Abstract: Current systems complexity has reached a degree that
requires addressing conception and design issues while taking into
account environmental, operational, social, legal and financial
aspects. Therefore, one of the main challenges is the way complex
systems are specified and designed. The exponential growing effort,
cost and time investment of complex systems in modeling phase
emphasize the need for a paradigm, a framework and an environment
to handle the system model complexity. For that, it is necessary to
understand the expectations of the human user of the model and his
limits. This paper presents a generic framework for designing
complex systems, highlights the requirements a system model needs
to fulfill to meet human user expectations, and suggests a graphbased
formalism for modeling complex systems. Finally, a set of
transformations are defined to handle the model complexity.
Abstract: In more complex systems, such as automotive
gearbox, a rigorous treatment of the data is necessary because there
are several moving parts (gears, bearings, shafts, etc.), and in this
way, there are several possible sources of errors and also noise. The
basic objective of this work is the detection of damage in automotive
gearbox. The detection methods used are the wavelet method, the
bispectrum; advanced filtering techniques (selective filtering) of
vibrational signals and mathematical morphology. Gearbox vibration
tests were performed (gearboxes in good condition and with defects)
of a production line of a large vehicle assembler. The vibration
signals are obtained using five accelerometers in different positions
of the sample. The results obtained using the kurtosis, bispectrum,
wavelet and mathematical morphology showed that it is possible to
identify the existence of defects in automotive gearboxes.
Abstract: The building sector is responsible, in many
industrialized countries, for about 40% of the total energy
requirements, so it seems necessary to devote some efforts in this
area in order to achieve a significant reduction of energy
consumption and of greenhouse gases emissions.
The paper presents a study aiming at providing a design
methodology able to identify the best configuration of the system
building/plant, from a technical, economic and environmentally point
of view.
Normally, the classical approach involves a building's energy
loads analysis under steady state conditions, and subsequent selection
of measures aimed at improving the energy performance, based on
previous experience made by architects and engineers in the design
team. Instead, the proposed approach uses a sequence of two wellknown
scientifically validated calculation methods (TRNSYS and
RETScreen), that allow quite a detailed feasibility analysis.
To assess the validity of the calculation model, an existing,
historical building in Central Italy, that will be the object of
restoration and preservative redevelopment, was selected as a casestudy.
The building is made of a basement and three floors, with a
total floor area of about 3,000 square meters.
The first step has been the determination of the heating and
cooling energy loads of the building in a dynamic regime by means,
which allows simulating the real energy needs of the building in
function of its use. Traditional methodologies, based as they are on
steady-state conditions, cannot faithfully reproduce the effects of
varying climatic conditions and of inertial properties of the structure.
With this model is possible to obtain quite accurate and reliable
results that allow identifying effective combinations building-HVAC
system.
The second step has consisted of using output data obtained as
input to the calculation model, which enables to compare different
system configurations from the energy, environmental and financial
point of view, with an analysis of investment, and operation and
maintenance costs, so allowing determining the economic benefit of
possible interventions.
The classical methodology often leads to the choice of
conventional plant systems, while our calculation model provides a
financial-economic assessment for innovative energy systems and
low environmental impact.
Computational analysis can help in the design phase, particularly
in the case of complex structures with centralized plant systems, by
comparing the data returned by the calculation model for different
design options.
