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With examples and clear explanation throughout, this step-by-step approach makes quantum theory of plasmons accessible to readers without specialized training in theory. Jacak uses original research results to offer a fully analytical theory formulation suitable for further development and applications. The theory is focused on the Random Phase Approximation description of plasmons in metallic nano-structures, previously defined for bulk metal. Particular attention is paid to large damping of plasmons in nanostructures including electron scattering and Lorentz friction losses, quantum description of plasmon photovoltaic effect is presented and there is in-depth analysis of plasmon-polariton kinetics in metallic nano-chains. Suitable for students in the field of plasmonics, opto-electronics and photonics, and for researchers active in the field of photo-voltaics, opto-electronics, nano-plasmonics and nano-photonics. Also of help to researchers in soft plasmonics with applications to electro-signalling in neurons.

The challenging question on possible plasmon type excitations in finite ionic systems is discussed. The related theoretical model is formulated and developed in order to describe surface and volume plasmons of ion liquid in finite electrolyte systems. The irradiation of ionic surface plasmon fluctuations is studied in terms of the Lorentz friction of oscillating charges. The attenuation of surface plasmons in the ionic sphere is calculated and minimized with respect to the sphere size. Various regimes of approximation for description of size effect for damping of ionic plasmons are determined and a cross-over in damping size-dependence is demonstrated. The most convenient dimension of finite electrolyte system for energy and information transfer by usage of ionic dipole plasmons is determined. The overall shift of size effect to micrometer scale for ions in comparison to nanometer scale for electrons in metals is found and by several orders red shift of plasmonic resonances in ion systems is predicted in a wide range of variation depending of ion system parameters. This convenient opportunity of tuning of resonances differs ionic plasmons from plasmons in metals where electron concentration was firmly fixed.

Propagation of plasmon-polariton in a metallic nano-chain in a dielectric surroundings is almost undamped and irradiation-less and such nano-chains are considered as quasi-perfect wave-guides for plasmon-polaritons. If, however, a chain is deposited on a semiconductor substrate or embedded in a semiconductor surroundings, plasmon-polaritons are strongly quenched due to the energy transfer from plasmon oscillations to band electrons in the semiconductor medium via the near-field coupling channel. For the reverse direction of this energy transfer the balance of the thermal losses of plasmon-polariton can be achieved. By a control of a dielectric spacing between the metallic nano-chain and the semiconductor substrate (and a type of the deposition), various regimes for coupling of two systems can be organized. We present the exact solution for propagation of strongly damped plasmon-polaritons in metallic nano-chain due to coupling of plasmons with band electrons in the semiconductor substrate.

Metallic nanoparticles are used to improve solar cell efficiency due to plasmon mediated photo-voltaic effect. We present various channels of this phenomenon in semiconductor solar cells with p − n junction and in chemical-type cells with exciton photovoltaic mechanism. Besides of previously known by plasmon strengthening of sun light absorption in metalized solar cells we have described the influence of plasmonic nanoparticles onto internal electricity of cells. The latter case we analyze on the example of hybridized perovskite solar cells regarded as most promising cells of III-rd generation. The explanation of recent experimental achievements with the metallization of perovskite cells is presented in comparison to the metallization of conventional Si-based cells.

Photovoltaics is amongst the most important technologies for renewable energy sources, and plays a key role in the development of a society with a smaller environmental footprint. Key parameters for solar cells are their energy conversion efficiency, their operating lifetime, and the cost of the energy obtained from a photovoltaic system compared to other sources. The optimization of these aspects involves the exploitation of new materials and development of novel solar cell concepts and designs. Both theoretical modeling and characterization of such devices require a comprehensive view including all scales from the atomic to the macroscopic and industrial scale. The different length scales of the electronic and optical degrees of freedoms specifically lead to an intrinsic need for multiscale simulation, which is accentuated in many advanced photovoltaics concepts including nanostructured regions. Therefore, multiscale modeling has found particular interest in the photovoltaics community, as a tool to advance the field beyond its current limits. In this article, we review the field of multiscale techniques applied to photovoltaics, and we discuss opportunities and remaining challenges. © T. Abu Hamed et al., published by EDP Sciences, 2018., European Cooperation in Science and Technology: MP1406, The authors are grateful for the financial support by the COST Action MP1406 “MultiscaleSolar.”

