Abstract: This paper proposes a new optimization techniques
for the optimization a gas processing plant uncertain feed and
product flows. The problem is first formulated using a continuous
linear deterministic approach. Subsequently, the single and joint
chance constraint models for steady state process with timedependent
uncertainties have been developed. The solution approach
is based on converting the probabilistic problems into their
equivalent deterministic form and solved at different confidence
levels Case study for a real plant operation has been used to
effectively implement the proposed model. The optimization results
indicate that prior decision has to be made for in-operating plant
under uncertain feed and product flows by satisfying all the
constraints at 95% confidence level for single chance constrained and
85% confidence level for joint chance constrained optimizations
cases.
Abstract: Over the past decade, mobile has experienced a
revolution that will ultimately change the way we communicate.All
these technologies have a common denominator exploitation of
computer information systems, but their operation can be tedious
because of problems with heterogeneous data sources.To overcome
the problems of heterogeneous data sources, we propose to use a
technique of adding an extra layer interfacing applications of
management or supervision at the different data sources.This layer
will be materialized by the implementation of a mediator between
different host applications and information systems frequently used
hierarchical and relational manner such that the heterogeneity is
completely transparent to the VoIP platform.
Abstract: Cooktop burners are widely used nowadays. In
cooktop burner design, nozzle efficiency and greenhouse
gas(GHG) emissions mainly depend on heat transfer from the
premixed flame to the impinging surface. This is a complicated
issue depending on the individual and combined effects of various
input combustion variables. Optimal operating conditions for
sustainable burner design were rarely addressed, especially in the
case of multiple slot-jet burners. Through evaluating the optimal
combination of combustion conditions for a premixed slot-jet
array, this paper develops a practical approach for the sustainable
design of gas cooktop burners. Efficiency, CO and NOx emissions
in respect of an array of slot jets using premixed flames were
analysed. Response surface experimental design were applied to
three controllable factors of the combustion process, viz.
Reynolds number, equivalence ratio and jet-to-vessel distance.
Desirability Function Approach(DFA) is the analytic technique
used for the simultaneous optimization of the efficiency and
emission responses.
Abstract: Bioprocesses are appreciated as difficult to control because their dynamic behavior is highly nonlinear and time varying, in particular, when they are operating in fed batch mode. The research objective of this study was to develop an appropriate control method for a complex bioprocess and to implement it on a laboratory plant. Hence, an intelligent control structure has been designed in order to produce biomass and to maximize the specific growth rate.
Abstract: Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.
Abstract: Business rules and data warehouse are concepts and
technologies that impact a wide variety of organizational tasks. In
general, each area has evolved independently, impacting application
development and decision-making. Generating knowledge from data
warehouse is a complex process. This paper outlines an approach to
ease import of information and knowledge from a data warehouse
star schema through an inference class of business rules. The paper
utilizes the Oracle database for illustrating the working of the
concepts. The star schema structure and the business rules are stored
within a relational database. The approach is explained through a
prototype in Oracle-s PL/SQL Server Pages.
Abstract: The present work demonstrates the design and simulation of a fuzzy control of an air conditioning system at different pressures. The first order Sugeno fuzzy inference system is utilized to model the system and create the controller. In addition, an estimation of the heat transfer rate and water mass flow rate injection into or withdraw from the air conditioning system is determined by the fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm along with least square estimation (LSE) generates the fuzzy rules that describe the relationship between input/output data. The fuzzy rules are tuned by Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that when the pressure increases the amount of water flow rate and heat transfer rate decrease within the lower ranges of inlet dry bulb temperatures. On the other hand, and as pressure increases the amount of water flow rate and heat transfer rate increases within the higher ranges of inlet dry bulb temperatures. The inflection in the pressure effect trend occurs at lower temperatures as the inlet air humidity increases.
Abstract: In this paper, based on the coupled-mode and carrier rate equations, derivation of a dynamic model and numerically analysis of a MQW chirped DFB-SOA all-optical flip-flop is done precisely. We have analyzed the effects of strains of QW and MQW and cross phase modulation (XPM) on the dynamic response, and rise and fall times of the DFB-SOA all optical flip flop. We have shown that strained MQW active region in under an optimized condition into a DFB-SOA with chirped grating can improve the switching ON speed limitation in such a of the device, significantly while the fall time is increased. The values of the rise times for such an all optical flip-flop, are obtained in an optimized condition, areas tr=255ps.
