Abstract: High Strength Concrete (HSC) is defined as concrete
that meets special combination of performance and uniformity
requirements that cannot be achieved routinely using conventional
constituents and normal mixing, placing, and curing procedures. It is
a highly complex material, which makes modeling its behavior a very
difficult task. This paper aimed to show possible applicability of
Neural Networks (NN) to predict the slump in High Strength
Concrete (HSC). Neural Network models is constructed, trained and
tested using the available test data of 349 different concrete mix
designs of High Strength Concrete (HSC) gathered from a particular
Ready Mix Concrete (RMC) batching plant. The most versatile
Neural Network model is selected to predict the slump in concrete.
The data used in the Neural Network models are arranged in a format
of eight input parameters that cover the Cement, Fly Ash, Sand,
Coarse Aggregate (10 mm), Coarse Aggregate (20 mm), Water,
Super-Plasticizer and Water/Binder ratio. Furthermore, to test the
accuracy for predicting slump in concrete, the final selected model is
further used to test the data of 40 different concrete mix designs of
High Strength Concrete (HSC) taken from the other batching plant.
The results are compared on the basis of error function (or
performance function).
Abstract: With increasing complexity in electronic systems
there is a need for system level anomaly detection and fault isolation.
Anomaly detection based on vector similarity to a training set is used
in this paper through two approaches, one the preserves the original
information, Mahalanobis Distance (MD), and the other that
compresses the data into its principal components, Projection Pursuit
Analysis. These methods have been used to detect deviations in
system performance from normal operation and for critical parameter
isolation in multivariate environments. The study evaluates the
detection capability of each approach on a set of test data with known
faults against a baseline set of data representative of such “healthy"
systems.
Abstract: Biological data has several characteristics that strongly differentiate it from typical business data. It is much more complex, usually large in size, and continuously changes. Until recently business data has been the main target for discovering trends, patterns or future expectations. However, with the recent rise in biotechnology, the powerful technology that was used for analyzing business data is now being applied to biological data. With the advanced technology at hand, the main trend in biological research is rapidly changing from structural DNA analysis to understanding cellular functions of the DNA sequences. DNA chips are now being used to perform experiments and DNA analysis processes are being used by researchers. Clustering is one of the important processes used for grouping together similar entities. There are many clustering algorithms such as hierarchical clustering, self-organizing maps, K-means clustering and so on. In this paper, we propose a clustering algorithm that imitates the ecosystem taking into account the features of biological data. We implemented the system using an Ant-Colony clustering algorithm. The system decides the number of clusters automatically. The system processes the input biological data, runs the Ant-Colony algorithm, draws the Topic Map, assigns clusters to the genes and displays the output. We tested the algorithm with a test data of 100 to1000 genes and 24 samples and show promising results for applying this algorithm to clustering DNA chip data.
Abstract: Automatic face detection is a complex problem in
image processing. Many methods exist to solve this problem such as
template matching, Fisher Linear Discriminate, Neural Networks,
SVM, and MRC. Success has been achieved with each method to
varying degrees and complexities. In proposed algorithm we used
upright, frontal faces for single gray scale images with decent
resolution and under good lighting condition. In the field of face
recognition technique the single face is matched with single face
from the training dataset. The author proposed a neural network
based face detection algorithm from the photographs as well as if any
test data appears it check from the online scanned training dataset.
Experimental result shows that the algorithm detected up to 95%
accuracy for any image.
Abstract: In this paper, a neural network technique is applied to
real-time classifying media while a projectile is penetrating through
them. A laboratory-scaled penetrating setup was built for the
experiment. Features used as the network inputs were extracted from
the acceleration of penetrator. 6000 set of features from a single
penetration with known media and status were used to train the neural
network. The trained system was tested on 30 different penetration
experiments. The system produced an accuracy of 100% on the
training data set. And, their precision could be 99% for the test data
from 30 tests.
Abstract: Analytical procedure was carried out in this paper to
calculate the ultimate load capacity of reinforced concrete corbels
strengthened or repaired externally with CFRP sheets. Strut and tie
method and shear friction method proposed earlier for analyzing
reinforced concrete corbels were modified to incorporate the effect of
external CFRP sheets bonded to the corbel. The points of weakness
of any method that lead to an inaccuracy, especially when
overestimating test results were checked and discussed. Comparison
of prediction with the test data indicates that the ratio of test /
calculated ultimate load is 0.82 and 1.17 using strut and tie method
and shear friction method, respectively. If the limits of maximum
shear stress is followed, the calculated ultimate load capacity using
shear friction method was found to underestimates test data
considerably.
