Abstract: In this paper, a comparative performance analysis of
mostly used four nonlinearity cancellation techniques used to realize
the passive resistor by MOS transistors, is presented. The comparison
is done by using an integrator circuit which is employing sequentially
Op-amp, OTRA and ICCII as active element. All of the circuits are
implemented by MOS-C realization and simulated by PSPICE
program using 0.35μm process TSMC MOSIS model parameters.
With MOS-C realization, the circuits became electronically tunable
and fully integrable which is very important in IC design. The output
waveforms, frequency responses, THD analysis results and features
of the nonlinearity cancellation techniques are also given.
Abstract: People, throughout the history, have made estimates
and inferences about the future by using their past experiences.
Developing information technologies and the improvements in the
database management systems make it possible to extract useful
information from knowledge in hand for the strategic decisions.
Therefore, different methods have been developed. Data mining by
association rules learning is one of such methods. Apriori algorithm,
one of the well-known association rules learning algorithms, is not
commonly used in spatio-temporal data sets. However, it is possible
to embed time and space features into the data sets and make Apriori
algorithm a suitable data mining technique for learning spatiotemporal
association rules. Lake Van, the largest lake of Turkey, is a
closed basin. This feature causes the volume of the lake to increase or
decrease as a result of change in water amount it holds. In this study,
evaporation, humidity, lake altitude, amount of rainfall and
temperature parameters recorded in Lake Van region throughout the
years are used by the Apriori algorithm and a spatio-temporal data
mining application is developed to identify overflows and newlyformed
soil regions (underflows) occurring in the coastal parts of
Lake Van. Identifying possible reasons of overflows and underflows
may be used to alert the experts to take precautions and make the
necessary investments.
Abstract: The critical concern of satellite operations is to ensure
the health and safety of satellites. The worst case in this perspective
is probably the loss of a mission, but the more common interruption
of satellite functionality can result in compromised mission
objectives. All the data acquiring from the spacecraft are known as
Telemetry (TM), which contains the wealth information related to the
health of all its subsystems. Each single item of information is
contained in a telemetry parameter, which represents a time-variant
property (i.e. a status or a measurement) to be checked. As a
consequence, there is a continuous improvement of TM monitoring
systems to reduce the time required to respond to changes in a
satellite's state of health. A fast conception of the current state of the
satellite is thus very important to respond to occurring failures.
Statistical multivariate latent techniques are one of the vital learning
tools that are used to tackle the problem above coherently.
Information extraction from such rich data sources using advanced
statistical methodologies is a challenging task due to the massive
volume of data. To solve this problem, in this paper, we present a
proposed unsupervised learning algorithm based on Principle
Component Analysis (PCA) technique. The algorithm is particularly
applied on an actual remote sensing spacecraft. Data from the
Attitude Determination and Control System (ADCS) was acquired
under two operation conditions: normal and faulty states. The models
were built and tested under these conditions, and the results show that
the algorithm could successfully differentiate between these
operations conditions. Furthermore, the algorithm provides
competent information in prediction as well as adding more insight
and physical interpretation to the ADCS operation.
Abstract: Revenue leakages are one of the major challenges
manufacturers face in production processes, as most of the input
materials that should emanate as products from the lines are lost as
waste. Rather than generating income from material input which is
meant to end-up as products, losses are further incurred as costs in
order to manage waste generated. In addition, due to the lack of a
clear view of the flow of resources on the lines from input to output
stage, acquiring information on the true cost of waste generated have
become a challenge. This has therefore given birth to the
conceptualization and implementation of waste minimization
strategies by several manufacturing industries. This paper reviews the
principles and applications of three environmental management
accounting tools namely Activity-based Costing (ABC), Life-Cycle
Assessment (LCA) and Material Flow Cost Accounting (MFCA) in
the manufacturing industry and their effectiveness in curbing revenue
leakages. The paper unveils the strengths and limitations of each of
the tools; beaming a searchlight on the tool that could allow for
optimal resource utilization, transparency in production process as
well as improved cost efficiency. Findings from this review reveal
that MFCA may offer superior advantages with regards to the
provision of more detailed information (both in physical and
monetary terms) on the flow of material inputs throughout the
production process compared to the other environmental accounting
tools. This paper therefore makes a case for the adoption of MFCA as
a viable technique for the identification and reduction of waste in
production processes, and also for effective decision making by
production managers, financial advisors and other relevant
stakeholders.
