Abstract: The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. An improved model for temperature prediction in Georgia was developed by including information on seasonality and modifying parameters of an existing artificial neural network model. Alternative models were compared by instantiating and training multiple networks for each model. The inclusion of up to 24 hours of prior weather information and inputs reflecting the day of year were among improvements that reduced average four-hour prediction error by 0.18°C compared to the prior model. Results strongly suggest model developers should instantiate and train multiple networks with different initial weights to establish appropriate model parameters.
Abstract: In this research, we propose a weighted class based
queuing (WCBQ) mechanism to provide class differentiation and to
reduce the load for the IMS (IP Multimedia Subsystem) presence
server (PS). The tasks of admission controller for the PS are
demonstrated. Analysis and simulation models are developed to
quantify the performance of WCBQ scheme. An optimized dropping
time frame has been developed based on which some of the preexisting
messages are dropped from the PS-buffer. Cost functions are
developed and simulation comparison has been performed with FCFS
(First Come First Served) scheme. The results show that the PS
benefits significantly from the proposed queuing and dropping
algorithm (WCBQ) during heavy traffic.
Abstract: An artificial neural network (ANN) model is
presented for the prediction of kinematic viscosity of binary mixtures
of poly (ethylene glycol) (PEG) in water as a function of temperature,
number-average molecular weight and mass fraction. Kinematic
viscosities data of aqueous solutions for PEG (0.55419×10-6 –
9.875×10-6 m2/s) were obtained from the literature for a wide range
of temperatures (277.15 - 338.15 K), number-average molecular
weight (200 -10000), and mass fraction (0.0 – 1.0). A three layer
feed-forward artificial neural network was employed. This model
predicts the kinematic viscosity with a mean square error (MSE) of
0.281 and the coefficient of determination (R2) of 0.983. The results
show that the kinematic viscosity of binary mixture of PEG in water
could be successfully predicted using an artificial neural network
model.
Abstract: Brick is one of the most common masonry units used as building material. Due to the demand, different types of waste have been investigated to be incorporated into the bricks. Many types of sludge have been incorporated in fired clay brick for example marble sludge, stone sludge, water sludge, sewage sludge, and ceramic sludge. The utilization of these waste materials in fired clay bricks usually has positive effects on the properties such as lightweight bricks with improved shrinkage, porosity, and strength. This paper reviews on utilization of different types of sludge wastes into fired clay bricks. Previous investigations have demonstrated positive effects on the physical and mechanical properties as well as less impact towards the environment. Thus, the utilizations of sludge waste could produce a good quality of brick and could be one of alternative disposal methods for the sludge wastes.
Abstract: This paper deals with an on-line identification method
of continuous-time Hammerstein systems by using the radial basis
function (RBF) networks and immune algorithm (IA). An unknown
nonlinear static part to be estimated is approximately represented
by the RBF network. The IA is efficiently combined with the
recursive least-squares (RLS) method. The objective function for the
identification is regarded as the antigen. The candidates of the RBF
parameters such as the centers and widths are coded into binary bit
strings as the antibodies and searched by the IA. On the other hand,
the candidates of both the weighting parameters of the RBF network
and the system parameters of the linear dynamic part are updated
by the RLS method. Simulation results are shown to illustrate the
proposed method.
Abstract: Solid dispersions (SD) of curcuminpolyvinylpyrrolidone
in the ratio of 1:2, 1:4, 1:5, 1:6, and 1:8 were
prepared in an attempt to increase the solubility and dissolution.
Solubility, dissolution, powder X-ray diffraction (XRD), differential
scanning calorimetry (DSC) and Fourier transform infrared
spectroscopy (FTIR) of solid dispersions, physical mixtures (PM)
and curcumin were evaluated. Both solubility and dissolution of
curcumin solid dispersions were significantly greater than those
observed for physical mixtures and intact curcumin. The powder
X-ray diffractograms indicated that the amorphous curcumin was
obtained from all solid dispersions. It was found that the optimum
weight ratio for curcumin:PVP K-30 is 1:6. The 1:6 solid dispersion
still in the amorphous from after storage at ambient temperature for 2
years and the dissolution profile did not significantly different from
freshly prepared.
Abstract: A kinetic model for propane dehydrogenation in an
industrial moving bed reactor is developed based on the reported
reaction scheme. The kinetic parameters and activity constant are
fine tuned with several sets of balanced plant data. Plant data at
different operating conditions is applied to validate the model and
the results show a good agreement between the model
predictions and plant observations in terms of the amount of main
product, propylene produced. The simulation analysis of key
variables such as inlet temperature of each reactor (Tinrx) and
hydrogen to total hydrocarbon ratio (H2/THC) affecting process
performance is performed to identify the operating condition to
maximize the production of propylene. Within the range of operating
conditions applied in the present studies, the operating condition to
maximize the propylene production at the same weighted average
inlet temperature (WAIT) is ΔTinrx1= -2, ΔTinrx2= +1, ΔTinrx3= +1 ,
ΔTinrx4= +2 and ΔH2/THC= -0.02. Under this condition, the surplus
propylene produced is 7.07 tons/day as compared with base case.
