Abstract: Injection molding is a very complicated process to
monitor and control. With its high complexity and many process
parameters, the optimization of these systems is a very challenging
problem. To meet the requirements and costs demanded by the
market, there has been an intense development and research with the
aim to maintain the process under control. This paper outlines the
latest advances in necessary algorithms for plastic injection process
and monitoring, and also a flexible data acquisition system that
allows rapid implementation of complex algorithms to assess their
correct performance and can be integrated in the quality control
process. This is the main topic of this paper. Finally, to demonstrate
the performance achieved by this combination, a real case of use is
presented.
Abstract: Total liquid ventilation can support gas exchange in animal models of lung injury. Clinical application awaits further technical improvements and performance verification. Our aim was to develop a liquid ventilator, able to deliver accurate tidal volumes, and a computerized system for measuring lung mechanics. The computer-assisted, piston-driven respirator controlled ventilatory parameters that were displayed and modified on a real-time basis. Pressure and temperature transducers along with a lineal displacement controller provided the necessary signals to calculate lung mechanics. Ten newborn lambs (
Abstract: The promises of component-based technology can only be fully realized when the system contains in its design a necessary level of separation of concerns. The authors propose to focus on the concerns that emerge throughout the life cycle of the system and use them as an architectural foundation for the design of a component-based framework. The proposed model comprises a set of superimposed views of the system describing its functional and non-functional concerns. This approach is illustrated by the design of a specific framework for data analysis and data acquisition and supplemented with experiences from using the systems developed with this framework at the Fermi National Accelerator Laboratory.
Abstract: The pipe inspection operation is the difficult detective
performance. Almost applications are mainly relies on a manual
recognition of defective areas that have carried out detection by an
engineer. Therefore, an automation process task becomes a necessary
in order to avoid the cost incurred in such a manual process. An
automated monitoring method to obtain a complete picture of the
sewer condition is proposed in this work. The focus of the research is
the automated identification and classification of discontinuities in
the internal surface of the pipe. The methodology consists of several
processing stages including image segmentation into the potential
defect regions and geometrical characteristic features. Automatic
recognition and classification of pipe defects are carried out by means
of using an artificial neural network technique (ANN) based on
Radial Basic Function (RBF). Experiments in a realistic environment
have been conducted and results are presented.
Abstract: Total liquid ventilation can support gas exchange in animal models of lung injury. Clinical application awaits further technical improvements and performance verification. Our aim was to develop a liquid ventilator, able to deliver accurate tidal volumes, and a computerized system for measuring lung mechanics. The computer-assisted, piston-driven respirator controlled ventilatory parameters that were displayed and modified on a real-time basis. Pressure and temperature transducers along with a lineal displacement controller provided the necessary signals to calculate lung mechanics. Ten newborn lambs (
Abstract: The Sensor Network consists of densely deployed
sensor nodes. Energy optimization is one of the most important
aspects of sensor application design. Data acquisition and aggregation
techniques for processing data in-network should be energy efficient.
Due to the cross-layer design, resource-limited and noisy nature
of Wireless Sensor Networks(WSNs), it is challenging to study
the performance of these systems in a realistic setting. In this
paper, we propose optimizing queries by aggregation of data and
data redundancy to reduce energy consumption without requiring
all sensed data and directed diffusion communication paradigm to
achieve power savings, robust communication and processing data
in-network. To estimate the per-node power consumption POWERTossim
mica2 energy model is used, which provides scalable and
accurate results. The performance analysis shows that the proposed
methods overcomes the existing methods in the aspects of energy
consumption in wireless sensor networks.
