Abstract: The main aim of this research is to investigate a novel technique for implementing a more natural and intelligent conversation system. Conversation systems are designed to converse like a human as much as their intelligent allows. Sometimes, we can think that they are the embodiment of Turing-s vision. It usually to return a predetermined answer in a predetermined order, but conversations abound with uncertainties of various kinds. This research will focus on an integrated natural language processing approach. This approach includes an integrated knowledge-base construction module, a conversation understanding and generator module, and a state manager module. We discuss effectiveness of this approach based on an experiment.
Abstract: Wind farms (WFs) with high level of penetration are
being established in power systems worldwide more rapidly than
other renewable resources. The Independent System Operator (ISO),
as a policy maker, should propose appropriate places for WF
installation in order to maximize the benefits for the investors. There
is also a possibility of congestion relief using the new installation of
WFs which should be taken into account by the ISO when proposing
the locations for WF installation. In this context, efficient wind farm
(WF) placement method is proposed in order to reduce burdens on
congested lines. Since the wind speed is a random variable and load
forecasts also contain uncertainties, probabilistic approaches are used
for this type of study. AC probabilistic optimal power flow (P-OPF)
is formulated and solved using Monte Carlo Simulations (MCS). In
order to reduce computation time, point estimate methods (PEM) are
introduced as efficient alternative for time-demanding MCS.
Subsequently, WF optimal placement is determined using generation
shift distribution factors (GSDF) considering a new parameter
entitled, wind availability factor (WAF). In order to obtain more
realistic results, N-1 contingency analysis is employed to find the
optimal size of WF, by means of line outage distribution factors
(LODF). The IEEE 30-bus test system is used to show and compare
the accuracy of proposed methodology.
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: R&D risk management has been suggested as one of
the management approaches for accomplishing the goals of public
R&D investment. The investment in basic science and core technology
development is the essential roles of government for securing the
social base needed for continuous economic growth. And, it is also an
important role of the science and technology policy sectors to generate
a positive environment in which the outcomes of public R&D can be
diffused in a stable fashion by controlling the uncertainties and risk
factors in advance that may arise during the application of such
achievements to society and industry. Various policies have already
been implemented to manage uncertainties and variables that may
have negative impact on accomplishing public R& investment goals.
But we may derive new policy measures for complementing the
existing policies and for exploring progress direction by analyzing
them in a policy package from the viewpoint of R&D risk
management.
Abstract: Since DVB-RCS has been successively implemented, the mobile communication on the multi-beam satellite communication is attractive attention. And the DVB-RCS standard sets up to support mobility of a RCST. In the case of the spot-beam satellite system, the received signal strength does not differ largely between the center and the boundary of the beam. Thus, the RSS based handoff detection algorithm is not benefit to the satellite system as a terrestrial system. Therefore we propose an Adaptive handoff detection algorithm based on RCST mobility information. Our handoff detection algorithm not only can be used as centralized handoff detection algorithm but also removes uncertainties of handoff due to the variation of RSS. Performances were compared with RSS based handoff algorithm. Simulation results show that the proposed handoff detection algorithm not only achieved better handoff and link degradation rate, but also achieved better forward link spectral efficiency.
Abstract: In conventional reliability assessment, the reliability data of system components are treated as crisp values. The collected data have some uncertainties due to errors by human beings/machines or any other sources. These uncertainty factors will limit the understanding of system component failure due to the reason of incomplete data. In these situations, we need to generalize classical methods to fuzzy environment for studying and analyzing the systems of interest. Fuzzy set theory has been proposed to handle such vagueness by generalizing the notion of membership in a set. Essentially, in a Fuzzy Set (FS) each element is associated with a point-value selected from the unit interval [0, 1], which is termed as the grade of membership in the set. A Vague Set (VS), as well as an Intuitionistic Fuzzy Set (IFS), is a further generalization of an FS. Instead of using point-based membership as in FS, interval-based membership is used in VS. The interval-based membership in VS is more expressive in capturing vagueness of data. In the present paper, vague set theory coupled with conventional Lambda-Tau method is presented for reliability analysis of repairable systems. The methodology uses Petri nets (PN) to model the system instead of fault tree because it allows efficient simultaneous generation of minimal cuts and path sets. The presented method is illustrated with the press unit of the paper mill.
Abstract: What influences microsystems (MEMS) and nanosystems (NEMS) innovation teams apart from technology complexity? Based on in-depth interviews with innovators, this research explores the key influences on innovation teams in the early phases of MEMS/NEMS. Projects are rare and may last from 5 to 10 years or more from idea to concept. As fundamental technology development in MEMS/NEMS is highly complex and interdisciplinary by involving expertise from different basic and engineering disciplines, R&D is rather a 'testing of ideas' with many uncertainties than a clearly structured process. The purpose of this study is to explore the innovation teams- environment and give specific insights for future management practices. The findings are grouped into three major areas: people, know-how and experience, and market. The results highlight the importance and differences of innovation teams- composition, transdisciplinary knowledge, project evaluation and management compared to the counterparts from new product development teams.