The Lorentz friction for surface plasmons in metallic nanospheres has been analyzed versus nanoparticle size upon random-phase approximation quasiclassical method. An accurate inclusion of the Lorentz friction to the plasmon damping has been performed. Total attenuation of surface plasmons in a metallic nanosphere has been calculated and minimized with respect to the radius of the nanosphere. Experimentally measured anomalous red-shift of plasmon resonance frequency, increasing with the metallic particle radius growth, especially pronounced for a large size limit, was explained in agreement with the experimental data for Au nanospheres of radii 10–75 nm (the measured quantity being the extinction of the light trans-passing through the water colloidal solution of metallic nanospheres with various radii). A very good agreement of the measured data and the theory with properly included Lorentz friction has been achieved.

We analyze the microscopic mechanism of the improvement of solar cell efficiency by plasmons in metallic components embedded in active optical medium of a cell. We focus on the explanation of the observed new channel of plasmon photovoltaic effect related to the influence of plasmons onto the internal cell electricity beyond the previously known plasmon mediated absorption of photons. The model situation we analyze is the hybrid chemical perovskite solar cell CH 3 NH 3 PbI 3 &minus, &alpha, Cl &alpha, with inclusion of core&ndash, shell Au/Si0 2 nanoparticles filling pores in the Al 2 O 3 or TiO 2 porous bases at the bottom of perovskite layer, application of which improved the cell efficiency from 10.7 to 11.4% and from 8.4 to 9.5%, respectively, as demonstrated experimentally, mostly due to the reduction by plasmons of the exciton binding energy.

Plasmons in noble metallic nanoparticles can be used in order to increase efficiency of energy transfer in solar cells of various configurations, mainly in diode-type setups. The origin of plasmonic effect is still not fully recognized and one of the important contributions to photocurrent enhancement results from the inclusion of non-conserved momentum interband transition in semiconductor as a consequence of absence of translational invariance in nanomodified system. We consider energy transfer efficiency via plasmons in nanoparticles and its dependency of nanoparticle radius. Fully-analytical, semiclassical approach for plasmons in spherical geometry is applied and efficiency of near-field absorption in semiconductor is estimated upon Fermi's golden rule. Finally, we link our results with experimental measurements of photocurrent gain in semiconductor silicon-diode setup with gold nanoparticles, which occur in positive qualitative agreement with the theoretical predictions.

An explanation of a large plasmon-induced PV efficiency enhancement of metallically surface-modified photo-cell is given by inclusion of all indirect inter-band electron transitions in semiconductor due to near-field coupling with plasmon radiation of a nano-scale metallic components. The model of nanosphere plasmon is formulated (of RPA-type, adjusted to large clusters) for both surface and volume modes. Damping of plasmons is analyzed including irradiation losses due to the Lorentz friction. Probability of the interband transition in substrate semiconductor caused by the coupling with plasmons in near-field regime turns out to be larger by 4-orders (in an idealised atomic regime) than for coupling of electrons with planar-wave photons. Inclusion of proximity and interference effects allows for explanation of photo-current growth measured in experimental metallically modified photo-diode systems.

We analyze the material dependence of the near-field coupling to band electrons of surface plasmons in metallic nanoparticles deposited on the top of a semiconductor substrate in nano-modified solar cells. Various materials for the metal and substrate are considered for comparison upon the Fermi Golden Rule approach to theoretical modeling of the plasmon-electron coupling. The material dependence of the plasmon effect in two types of solar cells, multi-crystalline Si and CIGS (copper-indium-gallium-diselenide), modified by various metallic nano-particles is illustrated by the experimental data and compared with the theoretical predictions.

In heterogeneous system design, partitioning of the functional specifications into hardware (HW) and software (SW) components is an important procedure. Often, an HW platform is chosen, and the SW is mapped onto the existing partial solution, or the actual partitioning is performed in an ad hoc manner. The partitioning approach presented is novel in that it uses Bayesian belief networks (BBNs) to categorize functional components into HW and SW classifications. The BBNpsilas ability to propagate evidence permits the effects of a classification decision that is made about one function to be felt throughout the entire network. In addition, because BBNs have a belief of hypotheses as their core, a quantitative measurement as to the correctness of a partitioning decision is achieved. A methodology for automatically generating the qualitative structural portion of BBN and the quantitative link matrices is given. A case study of a programmable thermostat is developed to illustrate the BBN approach. The outcomes of the partitioning process are discussed and placed in a larger design context, which is called model-based codesign.