Abstract: Ontologies offer a means for representing and sharing
information in many domains, particularly in complex domains. For
example, it can be used for representing and sharing information
of System Requirement Specification (SRS) of complex systems
like the SRS of ERTMS/ETCS written in natural language. Since
this system is a real-time and critical system, generic ontologies,
such as OWL and generic ERTMS ontologies provide minimal
support for modeling temporal information omnipresent in these SRS
documents. To support the modeling of temporal information, one
of the challenges is to enable representation of dynamic features
evolving in time within a generic ontology with a minimal redesign
of it. The separation of temporal information from other information
can help to predict system runtime operation and to properly design
and implement them. In addition, it is helpful to provide a reasoning
and querying techniques to reason and query temporal information
represented in the ontology in order to detect potential temporal
inconsistencies. To address this challenge, we propose a lightweight
3-layer temporal Quality of Service (QoS) ontology for representing,
reasoning and querying over temporal and non-temporal information
in a complex domain ontology. Representing QoS entities in separated
layers can clarify the distinction between the non QoS entities
and the QoS entities in an ontology. The upper generic layer of
the proposed ontology provides an intuitive knowledge of domain
components, specially ERTMS/ETCS components. The separation of
the intermediate QoS layer from the lower QoS layer allows us to
focus on specific QoS Characteristics, such as temporal or integrity
characteristics. In this paper, we focus on temporal information that
can be used to predict system runtime operation. To evaluate our
approach, an example of the proposed domain ontology for handover
operation, as well as a reasoning rule over temporal relations in this
domain-specific ontology, are presented.
Abstract: Recently, an increasing number of researchers have
been focusing on working out realistic solutions to sustainability
problems. As sustainability issues gain higher importance for
organisations, the management of such decisions becomes critical.
Knowledge representation is a fundamental issue of complex
knowledge based systems. Many types of sustainability problems
would benefit from models based on experts’ knowledge. Cognitive
maps have been used for analyzing and aiding decision making. A
cognitive map can be made of almost any system or problem. A
fuzzy cognitive map (FCM) can successfully represent knowledge
and human experience, introducing concepts to represent the essential
elements and the cause and effect relationships among the concepts to
model the behaviour of any system. Integrated waste management
systems (IWMS) are complex systems that can be decomposed to
non-related and related subsystems and elements, where many factors
have to be taken into consideration that may be complementary,
contradictory, and competitive; these factors influence each other and
determine the overall decision process of the system. The goal of the
present paper is to construct an efficient IWMS which considers
various factors. The authors’ intention is to propose an expert based
system design approach for implementing expert decision support in
the area of IWMSs and introduces an appropriate methodology for
the development and analysis of group FCM. A framework for such a
methodology consisting of the development and application phases is
presented.
Abstract: Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.
Abstract: Nonstationary and nonlinear signals generated by living complex systems defy traditional mechanistic approaches, which are based on homeostasis. Previous our studies have shown that the evaluation of the interactions of physiological signals by using special analysis methods is suitable for observation of physiological processes. It is demonstrated the possibility of using deep physiological model, based on the interpretation of the changes of the human body’s functional states combined with an application of the analytical method based on matrix theory for the physiological signals analysis, which was applied on high risk cardiac patients. It is shown that evaluation of cardiac signals interactions show peculiar for each individual functional changes at the onset of hemodynamic restoration procedure. Therefore, we suggest that the alterations of functional state of the body, after patients overcome surgery can be complemented by the data received from the suggested approach of the evaluation of functional variables’ interactions.
Abstract: Modelling is a widely used tool to facilitate the evaluation of disease management. The interest of epidemiological models lies in their ability to explore hypothetical scenarios and provide decision makers with evidence to anticipate the consequences of disease incursion and impact of intervention strategies.
All models are, by nature, simplification of more complex systems. Models that involve diseases can be classified into different categories depending on how they treat the variability, time, space, and structure of the population. Approaches may be different from simple deterministic mathematical models, to complex stochastic simulations spatially explicit.
Thus, epidemiological modelling is now a necessity for epidemiological investigations, surveillance, testing hypotheses and generating follow-up activities necessary to perform complete and appropriate analysis.
The state of the art presented in the following, allows us to position itself to the most appropriate approaches in the epidemiological study.
Abstract: Sometime it is difficult to determine the exact reliability for complex systems in analytical procedures. Approximate solution of this problem can be provided through discretization of random variables. In this paper we describe the usefulness of discretization of a random variable using the reversed hazard rate function of its continuous version. Discretization of the exponential distribution has been demonstrated. Applications of this approach have also been cited. Numerical calculations indicate that the proposed approach gives very good approximation of reliability of complex systems under stress-strength set-up. The performance of the proposed approach is better than the existing discrete concentration method of discretization. This approach is conceptually simple, handles analytic intractability and reduces computational time. The approach can be applied in manufacturing industries for producing high-reliable items.