Abstract: This paper evaluates the accrual based scheduling for
cloud in single and multi-resource system. Numerous organizations
benefit from Cloud computing by hosting their applications. The
cloud model provides needed access to computing with potentially
unlimited resources. Scheduling is tasks and resources mapping to a
certain optimal goal principle. Scheduling, schedules tasks to virtual
machines in accordance with adaptable time, in sequence under
transaction logic constraints. A good scheduling algorithm improves
CPU use, turnaround time, and throughput. In this paper, three realtime
cloud services scheduling algorithm for single resources and
multiple resources are investigated. Experimental results show
Resource matching algorithm performance to be superior for both
single and multi-resource scheduling when compared to benefit first
scheduling, Migration, Checkpoint algorithms.
Abstract: Imperialist Competitive Algorithm (ICA) is a recent
meta-heuristic method that is inspired by the social evolutions for
solving NP-Hard problems. The ICA is a population-based algorithm
which has achieved a great performance in comparison to other metaheuristics.
This study is about developing enhanced ICA approach to
solve the Cell Formation Problem (CFP) using sequence data. In
addition to the conventional ICA, an enhanced version of ICA,
namely EICA, applies local search techniques to add more
intensification aptitude and embed the features of exploration and
intensification more successfully. Suitable performance measures are
used to compare the proposed algorithms with some other powerful
solution approaches in the literature. In the same way, for checking
the proficiency of algorithms, forty test problems are presented. Five
benchmark problems have sequence data, and other ones are based on
0-1 matrices modified to sequence based problems. Computational
results elucidate the efficiency of the EICA in solving CFP problems.
Abstract: Government reports and published research have
flagged and brought to public attention the deteriorating condition of
a large percentage of bridges in Canada and the United States. With
the increasing number of deteriorated bridges in the US, Canada, and
around the globe, condition assessment techniques of concrete
bridges are evolving. Investigation for bridges’ defects such as
cracks, spalls, and delamination and their level of severity are the
main objectives of condition assessment. Inspection and
rehabilitation programs are being implemented to monitor and
maintain deteriorated bridge infrastructure. This paper highlights the
state-of-the art of current practices being performed for concrete
bridge inspection. The information is gathered from the literature and
through a distributed questionnaire. The current practices in concrete
bridge inspection rely on the use of hummer sounding and chain
dragging tests. Non-Destructive Testing (NDT) techniques are not
being utilized fully in the process. Nonetheless, they are being
partially utilized by the recommendation of the bridge inspector after
conducting visual inspection. Lanes are usually closed during the
performance of visual inspection and bridge inspection in general.
Abstract: Fresh water is one of the resources which is getting
depleted day by day. A wise method to address this issue is by the
application of renewable energy-sun irradiation and by means of
decentralized, cheap, energetically self-sufficient, robust and simple
to operate plants, distillates can be obtained from sea, river or even
sewage. Solar desalination is a technique used to desalinate water
using solar energy. The present work deals with the comprehensive
design and simulation of solar tracking system using LabVIEW,
temperature and mass flow rate control of the solar desalination plant
using LabVIEW and also analysis of single phase inverter circuit
with LC filters for solar pumping system in MATLAB. The main
objective of this work is to improve the performance of solar
desalination system using automatic tracking system, output control
using temperature and mass flow rate control system and also to
reduce the harmonic distortion in the solar pumping system by means
of LC filters. The simulation of single phase inverter was carried out
using MATLAB and the output waveforms were analyzed.
Simulations were performed for optimum output temperature control,
which in turn controls the mass flow rate of water in the thermal
collectors. Solar tracking system was accomplished using LABVIEW
and was tested successfully. The thermal collectors are tracked in
accordance with the sun’s irradiance levels, thereby increasing the
efficiency of the thermal collectors.
Abstract: This paper presents an extensive review of literature
relevant to the modelling techniques adopted in sediment yield and
hydrological modelling. Several studies relating to sediment yield are
discussed. Many research areas of sedimentation in rivers, runoff and
reservoirs are presented. Different types of hydrological models,
different methods employed in selecting appropriate models for
different case studies are analysed. Applications of evolutionary
algorithms and artificial intelligence techniques are discussed and
compared especially in water resources management and modelling.