This work studies the use of gold (Au) and silver (Ag) nanoparticles in multicrystalline silicon (mc-Si) and copper-indium-gallium-diselenide (CIGS) solar cells. Au and Ag nanoparticles are deposited by spin-coating method, which is a simple and low cost process. The random distribution of nanoparticles by spin coating broadens the resonance wavelength of the transmittance. This broadening favors solar cell applications. Metal shadowing competes with light scattering in a manner that varies with nanoparticle concentration. Experimental results reveal that the mc-Si solar cells that incorporate Au nanoparticles outperform those with Ag nanoparticles. The incorporation of suitable concentration of Au and Ag nanoparticles into mc-Si solar cells increases their efficiency enhancement by 5.6% and 4.8%, respectively. Incorporating Au and Ag nanoparticles into CIGS solar cells improve their efficiency enhancement by 1.2% and 1.4%, respectively. The enhancement of the photocurrent in mc-Si solar cells is lower than that in CIGS solar cells, owing to their different light scattering behaviors and material absorption coefficients.

An inclusion of the Lorentz friction to the damping of plasmons in metallic nanosphere is performed within the random phase approximation quasiclassical approach. The explanation of the experimentally observed anomalous red shift of plasmon resonance frequency with increase of the metallic particle radius for a large size limit is given and the perfect coincidence of the measured plasmon resonance red shift for Au nanospheres with radii 10 – 75 nm and the theory with accurately included Lorentz friction is demonstrated.

In equidistant infinite chain of metallic nanospheres the collective mode of surface plasmons propagates without radiative losses, i.e., the Lorentz friction losses in each nanosphere are compensated by energy income in near-, medium- and far-field from the rest of the chain. Within an approximate approach including numerical studies in Green function framework it has been indicated superluminal propagation of some plasmon-polariton modes. By the exact solution of the nonlinear dynamic equation we demonstrate that the superluminal modes were an artifact of the perturbation solution type and we show that the group velocities for both polarizations are limited by light velocity, though vary in large range depending on chain parameters and are typically one order lower than the light velocity.

In this chapter we present a novel method for scoring function specification and feature selection by combining unsupervised learning with supervised cross validation. Various clustering algorithms such as one dimensional Kohonen SOM, k-means, fuzzy c-means and hierarchical clustering procedures are used to perform a clustering of object-data for a chosen subset of input features and a given number of clusters. The resulting object clusters are compared with the predefined target classes and a matching factor (score) is calculated. This score is used as criterion function for heuristic sequential and cross feature selection.

This book offers an excellent presentation of intelligent engineering and informatics foundations for researchers in this field as well as many examples with industrial application. It contains extended versions ofselected papers presented at the inaugural ACASE 2012 Conference dedicated to the Applications of Systems Engineering. This conference was held from the 6th to the 8th of February 2012, at the University of Technology, Sydney, Australia, organized by the University of Technology, Sydney (Australia), Wroclaw University of Technology (Poland) and theUniversity of Applied Sciencesin Hagenberg (Austria). The book is organized into three main parts. Part I contains papers devoted to the heuristic approaches that are applicable in situations where the problem cannot be solved by exact methods, due to various characteristics or dimensionality problems. Part II covers essential issues of the network management, presents intelligent models of the next generation of networks and distributed systems as well as discusses applications of modern numerical methods in large intractable systems. Part III covers salient issues of complexity in intelligent system applications. This part also contains papers and articles which discuss concurrency issues that arise when multiple systems attempt to use the same radio space and the inter-connected system applications in the field of medical simulation and training.

In this chapter we present results of empirical research work done on the data based identification of estimation models for tumor markers and cancer diagnoses: Based on patients’ data records including standard blood parameters, tumor markers, and information about the diagnosis of tumors we have trained mathematical models that represent virtual tumor markers and predictors for cancer diagnoses, respectively. We have used a medical database compiled at the Central Laboratory of the General Hospital Linz, Austria, and applied several data based modeling approaches for identifying mathematical models for estimating selected tumor marker values on the basis of routinely available blood values; in detail, estimators for the tumor markers AFP, CA-125, CA15-3, CEA, CYFRA, and PSA have been identified and are discussed here. Furthermore, several data based modeling approaches implemented in HeuristicLab have been applied for identifying estimators for selected cancer diagnoses: Linear regression, k-nearest neighbor learning, artificial neural networks, and support vector machines (all optimized using evolutionary algorithms) as well as genetic programming. The investigated diagnoses of breast cancer, melanoma, and respiratory system cancer can be estimated correctly in up to 81%, 74%, and 91% of the analyzed test cases, respectively; without tumor markers up to 75%, 74%, and 87% of the test samples are correctly estimated, respectively.

