Abstract: In the present study, the oleaginous fungus
Mortierella alpina CBS 754.68 was screened for arachidonic
acidproduction using inexpensive agricultural by-products as
substrate. Four oilcakes were analysed to choose the best substrate
among them. Sunflower oilcake was the most effective substrate for
ARA production followed by soybean, colza and olive oilcakes. In
the next step, seven variables including substrate particle size,
moisture content, time, temperature, yeast extract supply, glucose
supply and glutamate supply were surveyed and effective variables
for ARA production were determined using a Plackett-Burman
screening design. Analysis results showed that time (12 days),
substrate particle size (1-1.4 mm) and temperature (20ºC) were the
most effective variables for the highest level of ARA production
respectively.
Abstract: In the present study, a support vector machine (SVM) learning approach to character recognition is proposed. Simple
feature detectors, similar to those found in the human visual system, were used in the SVM classifier. Alphabetic characters were rotated
to 8 different angles and using the proposed cognitive model, all characters were recognized with 100% accuracy and specificity.
These same results were found in psychiatric studies of human character recognition.
Abstract: Rhizopus oligosporus was used in the present study
for the production of protease enzyme under SSF. Sunflower meal
was used as by-product of oil industry incorporated with organic salts
was employed for the production of protease enzyme. The main
purpose of the present was to study different parameters of protease
productivity, its yields and to optimize basal fermentation conditions.
The optimal conditions found for protease production using
sunflower meal as a substrate in the present study were inoculum size
(1%), substrate (Sunflower meal), substrate concentration (20 g), pH
(3), cultivation period (72 h), incubation temperature (35oC),
substrate to diluent-s ratio (1:2) and tween 81 (1 mL). The maximum
production of protease in the presence of cheaper substrate at low
concentration and stability at acidic pH, these characteristics make
the strain and its enzymes useful in different industry.
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: Voltage stability has become an important issue to many power systems around the world due to the weak systems and long line on power system networks. In this paper, MATLAB load flow program is applied to obtain the weak points in the system combined with finding the voltage stability limit. The maximum permissible loading of a system, within the voltage stability limit, is usually determined. The methods for varying tap ratio (using tap changing transformer) and applying different values of shunt capacitor injection to improve the voltage stability within the limit are also provided.
Abstract: Emotions are related with learning processes and
physiological signals can be used to detect them for the
personalization of learning resources and to control the pace of
instruction. A model of relevant emotions has been developed, where
specific combinations of emotions and cognition processes are
connected and integrated with the concept of 'flow', in order to
improve learning. The cardiac pulse is a reliable signal that carries
useful information about the subject-s emotional condition; it is
detected using a classroom chair adapted with non invasive EMFi
sensor and an acquisition system that generates a ballistocardiogram
(BCG), the signal is processed by an algorithm to obtain
characteristics that match a specific emotional condition. The
complete chair system is presented in this work, along with a
framework for the personalization of learning resources.
Abstract: In this work, effects of catalysts (TiO2, and Nb2O5) were investigated on the hydrogen desorption of Mg(BH4)2. LiBH4 and MgCl2 with 2:1 molar ratio were mixed by using ball milling to prepare Mg(BH4)2. The desorption behaviors were measured by thermo-volumetric apparatus. The hydrogen desorption capacity of the mixed sample milled for 2 h was 4.78 wt% with a 2-step released. The first step occurred at 214 °C and the second step appeared at 374 °C. The addition of 16 wt% Nb2O5 decreased the desorption temperature in the second step about 66 °C and increased the hydrogen desorption capacity to 4.86 wt% hydrogen. The addition of TiO2 also improved the desorption temperature in the second step and the hydrogen desorption capacity. It decreased the desorption temperature about 71°C and showed a high amount of hydrogen, 5.27 wt%, released from the mixed sample. The hydrogen absorption after desorption of Mg(BH4)2 was also studied under 9.5 MPa and 350 °C for 12 h.
Abstract: To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combined classifier has high recognition rate with large variation range of SNR (success rates are over 99.9% when SNR is not lower than 5dB).
Abstract: The lecture represents significant advances in
understanding of the transfer processes mechanism in turbulent
separated flows. Based upon experimental data suggesting the
governing role of generated local pressure gradient that takes place in
the immediate vicinity of the wall in separated flow as a result of
intense instantaneous accelerations induced by large-scale vortex
flow structures similarity laws for mean velocity and temperature and
spectral characteristics and heat and mass transfer law for turbulent
separated flows have been developed. These laws are confirmed by
available experimental data. The results obtained were employed for
analysis of heat and mass transfer in some very complex processes
occurring in technological applications such as impinging jets, heat
transfer of cylinders in cross flow and in tube banks, packed beds
where processes manifest distinct properties which allow them to be
classified under turbulent separated flows. Many facts have got an
explanation for the first time.
