Abstract: SEMG (Surface Electromyogram) is one of the
bio-signals and is generated from the muscle. And there are many
research results that use forearm EMG to detect hand motions. In this
paper, we will talk about our developed the robot hand system that can
control grasping power by SEMG. In our system, we suppose that
muscle power is proportional to the amplitude of SEMG. The power is
estimated and the grip power of a robot hand is able to be controlled
using estimated muscle power in our system. In addition, to perform a
more precise control can be considered to build a closed loop feedback
system as an object to a subject to pressure from the edge of hand. Our
objectives of this study are the development of a method that makes
perfect detection of the hand grip force possible using SEMG patterns,
and applying this method to the man-machine interface.
Abstract: An Artificial Neural Network based modeling
technique has been used to study the influence of different
combinations of meteorological parameters on evaporation from a
reservoir. The data set used is taken from an earlier reported study.
Several input combination were tried so as to find out the importance
of different input parameters in predicting the evaporation. The
prediction accuracy of Artificial Neural Network has also been
compared with the accuracy of linear regression for predicting
evaporation. The comparison demonstrated superior performance of
Artificial Neural Network over linear regression approach. The
findings of the study also revealed the requirement of all input
parameters considered together, instead of individual parameters
taken one at a time as reported in earlier studies, in predicting the
evaporation. The highest correlation coefficient (0.960) along with
lowest root mean square error (0.865) was obtained with the input
combination of air temperature, wind speed, sunshine hours and
mean relative humidity. A graph between the actual and predicted
values of evaporation suggests that most of the values lie within a
scatter of ±15% with all input parameters. The findings of this study
suggest the usefulness of ANN technique in predicting the
evaporation losses from reservoirs.
Abstract: Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.
Abstract: Article presents the geometry and structure
reconstruction procedure of the aircraft model for flatter research
(based on the I22-IRYDA aircraft). For reconstruction the Reverse
Engineering techniques and advanced surface modeling CAD tools
are used. Authors discuss all stages of data acquisition process,
computation and analysis of measured data. For acquisition the three
dimensional structured light scanner was used. In the further sections,
details of reconstruction process are present. Geometry
reconstruction procedure transform measured input data (points
cloud) into the three dimensional parametric computer model
(NURBS solid model) which is compatible with CAD systems.
Parallel to the geometry of the aircraft, the internal structure
(structural model) are extracted and modeled. In last chapter the
evaluation of obtained models are discussed.
Abstract: In this study, a fuzzy-logic based control system was
designed to ensure that time and energy is saved during the operation
of load elevators which are used during the construction of tall
buildings. In the control system that was devised, for the load
elevators to work more efficiently, the energy interval where the
motor worked was taken as the output variable whereas the amount
of load and the building height were taken as input variables. The
most appropriate working intervals depending on the characteristics
of these variables were defined by the help of an expert. Fuzzy expert
system software was formed using Delphi programming language. In
this design, mamdani max-min inference mechanism was used and
the centroid method was employed in the clarification procedure. In
conclusion, it is observed that the system that was designed is
feasible and this is supported by statistical analyses..
Abstract: This work deals with modeling and simulation of SO2 removal in a ceramic membrane by means of FEM. A mass transfer model was developed to predict the performance of SO2 absorption in a chemical solvent. The model was based on solving conservation equations for gas component in the membrane. Computational fluid dynamics (CFD) of mass and momentum were used to solve the model equations. The simulations aimed to obtain the distribution of gas concentration in the absorption process. The effect of the operating parameters on the efficiency of the ceramic membrane was evaluated. The modeling findings showed that the gas phase velocity has significant effect on the removal of gas whereas the liquid phase does not affect the SO2 removal significantly. It is also indicated that the main mass transfer resistance is placed in the membrane and gas phase because of high tortuosity of the ceramic membrane.
Abstract: Advancement in Artificial Intelligence has lead to the
developments of various “smart" devices. Character recognition
device is one of such smart devices that acquire partial human
intelligence with the ability to capture and recognize various
characters in different languages. Firstly multiscale neural training
with modifications in the input training vectors is adopted in this
paper to acquire its advantage in training higher resolution character
images. Secondly selective thresholding using minimum distance
technique is proposed to be used to increase the level of accuracy of
character recognition. A simulator program (a GUI) is designed in
such a way that the characters can be located on any spot on the
blank paper in which the characters are written. The results show that
such methods with moderate level of training epochs can produce
accuracies of at least 85% and more for handwritten upper case
English characters and numerals.
Abstract: Modern information and communication technologies
offer a variety of support options for the efficient handling of
customer relationships. CRM systems have been developed, which
are designed to support the processes in the areas of marketing, sales
and service. Along with technological progress, CRM systems are
constantly changing, i.e. the systems are continually enhanced by
new functions. However, not all functions are suitable for every
company because of different frameworks and business processes. In
this context the question arises whether or not CRM systems are
widely used in Austrian companies and which business processes are
most frequently supported by CRM systems. This paper aims to shed
light on the popularity of CRM systems in Austrian companies in
general and the use of different functions to support their daily
business. First of all, the paper provides a theoretical overview of the
structure of modern CRM systems and proposes a categorization of
currently available software functionality for collaborative,
operational and analytical CRM processes, which provides the
theoretical background for the empirical study. Apart from these
theoretical considerations, the paper presents the empirical results of
a field survey on the use of CRM systems in Austrian companies and
analyzes its findings.
