Abstract: The greenhouse effect and limitations on carbon
dioxide emissions concern engine maker and the future of the
internal combustion engines should go toward substantially and
improved thermal efficiency engine. Homogeneous charge
compression ignition (HCCI) is an alternative high-efficiency
technology for combustion engines to reduce exhaust emissions and
fuel consumption. However, there are still tough challenges in the
successful operation of HCCI engines, such as controlling the
combustion phasing, extending the operating range, and high
unburned hydrocarbon and CO emissions. HCCI and the exploitation
of ethanol as an alternative fuel is one way to explore new frontiers
of internal combustion engines with an eye towards maintaining its
sustainability. This study was done to extend database knowledge
about HCCI with ethanol a fuel.
Abstract: The charge-exchange xenon (CEX) ion generated by ion thruster can backflow to the surface of spacecraft and threaten to the safety of spacecraft operation. In order to evaluate the effects of the induced plasma environment in backflow regions on the spacecraft, we designed a spherical single Langmuir probe of 5.8cm in diameter for measuring low-density plasma parameters in backflow region of ion thruster. In practice, the tests are performed in a two-dimensional array (40cm×60cm) composed of 20 sites. The experiment results illustrate that the electron temperature ranges from 3.71eV to 3.96eV, with the mean value of 3.82eV and the standard deviation of 0.064eV. The electron density ranges from 8.30×1012/m3 to 1.66×1013/m3, with the mean value of 1.30×1013/m3 and the standard deviation of 2.15×1012/m3. All data is analyzed according to the “ideal" plasma conditions of Maxwellian distributions.
Abstract: Intensive changes of environment and strong market
competition have raised management of information and knowledge
to the strategic level of companies. In a knowledge based economy
only those organizations are capable of living which have up-to-date,
special knowledge and they are able to exploit and develop it.
Companies have to know what knowledge they have by taking a
survey of organizational knowledge and they have to fix actual and
additional knowledge in organizational memory. The question is how
to identify, acquire, fix and use knowledge effectively. The paper will
show that over and above the tools of information technology
supporting acquisition, storage and use of information and
organizational learning as well as knowledge coming into being as a
result of it, fixing and storage of knowledge in the memory of a
company play an important role in the intelligence of organizations
and competitiveness of a company.
Abstract: There have been widespread applications of fluidized beds in industries which are related to the combination of gas-solid particles during the last decade. For instance, in order to crack the catalyses in petrochemical industries or as a drier in food industries. High capacity of fluidized bed in heat and mass transfer has made this device very popular. In order to achieve a higher efficiency of fluidized beds, a particular attention has been paid to beds with pulsating air flow. In this paper, a fluidized bed device with pulsating flow has been designed and constructed. Size of particles have been used during the test are in the range of 40 to 100μm. The purpose of this experimental test is to investigate the air flow regime, observe the particles- movement and measure the pressure loss along the bed. The effects of pulsation can be evaluated by comparing the results for both continuous and pulsating flow. Results of both situations are compared for various gas speeds. Moreover the above experiment is numerically simulated by using Fluent software and its numerical results are compared with the experimental results.
Abstract: This paper presents a novel approach for tuning unified power flow controller (UPFC) based damping controller in order to enhance the damping of power system low frequency oscillations. The design problem of damping controller is formulated as an optimization problem according to the eigenvalue-based objective function which is solved using iteration particle swarm optimization (IPSO). The effectiveness of the proposed controller is demonstrated through eigenvalue analysis and nonlinear time-domain simulation studies under a wide range of loading conditions. The simulation study shows that the designed controller by IPSO performs better than CPSO in finding the solution. Moreover, the system performance analysis under different operating conditions show that the δE based controller is superior to the mB based controller.
Abstract: The computer has become an essential tool in modern
life, and the combined use of a computer with a projector is very
common in teaching and presentations. However, as typical computer
operating devices involve a mouse or keyboard, when making
presentations, users often need to stay near the computer to execute
functions such as changing pages, writing, and drawing, thus, making
the operation time-consuming, and reducing interactions with the
audience. This paper proposes a laser pointer interaction system able
to simulate mouse functions in order that users need not remain near
the computer, but can directly use laser pointer operations from at a
distance. It can effectively reduce the users- time spent by the
computer, allowing for greater interactions with the audience.
Abstract: This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.
Abstract: This paper proposes a new parameter identification
method based on Linear Fractional Transformation (LFT). It is
assumed that the target linear system includes unknown parameters.
The parameter deviations are separated from a nominal system via
LFT, and identified by organizing I/O signals around the separated
deviations of the real system. The purpose of this paper is to apply LFT
to simultaneously identify the parameter deviations in systems with
fewer outputs than unknown parameters. As a fundamental example,
this method is implemented to one degree of freedom vibratory system.