Abstract: Currently, mathematical and computer modeling are widely used in different biological studies to predict or assess behavior of such a complex systems as a biological are. This study deals with mathematical and computer modeling of bi-substrate enzymatic reactions, which play an important role in different biochemical pathways. The main objective of this study is to represent the results from in silico investigation of bi-substrate enzymatic reactions in the presence of uncompetitive inhibitors, as well as to describe in details the inhibition effects. Four models of uncompetitive inhibition were designed using different software packages. Particularly, uncompetitive inhibitor to the first [ES1] and the second ([ES1S2]; [FS2]) enzyme-substrate complexes have been studied. The simulation, using the same kinetic parameters for all models allowed investigating the behavior of reactions as well as determined some interesting aspects concerning influence of different cases of uncompetitive inhibition. Besides, it has been shown that uncompetitive inhibitors exhibit specific selectivity depending on mechanism of bi-substrate enzymatic reaction.
Abstract: Economic models are complex dynamic systems with a lot of uncertainties and fuzzy data. Conventional modeling approaches using well known methods and techniques cannot provide realistic and satisfactory answers to today-s challenging economic problems. Qualitative modeling using fuzzy logic and intelligent system theories can be used to model macroeconomic models. Fuzzy Cognitive maps (FCM) is a new method been used to model the dynamic behavior of complex systems. For the first time FCMs and the Mamdani Model of Intelligent control is used to model macroeconomic models. This new model is referred as the Mamdani Rule-Based Fuzzy Cognitive Map (MBFCM) and provides the academic and research community with a new promising integrated advanced computational model. A new economic model is developed for a qualitative approach to Macroeconomic modeling. Fuzzy Controllers for such models are designed. Simulation results for an economic scenario are provided and extensively discussed
Abstract: It is by reason of the unified measure of varieties of resources and the unified processing of the disposal of varieties of resources, that these closely related three of new basic models called the resources assembled node and the disposition integrated node as well as the intelligent organizing node are put forth in this paper; the three closely related quantities of integrative analytical mechanics including the disposal intensity and disposal- weighted intensity as well as the charge of resource charge are set; and then the resources assembled space and the disposition integrated space as well as the intelligent organizing space are put forth. The system of fundamental equations and model of complete factor synergetics is preliminarily approached for the general situation in this paper, to form the analytical base of complete factor synergetics. By the essential variables constituting this system of equations we should set twenty variables respectively with relation to the essential dynamical effect, external synergetic action and internal synergetic action of the system.
Abstract: Modularized design approach can facilitate the
modeling of complex systems and support behavior analysis and
simulation in an iterative and thus complex engineering process, by
using encapsulated submodels of components and of their interfaces.
Therefore it can improve the design efficiency and simplify the
solving complicated problem. Multi-drivers off-road vehicle is
comparatively complicated. Driving-line is an important core part to a
vehicle; it has a significant contribution to the performance of a
vehicle. Multi-driver off-road vehicles have complex driving-line, so
its performance is heavily dependent on the driving-line. A typical
off-road vehicle-s driving-line system consists of torque converter,
transmission, transfer case and driving-axles, which transfer the
power, generated by the engine and distribute it effectively to the
driving wheels according to the road condition. According to its main
function, this paper puts forward a modularized approach for
designing and evaluation of vehicle-s driving-line. It can be used to
effectively estimate the performance of driving-line during concept
design stage. Through appropriate analysis and assessment method, an
optimal design can be reached. This method has been applied to the
practical vehicle design, it can improve the design efficiency and is
convenient to assess and validate the performance of a vehicle,
especially of multi-drivers off-road vehicle.