Abstract: The demand for autonomous resource
management for distributed systems has increased in recent
years. Distributed systems require an efficient and powerful
communication mechanism between applications running on
different hosts and networks. The use of mobile agent
technology to distribute and delegate management tasks
promises to overcome the scalability and flexibility limitations
of the currently used centralized management approach. This
work proposes a multiagent system that adopts mobile agents
as a technology for tasks distribution, results collection, and
management of resources in large-scale distributed systems. A
new mobile agent-based approach for collecting results from
distributed system elements is presented. The technique of
artificial intelligence based on intelligent agents giving the
system a proactive behavior. The presented results are based
on a design example of an application operating in a mobile
environment.
Abstract: A set of Artificial Neural Network (ANN) based methods
for the design of an effective system of speech recognition of
numerals of Assamese language captured under varied recording
conditions and moods is presented here. The work is related to
the formulation of several ANN models configured to use Linear
Predictive Code (LPC), Principal Component Analysis (PCA) and
other features to tackle mood and gender variations uttering numbers
as part of an Automatic Speech Recognition (ASR) system in
Assamese. The ANN models are designed using a combination of
Self Organizing Map (SOM) and Multi Layer Perceptron (MLP)
constituting a Learning Vector Quantization (LVQ) block trained in a
cooperative environment to handle male and female speech samples
of numerals of Assamese- a language spoken by a sizable population
in the North-Eastern part of India. The work provides a comparative
evaluation of several such combinations while subjected to handle
speech samples with gender based differences captured by a microphone
in four different conditions viz. noiseless, noise mixed, stressed
and stress-free.
Abstract: Process planning and production scheduling play
important roles in manufacturing systems. In this paper a multiobjective
mixed integer linear programming model is presented for
the integrated planning and scheduling of multi-product. The aim is
to find a set of high-quality trade-off solutions. This is a
combinatorial optimization problem with substantially large solution
space, suggesting that it is highly difficult to find the best solutions
with the exact search method. To account for it, a PSO-based
algorithm is proposed by fully utilizing the capability of the
exploration search and fast convergence. To fit the continuous PSO
in the discrete modeled problem, a solution representation is used in
the algorithm. The numerical experiments have been performed to
demonstrate the effectiveness of the proposed algorithm.
Abstract: Evidence-based medicine is a new direction in modern healthcare. Its task is to prevent, diagnose and medicate diseases using medical evidence. Medical data about a large patient population is analyzed to perform healthcare management and medical research. In order to obtain the best evidence for a given disease, external clinical expertise as well as internal clinical experience must be available to the healthcare practitioners at right time and in the right manner. External evidence-based knowledge can not be applied directly to the patient without adjusting it to the patient-s health condition. We propose a data warehouse based approach as a suitable solution for the integration of external evidence-based data sources into the existing clinical information system and data mining techniques for finding appropriate therapy for a given patient and a given disease. Through integration of data warehousing, OLAP and data mining techniques in the healthcare area, an easy to use decision support platform, which supports decision making process of care givers and clinical managers, is built. We present three case studies, which show, that a clinical data warehouse that facilitates evidence-based medicine is a reliable, powerful and user-friendly platform for strategic decision making, which has a great relevance for the practice and acceptance of evidence-based medicine.
Abstract: Airbag deployment has been known to be responsible
for huge death, incidental injuries and broken bones due to low crash
severity and wrong deployment decisions. Therefore, the authorities
and industries have been looking for more innovative and intelligent
products to be realized for future enhancements in the vehicle safety
systems (VSSs). Although the VSSs technologies have advanced
considerably, they still face challenges such as how to avoid
unnecessary and untimely airbag deployments that can be hazardous
and fatal. Currently, most of the existing airbag systems deploy
without regard to occupant size and position. As such, this paper will
focus on the occupant and crash sensing performances due to frontal
collisions for the new breed of so called smart airbag systems. It
intends to provide a thorough discussion relating to the occupancy
detection, occupant size classification, occupant off-position
detection to determine safe distance zone for airbag deployment,
crash-severity analysis and airbag decision algorithms via a computer
modeling. The proposed system model consists of three main
modules namely, occupant sensing, crash severity analysis and
decision fusion. The occupant sensing system module utilizes the
weight sensor to determine occupancy, classify the occupant size,
and determine occupant off-position condition to compute safe
distance for airbag deployment. The crash severity analysis module is
used to generate relevant information pertinent to airbag deployment
decision. Outputs from these two modules are fused to the decision
module for correct and efficient airbag deployment action. Computer
modeling work is carried out using Simulink, Stateflow,
SimMechanics and Virtual Reality toolboxes.