Abstract: From a set of shifted, blurred, and decimated image , super-resolution image reconstruction can get a high-resolution image. So it has become an active research branch in the field of image restoration. In general, super-resolution image restoration is an ill-posed problem. Prior knowledge about the image can be combined to make the problem well-posed, which contributes to some regularization methods. In the regularization methods at present, however, regularization parameter was selected by experience in some cases and other techniques have too heavy computation cost for computing the parameter. In this paper, we construct a new super-resolution algorithm by transforming the solving of the System stem Є=An into the solving of the equations X+A*X-1A=I , and propose an inverse iterative method.
Abstract: An aqueous methanol sensor for use in direct
methanol fuel cells (DMFCs) applications is demonstrated; the
methanol sensor is built using dispersed single-walled carbon
nanotubes (SWCNTs) with Nafion117 solution to detect the methanol
concentration in water. The study is aimed at the potential use of the
carbon nanotubes array as a methanol sensor for direct methanol fuel
cells (DMFCs). The concentration of methanol in the fuel circulation
loop of a DMFC system is an important operating parameter, because
it determines the electrical performance and efficiency of the fuel cell
system. The sensor is also operative even at ambient temperatures
and responds quickly to changes in the concentration levels of the
methanol. Such a sensor can be easily incorporated into the methanol
fuel solution flow loop in the DMFC system.
Abstract: Fisheries management all around the world is
hampered by the lack, or poor quality, of critical data on fish
resources and fishing operations. The main reasons for the chronic
inability to collect good quality data during fishing operations is the
culture of secrecy common among fishers and the lack of modern
data gathering technology onboard most fishing vessels. In response,
OLRAC-SPS, a South African company, developed fisheries datalogging
software (eLog in short) and named it Olrac. The Olrac eLog
solution is capable of collecting, analysing, plotting, mapping,
reporting, tracing and transmitting all data related to fishing
operations. Olrac can be used by skippers, fleet/company managers,
offshore mariculture farmers, scientists, observers, compliance
inspectors and fisheries management authorities. The authors believe
that using eLog onboard fishing vessels has the potential to
revolutionise the entire process of data collection and reporting
during fishing operations and, if properly deployed and utilised,
could transform the entire commercial fleet to a provider of good
quality data and forever change the way fish resources are managed.
In addition it will make it possible to trace catches back to the actual
individual fishing operation, to improve fishing efficiency and to
dramatically improve control of fishing operations and enforcement
of fishing regulations.
Abstract: Currently, the Malaysian construction industry is
focusing on transforming construction processes from conventional
building methods to the Industrialized Building System (IBS). Still,
research on the decision making of IBS technology adoption with the
influence of contextual factors is scarce. The purpose of this paper is
to explore how contextual factors influence the IBS decision making
in building projects which is perceived by those involved in
construction industry namely construction stakeholders and IBS
supply chain members. Theoretical background, theoretical
frameworks and literatures which identify possible contextual factors
that influence decision making towards IBS technology adoption are
presented. This paper also discusses the importance of contextual
factors in IBS decision making, highlighting some possible crossover
benefits and making some suggestions as to how these can be
utilized. Conclusions are drawn and recommendations are made with
respect to the perception of socio-economic, IBS policy and IBS
technology associated with building projects.
Abstract: Irradiated material is a typical example of a complex
system with nonlinear coupling between its elements. During
irradiation the radiation damage is developed and this development
has bifurcations and qualitatively different kinds of behavior.
The accumulation of primary defects in irradiated crystals is
considered in frame work of nonlinear evolution of complex system.
The thermo-concentration nonlinear feedback is carried out as a
mechanism of self-oscillation development.
It is shown that there are two ways of the defect density evolution
under stationary irradiation. The first is the accumulation of defects;
defect density monotonically grows and tends to its stationary state
for some system parameters. Another way that takes place for
opportune parameters is the development of self-oscillations of the
defect density.
The stationary state, its stability and type are found. The
bifurcation values of parameters (environment temperature, defect
generation rate, etc.) are obtained. The frequency of the selfoscillation
and the conditions of their development is found and
rated. It is shown that defect density, heat fluxes and temperature
during self-oscillations can reach much higher values than the
expected steady-state values. It can lead to a change of typical
operation and an accident, e.g. for nuclear equipment.
Abstract: LABVIEW is a graphical programming language that has its roots in automation control and data acquisition. In this paper we have utilized this platform to provide a powerful toolset for process identification and control of nonlinear systems based on artificial neural networks (ANN). This tool has been applied to the monitoring and control of a lab-scale distillation column DELTALAB DC-SP. The proposed control scheme offers high speed of response for changes in set points and null stationary error for dual composition control and shows robustness in presence of externally imposed disturbance.