Abstract: This paper is an extension of a previous work where a diagonally implicit harmonic balance method was developed and applied to simulate oscillatory motions of pitching airfoil and wing. A more detailed study on the accuracy, convergence, and the efficiency of the method is carried out in the current paperby varying the number of harmonics in the solution approximation. As the main advantage of the method is itsusage for the design optimization of the unsteady problems, its application to more practical case of rotor flow analysis during forward flight is carried out and compared with flight test data and time-accurate computation results.
Abstract: Model Predictive Control has been previously applied
to supply chain problems with promising results; however hitherto
proposed systems possessed no information on future demand. A
forecasting methodology will surely promote the efficiency of
control actions by providing insight on the future. A complete supply
chain management framework that is based on Model Predictive
Control (MPC) and Time Series Forecasting will be presented in this
paper. The proposed framework will be tested on industrial data in
order to assess the efficiency of the method and the impact of
forecast accuracy on overall control performance of the supply chain.
To this end, forecasting methodologies with different characteristics
will be implemented on test data to generate forecasts that will serve
as input to the Model Predictive Control module.
Abstract: It is important to predict yield in semiconductor test process in order to increase yield. In this study, yield prediction means finding out defective die, wafer or lot effectively. Semiconductor test process consists of some test steps and each test includes various test items. In other world, test data has a big and complicated characteristic. It also is disproportionably distributed as the number of data belonging to FAIL class is extremely low. For yield prediction, general data mining techniques have a limitation without any data preprocessing due to eigen properties of test data. Therefore, this study proposes an under-sampling method using support vector machine (SVM) to eliminate an imbalanced characteristic. For evaluating a performance, randomly under-sampling method is compared with the proposed method using actual semiconductor test data. As a result, sampling method using SVM is effective in generating robust model for yield prediction.
Abstract: A comprehensive program of laboratory testing on a
compacted kaolin in a modified triaxial cell was perform to
investigate the influence of hydraulic hysteresis on effective stress in
unsaturated soils. The test data are presented on a range of constant
suction shear tests along wetting and drying paths. The values of
effective stress parameter χ at different matric suction were
determined using the test results. The effect of hydraulic hysteresis
phenomenon on the effective stress was observed. The values of
effective stress parameter χ obtained from the experiments were
compared with those obtained from the expressions proposed in
literature.
Abstract: Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.
Abstract: In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively
Abstract: Protein 3D structure prediction has always been an
important research area in bioinformatics. In particular, the
prediction of secondary structure has been a well-studied research
topic. Despite the recent breakthrough of combining multiple
sequence alignment information and artificial intelligence algorithms
to predict protein secondary structure, the Q3 accuracy of various
computational prediction algorithms rarely has exceeded 75%. In a
previous paper [1], this research team presented a rule-based method
called RT-RICO (Relaxed Threshold Rule Induction from Coverings)
to predict protein secondary structure. The average Q3 accuracy on
the sample datasets using RT-RICO was 80.3%, an improvement
over comparable computational methods. Although this demonstrated
that RT-RICO might be a promising approach for predicting
secondary structure, the algorithm-s computational complexity and
program running time limited its use. Herein a parallelized
implementation of a slightly modified RT-RICO approach is
presented. This new version of the algorithm facilitated the testing of
a much larger dataset of 396 protein domains [2]. Parallelized RTRICO
achieved a Q3 score of 74.6%, which is higher than the
consensus prediction accuracy of 72.9% that was achieved for the
same test dataset by a combination of four secondary structure
prediction methods [2].
Abstract: Base on extenics theory, the statistical physical and mechanical properties from laboratory experiments are used to evaluate the bearing capacity of lateritic soil foundation. The properties include water content, bulk density, liquid limit, cohesion, and so on. The matter-element and the dependent function are defined. Then the synthesis dependent degree and the final grade index are calculated. The results show that predicted outcomes can be matched with the in-situ test data, and a evaluate grade associate with bearing capacity can be deduced. The results provide guidance to assess and determine the bearing capacity grade of lateritic soil foundation.