Via LFT, all physical parameters were simultaneously identified in this
system. Then, numerical simulations were conducted for this system to
verify the results. This study shows that all the physical parameters of a
system with fewer outputs than unknown parameters can be effectively
identified simultaneously using LFT.
Abstract: A simple and dexterous in situ method was introduced to load CdS nanocrystals into organofunctionalized mesoporous, which used an ion-exchange method. The products were extensively characterized by combined spectroscopic methods. X- ray diffraction (XRD) and high-resolution transmission electron microscopy (HRTEM) demonstrated both the maintenance of pore symmetry (space group p6mm) of SBA-15 and the presence of CdS nanocrystals with uniform sizes of about 6 - 8 nm inside the functionalized SBA-15 channels. These mesoporous silica-supported CdS composites showed room temperature photoluminescence properties with a blue shift, indicating the quantum size effect of nanocrystalline CdS.
Abstract: Tea is consumed by a big part of the world-s
population. It has an enormous importance for the Turkish culture.
Nearly it is brewed every morning and evening at the all houses. Also it is consumed with lemon wedge. Habitual drinking of tea
infusions may significantly contribute to daily dietary requirements of elements.
Different instrumental techniques are used for determination of
these elements. But atomic and mass spectroscopic methods are preferred most. In these study chromium, iron and selenium contents
after the hot water brewing of black and green tea were determined
by Optical Emission Spectroscopy (ICP-OES). Furthermore, effect
of lemon addition on chromium, iron and selenium concentration tea
infusions is investigated.
Results of the investigation showed that concentration of
chromium, iron and selenium increased in black tea with lemon addition. On the other hand only selenium is increased with lemon
addition in green tea. And iron concentration is not detected in green
tea but its concentration is determined as 1.420 ppm after lemon addition.
Abstract: Power flow (PF) study, which is performed to
determine the power system static states (voltage magnitudes and
voltage angles) at each bus to find the steady state operating
condition of a system, is very important and is the most frequently
carried out study by power utilities for power system planning,
operation and control. In this paper, a counterpropagation neural
network (CPNN) is proposed to solve power flow problem under
different loading/contingency conditions for computing bus voltage
magnitudes and angles of the power system. The counterpropagation
network uses a different mapping strategy namely
counterpropagation and provides a practical approach for
implementing a pattern mapping task, since learning is fast in this
network. The composition of the input variables for the proposed
neural network has been selected to emulate the solution process of a
conventional power flow program. The effectiveness of the proposed
CPNN based approach for solving power flow is demonstrated by
computation of bus voltage magnitudes and voltage angles for
different loading conditions and single line-outage contingencies in
IEEE 14-bus system.
Abstract: Hepatitis C is an infectious disease transmitted by
blood and due to hepatitis C virus (HCV), which attacks the liver.
The infection is characterized by liver inflammation (hepatitis) that is
often asymptomatic but can progress to chronic hepatitis and later
cirrhosis and liver cancer. Our problem tends to highlight on the one
hand the prevalence of infectious disease in the population of the
region of Batna and on other hand the biological characteristics of
this disease by a screening and a specific diagnosis based on
serological tests, liver checkup (measurement of haematological and
biochemical parameters).
The results showed:
The serology of hepatitis C establishes the diagnosis of infection
with hepatitis C. In this study and with the serological test, 24 cases
of the disease of hepatitis C were found in 1000 suspected cases (7
cases with normal transaminases and 17 cases with elevated
transaminases). The prevalence of this disease in this study
population was 2.4%.
The presence of hepatitis C disrupts liver function including the
onset of cytolysis, cholestasis, jaundice, thrombocytopenia, and
coagulation disorders.
Abstract: This paper deals with the tuning of parameters for Automatic Generation Control (AGC). A two area interconnected hydrothermal system with PI controller is considered. Genetic Algorithm (GA) and Particle Swarm optimization (PSO) algorithms have been applied to optimize the controller parameters. Two objective functions namely Integral Square Error (ISE) and Integral of Time-multiplied Absolute value of the Error (ITAE) are considered for optimization. The effectiveness of an objective function is considered based on the variation in tie line power and change in frequency in both the areas. MATLAB/SIMULINK was used as a simulation tool. Simulation results reveal that ITAE is a better objective function than ISE. Performances of optimization algorithms are also compared and it was found that genetic algorithm gives better results than particle swarm optimization algorithm for the problems of AGC.
Abstract: The development of Internet technology in recent years has led to a more active role of users in creating Web content. This has significant effects both on individual learning and collaborative knowledge building. This paper will present an integrative framework model to describe and explain learning and knowledge building with shared digital artifacts on the basis of Luhmann-s systems theory and Piaget-s model of equilibration. In this model, knowledge progress is based on cognitive conflicts resulting from incongruities between an individual-s prior knowledge and the information which is contained in a digital artifact. Empirical support for the model will be provided by 1) applying it descriptively to texts from Wikipedia, 2) examining knowledge-building processes using a social network analysis, and 3) presenting a survey of a series of experimental laboratory studies.