Abstract: The experimental study of position control of a light
weight and small size robotic finger during non-contact motion is
presented in this paper. The finger possesses fingertip pinching and
self adaptive grasping capabilities, and is made of a seven bar linkage
mechanism with a slider in the middle phalanx. The control system is
tested under the Proportional Integral Derivative (PID) control
algorithm and Recursive Least Square (RLS) based Feedback Error
Learning (FEL) control scheme to overcome the uncertainties present
in the plant. The experiments conducted in Matlab Simulink and xPC
Target environments show that the overall control strategy is efficient
in controlling the finger movement.
Abstract: This paper discusses two observers, which are used
for the estimation of parameters of PMSM. Former one, reduced
order observer, which is used to estimate the inaccessible parameters
of PMSM. Later one, full order observer, which is used to estimate
all the parameters of PMSM even though some of the parameters are
directly available for measurement, so as to meet with the
insensitivity to the parameter variation. However, the state space
model contains some nonlinear terms i.e. the product of different
state variables. The asymptotic state observer, which approximately
reconstructs the state vector for linear systems without uncertainties,
was presented by Luenberger. In this work, a modified form of such
an observer is used by including a non-linear term involving the
speed. So, both the observers are designed in the framework of
nonlinear control; their stability and rate of convergence is discussed.
Abstract: This paper present a new method for design of power
system stabilizer (PSS) based on sliding mode control (SMC)
technique. The control objective is to enhance stability and improve
the dynamic response of the multi-machine power system. In order to
test effectiveness of the proposed scheme, simulation will be carried
out to analyze the small signal stability characteristics of the system
about the steady state operating condition following the change in
reference mechanical torque and also parameters uncertainties. For
comparison, simulation of a conventional control PSS (lead-lag
compensation type) will be carried out. The main approach is
focusing on the control performance which later proven to have the
degree of shorter reaching time and lower spike.
Abstract: Planning community has been long discussing emerging paradigms within the planning theory in the face of the changing conditions of the world order. The paradigm shift concept was introduced by Thomas Kuhn, in 1960, who claimed the necessity of shifting within scientific knowledge boundaries; and following him in 1970 Imre Loktas also gave priority to the emergence of multi-paradigm societies [24]. Multi-paradigm is changing our predetermined lifeworld through uncertainties. Those uncertainties are reflected in two sides, the first one is uncertainty as a concept of possibility and creativity in public sphere and the second one is uncertainty as a risk. Therefore, it is necessary to apply a resilience planning approach to be more dynamic in controlling uncertainties which have the potential to transfigure present time and space definitions. In this way, stability of system can be achieved. Uncertainty is not only an outcome of worldwide changes but also a place-specific issue, i.e. it changes from continent to continent, a country to country; a region to region. Therefore, applying strategic spatial planning with respect to resilience principle contributes to: control, grasp and internalize uncertainties through place-specific strategies. In today-s fast changing world, planning system should follow strategic spatial projects to control multi-paradigm societies with adaptability capacities. Here, we have selected two alternatives to demonstrate; these are; 1.Tehran (Iran) from the Middle East 2.Bath (United Kingdom) from Europe. The study elaborates uncertainties and particularities in their strategic spatial planning processes in a comparative manner. Through the comparison, the study aims at assessing place-specific priorities in strategic planning. The approach is to a two-way stream, where the case cities from the extreme end of the spectrum can learn from each other. The structure of this paper is to firstly compare semi-periphery (Tehran) and coreperiphery (Bath) cities, with the focus to reveal how they equip to face with uncertainties according to their geographical locations and local particularities. Secondly, the key message to address is “Each locality requires its own strategic planning approach to be resilient.--
Abstract: In this paper, we present optimal control for
movement and trajectory planning for four degrees-of-freedom robot
using Fuzzy Logic (FL) and Genetic Algorithms (GAs). We have
evaluated using Fuzzy Logic (FL) and Genetic Algorithms (GAs)
for four degree-of-freedom (4 DOF) robotics arm, Uncertainties like;
Movement, Friction and Settling Time in robotic arm movement
have been compensated using Fuzzy logic and Genetic Algorithms.
The development of a fuzzy genetic optimization algorithm is
presented and discussed. The result are compared only GA and
Fuzzy GA. This paper describes genetic algorithms, which is
designed to optimize robot movement and trajectory. Though the
model represents is a general model for redundant structures and
could represent any n-link structures. The result is a complete
trajectory planning with Fuzzy logic and Genetic algorithms
demonstrating the flexibility of this technique of artificial
intelligence.
