Abstract: Detection, feature extraction and pose estimation of
people in images and video is made challenging by the variability of
human appearance, the complexity of natural scenes and the high
dimensionality of articulated body models and also the important
field in Image, Signal and Vision Computing in recent years. In this
paper, four types of people in 2D dimension image will be tested and
proposed. The system will extract the size and the advantage of them
(such as: tall fat, short fat, tall thin and short thin) from image. Fat
and thin, according to their result from the human body that has been
extract from image, will be obtained. Also the system extract every
size of human body such as length, width and shown them in output.
Abstract: A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.
Abstract: Multi-loop (De-centralized) Proportional-Integral-
Derivative (PID) controllers have been used extensively in process
industries due to their simple structure for control of multivariable
processes. The objective of this work is to design multiple-model
adaptive multi-loop PID strategy (Multiple Model Adaptive-PID)
and neural network based multi-loop PID strategy (Neural Net
Adaptive-PID) for the control of multivariable system. The first
method combines the output of multiple linear PID controllers,
each describing process dynamics at a specific level of operation.
The global output is an interpolation of the individual multi-loop
PID controller outputs weighted based on the current value of the
measured process variable. In the second method, neural network
is used to calculate the PID controller parameters based on the
scheduling variable that corresponds to major shift in the process
dynamics. The proposed control schemes are simple in structure with
less computational complexity. The effectiveness of the proposed
control schemes have been demonstrated on the CSTR process,
which exhibits dynamic non-linearity.
Abstract: This study suggests a model of a new set of evaluation criteria that will be used to measure the efficiency of real-world E-commerce websites. Evaluation criteria include design, usability and performance for websites, the Data Envelopment Analysis (DEA) technique has been used to measure the websites efficiency. An efficient Web site is defined as a site that generates the most outputs, using the smallest amount of inputs. Inputs refer to measurements representing the amount of effort required to build, maintain and perform the site. Output is amount of traffic the site generates. These outputs are measured as the average number of daily hits and the average number of daily unique visitors.
Abstract: The tree structured approach of non-uniform filterbank
(NUFB) is normally used in perfect reconstruction (PR). The PR is
not always feasible due to certain limitations, i.e, constraints in
selecting design parameters, design complexity and some times
output is severely affected by aliasing error if necessary and
sufficient conditions of PR is not satisfied perfectly. Therefore, there
has been generalized interest of researchers to go for near perfect
reconstruction (NPR). In this proposed work, an optimized tree
structure technique is used for the design of NPR non-uniform
filterbank. Window functions of Blackman family are used to design
the prototype FIR filter. A single variable linear optimization is used
to minimize the amplitude distortion. The main feature of the
proposed design is its simplicity with linear phase property.
Abstract: This paper uses the radial basis function neural
network (RBFNN) for system identification of nonlinear systems.
Five nonlinear systems are used to examine the activity of RBFNN in
system modeling of nonlinear systems; the five nonlinear systems are
dual tank system, single tank system, DC motor system, and two
academic models. The feed forward method is considered in this
work for modelling the non-linear dynamic models, where the KMeans
clustering algorithm used in this paper to select the centers of
radial basis function network, because it is reliable, offers fast
convergence and can handle large data sets. The least mean square
method is used to adjust the weights to the output layer, and
Euclidean distance method used to measure the width of the Gaussian
function.
Abstract: This paper proposes the authentication method using
ESA algorithm instead of using CAVE algorithm in the CDMA
mobile communication systems including IS-95 and CDMA2000 1x.
And, we analyze to apply ESA mechanism on behalf of CAVE
mechanism without the change of message format and air interface in
the existing CDMA systems. If ESA algorithm can be used as the
substitution of CAVE algorithm, security strength of authentication
algorithm is intensified without protocol change. An algorithm
replacement proposed in this paper is not to change an authentication
mechanism, but to configure input of ESA algorithm and to produce
output. Therefore, our proposal can be the compatible to the existing
systems.
Abstract: It is suggested to evaluate environmental performance
of energy sector using Data Envelopment Analysis with nondiscretionary
factors (DEA-ND) with relative indicators as inputs and
outputs. The latter allows for comparison of the objects essentially
different in size. Inclusion of non-discretionary factors serves
separation of the indicators that are beyond the control of the objects.
