Abstract: As German companies roll out their standardized
production systems to offshore manufacturing plants, they face the
challenge of implementing them in different cultural environments.
Studies show that the local adaptation is one of the key factors for a
successful implementation. Thus the question arises of where the line
between standardization and adaptation can be drawn. To answer
this question the influence of culture on production systems is
analysed in this paper. The culturally contingent components of
production systems are identified. Also the contingency factors are
classified according to their impact on the necessary adaptation
changes and implementation effort. Culturally specific decision
making, coordination, communication and motivation patterns
require one-time changes in organizational and process design. The
attitude towards rules requires more intense coaching and controlling.
Lastly a framework is developed to depict standardization and
adaption needs when transplanting production systems into different
cultural environments.
Abstract: Mining frequent tree patterns have many useful
applications in XML mining, bioinformatics, network routing, etc.
Most of the frequent subtree mining algorithms (i.e. FREQT,
TreeMiner and CMTreeMiner) use anti-monotone property in the
phase of candidate subtree generation. However, none of these
algorithms have verified the correctness of this property in tree
structured data. In this research it is shown that anti-monotonicity
does not generally hold, when using weighed support in tree pattern
discovery. As a result, tree mining algorithms that are based on this
property would probably miss some of the valid frequent subtree
patterns in a collection of trees. In this paper, we investigate the
correctness of anti-monotone property for the problem of weighted
frequent subtree mining. In addition we propose W3-Miner, a new
algorithm for full extraction of frequent subtrees. The experimental
results confirm that W3-Miner finds some frequent subtrees that the
previously proposed algorithms are not able to discover.
Abstract: Laser interferometric methods have been utilized for the measurement of natural convection heat transfer from a heated vertical flat plate, in the investigation presented here. The study mainly aims at comparing two different fringe orientations in the wedge fringe setting of Mach-Zehnder interferometer (MZI), used for the measurements. The interference fringes are set in horizontal and vertical orientations with respect to the heated surface, and two different fringe analysis methods, namely the stepping method and the method proposed by Naylor and Duarte, are used to obtain the heat transfer coefficients. The experimental system is benchmarked with theoretical results, thus validating its reliability in heat transfer measurements. The interference fringe patterns are analyzed digitally using MATLAB 7 and MOTIC Plus softwares, which ensure improved efficiency in fringe analysis, hence reducing the errors associated with conventional fringe tracing. The work also discuss the relative merits and limitations of the two methods used.
Abstract: A numerical study is made of laminar, unsteady flow
behind a rotationally oscillating circular cylinder using a recently
developed higher order compact (HOC) scheme. The stream function
vorticity formulation of Navier-Stokes (N-S) equations in cylindrical
polar coordinates are considered as the governing equations. The
temporal behaviour of vortex formation and relevant streamline
patterns of the flow are scrutinized over broad ranges of two
externally specified parameters namely dimensionless forced
oscillating frequency Sf and dimensionless peak rotation rate αm for
the Reynolds-s number Re = 200. Excellent agreements are found
both qualitatively and quantitatively with the existing experimental
and standard numerical results.
Abstract: Home Automation is a field that, among other
subjects, is concerned with the comfort, security and energy
requirements of private homes. The configuration of automatic
functions in this type of houses is not always simple to its inhabitants
requiring the initial setup and regular adjustments. In this work, the
ubiquitous computing system vision is used, where the users- action
patterns are captured, recorded and used to create the contextawareness
that allows the self-configuration of the home automation
system. The system will try to free the users from setup adjustments
as the home tries to adapt to its inhabitants- real habits. In this paper
it is described a completely automated process to determine the light
state and act on them, taking in account the users- daily habits.
Artificial Neural Network (ANN) is used as a pattern recognition
method, classifying for each moment the light state. The work
presented uses data from a real house where a family is actually
living.
