid-term, but to have economic growth and
competitive advantage in long term, radical innovations are
necessary. Radical innovations can lead to new products and
materials which their applications in packaging industry can produce
value added. However application of nanotechnology in this industry
can be costly, it can be done in cooperation with other industries to
make the maximum use of nanotechnology possible. Therefore this
technology can be used in all the production process resulting in the
mass production of simple and flexible papers with low cost and
special properties such as facility at shape, form, easy transportation,
light weight, recovery and recycle marketing abilities, and sealing.
Improving the resistance of the packaging materials without reducing
the performance of packaging materials enhances the quality and the
value added of packaging. Improving the cellulose at nano scale can
have considerable electron optical and magnetic effects leading to
improvement in packaging and value added. Comparing to the
specifications of thermoplastic products and ordinary papers,
nanopapers show much better performance in terms of effective
mechanical indexes such as the modulus of elasticity, tensile strength,
and strain-stress. In densities lower than 640 kgm -3, due to the
network structure of nanofibers and the balanced and randomized
distribution of NFC in flat space, these specifications will even
improve more. For nanopapers, strains are 1,4Gpa, 84Mpa and 17%,
13,3 Gpa, 214Mpa and 10% respectively. In layer by layer self
assembly method (LbL) the tensile strength of nanopaper with Tio3
particles and Sio2 and halloysite clay nanotube are 30,4 ±7.6Nm/g
and 13,6 ±0.8Nm/g and 14±0.3,3Nm/g respectively that fall within
acceptable range of similar samples with virgin fiber. The usage of
improved brightness and porosity index in nanopapers can create
more competitive advantages at packaging industry.
Abstract: In modern agriculture, polymeric hydrogels are
known as a component able to hold an amount of water due to their
3-dimensional network structure and their tendency to absorb water
in humid environments. In addition, these hydrogels are able to
controllably release the fertilisers and pesticides loaded in them.
Therefore, they deliver these materials to the plants' roots and help
them with growing. These hydrogels also reduce the pollution of
underground water sources by preventing the active components
from leaching. In this study, sIPN acrylamide based hydrogels are
synthesised by using acrylamide free radical, potassium acrylate, and
linear polyvinyl alcohol. Ammonium nitrate is loaded in the hydrogel
as the fertiliser. The effect of various amounts of monomers and
linear polymer, measured in molar ratio, on the swelling rate,
equilibrium swelling, and release of ammonium nitrate is studied.
Abstract: This paper presents the results of enhancing images from a left and right stereo pair in order to increase the resolution of a 3D representation of a scene generated from that same pair. A new neural network structure known as a Self Delaying Dynamic Network (SDN) has been used to perform the enhancement. The advantage of SDNs over existing techniques such as bicubic interpolation is their ability to cope with motion and noise effects. SDNs are used to generate two high resolution images, one based on frames taken from the left view of the subject, and one based on the frames from the right. This new high resolution stereo pair is then processed by a disparity map generator. The disparity map generated is compared to two other disparity maps generated from the same scene. The first is a map generated from an original high resolution stereo pair and the second is a map generated using a stereo pair which has been enhanced using bicubic interpolation. The maps generated using the SDN enhanced pairs match more closely the target maps. The addition of extra noise into the input images is less problematic for the SDN system which is still able to out perform bicubic interpolation.
Abstract: This paper maps the structure of the social network of
the 2011 class ofsixty graduate students of the Masters of Science
(Knowledge Management) programme at the Nanyang Technological
University, based on their friending relationships on Facebook. To
ensure anonymity, actual names were not used. Instead, they were
replaced with codes constructed from their gender, nationality, mode
of study, year of enrollment and a unique number. The relationships
between friends within the class, and among the seniors and alumni
of the programme wereplotted. UCINet and Pajek were used to plot
the sociogram, to compute the density, inclusivity, and degree,
global, betweenness, and Bonacich centralities, to partition the
students into two groups, namely, active and peripheral, and to
identify the cut-points. Homophily was investigated, and it was
observed for nationality and study mode. The groups students formed
on Facebook were also studied, and of fifteen groups, eight were
classified as dead, which we defined as those that have been inactive
for over two months.
Abstract: In this research, we study a control method of a multivehicle
system while considering the limitation of communication
range for each vehicles. When we control networked vehicles with
limitation of communication range, it is important to control the
communication network structure of a multi-vehicle system in order
to keep the network-s connectivity. From this, we especially aim to
control the network structure to the target structure. We formulate
the networked multi-vehicle system with some disturbance and the
communication constraints as a hybrid dynamical system, and then
we study the optimal control problems of the system. It is shown
that the system converge to the objective network structure in finite
time when the system is controlled by the receding horizon method.
Additionally, the optimal control probrems are convertible into the
mixed integer problems and these problems are solvable by some
branch and bound algorithm.
Abstract: IT infrastructures are becoming more and more
difficult. Therefore, in the first industrial IT systems, the P2P
paradigm has replaced the traditional client server and methods of
self-organization are gaining more and more importance. From the
past it is known that especially regular structures like grids may
significantly improve the system behavior and performance. This
contribution introduces a new algorithm based on a biologic
analogue, which may provide the growth of several regular structures
on top of anarchic grown P2P- or social network structures.