Abstract: the reliability analysis of the medical equipments can
help to increase the availability and the efficiency of the systems. In
this manuscript we present a simple method of decomposition that
could be easily applied on the complex medical systems. Using this
method we can easily calculate the effect of the subsystems or
components on the reliability of the overall system. Furthermore, to
investigate the effect of subsystems or components on system
performance, we perform a numerical study varying every time the
worst reliability of subsystem or component with another which has
higher reliability. It can also be useful to engineers and designers of
medical equipment, who wishes to optimize the complex systems.
Abstract: In this paper, a novel approach for robust trajectory tracking of induction motor drive is presented. By combining variable structure systems theory with fuzzy logic concept and neural network techniques, a new algorithm is developed. Fuzzy logic was used for the adaptation of the learning algorithm to improve the robustness of learning and operating of the neural network. The developed control algorithm is robust to parameter variations and external influences. It also assures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the designed controller of induction motor drives which considered as highly non linear dynamic complex systems and variable characteristics over the operating conditions.
Abstract: Fuzzy Cognitive Maps (FCMs) is a causal graph, which shows the relations between essential components in complex systems. Experts who are familiar with the system components and their relations can generate a related FCM. There is a big gap when human experts cannot produce FCM or even there is no expert to produce the related FCM. Therefore, a new mechanism must be used to bridge this gap. In this paper, a novel learning method is proposed to construct causal graph based on historical data and by using metaheuristic such Tabu Search (TS). The efficiency of the proposed method is shown via comparison of its results of some numerical examples with those of some other methods.
Abstract: Measures of complexity and entropy have not converged to a single quantitative description of levels of organization of complex systems. The need for such a measure is increasingly necessary in all disciplines studying complex systems. To address this problem, starting from the most fundamental principle in Physics, here a new measure for quantity of organization and rate of self-organization in complex systems based on the principle of least (stationary) action is applied to a model system - the central processing unit (CPU) of computers. The quantity of organization for several generations of CPUs shows a double exponential rate of change of organization with time. The exact functional dependence has a fine, S-shaped structure, revealing some of the mechanisms of self-organization. The principle of least action helps to explain the mechanism of increase of organization through quantity accumulation and constraint and curvature minimization with an attractor, the least average sum of actions of all elements and for all motions. This approach can help describe, quantify, measure, manage, design and predict future behavior of complex systems to achieve the highest rates of self organization to improve their quality. It can be applied to other complex systems from Physics, Chemistry, Biology, Ecology, Economics, Cities, network theory and others where complex systems are present.
Abstract: A logic model for analyzing complex systems- stability
is very useful to many areas of sciences. In the real world, we are
enlightened from some natural phenomena such as “biosphere", “food
chain", “ecological balance" etc. By research and practice, and taking
advantage of the orthogonality and symmetry defined by the theory of
multilateral matrices, we put forward a logic analysis model of
stability of complex systems with three relations, and prove it by
means of mathematics. This logic model is usually successful in
analyzing stability of a complex system. The structure of the logic
model is not only clear and simple, but also can be easily used to
research and solve many stability problems of complex systems. As an
application, some examples are given.
Abstract: Most of the well known methods for generating
Gaussian variables require at least one standard uniform distributed
value, for each Gaussian variable generated. The length of the
random number generator therefore, limits the number of
independent Gaussian distributed variables that can be generated
meanwhile the statistical solution of complex systems requires a
large number of random numbers for their statistical analysis. We
propose an alternative simple method of generating almost infinite
number of Gaussian distributed variables using a limited number of
standard uniform distributed random numbers.
Abstract: A new strategy of control is formulated for chaos synchronization of non-identical chaotic systems with different orders using the Borne and Gentina practical criterion associated with the Benrejeb canonical arrow form matrix, to drift the stability property of dynamic complex systems. The designed controller ensures that the state variables of controlled chaotic slave systems globally synchronize with the state variables of the master systems, respectively. Numerical simulations are performed to illustrate the efficiency of the proposed method.