The energy transfer from plasmons in metallic nano-sphere deposited on the semiconductor surface to substrate electron band-system is investigated upon the scheme of Fermi golden rule. The analysis of PV efficiency dependence with respect to the nano-sphere size for plasmon mediated channel is presented for the case of photo-diode system with metallic nano-components deposited on the photo-active surface. The trade off between PV efficiency enhancement accompanying surface plasmon dipole magnitude increase with the nano-sphere size growth versus lowering tendency due to quenching of indirect inter-band transitions in substrate semiconductor, significant for smaller radii, is demonstrated in an analytical way. Plasmons in metallic nano-sphere are described within the semiclassical random phase approximation (RPA) framework, sufficiently accurate for large nanoparticles, with radius a ∼ 5 − 60 nm (for Au or Ag). Irradiation induced plasmon damping is analyzed via the Lorentz friction mechanism. The comparison with the experimental data is given supporting the formulated explanation of giant PV efficiency increase due to plasmon effect.

Experimental and theoretical investigations of plasmon excitations in metallic nano-crystals rapidly grew up mainly due to possible applications in photo-voltaics and microelectronics. A significant enhancement of absorption of incident light in photo-diode-systems with active surfaces covered with nano-size metallic particles (of Au, Ag or Cu) with planar density 108-1010/cm2 was observed [24, 25, 27, 30, 32, 34]. These findings are of practical importance for enhancement of solar cell efficiency, especially for development of thin film cell technology. On the other hand, hybridized states of surface plasmons and photons result in plasmon-polaritons [20, 36], which are of high importance for applications in photonics and microelectronics [10, 20], in particular, for sub-diffraction transportation of converted light energy and information in metallically modified structures in nano-scale [21, 36].

In this paper we report on the use of evolutionary algorithms for optimizing the identification of classification models for selected tumor markers. Our goal is to identify mathematical models that can be used for classifying tumor marker values as normal or as elevated; evolutionary algorithms are used for optimizing the parameters for learning classification models. The sets of variables used as well as the parameter settings for concrete modeling methods are optimized using evolution strategies and genetic algorithms. The performance of these algorithms is analyzed as well as the population diversity progress. In the empirical part of this paper we document modeling results achieved for tumor markers CA 125 and CYFRA using a medical data base provided by the Central Laboratory of the General Hospital Linz; empirical tests are executed using HeuristicLab.

In this paper we present results of empirical research work done on the data based identification of estimation models for cancer diagnoses: Based on patients' data records including standard blood parameters, tumor markers, and information about the diagnosis of tumors we have trained mathematical models for estimating cancer diagnoses. Several data based modeling approaches implemented in HeuristicLab have been applied for identifying estimators for selected cancer diagnoses: Linear regression, k-nearest neighbor learning, artificial neural networks, and support vector machines (all optimized using evolutionary algorithms) as well as genetic programming. The investigated diagnoses of breast cancer, melanoma, and respiratory system cancer can be estimated correctly in up to 81%, 74%, and 91% of the analyzed test cases, respectively; without tumor markers up to 75%, 74%, and 87% of the test samples are correctly estimated, respectively.

Metallic nanospheres (Au, Ag, Cu) deposited on PV-active semiconductor surface can act as light converters, collecting energy of incident photons in plasmon oscillations. This energy can be next transferred to semiconductor substrate via a near-field channel, in a more efficient manner in comparison to the direct photo-effect. We explain this enhancement by inclusion of all indirect inter-band transitions in semiconductor layer due to near-field coupling with plasmon radiation in nanoscale of the metallic components, where the momentum is not conserved as the system is not translationally invariant. The model of nano-sphere plasmon is formulated (RPA, analytical version, adjusted to description of large metallic clusters, with radius of 10-100 nm) including surface and volume modes. Damping of plasmons is analyzed including Lorentz friction, and irradiation losses in far- and near-field regimes. Resulting resonance shifts are verified experimentally for Au and Ag (10-80 nm) colloidal water solutions with respect to particle size. Probability of interband transition (within the Fermi golden rule) caused by coupling to plasmons in near-field regime turns out to be 4-order larger than for coupling of electrons to planar-wave photons. Inclusion of proximity effects (for type of deposition of nano-components and their shape) allows for explanation of photo-current growth experimentally measured. We describe also a non-dissipative collective mode of surface plasmons in a chain of near-field-coupled metallic nanospheres, for particular size/separation parameters and wave-lengths.

