Abstract: This paper presents an analytical method to solve
governing consolidation parabolic partial differential equation (PDE)
for inelastic porous Medium (soil) with consideration of variation of
equation coefficient under cyclic loading. Since under cyclic loads,
soil skeleton parameters change, this would introduce variable
coefficient of parabolic PDE. Classical theory would not rationalize
consolidation phenomenon in such condition. In this research, a
method based on time space mapping to a virtual time space along
with superimposing rule is employed to solve consolidation of
inelastic soils in cyclic condition. Changes of consolidation
coefficient applied in solution by modification of loading and
unloading duration by introducing virtual time. Mapping function is
calculated based on consolidation partial differential equation results.
Based on superimposing rule a set of continuous static loads in
specified times used instead of cyclic load. A set of laboratory
consolidation tests under cyclic load along with numerical
calculations were performed in order to verify the presented method.
Numerical solution and laboratory tests results showed accuracy of
presented method.
Abstract: Development of levels of service in municipal context
is a flexible vehicle to assist in performing quality-cost trade-off
analysis for municipal services. This trade-off depends on the
willingness of a community to pay as well as on the condition of the
assets. Community perspective of the performance of an asset from
service point of view may be quite different from the municipality
perspective of the performance of the same asset from condition
point of view. This paper presents a three phased level of service
based methodology for water mains that consists of :1)development
of an Analytical Hierarchy model of level of service 2) development
of Fuzzy Weighted Sum model of water main condition index and 3)
deriving a Fuzzy logic based function that maps level of service to
asset condition index. This mapping will assist asset managers in
quantifying condition improvement requirement to meet service
goals and to make more informed decisions on interventions and
relayed priorities.
Abstract: Robotic system is an important area in artificial intelligence that aims at developing the performance techniques of the robot and making it more efficient and more effective in choosing its correct behavior. In this paper the distributed learning classifier system is used for designing a simulated control system for robot to perform complex behaviors. A set of enhanced approaches that support default hierarchies formation is suggested and compared with each other in order to make the simulated robot more effective in mapping the input to the correct output behavior.
Abstract: The principal purpose of this article is to present a new method based on Adaptive Neural Network Fuzzy Inference System (ANFIS) to generate additional artificial earthquake accelerograms from presented data, which are compatible with specified response spectra. The proposed method uses the learning abilities of ANFIS to develop the knowledge of the inverse mapping from response spectrum to earthquake records. In addition, wavelet packet transform is used to decompose specified earthquake records and then ANFISs are trained to relate the response spectrum of records to their wavelet packet coefficients. Finally, an interpretive example is presented which uses an ensemble of recorded accelerograms to demonstrate the effectiveness of the proposed method.
Abstract: Recently many research has been conducted to
retrieve pertinent parameters and adequate models for automatic
music genre classification. In this paper, two measures based upon
information theory concepts are investigated for mapping the features
space to decision space. A Gaussian Mixture Model (GMM) is used
as a baseline and reference system. Various strategies are proposed
for training and testing sessions with matched or mismatched
conditions, long training and long testing, long training and short
testing. For all experiments, the file sections used for testing are
never been used during training. With matched conditions all
examined measures yield the best and similar scores (almost 100%).
With mismatched conditions, the proposed measures yield better
scores than the GMM baseline system, especially for the short testing
case. It is also observed that the average discrimination information
measure is most appropriate for music category classifications and on
the other hand the divergence measure is more suitable for music
subcategory classifications.
Abstract: During signal transmission, the combined effect of the
transmitter filter, the transmission medium, and additive white
Gaussian noise (AWGN) are included in the channel which distort
and add noise to the signal. This causes the well defined signal
constellation to spread causing errors in bit detection. A compact pi
neural network with minimum number of nodes is proposed. The
replacement of summation at each node by multiplication results in
more powerful mapping. The resultant pi network is tested on six
different channels.