Abstract: The experimental study of position control of a light
weight and small size robotic finger during non-contact motion is
presented in this paper. The finger possesses fingertip pinching and
self adaptive grasping capabilities, and is made of a seven bar linkage
mechanism with a slider in the middle phalanx. The control system is
tested under the Proportional Integral Derivative (PID) control
algorithm and Recursive Least Square (RLS) based Feedback Error
Learning (FEL) control scheme to overcome the uncertainties present
in the plant. The experiments conducted in Matlab Simulink and xPC
Target environments show that the overall control strategy is efficient
in controlling the finger movement.
Abstract: The aim of this paper is to present a comparative
study on two different methods for the evaluation of the equilibrium
point of a ship, core issue for designing an On Board Stability System
(OBSS) module that, starting from geometry information of a ship
hull, described by a discrete model in a standard format, and the
distribution of all weights onboard calculates the ship floating
conditions (in draught, heel and trim).
Abstract: In order to reduce cost, increase quality, and for
timely supplying production systems has considerably taken the
advantages of supply chain management and these advantages are
also competitive. Selection of appropriate supplier has an important
role in improvement and efficiency of systems.
The models of supplier selection which have already been used by
researchers have considered selection one or more suppliers from
potential suppliers but in this paper selecting one supplier as partner
from one supplier that have minimum one period supplying to buyer
is considered.
This paper presents a conceptual model for partner selection and
application of Degree of Adoptive (DOA) model for final selection.
The attributes weight in this model is prepared through AHP
model. After making the descriptive model, determining the
attributes and measuring the parameters of the adaptive is examined
in an auto industry of Iran(Zagross Khodro co.) and results are
presented.
Abstract: Fast forecasting of stock market prices is very important for
strategic planning. In this paper, a new approach for fast forecasting of
stock market prices is presented. Such algorithm uses new high speed
time delay neural networks (HSTDNNs). The operation of these
networks relies on performing cross correlation in the frequency
domain between the input data and the input weights of neural
networks. It is proved mathematically and practically that the number
of computation steps required for the presented HSTDNNs is less
than that needed by traditional time delay neural networks
(TTDNNs). Simulation results using MATLAB confirm the
theoretical computations.
Abstract: In this paper, a method for deriving a group priority vector in the Fuzzy Analytic Network Process (FANP) is proposed. By introducing importance weights of multiple decision makers (DMs) based on their experiences, the Fuzzy Preferences Programming Method (FPP) is extended to a fuzzy group prioritization problem in the FANP. Additionally, fuzzy pair-wise comparison judgments are presented rather than exact numerical assessments in order to model the uncertainty and imprecision in the DMs- judgments and then transform the fuzzy group prioritization problem into a fuzzy non-linear programming optimization problem which maximize the group satisfaction. Unlike the known fuzzy prioritization techniques, the new method proposed in this paper can easily derive crisp weights from incomplete and inconsistency fuzzy set of comparison judgments and does not require additional aggregation producers. Detailed numerical examples are used to illustrate the implement of our approach and compare with the latest fuzzy prioritization method.
Abstract: The development of sustainable utilization water resources is crucial. The ecological environment and water resources systems form the foundation of the existence and development of the social economy. The urban ecological support system depends on these resources as well. This research studies the vulnerability, criticality, and risk of climate change on water supply and demand in the main administrative district of the Taijiang Area (Tainan City). Based on the two situations set in this paper and various factors (indexes), this research adopts two kinds of weights (equal and AHP) to conduct the calculation and establish the water supply and demand risk map for the target year 2039. According to the risk analysis result, which is based on equal weight, only one district belongs to a high-grade district (Grade 4). Based on the AHP weight, 16 districts belong to a high-grade or higher-grade district (Grades 4 and 5), and from among them, two districts belong to the highest grade (Grade 5). These results show that the risk level of water supply and demand in cities is higher than that in towns. The government generally gives more attention to the adjustment strategy in the “cities." However, it should also provide proper adjustment strategies for the “towns" to be able to cope with the risks of water supply and demand.
Abstract: This work presents a novel means of extracting fixedlength parameters from voice signals, such that words can be recognized
in linear time. The power and the zero crossing rate are first
calculated segment by segment from a voice signal; by doing so, two
feature sequences are generated. We then construct an FIR system
across these two sequences. The parameters of this FIR system, used
as the input of a multilayer proceptron recognizer, can be derived by
recursive LSE (least-square estimation), implying that the complexity of overall process is linear to the signal size. In the second part of
this work, we introduce a weighting factor λ to emphasize recent
input; therefore, we can further recognize continuous speech signals.
Experiments employ the voice signals of numbers, from zero to nine, spoken in Mandarin Chinese. The proposed method is verified to
recognize voice signals efficiently and accurately.
Abstract: Since the actuator capacity is limited, in the real
application of active control systems under sever earthquakes it is
conceivable that the actuators saturate, hence the actuator saturation
should be considered as a constraint in design of optimal controllers.