A virtual perfect object comprised of maximal outputs and minimal
inputs was added to the group of actual ones. In this setting, explicit
solution of the DEA-ND problem was obtained. Energy sector of the
United States was analyzed using suggested approach for the period
of 1980 – 2006 with expected values of economic indicators for 2030
used for forming the perfect object. It was obtained that
environmental performance has been increasing steadily for the
period from 7.7% through 50.0% but still remains well below the
prospected level
Abstract: This study presents the application of artificial
neural network for modeling the phenolic compound
migration through vertical soil column. A three layered feed
forward neural network with back propagation training
algorithm was developed using forty eight experimental data
sets obtained from laboratory fixed bed vertical column tests.
The input parameters used in the model were the influent
concentration of phenol(mg/L) on the top end of the soil
column, depth of the soil column (cm), elapsed time after
phenol injection (hr), percentage of clay (%), percentage of
silt (%) in soils. The output of the ANN was the effluent
phenol concentration (mg/L) from the bottom end of the soil
columns. The ANN predicted results were compared with the
experimental results of the laboratory tests and the accuracy of
the ANN model was evaluated.
Abstract: Multilevel inverters supplied from equal and constant
dc sources almost don-t exist in practical applications. The variation
of the dc sources affects the values of the switching angles required
for each specific harmonic profile, as well as increases the difficulty
of the harmonic elimination-s equations. This paper presents an
extremely fast optimal solution of harmonic elimination of multilevel
inverters with non-equal dc sources using Tanaka's fuzzy linear
regression formulation. A set of mathematical equations describing
the general output waveform of the multilevel inverter with nonequal
dc sources is formulated. Fuzzy linear regression is then
employed to compute the optimal solution set of switching angles.
Abstract: This paper gives an introduction to Web mining, then
describes Web Structure mining in detail, and explores the data
structure used by the Web. This paper also explores different Page
Rank algorithms and compare those algorithms used for Information
Retrieval. In Web Mining, the basics of Web mining and the Web
mining categories are explained. Different Page Rank based
algorithms like PageRank (PR), WPR (Weighted PageRank), HITS
(Hyperlink-Induced Topic Search), DistanceRank and DirichletRank
algorithms are discussed and compared. PageRanks are calculated for
PageRank and Weighted PageRank algorithms for a given hyperlink
structure. Simulation Program is developed for PageRank algorithm
because PageRank is the only ranking algorithm implemented in the
search engine (Google). The outputs are shown in a table and chart
format.
Abstract: Amount of dissolve oxygen in a river has a great direct affect on aquatic macroinvertebrates and this would influence on the region ecosystem indirectly. In this paper it is tried to predict dissolved oxygen in rivers by employing an easy Fuzzy Logic Modeling, Wang Mendel method. This model just uses previous records to estimate upcoming values. For this purpose daily and hourly records of eight stations in Au Sable watershed in Michigan, United States are employed for 12 years and 50 days period respectively. Calculations indicate that for long period prediction it is better to increase input intervals. But for filling missed data it is advisable to decrease the interval. Increasing partitioning of input and output features influence a little on accuracy but make the model too time consuming. Increment in number of input data also act like number of partitioning. Large amount of train data does not modify accuracy essentially, so, an optimum training length should be selected.
Abstract: Data Envelopment Analysis (DEA) is a methodology
that computes efficiency values for decision making units (DMU) in a
given period by comparing the outputs with the inputs. In many cases,
there are some time lag between the consumption of inputs and the
production of outputs. For a long-term research project, it is hard to
avoid the production lead time phenomenon. This time lag effect
should be considered in evaluating the performance of organizations.
This paper suggests a model to calculate efficiency values for the
performance evaluation problem with time lag. In the experimental
part, the proposed methods are compared with the CCR and an
existing time lag model using the data set of the 21st century frontier
R&D program which is a long-term national R&D program of Korea.
Abstract: A fully on-chip low drop-out (LDO) voltage regulator with 100pF output load capacitor is presented. A novel frequency compensation scheme using current buffer is adopted to realize single dominant pole within the unit gain frequency of the regulation loop, the phase margin (PM) is at least 50 degree under the full range of the load current, and the power supply rejection (PSR) character is improved compared with conventional Miller compensation. Besides, the differentiator provides a high speed path during the load current transient. Implemented in 0.18μm CMOS technology, the LDO voltage regulator provides 100mA load current with a stable 1.8V output voltage consuming 80μA quiescent current.