Abstract: This paper presents an Extended Kaman Filter
implementation of a single-camera Visual Simultaneous Localization
and Mapping algorithm, a novel algorithm for simultaneous
localization and mapping problem widely studied in mobile robotics
field. The algorithm is vision and odometry-based, The odometry
data is incremental, and therefore it will accumulate error over time,
since the robot may slip or may be lifted, consequently if the
odometry is used alone we can not accurately estimate the robot
position, in this paper we show that a combination of odometry and
visual landmark via the extended Kalman filter can improve the robot
position estimate. We use a Pioneer II robot and motorized pan tilt
camera models to implement the algorithm.
Abstract: Domain-specific languages describe specific solutions to problems in the application domain. Traditionally they form a solution composing black-box abstractions together. This, usually, involves non-deep transformations over the target model. In this paper we argue that it is potentially powerful to operate with grey-box abstractions to build a domain-specific software system. We present parametric code templates as grey-box abstractions and conceptual tools to encapsulate and manipulate these templates. Manipulations introduce template-s merging routines and can be defined in a generic way. This involves reasoning mechanisms at the code templates level. We introduce the concept of Neurath Modelling Language (NML) that operates with parametric code templates and specifies a visualisation mapping mechanism for target models. Finally we provide an example of calculating a domain-specific software system with predefined NML elements.
Abstract: Poly-β-hydroxybutyrate (PHB) is one of the most
famous biopolymers that has various applications in production of
biodegradable carriers. The most important strategy for enhancing
efficiency in production process and reducing the price of PHB, is the
accurate expression of kinetic model of products formation and
parameters that are effective on it, such as Dry Cell Weight (DCW)
and substrate consumption. Considering the high capabilities of
artificial neural networks in modeling and simulation of non-linear
systems such as biological and chemical industries that mainly are
multivariable systems, kinetic modeling of microbial production of
PHB that is a complex and non-linear biological process, the three
layers perceptron neural network model was used in this study.
Artificial neural network educates itself and finds the hidden laws
behind the data with mapping based on experimental data, of dry cell
weight, substrate concentration as input and PHB concentration as
output. For training the network, a series of experimental data for
PHB production from Hydrogenophaga Pseudoflava by glucose
carbon source was used. After training the network, two other
experimental data sets that have not intervened in the network
education, including dry cell concentration and substrate
concentration were applied as inputs to the network, and PHB
concentration was predicted by the network. Comparison of predicted
data by network and experimental data, indicated a high precision
predicted for both fructose and whey carbon sources. Also in present
study for better understanding of the ability of neural network in
modeling of biological processes, microbial production kinetic of
PHB by Leudeking-Piret experimental equation was modeled. The
Observed result indicated an accurate prediction of PHB
concentration by artificial neural network higher than Leudeking-
Piret model.
Abstract: In this paper, an efficient technique is proposed to manage the cache memory. The proposed technique introduces some modifications on the well-known set associative mapping technique. This modification requires a little alteration in the structure of the cache memory and on the way by which it can be referenced. The proposed alteration leads to increase the set size virtually and consequently to improve the performance and the utilization of the cache memory. The current mapping techniques have accomplished good results. In fact, there are still different cases in which cache memory lines are left empty and not used, whereas two or more processes overwrite the lines of each other, instead of using those empty lines. The proposed algorithm aims at finding an efficient way to deal with such problem.
Abstract: Recent years have witnessed the rapid development of
the Internet and telecommunication techniques. Information security
is becoming more and more important. Applications such as covert
communication, copyright protection, etc, stimulate the research of
information hiding techniques. Traditionally, encryption is used to
realize the communication security. However, important information
is not protected once decoded. Steganography is the art and science
of communicating in a way which hides the existence of the communication.
Important information is firstly hidden in a host data, such
as digital image, video or audio, etc, and then transmitted secretly
to the receiver.In this paper a data hiding model with high security
features combining both cryptography using finite state sequential
machine and image based steganography technique for communicating
information more securely between two locations is proposed.