In this paper optimal design of active controllers for nonlinear
structures by considering actuator saturation, has been studied. The
proposed method for designing optimal controllers is based on
defining an optimization problem which the objective has been to
minimize the maximum displacement of structure when a limited
capacity for actuator has been used. To this end a single degree of
freedom (SDF) structure with a bilinear hysteretic behavior has been
simulated under a white noise ground acceleration of different
amplitudes. Active tendon control mechanism, comprised of prestressed
tendons and an actuator, and extended nonlinear Newmark
method based instantaneous optimal control algorithm have been
used. To achieve the best results, the weights corresponding to
displacement, velocity, acceleration and control force in the
performance index have been optimized by the Distributed Genetic
Algorithm (DGA). Results show the effectiveness of the proposed
method in considering actuator saturation. Also based on the
numerical simulations it can be concluded that the actuator capacity
and the average value of required control force are two important
factors in designing nonlinear controllers which consider the actuator
saturation.
Abstract: Variable speed drives are growing and varying. Drives expanse depend on progress in different part of science like power system, microelectronic, control methods, and so on. Artificial intelligent contains hard computation and soft computation. Artificial intelligent has found high application in most nonlinear systems same as motors drive. Because it has intelligence like human but there are no sentimental against human like angriness and.... Artificial intelligent is used for various points like approximation, control, and monitoring. Because artificial intelligent techniques can use as controller for any system without requirement to system mathematical model, it has been used in electrical drive control. With this manner, efficiency and reliability of drives increase and volume, weight and cost of them decrease.
Abstract: Segmentation techniques based on Active Contour
Models have been strongly benefited from the use of prior information
during their evolution. Shape prior information is captured from
a training set and is introduced in the optimization procedure to
restrict the evolution into allowable shapes. In this way, the evolution
converges onto regions even with weak boundaries. Although
significant effort has been devoted on different ways of capturing
and analyzing prior information, very little thought has been devoted
on the way of combining image information with prior information.
This paper focuses on a more natural way of incorporating the
prior information in the level set framework. For proof of concept
the method is applied on hippocampus segmentation in T1-MR
images. Hippocampus segmentation is a very challenging task, due
to the multivariate surrounding region and the missing boundary
with the neighboring amygdala, whose intensities are identical. The
proposed method, mimics the human segmentation way and thus
shows enhancements in the segmentation accuracy.
Abstract: The main goal of the present work is to decrease the
computational burden for optimum design of steel frames with
frequency constraints using a new type of neural networks called
Wavelet Neural Network. It is contested to train a suitable neural
network for frequency approximation work as the analysis program.
The combination of wavelet theory and Neural Networks (NN)
has lead to the development of wavelet neural networks.
Wavelet neural networks are feed-forward networks using
wavelet as activation function. Wavelets are mathematical
functions within suitable inner parameters, which help them to
approximate arbitrary functions. WNN was used to predict the
frequency of the structures. In WNN a RAtional function with
Second order Poles (RASP) wavelet was used as a transfer
function. It is shown that the convergence speed was faster
than other neural networks. Also comparisons of WNN with
the embedded Artificial Neural Network (ANN) and with
approximate techniques and also with analytical solutions are
available in the literature.
Abstract: In this study we present our developed formative
assessment tool for students' assignments. The tool enables lecturers
to define assignments for the course and assign each problem in each
assignment a list of criteria and weights by which the students' work
is evaluated. During assessment, the lecturers feed the scores for each
criterion with justifications. When the scores of the current
assignment are completely fed in, the tool automatically generates
reports for both students and lecturers. The students receive a report
by email including detailed description of their assessed work, their
relative score and their progress across the criteria along the course
timeline. This information is presented via charts generated
automatically by the tool based on the scores fed in. The lecturers
receive a report that includes summative (e.g., averages, standard
deviations) and detailed (e.g., histogram) data of the current
assignment. This information enables the lecturers to follow the class
achievements and adjust the learning process accordingly. The tool
was examined on two pilot groups of college students that study a
course in (1) Object-Oriented Programming (2) Plane Geometry.
Results reveal that most of the students were satisfied with the
assessment process and the reports produced by the tool. The
lecturers who used the tool were also satisfied with the reports and
their contribution to the learning process.
Abstract: Adhesively bonded joints are preferred over the
conventional methods of joining such as riveting, welding, bolting
and soldering. Some of the main advantages of adhesive joints
compared to conventional joints are the ability to join dissimilar
materials and damage-sensitive materials, better stress distribution,
weight reduction, fabrication of complicated shapes, excellent
thermal and insulation properties, vibration response and enhanced
damping control, smoother aerodynamic surfaces and an
improvement in corrosion and fatigue resistance. This paper presents
the behavior of adhesively bonded joints subjected to combined
thermal loadings, using the numerical methods. The joint
configuration considers aluminum as central adherend with six
different outer adherends including aluminum, steel, titanium, boronepoxy,
unidirectional graphite-epoxy and cross-ply graphite-epoxy
and epoxy-based adhesives. Free expansion of the joint in x
direction was permitted and stresses in adhesive layer and interfaces
calculated for different adherends.