Abstract: Thailand-s health system is challenged by the rising
number of patients and decreasing ratio of medical
practitioners/patients, especially in rural areas. This may tempt
inexperienced GPs to rush through the process of anamnesis with the
risk of incorrect diagnosis. Patients have to travel far to the hospital
and wait for a long time presenting their case. Many patients try to
cure themselves with traditional Thai medicine. Many countries are
making use of the Internet for medical information gathering,
distribution and storage. Telemedicine applications are a relatively
new field of study in Thailand; the infrastructure of ICT had
hampered widespread use of the Internet for using medical
information. With recent improvements made health and technology
professionals can work out novel applications and systems to help
advance telemedicine for the benefit of the people. Here we explore
the use of telemedicine for people with health problems in rural areas
in Thailand and present a Telemedicine Diagnosis System for Rural
Thailand (TEDIST) for diagnosing certain conditions that people
with Internet access can use to establish contact with Community
Health Centers, e.g. by mobile phone. The system uses a Web-based
input method for individual patients- symptoms, which are taken by
an expert system for the analysis of conditions and appropriate
diseases. The analysis harnesses a knowledge base and a backward
chaining component to find out, which health professionals should be
presented with the case. Doctors have the opportunity to exchange
emails or chat with the patients they are responsible for or other
specialists. Patients- data are then stored in a Personal Health Record.
Abstract: In this paper the development of a heat exchanger as a
pilot plant for educational purpose is discussed and the use of neural
network for controlling the process is being presented. The aim of the
study is to highlight the need of a specific Pseudo Random Binary
Sequence (PRBS) to excite a process under control. As the neural
network is a data driven technique, the method for data generation
plays an important role. In light of this a careful experimentation
procedure for data generation was crucial task. Heat exchange is a
complex process, which has a capacity and a time lag as process
elements. The proposed system is a typical pipe-in- pipe type heat
exchanger. The complexity of the system demands careful selection,
proper installation and commissioning. The temperature, flow, and
pressure sensors play a vital role in the control performance. The
final control element used is a pneumatically operated control valve.
While carrying out the experimentation on heat exchanger a welldrafted
procedure is followed giving utmost attention towards safety
of the system. The results obtained are encouraging and revealing
the fact that if the process details are known completely as far as
process parameters are concerned and utilities are well stabilized then
feedback systems are suitable, whereas neural network control
paradigm is useful for the processes with nonlinearity and less
knowledge about process. The implementation of NN control
reinforces the concepts of process control and NN control paradigm.
The result also underlined the importance of excitation signal
typically for that process. Data acquisition, processing, and
presentation in a typical format are the most important parameters
while validating the results.
Abstract: This paper proposes a novel architecture for At-
Home medical care which enables senior citizens, patients
with chronic ailments and patients requiring post- operative
care to be remotely monitored in the comfort of their homes.
This architecture is implemented using sensors and wireless
networking for transmitting patient data to the hospitals,
health- care centers for monitoring by medical professionals.
Patients are equipped with sensors to measure their
physiological parameters, like blood pressure, pulse rate etc.
and a Wearable Data Acquisition Unit is used to transmit the
patient sensor data. Medical professionals can be alerted to
any abnormal variations in these values for diagnosis and
suitable treatment. Security threats and challenges inherent to
wireless communication and sensor network have been
discussed and a security mechanism to ensure data
confidentiality and source authentication has been proposed.
Symmetric key algorithm AES has been used for encrypting
the data and a patent-free, two-pass block cipher mode CCFB
has been used for implementing semantic security.
Abstract: This paper presents a real time force sensing
instrument that is designed for human gait analysis purposes. It is
capable of recording and monitoring ground reaction forces exerted
by human foot during various activities such as walking, running and
jumping in real time. In overall, force sensing mat mainly consists of
three elements: the force sensing mat, signal conditioning circuit and
data acquisition device. Force sensing mat is the mat that contains an
array of force sensing elements. To control and process the incoming
signal from the force sensing mat, Force-Logger and Force-Reloader
are developed using National Instrument Labview. This paper
describes the architecture of the force sensing mat, signal
conditioning circuit and the real time streaming of the incoming data
from the force sensing mat. Additionally, a preliminary experiment
dataset is presented in this paper.
Abstract: Modern manufacturing facilities are large scale,
highly complex, and operate with large number of variables under
closed loop control. Early and accurate fault detection and diagnosis
for these plants can minimise down time, increase the safety of plant
operations, and reduce manufacturing costs. Fault detection and
isolation is more complex particularly in the case of the faulty analog
control systems. Analog control systems are not equipped with
monitoring function where the process parameters are continually
visualised. In this situation, It is very difficult to find the relationship
between the fault importance and its consequences on the product
failure. We consider in this paper an approach to fault detection and
analysis of its effect on the production quality using an adaptive
centring and scaling in the pickling process in cold rolling. The fault
appeared on one of the power unit driving a rotary machine, this
machine can not track a reference speed given by another machine.
