Abstract: Accounts of language acquisition differ significantly in their treatment of the role of prediction in language learning. In particular, nativist accounts posit that probabilistic learning about words and word sequences has little to do with how children come to use language. The accuracy of this claim was examined by testing whether distributional probabilities and frequency contributed to how well 3-4 year olds repeat simple word chunks. Corresponding chunks were the same length, expressed similar content, and were all grammatically acceptable, yet the results of the study showed marked differences in performance when overall distributional frequency varied. It was found that a distributional model of language predicted the empirical findings better than a number of other models, replicating earlier findings and showing that children attend to distributional probabilities in an adult corpus. This suggested that language is more prediction-and-error based, rather than on abstract rules which nativist camps suggest.
Abstract: A new observer based fault detection and diagnosis
scheme for predicting induction motors- faults is proposed in this
paper. Prediction of incipient faults, using different variants of
Kalman filter and their relative performance are evaluated. Only soft
faults are considered for this work. The data generation, filter
convergence issues, hypothesis testing and residue estimates are
addressed. Simulink model is used for data generation and various
types of faults are considered. A comparative assessment of the
estimates of different observers associated with these faults is
included.
Abstract: The objective of this study is to investigate fire
behaviors, experimentally and numerically, in a scaled version of an
underground station. The effect of ventilation velocity on the fire is
examined. Fire experiments are simulated by burning 10 ml
isopropyl alcohol fuel in a fire pool with dimensions 5cm x 10cm x 4
mm at the center of 1/100 scaled underground station model. A
commercial CFD program FLUENT was used in numerical
simulations. For air flow simulations, k-ω SST turbulence model and
for combustion simulation, non-premixed combustion model are
used. This study showed that, the ventilation velocity is increased
from 1 m/s to 3 m/s the maximum temperature in the station is found
to be less for ventilation velocity of 1 m/s. The reason for these
experimental result lies on the relative dominance of oxygen supply
effect on cooling effect. Without piston effect, maximum temperature
occurs above the fuel pool. However, when the ventilation velocity
increased the flame was tilted in the direction of ventilation and the
location of maximum temperature moves along the flow direction.
The velocities measured experimentally in the station at different
locations are well matched by the CFD simulation results. The
prediction of general flow pattern is satisfactory with the smoke
visualization tests. The backlayering in velocity is well predicted by
CFD simulation. However, all over the station, the CFD simulations
predicted higher temperatures compared to experimental
measurements.
Abstract: In this study is presented a general methodology to
predict the performance of a continuous near-critical fluid extraction
process to remove compounds from aqueous solutions using hollow
fiber membrane contactors. A comprehensive 2D mathematical
model was developed to study Porocritical extraction process. The
system studied in this work is a membrane based extractor of ethanol
and acetone from aqueous solutions using near-critical CO2.
Predictions of extraction percentages obtained by simulations have
been compared to the experimental values reported by Bothun et al.
[5]. Simulations of extraction percentage of ethanol and acetone
show an average difference of 9.3% and 6.5% with the experimental
data, respectively. More accurate predictions of the extraction of
acetone could be explained by a better estimation of the transport
properties in the aqueous phase that controls the extraction of this
solute.
Abstract: This paper presents the possibilities of using Weibull statistical distribution in modeling the distribution of defects in ERP systems. There follows a case study, which examines helpdesk records of defects that were reported as the result of one ERP subsystem upgrade. The result of the applied modeling is in modeling the reliability of the ERP system from a user perspective with estimated parameters like expected maximum number of defects in one day or predicted minimum of defects between two upgrades. Applied measurement-based analysis framework is proved to be suitable in predicting future states of the reliability of the observed ERP subsystems.
Abstract: Several valve stiction models have been proposed in the literature to help understand and study the behavior of sticky valves. In this paper, an alternative black-box modeling approach based on Neural Network (NN) is presented. It is shown that with proper network type and optimum model structures, the performance of the developed NN stiction model is comparable to other established method. The resulting NN model is also tested for its robustness against the uncertainty in the stiction parameter values. Predictive mode operation also shows excellent performance of the proposed model for multi-steps ahead prediction.