Abstract: This paper presents the analysis of similarity between local decisions, in the process of alphanumeric hand-prints classification. From the analysis of local characteristics of handprinted numerals and characters, extracted by a zoning method, the set of classification decisions is obtained and the similarity among them is investigated. For this purpose the Similarity Index is used, which is an estimator of similarity between classifiers, based on the analysis of agreements between their decisions. The experimental tests, carried out using numerals and characters from the CEDAR and ETL database, respectively, show to what extent different parts of the patterns provide similar classification decisions.
Abstract: Performance of vehicle depends on driving patterns
and vehicle drive train configuration. Driving patterns depends on
traffic condition, road condition and driver behavior. HEV design is
carried out under certain constrain like vehicle operating range,
acceleration, decelerations, maximum speed and road grades which
are directly related to the driving patterns. Therefore the detailed
study on HEV performance over a different drive cycle is required
for selection and sizing of HEV components. A simple hardware is
design to measured velocity v/s time profile of the vehicle by
operating vehicle on Indian roads under real traffic conditions. To
size the HEV components, a detailed dynamic model of the vehicle is
developed considering the effect of inertia of rotating components
like wheels, drive chain, engine and electric motor. Using vehicle
model and different Indian drive cycles data, total tractive power
demanded by vehicle and power supplied by individual components
has been calculated.Using above information selection and estimation
of component sizing for HEV is carried out so that HEV performs
efficiently under hostile driving condition. Complete analysis is
carried out in LABVIEW.
Abstract: With increasing data in medical databases, medical
data retrieval is growing in popularity. Some of this analysis
including inducing propositional rules from databases using many
soft techniques, and then using these rules in an expert system.
Diagnostic rules and information on features are extracted from
clinical databases on diseases of congenital anomaly. This paper
explain the latest soft computing techniques and some of the
adaptive techniques encompasses an extensive group of methods
that have been applied in the medical domain and that are used for
the discovery of data dependencies, importance of features,
patterns in sample data, and feature space dimensionality
reduction. These approaches pave the way for new and interesting
avenues of research in medical imaging and represent an important
challenge for researchers.
Abstract: Temperature is one of the most principle factors affects aquaculture system. It can cause stress and mortality or superior environment for growth and reproduction. This paper presents the control of pond water temperature using artificial intelligence technique. The water temperature is very important parameter for shrimp growth. The required temperature for optimal growth is 34oC, if temperature increase up to 38oC it cause death of the shrimp, so it is important to control water temperature. Solar thermal water heating system is designed to supply an aquaculture pond with the required hot water in Mersa Matruh in Egypt. Neural networks are massively parallel processors that have the ability to learn patterns through a training experience. Because of this feature, they are often well suited for modeling complex and non-linear processes such as those commonly found in the heating system. Artificial neural network is proposed to control water temperature due to Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques. They have been used to solve complicated practical problems. Moreover this paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the aquaculture system. The simulation results indicate that, the control unit success in keeping water temperature constant at the desired temperature by controlling the hot water flow rate.
Abstract: Surface currents play a major role in the distribution
of contaminants, the connectivity of marine populations, and can
influence the vertical and horizontal distribution of nutrients within
the water column. This paper aims to determine the effects of sea
breeze-wind patterns on the climatology of the surface currents on the
continental shelf surrounding Rottnest Island, WA Australia. The
alternating wind patterns allow for full cyclic rotations of wind
direction, permitting the interpretation of the effect of the wind on the
surface currents. It was found that the surface currents only clearly
follow the northbound Capes Current in times when the Fremantle
Doctor sets in. Surface currents react within an hour to a change of
direction of the wind, allowing southerly currents to dominate during
strong northerly sea breezes, often followed by mixed currents
dominated by eddies in the inter-lying times.