SUMMARYA new method of optimal cost trajectory planning based on a graph searching algorithm is presented. Various heuristic functions which play the key role in the construction of effective algorithms for solving such problems are proposed. Some numerical examples are given based on the kinematic and dynamic models of a IRb-6 ASEA robot. This method rooted in AI has less computational complexity than a dynamical programming method applying to the same optimal-cost trajectory planning problem.

The random-phase-approximation semiclassical scheme for description of plasmon excitations in large metallic nanospheres, with radius range 10-60 nm, is formulated in an all-analytical version. The spectrum of plasmons is determined including both surface and volume type excitations and their mutual connections. The various channels for damping of surface plasmons are evaluated and the relevant resonance shifts are compared with the experimental data for metallic nanoparticles of different size located in dielectric medium or on the semiconductor substrate. The strong enhancement of energy transfer from the surface plasmon oscillations to the substrate semiconductor is explained in the regime of a near-field coupling in agreement with recent experimental observations for metallically nanomodified photo-diode systems.

The paper presents methods that allow an intelligent multiagent system to coordinate and negotiate their actions in order to achieve a common goal.

The design of intelligent and sensor-based autonomous agents learning by themselves to perform complex real-world tasks is a still-open challenge for artificial and computational intelligence. In this paper a concept of a framework for conflict resolution in an autonomous robotic agent system is presented. The structure of an intelligent robotic agent consists of two independent subsystems: the action and motion planning system and the action and motion reactive control system with integrated conflict resolution methods.

The paper presents a method that allows an intelligent multi-agent system to coordinate and negotiate their actions in order to achieve a common goal. Each individual agent consists of several autonomous components that allow the agent to perceive and react to its environment, to plan and execute an action, and to negotiate with other agents in an intelligent manner. A heuristic approach for conflict solution is presented, which is used for coordination of a society of independently acting agents in common environment. The application of the method is shown on an intelligent multiagent robotic system realizing complex transfer operations simultanously.

The data-driven decision support tool built around the SAS technology has been developed to support the evaluation and monitoring of the quality of educational process. The tool forms an integrated framework that can be used for managing of teaching and learning processes and for performing comparative studies in the participating institutions. The tool includes: the engine for the comparative statistical analysis of the quality of teaching, the mechanism for dissemination of analytical results and the reporting facility. The main aim of our study is to extract and to compare the information on the quality of courses and teaching obtained from the different sets of databases at the Faculty of Electronic Engineering (FEE) of the Wroclaw, University of Technology (WUT), Poland; the Faculty of Computer Science (FCS) at University of Las Palmas de Grand Canaria (ULPGC), Spain; the Faculty of Engineering, Software, the Polytechnic University of Upper Austria in Hagenberg, Austria; the Electrical and Computer Science Department at the University of Arizona in Tucson, USA and Software Engineering Group at the Faculty of Engineering University of Technology, Sydney (UTS), Australia. In this paper we describe the process involved, the methodology, the tools for the analysis; and we present the results of our study.

In this paper, requirements for design of high autonomy manufacturing systems are stipulated. Effods towards amalgamating the autonomous architecture and its real world component (i.e., a flexible manufacturing system) are presented. Planning and control principles derived from discrete event modeling techniques are summarized.

This paper presents an approach for a multilevel knowledge base system for evidence-based medicine. A sequence of events called patient trial is extracted from computer patient records. These events describe one flow of therapy for a concrete disease. Each event is represented by state and time. We introduce a measure between states, which is used to calculate the best alignment between different patient trials. The alignment measure calculates the distance between two sequences of patient states, which represents the similarity of the course of disease. Based on that similarity- value classes are introduced by using specific clustering methods. These classes can be extended by gene expression data on micro-arrays leading to finer clustering containing similar trials – called trial families. For easy checking if a new trial belongs to a family we use profiles of Hidden Markov models to detect potential membership in a family.