Abstract: In this paper, the discrete-time fuzzy BAM neural network with delays and impulses is studied. Sufficient conditions are obtained for the existence and global stability of a unique equilibrium of this class of fuzzy BAM neural networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time. Some numerical examples are given to demonstrate the effectiveness of the obtained results.
Abstract: How to coordinate the behaviors of the agents through
learning is a challenging problem within multi-agent domains.
Because of its complexity, recent work has focused on how
coordinated strategies can be learned. Here we are interested in using
reinforcement learning techniques to learn the coordinated actions of a
group of agents, without requiring explicit communication among
them. However, traditional reinforcement learning methods are based
on the assumption that the environment can be modeled as Markov
Decision Process, which usually cannot be satisfied when multiple
agents coexist in the same environment. Moreover, to effectively
coordinate each agent-s behavior so as to achieve the goal, it-s
necessary to augment the state of each agent with the information
about other existing agents. Whereas, as the number of agents in a
multiagent environment increases, the state space of each agent grows
exponentially, which will cause the combinational explosion problem.
Profit sharing is one of the reinforcement learning methods that allow
agents to learn effective behaviors from their experiences even within
non-Markovian environments. In this paper, to remedy the drawback
of the original profit sharing approach that needs much memory to
store each state-action pair during the learning process, we firstly
address a kind of on-line rational profit sharing algorithm. Then, we
integrate the advantages of modular learning architecture with on-line
rational profit sharing algorithm, and propose a new modular
reinforcement learning model. The effectiveness of the technique is
demonstrated using the pursuit problem.
Abstract: A microchannel with two inlets and two outlets was tested as a potential reactor to carry out two-phase catalytic phase transfer reaction with phase separation at the exit of the microchannel. The catalytic phase transfer reaction between benzyl chloride and sodium sulfide was chosen as a model reaction. The effect of operational time on the conversion was studied. By utilizing a multiphase parallel flow inside the microchannel reactor with the aid of a guideline structure, the catalytic phase reaction followed by phase separation could be ensured. The organic phase could be separated completely from one exit and part of the aqueous phase was separated purely and could be reused with slightly affecting the catalytic phase transfer reaction.
Abstract: Long Term Evolution (LTE) is a 4G wireless
broadband technology developed by the Third Generation
Partnership Project (3GPP) release 8, and it's represent the
competitiveness of Universal Mobile Telecommunications System
(UMTS) for the next 10 years and beyond. The concepts for LTE
systems have been introduced in 3GPP release 8, with objective of
high-data-rate, low-latency and packet-optimized radio access
technology. In this paper, performance of different TCP variants
during LTE network investigated. The performance of TCP over
LTE is affected mostly by the links of the wired network and total
bandwidth available at the serving base station. This paper describes
an NS-2 based simulation analysis of TCP-Vegas, TCP-Tahoe, TCPReno,
TCP-Newreno, TCP-SACK, and TCP-FACK, with full
modeling of all traffics of LTE system. The Evaluation of the
network performance with all TCP variants is mainly based on
throughput, average delay and lost packet. The analysis of TCP
performance over LTE ensures that all TCP's have a similar
throughput and the best performance return to TCP-Vegas than other
variants.
Abstract: This paper concerns about the experimental and
numerical investigations of energy absorption and axial tearing
behaviour of aluminium 6060 circular thin walled tubes under static
axial compression. The tubes are received in T66 heat treatment
condition with fixed outer diameter of 42mm, thickness of 1.5mm
and length of 120mm. The primary variables are the conical die
angles (15°, 20° and 25°). Numerical simulations are carried on
ANSYS/LS-DYNA software tool, for investigating the effect of
friction between the tube and the die.
Abstract: In recent years, IT convergence technology has been developed to get creative solution by combining robotics or sports science technology. Object detection and recognition have mainly applied to sports science field that has processed by recognizing face and by tracking human body. But object detection and recognition using vision sensor is challenge task in real world because of illumination. In this paper, object detection and recognition using vision sensor applied to sports simulator has been introduced. Face recognition has been processed to identify user and to update automatically a person athletic recording. Human body has tracked to offer a most accurate way of riding horse simulator. Combined image processing has been processed to reduce illumination adverse affect because illumination has caused low performance in detection and recognition in real world application filed. Face has recognized using standard face graph and human body has tracked using pose model, which has composed of feature nodes generated diverse face and pose images. Face recognition using Gabor wavelet and pose recognition using pose graph is robust to real application. We have simulated using ETRI database, which has constructed on horse riding simulator.