Abstract: Extensive rainfall disaggregation approaches have been developed and applied in climate change impact studies such as flood risk assessment and urban storm water management.In this study, five rainfall models that were capable ofdisaggregating daily rainfall data into hourly one were investigated for the rainfall record in theChangi Airport, Singapore. The objectives of this study were (i) to study the temporal characteristics of hourly rainfall in Singapore, and (ii) to evaluate the performance of variousdisaggregation models. The used models included: (i) Rectangular pulse Poisson model (RPPM), (ii) Bartlett-Lewis Rectangular pulse model (BLRPM), (iii) Bartlett-Lewis model with 2 cell types (BL2C), (iv) Bartlett-Lewis Rectangular with cell depth distribution dependent on duration (BLRD), and (v) Neyman-Scott Rectangular pulse model (NSRPM). All of these models werefitted using hourly rainfall data ranging from 1980 to 2005 (which was obtained from Changimeteorological station).The study results indicated that the weight scheme of inversely proportional variance could deliver more accurateoutputs for fitting rainfall patterns in tropical areas, and BLRPM performedrelatively better than other disaggregation models.
Abstract: Microarrays have become the effective, broadly used tools in biological and medical research to address a wide range of problems, including classification of disease subtypes and tumors. Many statistical methods are available for analyzing and systematizing these complex data into meaningful information, and one of the main goals in analyzing gene expression data is the detection of samples or genes with similar expression patterns. In this paper, we express and compare the performance of several clustering methods based on data preprocessing including strategies of normalization or noise clearness. We also evaluate each of these clustering methods with validation measures for both simulated data and real gene expression data. Consequently, clustering methods which are common used in microarray data analysis are affected by normalization and degree of noise and clearness for datasets.
Abstract: We propose a method for discrimination and
classification of ovarian with benign, malignant and normal tissue
using independent component analysis and neural networks. The
method was tested for a proteomic patters set from A database, and
radial basis functions neural networks. The best performance was
obtained with probabilistic neural networks, resulting I 99% success
rate, with 98% of specificity e 100% of sensitivity.
Abstract: Microarrays technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify the dynamics of the gene expression time series. By recourse of principal component analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis. We applied PCA to reduce the dimensionality of the data set. Examination of the components also provides insight into the underlying factors measured in the experiments. Our results suggest that all rhythmic content of data can be reduced to three main components.
Abstract: A simple approach is demonstrated for growing large
scale, nearly vertically aligned ZnO nanowire arrays by thermal
oxidation method. To reveal effect of temperature on growth and
physical properties of the ZnO nanowires, gold coated zinc substrates
were annealed at 300 °C and 400 °C for 4 hours duration in air. Xray
diffraction patterns of annealed samples indicated a set of well
defined diffraction peaks, indexed to the wurtzite hexagonal phase of
ZnO. The scanning electron microscopy studies show formation of
ZnO nanowires having length of several microns and average of
diameter less than 500 nm. It is found that the areal density of wires
is relatively higher, when the annealing is carried out at higher
temperature i.e. at 400°C. From the field emission studies, the values
of the turn-on and threshold field, required to draw emission current
density of 10 μA/cm2 and 100 μA/cm2 are observed to be 1.2 V/μm
and 1.7 V/μm for the samples annealed at 300 °C and 2.9 V/μm and
3.7 V/μm for that annealed at 400 °C, respectively. The field
emission current stability, investigated over duration of more than 2
hours at the preset value of 1 μA, is found to be fairly good in both
cases. The simplicity of the synthesis route coupled with the
promising field emission properties offer unprecedented advantage
for the use of ZnO field emitters for high current density
applications.
Abstract: This paper aims to improve a fine lapping process of
hard disk drive (HDD) lapping machines by removing materials from
each slider together with controlling the strip height (SH) variation to
minimum value. The standard deviation is the key parameter to
evaluate the strip height variation, hence it is minimized. In this
paper, a design of experiment (DOE) with factorial analysis by twoway
analysis of variance (ANOVA) is adopted to obtain a
statistically information. The statistics results reveal that initial stripe
height patterns affect the final SH variation. Therefore, initial SH
classification using a radial basis function neural network is
implemented to achieve the proportional gain prediction.