The authors incorporated the idea of secret key for authentication
at both ends in order to achieve high level of security. Before the
embedding operation the secret information has been encrypted with
the help of finite-state sequential machine and segmented in different
parts. The cover image is also segmented in different objects through
normalized cut.Each part of the encoded secret information has been
embedded with the help of a novel image steganographic method
(PMM) on different cuts of the cover image to form different stego
objects. Finally stego image is formed by combining different stego
objects and transmit to the receiver side. At the receiving end different
opposite processes should run to get the back the original secret
message.
Abstract: In this paper, we investigate the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks, the neutral system has mixed time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we drive a new criterion for the robust stability of a class of neural networks with time delays by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Numerical examples are given to illustrate the effectiveness and the advantage of the proposed main results.
Abstract: Soft topological spaces are considered as mathematical tools for dealing with uncertainties, and a fuzzy topological space
is a special case of the soft topological space. The purpose of this paper is to study soft topological spaces. We introduce some new concepts in soft topological spaces such as soft closed mapping, soft open mappings, soft connected spaces and soft paracompact spaces. We also redefine the concept of soft points such that it is reasonable in soft topological spaces. Moreover, some basic properties of these concepts are explored.
Abstract: This paper has introduced a slope photogrammetric mapping using unmanned aerial vehicle. There are two units of UAV has been used in this study; namely; fixed wing and multi-rotor. Both UAVs were used to capture images at the study area. A consumer digital camera was mounted vertically at the bottom of UAV and captured the images at an altitude. The objectives of this study are to obtain three dimensional coordinates of slope area and to determine the accuracy of photogrammetric product produced from both UAVs. Several control points and checkpoints were established Real Time Kinematic Global Positioning System (RTK-GPS) in the study area. All acquired images from both UAVs went through all photogrammetric processes such as interior orientation, exterior orientation, aerial triangulation and bundle adjustment using photogrammetric software. Two primary results were produced in this study; namely; digital elevation model and digital orthophoto. Based on results, UAV system can be used to mapping slope area especially for limited budget and time constraints project.
Abstract: Structural representation and technology mapping of
a Boolean function is an important problem in the design of nonregenerative
digital logic circuits (also called combinational logic
circuits). Library aware function manipulation offers a solution to
this problem. Compact multi-level representation of binary networks,
based on simple circuit structures, such as AND-Inverter Graphs
(AIG) [1] [5], NAND Graphs, OR-Inverter Graphs (OIG), AND-OR
Graphs (AOG), AND-OR-Inverter Graphs (AOIG), AND-XORInverter
Graphs, Reduced Boolean Circuits [8] does exist in
literature. In this work, we discuss a novel and efficient graph
realization for combinational logic circuits, represented using a
NAND-NOR-Inverter Graph (NNIG), which is composed of only
two-input NAND (NAND2), NOR (NOR2) and inverter (INV) cells.
The networks are constructed on the basis of irredundant disjunctive
and conjunctive normal forms, after factoring, comprising terms with
minimum support. Construction of a NNIG for a non-regenerative
function in normal form would be straightforward, whereas for the
complementary phase, it would be developed by considering a virtual
instance of the function. However, the choice of best NNIG for a
given function would be based upon literal count, cell count and
DAG node count of the implementation at the technology
independent stage. In case of a tie, the final decision would be made
after extracting the physical design parameters.
We have considered AIG representation for reduced disjunctive
normal form and the best of OIG/AOG/AOIG for the minimized
conjunctive normal forms. This is necessitated due to the nature of
certain functions, such as Achilles- heel functions. NNIGs are found
to exhibit 3.97% lesser node count compared to AIGs and
OIG/AOG/AOIGs; consume 23.74% and 10.79% lesser library cells
than AIGs and OIG/AOG/AOIGs for the various samples considered.