Abstract: The three steps of the standard one-way nested grid
for a regional scale of the third generation WAve Model Cycle 4
(WAMC4) is scrutinized. The model application is enabled to solve
the energy balance equation on a coarse resolution grid in order to
produce boundary conditions for a smaller area by the nested grid
technique. In the present study, the model takes a full advantage of the
fine resolution of wind fields in space and time produced by the available
U.S. Navy Global Atmospheric Prediction System (NOGAPS)
model with 1 degree resolution. The nested grid application of the
model is developed in order to gradually increase the resolution from
the open ocean towards the South China Sea (SCS) and the Gulf of
Thailand (GoT) respectively. The model results were compared with
buoy observations at Ko Chang, Rayong and Huahin locations which
were obtained from the Seawatch project. In addition, the results were
also compared with Satun based weather station which was provided
from Department of Meteorology, Thailand. The data collected from
this station presented the significant wave height (Hs) reached 12.85
m. The results indicated that the tendency of the Hs from the model
in the spherical coordinate propagation with deep water condition in
the fine grid domain agreed well with the Hs from the observations.
Abstract: This paper proposes transient angle stability
agents to enhance power system stability. The proposed transient
angle stability agents divided into two strategy agents. The
first strategy agent is a prediction agent that will predict power
system instability. According to the prediction agent-s output,
the second strategy agent, which is a control agent, is automatically
calculating the amount of active power reduction that can
stabilize the system and initiating a control action. The control
action considered is turbine fast valving. The proposed strategies
are applied to a realistic power system, the IEEE 50-
generator system. Results show that the proposed technique can
be used on-line for power system instability prediction and control.
Abstract: Environmental considerations have become an integral part of developmental thinking and decision making in many countries. It is growing rapidly in importance as a discipline of its own. Preventive approaches have been used at the evolutional process of environmental management as a broad and dynamic system for dealing with pollution and environmental degradation. In this regard, Environmental Assessment as an activity for identification and prediction of project’s impacts carried out in the world and its legal significance dates back to late 1960. In Iran, according to the Article 2 of Environmental Protection Act, Environmental Impact Assessment (EIA) should be prepared for seven categories of project. This article has been actively implementing by Department of Environment at 1997. World Bank in 1989 attempted to introducing application of Environmental Assessment for making decision about projects which are required financial assistance in developing countries. So, preparing EIA for obtaining World Bank loan was obligated. Alborz Project is one of the World Bank Projects in Iran which is environmentally significant. Seven out of ten W.B safeguard policies were considered at this project. In this paper, Alborz project, objectives, safeguard policies and role of environmental management will be elaborated
Abstract: The protein domain structure has been widely used as the most informative sequence feature to computationally predict protein-protein interactions. However, in a recent study, a research group has reported a very high accuracy of 94% using hydrophobicity feature. Therefore, in this study we compare and verify the usefulness of protein domain structure and hydrophobicity properties as the sequence features. Using the Support Vector Machines (SVM) as the learning system, our results indicate that both features achieved accuracy of nearly 80%. Furthermore, domains structure had receiver operating characteristic (ROC) score of 0.8480 with running time of 34 seconds, while hydrophobicity had ROC score of 0.8159 with running time of 20,571 seconds (5.7 hours). These results indicate that protein-protein interaction can be predicted from domain structure with reliable accuracy and acceptable running time.
Abstract: Embedded systems need to respect stringent real
time constraints. Various hardware components included in such
systems such as cache memories exhibit variability and therefore
affect execution time. Indeed, a cache memory access from an
embedded microprocessor might result in a cache hit where the
data is available or a cache miss and the data need to be fetched
with an additional delay from an external memory. It is therefore
highly desirable to predict future memory accesses during
execution in order to appropriately prefetch data without incurring
delays. In this paper, we evaluate the potential of several artificial
neural networks for the prediction of instruction memory
addresses. Neural network have the potential to tackle the nonlinear
behavior observed in memory accesses during program
execution and their demonstrated numerous hardware
implementation emphasize this choice over traditional forecasting
techniques for their inclusion in embedded systems. However,
embedded applications execute millions of instructions and
therefore millions of addresses to be predicted. This very
challenging problem of neural network based prediction of large
time series is approached in this paper by evaluating various neural
network architectures based on the recurrent neural network
paradigm with pre-processing based on the Self Organizing Map
(SOM) classification technique.