Abstract: This paper proposes fractal patterns for power quality
(PQ) detection using color relational analysis (CRA) based classifier.
Iterated function system (IFS) uses the non-linear interpolation in the
map and uses similarity maps to construct various fractal patterns of
power quality disturbances, including harmonics, voltage sag, voltage
swell, voltage sag involving harmonics, voltage swell involving
harmonics, and voltage interruption. The non-linear interpolation
functions (NIFs) with fractal dimension (FD) make fractal patterns
more distinguishing between normal and abnormal voltage signals.
The classifier based on CRA discriminates the disturbance events in a
power system. Compared with the wavelet neural networks, the test
results will show accurate discrimination, good robustness, and faster
processing time for detecting disturbing events.
Abstract: This paper presents a novel method for prediction of
the mechanical behavior of proximal femur using the general
framework of the quantitative computed tomography (QCT)-based
finite element Analysis (FEA). A systematic imaging and modeling
procedure was developed for reliable correspondence between the
QCT-based FEA and the in-vitro mechanical testing. A speciallydesigned
holding frame was used to define and maintain a unique
geometrical reference system during the analysis and testing. The
QCT images were directly converted into voxel-based 3D finite
element models for linear and nonlinear analyses. The equivalent
plastic strain and the strain energy density measures were used to
identify the critical elements and predict the failure patterns. The
samples were destructively tested using a specially-designed gripping
fixture (with five degrees of freedom) mounted within a universal
mechanical testing machine. Very good agreements were found
between the experimental and the predicted failure patterns and the
associated load levels.
Abstract: Horizontal wells are proven to be better producers
because they can be extended for a long distance in the pay zone.
Engineers have the technical means to forecast the well productivity
for a given horizontal length. However, experiences have shown that
the actual production rate is often significantly less than that of
forecasted. It is a difficult task, if not impossible to identify the real
reason why a horizontal well is not producing what was forecasted.
Often the source of problem lies in the drilling of horizontal section
such as permeability reduction in the pay zone due to mud invasion
or snaky well patterns created during drilling. Although drillers aim
to drill a constant inclination hole in the pay zone, the more frequent
outcome is a sinusoidal wellbore trajectory. The two factors, which
play an important role in wellbore tortuosity, are the inclination and
side force at bit. A constant inclination horizontal well can only be
drilled if the bit face is maintained perpendicular to longitudinal axis
of bottom hole assembly (BHA) while keeping the side force nil at
the bit. This approach assumes that there exists no formation force at
bit. Hence, an appropriate BHA can be designed if bit side force and
bit tilt are determined accurately. The Artificial Neural Network
(ANN) is superior to existing analytical techniques. In this study, the
neural networks have been employed as a general approximation tool
for estimation of the bit side forces. A number of samples are
analyzed with ANN for parameters of bit side force and the results
are compared with exact analysis. Back Propagation Neural network
(BPN) is used to approximation of bit side forces. Resultant low
relative error value of the test indicates the usability of the BPN in
this area.
Abstract: Moral decisions are considered as an intuitive process,
while conscious reasoning is mostly used only to justify those
intuitions. This problem is described in few different dual-process
theories of mind, that are being developed e.g. by Frederick and
Kahneman, Stanovich and Evans. Those theories recently evolved
into tri-process theories with a proposed process that makes ultimate
decision or allows to paraformal processing with focal bias..
Presented experiment compares the decision patterns to the
implications of those models.
In presented study participants (n=179) considered different
aspects of trolley dilemma or its footbridge version and decided after
that.
Results show that in the control group 70% of people decided to
use the lever to change tracks for the running trolley, and 20% chose
to push the fat man down the tracks. In contrast, after experimental
manipulation almost no one decided to act. Also the decision time
difference between dilemmas disappeared after experimental
manipulation.
The result supports the idea of three co-working processes:
intuitive (TASS), paraformal (reflective mind) and algorithmic
process.