Abstract: In this paper, the requirement for Coke quality
prediction, its role in Blast furnaces, and the model output is
explained. By applying method of Artificial Neural Networking
(ANN) using back propagation (BP) algorithm, prediction model has
been developed to predict CSR. Important blast furnace functions
such as permeability, heat exchanging, melting, and reducing
capacity are mostly connected to coke quality. Coke quality is further
dependent upon coal characterization and coke making process
parameters. The ANN model developed is a useful tool for process
experts to adjust the control parameters in case of coke quality
deviations. The model also makes it possible to predict CSR for new
coal blends which are yet to be used in Coke Plant. Input data to the
model was structured into 3 modules, for tenure of past 2 years and
the incremental models thus developed assists in identifying the
group causing the deviation of CSR.
Abstract: Cooperative communication scheme can be substituted
for multiple-input multiple-output (MIMO) technique when it may
not be able to support multiple antennas due to size, cost or
hardware limitations. In other words, cooperative communication
scheme is an efficient method to achieve spatial diversity without
multiple antennas. For satisfaction of rising QoS, we propose a
reliable cooperative communication scheme with M-QAM based Dual
Carrier Modulation (M-DCM), which can increase diversity gain.
Although our proposed scheme is very simple method, it gives us
frequency and spatial diversity. Simulation result shows our proposed
scheme obtains diversity gain more than the conventional cooperative
communication scheme.
Abstract: Gasoline Octane Number is the standard measure of
the anti-knock properties of a motor in platforming processes, that is
one of the important unit operations for oil refineries and can be
determined with online measurement or use CFR (Cooperative Fuel
Research) engines. Online measurements of the Octane number can
be done using direct octane number analyzers, that it is too
expensive, so we have to find feasible analyzer, like ANFIS
estimators.
ANFIS is the systems that neural network incorporated in fuzzy
systems, using data automatically by learning algorithms of NNs.
ANFIS constructs an input-output mapping based both on human
knowledge and on generated input-output data pairs.
In this research, 31 industrial data sets are used (21 data for training
and the rest of the data used for generalization). Results show that,
according to this simulation, hybrid method training algorithm in
ANFIS has good agreements between industrial data and simulated
results.
Abstract: One-way functions are functions that are easy to
compute but hard to invert. Their existence is an open conjecture; it
would imply the existence of intractable problems (i.e. NP-problems
which are not in the P complexity class).
If true, the existence of one-way functions would have an impact
on the theoretical framework of physics, in particularly, quantum
mechanics. Such aspect of one-way functions has never been shown
before.
In the present work, we put forward the following.
We can calculate the microscopic state (say, the particle spin in the
z direction) of a macroscopic system (a measuring apparatus
registering the particle z-spin) by the system macroscopic state (the
apparatus output); let us call this association the function F. The
question is: can we compute the function F in the inverse direction?
In other words, can we compute the macroscopic state of the system
through its microscopic state (the preimage F -1)?
In the paper, we assume that the function F is a one-way function.
The assumption implies that at the macroscopic level the Schrödinger
equation becomes unfeasible to compute. This unfeasibility plays a
role of limit of the validity of the linear Schrödinger equation.
Abstract: In this paper we present two novel 1-bit full adder
cells in dynamic logic style. NP-CMOS (Zipper) and Multi-Output
structures are used to design the adder blocks. Characteristic of
dynamic logic leads to higher speeds than the other standard static
full adder cells. Using HSpice and 0.18┬Ám CMOS technology
exhibits a significant decrease in the cell delay which can result in a
considerable reduction in the power-delay product (PDP). The PDP
of Multi-Output design at 1.8v power supply is around 0.15 femto
joule that is 5% lower than conventional dynamic full adder cell and
at least 21% lower than other static full adders.
Abstract: Optimal reactive power flow is an optimization problem
with one or more objective of minimizing the active power losses for
fixed generation schedule. The control variables are generator bus
voltages, transformer tap settings and reactive power output of the
compensating devices placed on different bus bars. Biogeography-
Based Optimization (BBO) technique has been applied to solve
different kinds of optimal reactive power flow problems subject
to operational constraints like power balance constraint, line flow
and bus voltages limits etc. BBO searches for the global optimum
mainly through two steps: Migration and Mutation. In the present
work, BBO has been applied to solve the optimal reactive power
flow problems on IEEE 30-bus and standard IEEE 57-bus power
systems for minimization of active power loss. The superiority of the
proposed method has been demonstrated. Considering the quality of
the solution obtained, the proposed method seems to be a promising
one for solving these problems.