The design techniques discussed here are concerned with computer integrated, cellular manufacturing systems. Such systems consist of automated robotic cells. Planning and control within a cell is carried out in off- and on-line modes by a hierarchical controller. A workstation and its control modules are called computer assisted workcell (CAW). An intelligent CAW should determine control laws so that manufacturing tasks can be carried out optimally. Techniques are presented to synthesize high autonomous computer assisted workcells. At the core of the CAW's architecture are three layers: execution, coordination, and organization. The architecture and its layers are discussed in detail. >

The purpose of a flexible manufacturing system (FMS) is to perform a series of well defined operations on a family of similar pads. The operations (e.g. milling or drilling) are realized by machines which are serviced by robots. A cell controller coordinates the flow of parts through the cell. Monitoring, i.e., to watch the system behavior during its operation and detect possible anomalies, is an important task of an intelligent manufacturing controller. This paper present an approach for manufacturing system monitoring which has its foundation in the DEVS-based intelligent control paradigm. The work is part of a bigger research project whose objective is to develop techniques for automatic synthesis of intelligent flexible manufacturing system controllers. Important additional parts of the intelligent control system will provide fault diagnosis and self repair capabilities. >

A framework is proposed for support of design, task planning, and simulation of automated manufacturing systems. The framework establishes a hierarchy of method banks essential for improving the efficiency and cost effectiveness of manufacturing processes. The methods should support automatic generation of sequencing rules, design and configuration of the manufacturing facility and equipment, synthesis of task oriented robot programs, and generation and execution of simulation models of a manufacturing system. In this paper, each layer is addressed and preliminary results that apply simulation to interpret and test task oriented robot programs are discussed. >

In this paper we present a method and a tool for modelling a teletraining session in heterogenous, distributed open environments. We propose a mathematical notion for the training process. Therefore we divide a whole training session into presentation units, define some relations on these units and develop a controller for running the session. Units consist of multimedia objects, such as text, graphics, video and audio, which have to be displayed with time and space synchronisation and coordination. In a related project at the Department of Software Engineering at the Upper Austria Polytechnic University a tool for modelling and running such teletraining sessions was developed. It is based on the herein defined formalism and compiles well defined unit models into JAVAT/sup TM/ code, which can be executed by usual WEB-Browsers.

In this work, we present our view of an intelligent robotic manipulator that can work autonomously in a dynamically changing environment. The increased intelligence is needed to enable the manipulator to react to changing environments without contacting a supervising coordinator. This approach requires increased computational capabilities on the part of the manipulator. We show how the computational requirements of such an autonomous manipulator can be satisfied by a combination of neural network-based processing modules. We use symbolic computation methods to construct these neural networks (thereby eliminating training times), and sensors to provide the signals to recognize changes in the environment.

The modeling of nonlinear dynamical systems is one of the emergent application areas of artificial neural networks. In this paper, we present a general methodology based on neural networks and genetic algorithms that can be applied to modeling of nonlinear dynamical systems. We describe a general methodology for modeling nonlinear systems with known rank (i.e. state-space dimension) by feedforward networks with external delay units. We point out the shortcomings of this approach when the rank of the system is not known a priori. In this case, it is beneficial to employ genetic algorithms to search for neural networks that can model the nonlinear dynamical systems. Two genetic algorithms are presented for this case: one that determines the best feedforward network with external delay, and one that searches for a network with arbitrary topology and memory cells within each neuron.

The paper presents a methodology and methods that allows a robotic system comprised of intelligent robotic agents to coordinate and negotiate their actions in order to achieve common goals. Each individual robotic agent consists of several autonomous components of its control system that allow the agent to perceive and react to its environment, to plan and execute an action, and to negotiate with other agents in an intelligent manner. Particular emphasis is placed on the negotiation and coordination process between two conflicting agents. Here, the workspace shared by both agents is treated as a common resource, and the coordination problem becomes a resource allocation problem. Strategies for conflict prevention and compromise strategies are presented in detail and implemented in a simulation.

This paper presents a system-theoretical approach for an intelligent multiagent system which is used to control and coordinate a society of robotic agents acting independently in a partially known environment. We focus our presentation on only one aspect of the coordination of the robotic agent group, namely on the conflict management. We present solutions for detecting and resolving conflicts between two or more robotic agents.