Abstract: In this paper we have proposed a methodology to
develop an amperometric biosensor for the analysis of glucose
concentration using a simple microcontroller based data acquisition
system. The work involves the development of Detachable
Membrane Unit (enzyme based biomembrane) with immobilized
glucose oxidase on the membrane and interfacing the same to the
signal conditioning system. The current generated by the biosensor
for different glucose concentrations was signal conditioned, then
acquired and computed by a simple AT89C51-microcontroller. The
optimum operating parameters for the better performance were found
and reported. The detailed performance evaluation of the biosensor
has been carried out. The proposed microcontroller based biosensor
system has the sensitivity of 0.04V/g/dl, with a resolution of
50mg/dl. It has exhibited very good inter day stability observed up to
30 days. Comparing to the reference method such as HPLC, the
accuracy of the proposed biosensor system is well within ± 1.5%.
The system can be used for real time analysis of glucose
concentration in the field such as, food and fermentation and clinical
(In-Vitro) applications.
Abstract: Partial discharge (PD) detection is an important
method to evaluate the insulation condition of metal-clad apparatus.
Non-intrusive sensors which are easy to install and have no
interruptions on operation are preferred in onsite PD detection.
However, it often lacks of accuracy due to the interferences in PD
signals. In this paper a novel PD extraction method that uses frequency
analysis and entropy based time-frequency (TF) analysis is introduced.
The repetitive pulses from convertor are first removed via frequency
analysis. Then, the relative entropy and relative peak-frequency of
each pulse (i.e. time-indexed vector TF spectrum) are calculated and
all pulses with similar parameters are grouped. According to the
characteristics of non-intrusive sensor and the frequency distribution
of PDs, the pulses of PD and interferences are separated. Finally the
PD signal and interferences are recovered via inverse TF transform.
The de-noised result of noisy PD data demonstrates that the
combination of frequency and time-frequency techniques can
discriminate PDs from interferences with various frequency
distributions.
Abstract: As the Internet continues to grow at a rapid pace as
the primary medium for communications and commerce and as
telecommunication networks and systems continue to expand their
global reach, digital information has become the most popular and
important information resource and our dependence upon the
underlying cyber infrastructure has been increasing significantly.
Unfortunately, as our dependency has grown, so has the threat to the
cyber infrastructure from spammers, attackers and criminal
enterprises. In this paper, we propose a new machine learning based
network intrusion detection framework for cyber security. The
detection process of the framework consists of two stages: model
construction and intrusion detection. In the model construction stage,
a semi-supervised machine learning algorithm is applied to a
collected set of network audit data to generate a profile of normal
network behavior and in the intrusion detection stage, input network
events are analyzed and compared with the patterns gathered in the
profile, and some of them are then flagged as anomalies should these
events are sufficiently far from the expected normal behavior. The
proposed framework is particularly applicable to the situations where
there is only a small amount of labeled network training data
available, which is very typical in real world network environments.
Abstract: As the enormous amount of on-line text grows on the
World-Wide Web, the development of methods for automatically
summarizing this text becomes more important. The primary goal of
this research is to create an efficient tool that is able to summarize
large documents automatically. We propose an Evolving
connectionist System that is adaptive, incremental learning and
knowledge representation system that evolves its structure and
functionality. In this paper, we propose a novel approach for Part of
Speech disambiguation using a recurrent neural network, a paradigm
capable of dealing with sequential data. We observed that
connectionist approach to text summarization has a natural way of
learning grammatical structures through experience. Experimental
results show that our approach achieves acceptable performance.
Abstract: Ontology is widely being used as a tool for organizing
information, creating the relation between the subjects within the
defined knowledge domain area. Various fields such as Civil,
Biology, and Management have successful integrated ontology in
decision support systems for managing domain knowledge and to
assist their decision makers. Gross pollutant traps (GPT) are devices
used in trapping and preventing large items or hazardous particles in
polluting and entering our waterways. However choosing and
determining GPT is a challenge in Malaysia as there are inadequate
GPT data repositories being captured and shared. Hence ontology is
needed to capture, organize and represent this knowledge into
meaningful information which can be contributed to the efficiency of
GPT selection in Malaysia urbanization. A GPT Ontology framework
is therefore built as the first step to capture GPT knowledge which
will then be integrated into the decision support system. This paper
will provide several examples of the GPT ontology, and explain how
it is constructed by using the Protégé tool.
Abstract: The third phase of web means semantic web requires many web pages which are annotated with metadata. Thus, a crucial question is where to acquire these metadata. In this paper we propose our approach, a semi-automatic method to annotate the texts of documents and web pages and employs with a quite comprehensive knowledge base to categorize instances with regard to ontology. The approach is evaluated against the manual annotations and one of the most popular annotation tools which works the same as our tool. The approach is implemented in .net framework and uses the WordNet for knowledge base, an annotation tool for the Semantic Web.