Abstract: In the present study, a steady-state simulation model
has been developed to evaluate the system performance of a
transcritical carbon dioxide heat pump system for simultaneous water
cooling and heating. Both the evaporator (including both two-phase
and superheated zone) and gas cooler models consider the highly
variable heat transfer characteristics of CO2 and pressure drop. The
numerical simulation model of transcritical CO2 heat pump has been
validated by test data obtained from experiments on the heat pump
prototype. Comparison between the test results and the model
prediction for system COP variation with compressor discharge
pressure shows a modest agreement with a maximum deviation of
15% and the trends are fairly similar. Comparison for other operating
parameters also shows fairly similar deviation between the test
results and the model prediction. Finally, the simulation results are
presented to study the effects of operating parameters such as,
temperature of heat exchanger fluid at the inlet, discharge pressure,
compressor speed on system performance of CO2 heat pump, suitable
in a dairy plant where simultaneous cooling at 4oC and heating at
73oC are required. Results show that good heat transfer properties of
CO2 for both two-phase and supercritical region and efficient
compression process contribute a lot for high system COPs.
Abstract: Research into the problem of classification of sonar signals has been taken up as a challenging task for the neural networks. This paper investigates the design of an optimal classifier using a Multi layer Perceptron Neural Network (MLP NN) and Support Vector Machines (SVM). Results obtained using sonar data sets suggest that SVM classifier perform well in comparison with well-known MLP NN classifier. An average classification accuracy of 91.974% is achieved with SVM classifier and 90.3609% with MLP NN classifier, on the test instances. The area under the Receiver Operating Characteristics (ROC) curve for the proposed SVM classifier on test data set is found as 0.981183, which is very close to unity and this clearly confirms the excellent quality of the proposed classifier. The SVM classifier employed in this paper is implemented using kernel Adatron algorithm is seen to be robust and relatively insensitive to the parameter initialization in comparison to MLP NN.
Abstract: State-based testing is frequently used in software testing. Test data generation is one of the key issues in software testing. A properly generated test suite may not only locate the errors in a software system, but also help in reducing the high cost associated with software testing. It is often desired that test data in the form of test sequences within a test suite can be automatically generated to achieve required test coverage. This paper proposes an Ant Colony Optimization approach to test data generation for the state-based software testing.
Abstract: The dynamic or complex modulus test is considered
to be a mechanistically based laboratory test to reliably characterize
the strength and load-resistance of Hot-Mix Asphalt (HMA) mixes
used in the construction of roads. The most common observation is
that the data collected from these tests are often noisy and somewhat
non-sinusoidal. This hampers accurate analysis of the data to obtain
engineering insight. The goal of the work presented in this paper is to
develop and compare automated evolutionary computational
techniques to filter test noise in the collection of data for the HMA
complex modulus test. The results showed that the Covariance
Matrix Adaptation-Evolutionary Strategy (CMA-ES) approach is
computationally efficient for filtering data obtained from the HMA
complex modulus test.
Abstract: A Vehicle-s door wireing harness arrangement structure is provided. In vehicle-s door wiring harness(W/H) system is more toward to arrange a passenger compartment than a hinge and a weatherstrip. This article gives some insight into the dimensioning process, with special focus on large deflection analysis of wiring harness(W/H) in vehicle-s door structures for durability problem. An Finite elements analysis for door wiring harness(W/H) are used for residual stresses and dimensional stability with bending flexible. Durability test data for slim test specimens were compared with the numerical predicted fatigue life for verification. The final lifing of the component combines the effects of these microstructural features with the complex stress state arising from the combined service loading and residual stresses.
Abstract: Automatic reading of handwritten cheque is a computationally
complex process and it plays an important role in financial
risk management. Machine vision and learning provide a viable
solution to this problem. Research effort has mostly been focused
on recognizing diverse pitches of cheques and demand drafts with an
identical outline. However most of these methods employ templatematching
to localize the pitches and such schemes could potentially
fail when applied to different types of outline maintained by the
bank. In this paper, the so-called outline problem is resolved by
a cheque information tree (CIT), which generalizes the localizing
method to extract active-region-of-entities. In addition, the weight
based density plot (WBDP) is performed to isolate text entities and
read complete pitches. Recognition is based on texture features using
neural classifiers. Legal amount is subsequently recognized by both
texture and perceptual features. A post-processing phase is invoked
to detect the incorrect readings by Type-2 grammar using the Turing
machine. The performance of the proposed system was evaluated
using cheque and demand drafts of 22 different banks. The test data
consists of a collection of 1540 leafs obtained from 10 different
account holders from each bank. Results show that this approach
can easily be deployed without significant design amendments.