Abstract: The hydraulic actuated excavator, being a non-linear
mobile machine, encounters many uncertainties. There are
uncertainties in the hydraulic system in addition to the uncertain
nature of the load. The simulation results obtained in this study show
that there is a need for intelligent control of such machines and in
particular interval type-2 fuzzy controller is most suitable for
minimizing the position error of a typical excavator-s bucket under
load variations. We consider the model parameter uncertainties such
as hydraulic fluid leakage and friction. These are uncertainties which
also depend up on the temperature and alter bulk modulus and
viscosity of the hydraulic fluid. Such uncertainties together with the
load variations cause chattering of the bucket position. The interval
type-2 fuzzy controller effectively eliminates the chattering and
manages to control the end-effecter (bucket) position with positional
error in the order of few millimeters.
Abstract: Imperfect knowledge cannot be avoided all the time. Imperfections may have several forms; uncertainties, imprecision and incompleteness. When we look to classification of methods for the management of imperfect knowledge we see fuzzy set-based techniques. The choice of a method to process data is linked to the choice of knowledge representation, which can be numerical, symbolic, logical or semantic and it depends on the nature of the problem to be solved for example decision support, which will be mentioned in our study. Fuzzy Logic is used for its ability to manage imprecise knowledge, but it can take advantage of the ability of neural networks to learn coefficients or functions. Such an association of methods is typical of so-called soft computing. In this study a new method was used for the management of imprecision for collected knowledge which related to economic analysis of construction industry in Turkey. Because of sudden changes occurring in economic factors decrease competition strength of construction companies. The better evaluation of these changes in economical factors in view of construction industry will made positive influence on company-s decisions which are dealing construction.
Abstract: This present paper proposes the modified Elastic Strip
method for mobile robot to avoid obstacles with a real time system in
an uncertain environment. The method deals with the problem of
robot in driving from an initial position to a target position based on
elastic force and potential field force. To avoid the obstacles, the
robot has to modify the trajectory based on signal received from the
sensor system in the sampling times. It was evident that with the
combination of Modification Elastic strip and Pseudomedian filter to
process the nonlinear data from sensor uncertainties in the data
received from the sensor system can be reduced. The simulations and
experiments of these methods were carried out.
Abstract: This study is concerned with a new adaptive impedance control strategy to compensate for unknown time-varying environment stiffness and position. The uncertainties are expressed by Function Approximation Technique (FAT), which allows the update laws to be derived easily using Lyapunov stability theory. Computer simulation results are presented to validate the effectiveness of the proposed strategy.
Abstract: In this research paper we have presented control
architecture for robotic arm movement and trajectory planning using
Fuzzy Logic (FL) and Genetic Algorithms (GAs). This architecture is
used to compensate the uncertainties like; movement, friction and
settling time in robotic arm movement. The genetic algorithms and
fuzzy logic is used to meet the objective of optimal control
movement of robotic arm. This proposed technique represents a
general model for redundant structures and may extend to other
structures. Results show optimal angular movement of joints as result
of evolutionary process. This technique has edge over the other
techniques as minimum mathematics complexity used.
Abstract: Many recent high energy physics calculations
involving charm and beauty invoke wave function at the origin
(WFO) for the meson bound state. Uncertainties of charm and beauty
quark masses and different models for potentials governing these
bound states require a simple numerical algorithm for evaluation of
the WFO's for these bound states. We present a simple algorithm for
this propose which provides WFO's with high precision compared
with similar ones already obtained in the literature.
Abstract: Fuzzy logic control (FLC) systems have been tested in
many technical and industrial applications as a useful modeling tool
that can handle the uncertainties and nonlinearities of modern control
systems. The main drawback of the FLC methodologies in the
industrial environment is challenging for selecting the number of
optimum tuning parameters.
In this paper, a method has been proposed for finding the optimum
membership functions of a fuzzy system using particle swarm
optimization (PSO) algorithm. A synthetic algorithm combined from
fuzzy logic control and PSO algorithm is used to design a controller
for a continuous stirred tank reactor (CSTR) with the aim of
achieving the accurate and acceptable desired results. To exhibit the
effectiveness of proposed algorithm, it is used to optimize the
Gaussian membership functions of the fuzzy model of a nonlinear
CSTR system as a case study. It is clearly proved that the optimized
membership functions (MFs) provided better performance than a
fuzzy model for the same system, when the MFs were heuristically
defined.
Abstract: Actual load, material characteristics and other
quantities often differ from the design values. This can cause worse
function, shorter life or failure of a civil engineering structure, a
machine, vehicle or another appliance. The paper shows main causes
of the uncertainties and deviations and presents a systematic
approach and efficient tools for their elimination or mitigation of
consequences. Emphasis is put on the design stage, which is most
important for reliability ensuring. Principles of robust design and
important tools are explained, including FMEA, sensitivity analysis
and probabilistic simulation methods. The lifetime prediction of
long-life objects can be improved by long-term monitoring of the
load response and damage accumulation in operation. The condition
evaluation of engineering structures, such as bridges, is often based
on visual inspection and verbal description. Here, methods based on
fuzzy logic can reduce the subjective influences.