Abstract: A Rotary Disc Contactor with inner diameter of
9.1cm and maximum operating height of 40cm has been used to
investigate break up phenomenon. Water-Toluene, Water as
continuous phase and Toluene as dispersed phase, was selected as
chemical system in the experiments. The mentioned chemical system
has high interfacial tension so it was possible to form big drops
which permit accurate investigation on break up phenomenon as well
as the first and second critical rotor speeds.
In this study, Break up phenomenon has been studied as a function
of mother drop size, rotor speed and continuous phase height. Further
more; the effects of mother drop size and continuous phase height on
the first and second critical rotor speeds were investigated. Finally,
two modified correlations were proposed to estimate the first and
second critical speeds.
Abstract: The main mission of Ezilla is to provide a friendly
interface to access the virtual machine and quickly deploy the high
performance computing environment. Ezilla has been developed by
Pervasive Computing Team at National Center for High-performance
Computing (NCHC). Ezilla integrates the Cloud middleware,
virtualization technology, and Web-based Operating System (WebOS)
to form a virtual computer in distributed computing environment. In
order to upgrade the dataset and speedup, we proposed the sensor
observation system to deal with a huge amount of data in the
Cassandra database. The sensor observation system is based on the
Ezilla to store sensor raw data into distributed database. We adopt the
Ezilla Cloud service to create virtual machines and login into virtual
machine to deploy the sensor observation system. Integrating the
sensor observation system with Ezilla is to quickly deploy experiment
environment and access a huge amount of data with distributed
database that support the replication mechanism to protect the data
security.
Abstract: The regional innovative competitiveness is an integrating characteristic of the innovative sphere of the region. It depends on a big variety of different parameters connected with all kinds of economic entities- activities. But management parameters shouldn't be irregular, so in order to avoid it, an institutional system should be formed. This system should carry out strategic management of factors having the greatest influence on the region's innovative development. This article is devoted to different aspects of organization of the region's development institutional mechanism, which is based on management of regional innovative competitiveness parameters. The base of the analysis is innovatively-active Russian regions which were compared according to the level of the innovative competitiveness. After that the most important parameters of successful innovative development of the region were revealed with the help of the correlation-regression analysis. The results of the research could be used for investigation of the region's innovative policy.
Abstract: In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.
Abstract: This paper is to explore the relationship and the level
of stock market integration of the Asian countries, primarily
concentrating on Malaysia, Thailand, Indonesia, and South Korea,
with the world from January 1997 to December 2009. The degree of
short-run and long-run stock market integration of those Asian
countries are analyzed in order to determine the significance of series
of regional and world financial crises, liberalization policies and
other financial reforms in influencing the level of stock market
integration. To test for cointegration, this paper applies coefficient
correlation, univariate regression analyses, cointegration tests, and
vector autoregressive models (VAR) by using the four Asian stock
markets main indices and the MSCI World index. The empirical
findings from this work reveal that there is no long-run stock market
integration for the four countries and the world market. However,
there is short run integration.
Abstract: In pattern recognition applications the low level
segmentation and the high level object recognition are generally
considered as two separate steps. The paper presents a method that
bridges the gap between the low and the high level object
recognition. It is based on a Bayesian network representation and
network propagation algorithm. At the low level it uses hierarchical
structure of quadratic spline wavelet image bases. The method is
demonstrated for a simple circuit diagram component identification
problem.
Abstract: In the project FleGSens, a wireless sensor network
(WSN) for the surveillance of critical areas and properties is currently developed which incorporates mechanisms to ensure information
security. The intended prototype consists of 200 sensor nodes for
monitoring a 500m long land strip. The system is focused on ensuring
integrity and authenticity of generated alarms and availability in the
presence of an attacker who may even compromise a limited number
of sensor nodes. In this paper, two of the main protocols developed
in the project are presented, a tracking protocol to provide secure
detection of trespasses within the monitored area and a protocol for secure detection of node failures. Simulation results of networks
containing 200 and 2000 nodes as well as the results of the first prototype comprising a network of 16 nodes are presented. The focus of the simulations and prototype are functional testing of the protocols
and particularly demonstrating the impact and cost of several attacks.