This review concentrates on Genetic Programming (GP) and fully
discusses its theories and applications. The successful applications of
GP as a soft computing technique were reviewed in sediment
modelling. Some fundamental issues such as benchmark,
generalization ability, bloat, over-fitting and other open issues
relating to the working principles of GP are highlighted. This paper
concludes with the identification of some research gaps in
hydrological modelling and sediment yield.
Abstract: Analytical techniques for measuring and planning
railway capacity expansion activities have been considered in this
article. A preliminary mathematical framework involving track
duplication and section sub divisions is proposed for this task. In
railways, these features have a great effect on network performance
and for this reason they have been considered. Additional motivations
have also arisen from the limitations of prior models that have not
included them.
Abstract: In this paper, we present an optimization technique or
a learning algorithm using the hybrid architecture by combining the
most popular sequence recognition models such as Recurrent Neural
Networks (RNNs) and Hidden Markov models (HMMs). In order to
improve the sequence/pattern recognition/classification performance
by applying a hybrid/neural symbolic approach, a gradient descent
learning algorithm is developed using the Real Time Recurrent
Learning of Recurrent Neural Network for processing the knowledge
represented in trained Hidden Markov Models. The developed hybrid
algorithm is implemented on automata theory as a sample test beds
and the performance of the designed algorithm is demonstrated and
evaluated on learning the deterministic finite state automata.
Abstract: This article presents the main results of a numerical
investigation on the uncertainty of dynamic response of structures
with statistically correlated random damping Gamma distributed. A
computational method based on a Linear Statistical Model (LSM) is
implemented to predict second order statistics for the response of a
typical industrial building structure. The significance of random
damping with correlated parameters and its implications on the
sensitivity of structural peak response in the neighborhood of a
resonant frequency are discussed in light of considerable ranges of
damping uncertainties and correlation coefficients. The results are
compared to those generated using Monte Carlo simulation
techniques. The numerical results obtained show the importance of
damping uncertainty and statistical correlation of damping
coefficients when obtaining accurate probabilistic estimates of
dynamic response of structures. Furthermore, the effectiveness of the
LSM model to efficiently predict uncertainty propagation for
structural dynamic problems with correlated damping parameters is
demonstrated.
Abstract: The present study focused on the investigation of the
effects of roughness elements on heat transfer during natural
convection in a rectangular cavity using numerical technique.
Roughness elements were introduced on the bottom hot wall with a
normalized amplitude (A*/H) of 0.1. Thermal and hydrodynamic
behaviors were studied using computational method based on Lattice
Boltzmann method (LBM). Numerical studies were performed for a
laminar flow in the range of Rayleigh number (Ra) from 103 to 106
for a rectangular cavity of aspect ratio (L/H) 2.0 with a fluid of
Prandtl number (Pr) 1.0. The presence of the sinusoidal roughness
elements caused a minimum to maximum decrease in the heat
transfer as 7% to 17% respectively compared to smooth enclosure.
The results are presented for mean Nusselt number (Nu), isotherms
and streamlines.
Abstract: Rapid prototyping is a new group of manufacturing
processes, which allows fabrication of physical of any complexity
using a layer by layer deposition technique directly from a computer
system. The rapid prototyping process greatly reduces the time and
cost necessary to bring a new product to market. The prototypes
made by these systems are used in a range of industrial application
including design evaluation, verification, testing, and as patterns for
casting processes. These processes employ a variety of materials and
mechanisms to build up the layers to build the part. The present work
was to build a FDM prototyping machine that could control the X-Y
motion and material deposition, to generate two-dimensional and
three-dimensional complex shapes. This study focused on the
deposition of wax material. This work was to find out the properties
of the wax materials used in this work in order to enable better
control of the FDM process. This study will look at the integration of
a computer controlled electro-mechanical system with the traditional
FDM additive prototyping process. The characteristics of the wax
were also analysed in order to optimise the model production process.
These included wax phase change temperature, wax viscosity and
wax droplet shape during processing.