The length of metal loop is then in continuous oscillation, this affects
the product quality. Using a computerised data acquisition system,
the main machine parameters have been monitored. The fault has
been detected and isolated on basis of analysis of monitored data.
Normal and faulty situation have been obtained by an artificial neural
network (ANN) model which is implemented to simulate the normal
and faulty status of rotary machine. Correlation between the product
quality defined by an index and the residual is used to quality
classification.
Abstract: This paper presents a real time force sensing instrument that is designed for human gait analysis purposes. This instrument mainly consists of three main elements: the force sensing mat, signal conditioning and switching circuit and data acquisition
device. In order to control and to process the incoming signals from the force sensing mat, Force-Logger and Force-Reloader program are
developed using Labview 8.0. This paper describes the architecture of the force sensing mat, signal conditioning and switching circuit and the real time streaming of the incoming data from the force
sensing mat.
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: 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: Imaging is defined as the process of obtaining
geometric images either two dimensional or three dimensional by scanning or digitizing the existing objects or products. In this research, it applied to retrieve 3D information of the human skin
surface in medical application. This research focuses on analyzing
and determining volume of leg ulcers using imaging devices. Volume
determination is one of the important criteria in clinical assessment of leg ulcer. The volume and size of the leg ulcer wound will give the
indication on responding to treatment whether healing or worsening.
Different imaging techniques are expected to give different result (and accuracies) in generating data and images. Midpoint projection
algorithm was used to reconstruct the cavity to solid model and compute the volume. Misinterpretation of the results can affect the
treatment efficacy. The objectives of this paper is to compare the
accuracy between two 3D data acquisition method, which is laser
triangulation and structured light methods, It was shown that using models with known volume, that structured-light-based 3D technique
produces better accuracy compared with laser triangulation data
acquisition method for leg ulcer volume determination.
Abstract: Quality of 2D and 3D cross-sectional images produce
by Computed Tomography primarily depend upon the degree of
precision of primary and secondary X-Ray intensity detection.
Traditional method of primary intensity detection is apt to errors.
Recently the X-Ray intensity measurement system along with smart
X-Ray sensors is developed by our group which is able to detect
primary X-Ray intensity unerringly. In this study a new smart X-Ray
sensor is developed using Light-to-Frequency converter TSL230
from Texas Instruments which has numerous advantages in terms of
noiseless data acquisition and transmission. TSL230 construction is
based on a silicon photodiode which converts incoming X-Ray
radiation into the proportional current signal. A current to frequency
converter is attached to this photodiode on a single monolithic CMOS
integrated circuit which provides proportional frequency count to
incoming current signal in the form of the pulse train. The frequency
count is delivered to the center of PICDEM FS USB board with
PIC18F4550 microcontroller mounted on it. With highly compact
electronic hardware, this Demo Board efficiently read the smart
sensor output data. The frequency output approaches overcome
nonlinear behavior of sensors with analog output thus un-attenuated
X-Ray intensities could be measured precisely and better
normalization could be acquired in order to attain high resolution.
Abstract: This paper applies Bayesian Networks to support
information extraction from unstructured, ungrammatical, and
incoherent data sources for semantic annotation. A tool has been
developed that combines ontologies, machine learning, and
information extraction and probabilistic reasoning techniques to
support the extraction process. Data acquisition is performed with the
aid of knowledge specified in the form of ontology. Due to the
variable size of information available on different data sources, it is
often the case that the extracted data contains missing values for
certain variables of interest. It is desirable in such situations to
predict the missing values. The methodology, presented in this paper,
first learns a Bayesian network from the training data and then uses it
to predict missing data and to resolve conflicts. Experiments have
been conducted to analyze the performance of the presented
methodology. The results look promising as the methodology
achieves high degree of precision and recall for information
extraction and reasonably good accuracy for predicting missing
values.