Abstract: In this work Artificial Intelligence (AI) techniques like Fuzzy logic, Genetic Algorithms and Particle Swarm Optimization have been used to improve the performance of the Automatic Generation Control (AGC) system. Instead of applying Genetic Algorithms and Particle swarm optimization independently for optimizing the parameters of the conventional AGC with PI controller, an intelligent tuned Fuzzy logic controller (acting as the secondary controller in the AGC system) has been designed. The controller gives an improved dynamic performance for both hydrothermal and thermal-thermal power systems under a variety of operating conditions.
Abstract: In this study, we developed a model to predict the
temperature and the pressure variation in an internal combustion
engine operated in HCCI (Homogeneous charge compression ignition)
mode. HCCI operation begins from aspirating of homogeneous charge
mixture through intake valve like SI (Spark ignition) engine and the
premixed charge is compressed until temperature and pressure of
mixture reach autoignition point like diesel engine. Combustion phase
was described by double-Wiebe function. The single zone model
coupled with an double-Wiebe function were performed to simulated
pressure and temperature between the period of IVC (Inlet valve close)
and EVO (Exhaust valve open). Mixture gas properties were
implemented using STANJAN and transfer the results to main model.
The model has considered the engine geometry and enables varying in
fuelling, equivalence ratio, manifold temperature and pressure. The
results were compared with the experiment and showed good
correlation with respect to combustion phasing, pressure rise, peak
pressure and temperature. This model could be adapted and use to
control start of combustion for HCCI engine.
Abstract: This paper presents the design and implementation of a fully integrated transimpedance amplifier (TIA) as the analog frontend receiver for Capacitive Micromachined Ultrasound Transducers (CMUTs) for ultrasound biomicroscope imaging application. The amplifier is designed to amplify the received signals from 17.5MHz to 52.5MHz with a center frequency of 35MHz. The TIA was fabricated in GF 0.18μm 1P6M 30V high voltage process. The measurement results show that the designed amplifier can reach a transimpedance gain of 61.08dBΩ and operating frequency from 17.5MHz to 100MHz with 1VP-P output voltage under 6V power supply.
Abstract: Along with forward supply chain organization needs
to consider the impact of reverse logistics due to its economic
advantage, social awareness and strict legislations. In this paper, we
develop a system dynamics framework for a closed-loop supply
chain with fuzzy demand and fuzzy collection rate by incorporating
product exchange policy in forward channel and various recovery
options in reverse channel. The uncertainty issues associated with
acquisition and collection of used product have been quantified using
possibility measures. In the simulation study, we analyze order
variation at both retailer and distributor level and compare bullwhip
effects of different logistics participants over time between the
traditional forward supply chain and the closed-loop supply chain.
Our results suggest that the integration of reverse logistics can reduce
order variation and bullwhip effect of a closed-loop system. Finally,
sensitivity analysis is performed to examine the impact of various
parameters on recovery process and bullwhip effect.
Abstract: Finger spelling is an art of communicating by signs
made with fingers, and has been introduced into sign language to serve
as a bridge between the sign language and the verbal language.
Previous approaches to finger spelling recognition are classified into
two categories: glove-based and vision-based approaches. The
glove-based approach is simpler and more accurate recognizing work
of hand posture than vision-based, yet the interfaces require the user to
wear a cumbersome and carry a load of cables that connected the
device to a computer. In contrast, the vision-based approaches provide
an attractive alternative to the cumbersome interface, and promise
more natural and unobtrusive human-computer interaction. The
vision-based approaches generally consist of two steps: hand
extraction and recognition, and two steps are processed independently.
This paper proposes real-time vision-based Korean finger spelling
recognition system by integrating hand extraction into recognition.
First, we tentatively detect a hand region using CAMShift algorithm.
Then fill factor and aspect ratio estimated by width and height
estimated by CAMShift are used to choose candidate from database,
which can reduce the number of matching in recognition step. To
recognize the finger spelling, we use DTW(dynamic time warping)
based on modified chain codes, to be robust to scale and orientation
variations. In this procedure, since accurate hand regions, without
holes and noises, should be extracted to improve the precision, we use
graph cuts algorithm that globally minimize the energy function
elegantly expressed by Markov random fields (MRFs). In the
experiments, the computational times are less than 130ms, and the
times are not related to the number of templates of finger spellings in
database, as candidate templates are selected in extraction step.