Abstract: Myocardial infarction is one of the leading causes of
death in the world. Some of these deaths occur even before the
patient reaches the hospital. Myocardial infarction occurs as a result
of impaired blood supply. Because the most of these deaths are due to
coronary artery disease, hence the awareness of the warning signs of
a heart attack is essential. Some heart attacks are sudden and intense,
but most of them start slowly, with mild pain or discomfort, then
early detection and successful treatment of these symptoms is vital to
save them. Therefore, importance and usefulness of a system
designing to assist physicians in early diagnosis of the acute heart
attacks is obvious. The main purpose of this study would be to enable patients to
become better informed about their condition and to encourage them
to seek professional care at an earlier stage in the appropriate
situations. For this purpose, the data were collected on 711 heart
patients in Iran hospitals. 28 attributes of clinical factors can be
reported by patients; were studied. Three logistic regression models
were made on the basis of the 28 features to predict the risk of heart
attacks. The best logistic regression model in terms of performance
had a C-index of 0.955 and with an accuracy of 94.9%. The variables,
severe chest pain, back pain, cold sweats, shortness of breath, nausea
and vomiting, were selected as the main features.
Abstract: In order to be capable of dealing with uncertainties,
subjectivities, including vagueness arising in building construction
projects, the application of fuzzy reasoning technique based on fuzzy
set theory is proposed. This study contributes significantly to the
development of a fuzzy reasoning safety risk assessment model for
building construction projects that could be employed to assess the
risk magnitude of each hazardous event identified during
construction, and a third parameter of probability of consequence is
incorporated in the model. By using the proposed safety risk analysis
methodology, more reliable and less ambiguities, which provide the
safety risk management project team for decision-making purposes.
Abstract: This study presents a kinematic positioning approach
that uses a global positioning system (GPS) buoy for precise ocean
surface monitoring. The GPS buoy data from the two experiments are
processed using an accurate, medium-range differential kinematic
technique. In each case, the data from a nearby coastal site are
collected at a high rate (1 Hz) for more than 24 hours, and
measurements are conducted in neighboring tidal stations to verify
the estimated sea surface heights. The GPS buoy kinematic
coordinates are estimated using epoch-wise pre-elimination and a
backward substitution algorithm. Test results show that centimeterlevel
accuracy can be successfully achieved in determining sea
surface height using the proposed technique. The centimeter-level
agreement between the two methods also suggests the possibility of
using this inexpensive and more flexible GPS buoy equipment to
enhance (or even replace) current tidal gauge stations.
Abstract: Friction stir welding and tungsten inert gas welding
techniques were employed to weld armor grade aluminum alloy to
investigate the effect of welding processes on tensile behavior of
weld joints. Tensile tests, Vicker microhardness tests and optical
microscopy were performed on developed weld joints and base metal.
Welding process influenced tensile behavior and microstructure of
weld joints. Friction stir welded joints showed tensile behavior better
than tungsten inert gas weld joints.
Abstract: Optimizing the parameters in the controller plays a
vital role in the control theory and its applications. Optimizing the
PID parameters is finding out the best value from the feasible
solutions. Finding the optimal value is an optimization problem.
Inverted Pendulum is a very good platform for control engineers to
verify and apply different logics in the field of control theory. It is
necessary to find an optimization technique for the controller to tune
the values automatically in order to minimize the error within the
given bounds. In this paper, the algorithmic concepts of Harmony
search (HS) and Genetic Algorithm (GA) have been analyzed for the
given range of values. The experimental results show that HS
performs well than GA.
Abstract: Multiple Input Multiple Output (MIMO) systems are
wireless systems with multiple antenna elements at both ends of the
link. Wireless communication systems demand high data rate and
spectral efficiency with increased reliability. MIMO systems have
been popular techniques to achieve these goals because increased
data rate is possible through spatial multiplexing scheme and
diversity. Spatial Multiplexing (SM) is used to achieve higher
possible throughput than diversity. In this paper, we propose a Zero-
Forcing (ZF) detection using a combination of Ordered Successive
Interference Cancellation (OSIC) and Zero Forcing using
Interference Cancellation (ZF-IC). The proposed method used an
OSIC based on Signal to Noise Ratio (SNR) ordering to get the
estimation of last symbol, then the estimated last symbol is
considered to be an input to the ZF-IC. We analyze the Bit Error Rate
(BER) performance of the proposed MIMO system over Rayleigh
Fading Channel, using Binary Phase Shift Keying (BPSK)
modulation scheme. The results show better performance than the
previous methods.