Abstract: Manufacturing Industries face a crucial change as products and processes are required to, easily and efficiently, be reconfigurable and reusable. In order to stay competitive and flexible, situations also demand distribution of enterprises globally, which requires implementation of efficient communication strategies. A prototype system called the “Broadcaster" has been developed with an assumption that the control environment description has been engineered using the Component-based system paradigm. This prototype distributes information to a number of globally distributed partners via an adoption of the circular-based data processing mechanism. The work highlighted in this paper includes the implementation of this mechanism in the domain of the manufacturing industry. The proposed solution enables real-time remote propagation of machine information to a number of distributed supply chain client resources such as a HMI, VRML-based 3D views and remote client instances regardless of their distribution nature and/ or their mechanisms. This approach is presented together with a set of evaluation results. Authors- main concentration surrounds the reliability and the performance metric of the adopted approach. Performance evaluation is carried out in terms of the response times taken to process the data in this domain and compared with an alternative data processing implementation such as the linear queue mechanism. Based on the evaluation results obtained, authors justify the benefits achieved from this proposed implementation and highlight any further research work that is to be carried out.
Abstract: The paper describes a self supervised parallel self organizing neural network (PSONN) architecture for true color image segmentation. The proposed architecture is a parallel extension of the standard single self organizing neural network architecture (SONN) and comprises an input (source) layer of image information, three single self organizing neural network architectures for segmentation of the different primary color components in a color image scene and one final output (sink) layer for fusion of the segmented color component images. Responses to the different shades of color components are induced in each of the three single network architectures (meant for component level processing) by applying a multilevel version of the characteristic activation function, which maps the input color information into different shades of color components, thereby yielding a processed component color image segmented on the basis of the different shades of component colors. The number of target classes in the segmented image corresponds to the number of levels in the multilevel activation function. Since the multilevel version of the activation function exhibits several subnormal responses to the input color image scene information, the system errors of the three component network architectures are computed from some subnormal linear index of fuzziness of the component color image scenes at the individual level. Several multilevel activation functions are employed for segmentation of the input color image scene using the proposed network architecture. Results of the application of the multilevel activation functions to the PSONN architecture are reported on three real life true color images. The results are substantiated empirically with the correlation coefficients between the segmented images and the original images.
Abstract: Electric impedance imaging is a method of
reconstructing spatial distribution of electrical conductivity inside a
subject. In this paper, a new method of electrical impedance imaging
using eddy current is proposed. The eddy current distribution in the
body depends on the conductivity distribution and the magnetic field
pattern. By changing the position of magnetic core, a set of voltage
differences is measured with a pair of electrodes. This set of voltage
differences is used in image reconstruction of conductivity
distribution. The least square error minimization method is used as a
reconstruction algorithm. The back projection algorithm is used to
get two dimensional images. Based on this principle, a measurement
system is developed and some model experiments were performed
with a saline filled phantom. The shape of each model in the
reconstructed image is similar to the corresponding model,
respectively. From the results of these experiments, it is confirmed
that the proposed method is applicable in the realization of electrical
imaging.
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: An optimal mean-square fusion formulas with scalar
and matrix weights are presented. The relationship between them is
established. The fusion formulas are compared on the continuous-time
filtering problem. The basic differential equation for cross-covariance
of the local errors being the key quantity for distributed fusion is
derived. It is shown that the fusion filters are effective for multi-sensor
systems containing different types of sensors. An example
demonstrating the reasonable good accuracy of the proposed filters is
given.
Abstract: In this paper, zigbee communication based wireless energy surveillance system is presented. The proposed system consists of multiple energy surveillance devices and an energy surveillance monitor. Each different standby power-off value of electric device is set automatically by using learning function of energy surveillance device. Thus adaptive standby power-off function provides user convenience and it maximizes the energy savings. Also, power consumption monitoring function is helpful to reduce inefficient energy consumption in home. The zigbee throughput simulator is designed to evaluate minimum transmission power and maximum allowable information quantity in the proposed system. The test result of prototype has been satisfied all the requirements. The proposed system has confirmed that can be used as an intelligent energy surveillance system for energy savings in home or office.