We compare the power efficiency and delay improvement achieved
by optimal NNIGs over minimal AIGs and OIG/AOG/AOIGs for
various case studies. In comparison with functionally equivalent,
irredundant and compact AIGs, NNIGs report mean savings in power
and delay of 43.71% and 25.85% respectively, after technology
mapping with a 0.35 micron TSMC CMOS process. For a
comparison with OIG/AOG/AOIGs, NNIGs demonstrate average
savings in power and delay by 47.51% and 24.83%. With respect to
device count needed for implementation with static CMOS logic
style, NNIGs utilize 37.85% and 33.95% lesser transistors than their
AIG and OIG/AOG/AOIG counterparts.
Abstract: The rapid development of manufacturing and information systems has caused significant changes in manufacturing environments in recent decades. Mass production has given way to flexible manufacturing systems, in which an important characteristic is customized or "on demand" production. In this scenario, the seamless and without gaps information flow becomes a key factor for success of enterprises. In this paper we present a framework to support the mapping of features into machining workingsteps compliant with the ISO 14649 standard (known as STEP-NC). The system determines how the features can be made with the available manufacturing resources. Examples of the mapping method are presented for features such as a pocket with a general surface.
Abstract: Landslide susceptibility map delineates the potential
zones for landslide occurrence. Previous works have applied
multivariate methods and neural networks for mapping landslide
susceptibility. This study proposed a new approach to integrate
decision tree model and spatial cluster statistic for assessing landslide
susceptibility spatially. A total of 2057 landslide cells were digitized
for developing the landslide decision tree model. The relationships of
landslides and instability factors were explicitly represented by using
tree graphs in the model. The local Getis-Ord statistics were used to
cluster cells with high landslide probability. The analytic result from
the local Getis-Ord statistics was classed to create a map of landslide
susceptibility zones. The map was validated using new landslide data
with 482 cells. Results of validation show an accuracy rate of 86.1% in
predicting new landslide occurrence. This indicates that the proposed
approach is useful for improving landslide susceptibility mapping.
Abstract: Organ motion, especially respiratory motion, is a technical challenge to radiation therapy planning and dosimetry. This motion induces displacements and deformation of the organ tissues within the irradiated region which need to be taken into account when simulating dose distribution during treatment. Finite element modeling (FEM) can provide a great insight into the mechanical behavior of the organs, since they are based on the biomechanical material properties, complex geometry of organs, and anatomical boundary conditions. In this paper we present an original approach that offers the possibility to combine image-based biomechanical models with particle transport simulations. We propose a new method to map material density information issued from CT images to deformable tetrahedral meshes. Based on the principle of mass conservation our method can correlate density variation of organ tissues with geometrical deformations during the different phases of the respiratory cycle. The first results are particularly encouraging, as local error quantification of density mapping on organ geometry and density variation with organ motion are performed to evaluate and validate our approach.
Abstract: The increase on the demand of IT resources diverts
the enterprises to use the cloud as a cheap and scalable solution.
Cloud computing promises achieved by using the virtual machine as a
basic unite of computation. However, the virtual machine pre-defined
settings might be not enough to handle jobs QoS requirements. This
paper addresses the problem of mapping jobs have critical start
deadlines to virtual machines that have predefined specifications.
These virtual machines hosted by physical machines and shared a
fixed amount of bandwidth. This paper proposed an algorithm that
uses the idle virtual machines bandwidth to increase the quote of other
virtual machines nominated as executors to urgent jobs. An algorithm
with empirical study have been given to evaluate the impact of the
proposed model on impatient jobs. The results show the importance
of dynamic bandwidth allocation in virtualized environment and its
affect on throughput metric.
Abstract: Selected Mapping (SLM) is a PAPR reduction technique, which converts the OFDM signal into several independent signals by multiplication with the phase sequence set and transmits one of the signals with lowest PAPR. But it requires the index of the selected signal i.e. side information (SI) to be transmitted with each OFDM symbol. The PAPR reduction capability of the SLM scheme depends on the selection of phase sequence set. In this paper, we have proposed a new phase sequence set generation scheme based on M-ary chaotic sequence and a mapping scheme to map quaternary data to concentric circle constellation (CCC) is used. It is shown that this method does not require SI and provides better SER performance with good PAPR reduction capability as compared to existing SLMOFDM methods.