Abstract: Developing a stable early warning system (EWS)
model that is capable to give an accurate prediction is a challenging
task. This paper introduces k-nearest neighbour (k-NN) method
which never been applied in predicting currency crisis before with the
aim of increasing the prediction accuracy. The proposed k-NN
performance depends on the choice of a distance that is used where in
our analysis; we take the Euclidean distance and the Manhattan as a
consideration. For the comparison, we employ three other methods
which are logistic regression analysis (logit), back-propagation neural
network (NN) and sequential minimal optimization (SMO). The
analysis using datasets from 8 countries and 13 macro-economic
indicators for each country shows that the proposed k-NN method
with k = 4 and Manhattan distance performs better than the other
methods.
Abstract: The paper presents a one-dimensional transient
mathematical model of compressible thermal multi-component gas
mixture flows in pipes. The set of the mass, momentum and enthalpy
conservation equations for gas phase is solved. Thermo-physical
properties of multi-component gas mixture are calculated by solving
the Equation of State (EOS) model. The Soave-Redlich-Kwong
(SRK-EOS) model is chosen. Gas mixture viscosity is calculated on
the basis of the Lee-Gonzales-Eakin (LGE) correlation. Numerical
analysis on rapid decompression in conventional dry gases is
performed by using the proposed mathematical model. The model is
validated on measured values of the decompression wave speed in
dry natural gas mixtures. All predictions show excellent agreement
with the experimental data at high and low pressure. The presented
model predicts the decompression in dry natural gas mixtures much
better than GASDECOM and OLGA codes, which are the most
frequently-used codes in oil and gas pipeline transport service.
Abstract: Partitioning is a critical area of VLSI CAD. In order to build complex digital logic circuits its often essential to sub-divide multi -million transistor design into manageable Pieces. This paper looks at the various partitioning techniques aspects of VLSI CAD, targeted at various applications. We proposed an evolutionary time-series model and a statistical glitch prediction system using a neural network with selection of global feature by making use of clustering method model, for partitioning a circuit. For evolutionary time-series model, we made use of genetic, memetic & neuro-memetic techniques. Our work focused in use of clustering methods - K-means & EM methodology. A comparative study is provided for all techniques to solve the problem of circuit partitioning pertaining to VLSI design. The performance of all approaches is compared using benchmark data provided by MCNC standard cell placement benchmark net lists. Analysis of the investigational results proved that the Neuro-memetic model achieves greater performance then other model in recognizing sub-circuits with minimum amount of interconnections between them.
Abstract: In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.
Abstract: The γ-turns play important roles in protein folding and
molecular recognition. The prediction and analysis of γ-turn types are
important for both protein structure predictions and better
understanding the characteristics of different γ-turn types. This study
proposed a physicochemical property-based decision tree (PPDT)
method to interpretably predict γ-turn types. In addition to the good
prediction performance of PPDT, three simple and human
interpretable IF-THEN rules are extracted from the decision tree
constructed by PPDT. The identified informative physicochemical
properties and concise rules provide a simple way for discriminating
and understanding γ-turn types.
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: This paper proposes a methodology for mitigating the occurrence of cascading failure in stressed power systems. The methodology is essentially based on predicting voltage instability in the power system using a voltage stability index and then devising a corrective action in order to increase the voltage stability margin. The paper starts with a brief description of the cascading failure mechanism which is probable root cause of severe blackouts. Then, the voltage instability indices are introduced in order to evaluate stability limit. The aim of the analysis is to assure that the coordination of protection, by adopting load shedding scheme, capable of enhancing performance of the system after the major location of instability is determined. Finally, the proposed method to generate instability prediction is introduced.
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: 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.