Within the last years the paradigm of intelligent software agents became an emerging topic in computer science research and development. Intelligent software agents are computational systems that populate complex dynamic environment, act and react there in an autonomous way in order realize a specific task based on a set of goals given to them. In multi agent systems (= MAS) intelligent software agents are grouped together and represent a network of problem solvers designed to achieve a common goal, which would be too large for a single centralized software agent. The member agents of such a multi agent system have a sufficient degree of decision-making autonomy and interact with other agents by explicit communication. Therefore the main fields of interest in multi agent based system design cover coordination, communication and negotiation based on uncertain data and knowledge of each member agent of the multi agent system. This article proposes s system theoretical approach towards the design and synthesis of a multi agent system for controlling a robotic agent.

The paper presents the system Telesession Designer and Transmitter for computer supported creation of interactive multimedia telepresentations of training session and the event based control system for telepresentation execution in an open and distributed environment. We propose a way of unifying these different objects into one standardized format, based on MHEG-Standard. Thus we call these objects dynamic multimedia documents. The presentation's objects will be stored in a distributed database, which elements (multimedia objects) can be stored somewhere in the open environment. An intelligent navigation system allows selection and preview of these objects from all over the world. The presentation is modelled in form of an event depending graph, representing the general flow of the education session. The final result - the telepresentation - will run in a both i.e. the peer to peer and the client/server environment.

In this paper, we presented an application of neural network-based models in the intelligent agent domain. The use of neural networks has the advantage that the model can adapt itself to changing environment conditions by simple retraining steps. This is illustrated by two functions needed for the lifelong learning-based modeling of an intelligent robotic agent: The function for generalizing sensor observations to conceptual states, and the function for modeling the effects of robot actions on the conceptual state space. An example shows that the algorithms can be applied in real-world environments.

In this article, we describe the use of tumour marker estimation models in the prediction of tumour diagnoses. In previous works, we have identified classification models for tumour markers that can be used for estimating tumour marker values on the basis of standard blood parameters. These virtual tumour markers are now used in combination with standard blood parameters for learning classifiers that are used for predicting tumour diagnoses. Several data-based modelling approaches implemented in HeuristicLab have been applied for identifying estimators for selected tumour markers and cancer diagnoses: Linear regression, k-nearest neighbour (k-NN) learning, artificial neural networks (ANNs) and support vector machines (SVMs) (all optimised using evolutionary algorithms), as well as genetic programming (GP). We have applied these modelling approaches for identifying models for breast cancer diagnoses; in the results section, we summarise classification accuracies for breast cancer and we compare classification results achieved by models that use measured marker values as well as models that use virtual tumour markers.

This paper discusses similarities of computer support in three areas: development of software, supporting the flow of work/documents in an office and the production of goods in a flexible work cell. All three areas obey the same underlying principle, i.e. defining a process and then interpreting the process by an enactment (interpretation) mechanism. In the paper elementary questions with respect to the similarity are raised and tentative answers given. The conclusion is that the use of the same paradigm is valid, that considerable difference have to be considered in its implementation.

With the development of new computing paradigms, such as neural networks and genetic algorithms, new tools have become available in computer-aided systems theory. These tools can be used to tackle problems that are considered “hard” in traditional systems theory, like the modeling and identification of nonlinear dynamical systems.

A comprehensive framework for design of an intelligent cell-controller requires integration of several layers of support methods and tools. We have proposed an architecture that facilitates an automatic generation of different plans of sequencing operations, synthesis of action plan for robots servicing the devices, synthesis of the workcell's simulation model, and verification of control variants based on simulation of the overall cell's architecture. The real-time discrete event simulator is used next to generate a sequence of future events of the virtual cell in a given time-window. These events are compared with current states of the real cell and are used to predict motion commands of robots and to monitor the process flow. The architecture, called Computer Assisted Workcell, offers support methods and tools at the following layers of control: organization, coordination, and execution.

International audience; Metallic nanospheres (Au, Ag, Cu) deposited on PV-active semiconductor surface can act as light converters, collecting energy of incident photons in plasmon oscillations. This energy can be next transferred to semiconductor substrate via a near-field channel, in a more efficient manner in comparison to the direct photo-effect. We explain this enhancement by inclusion of indirect inter-band transitions in semiconductor layer due to the near-field coupling with plasmon radiation in nanoscale of the metallic components, where the momentum is not conserved as the system is not translationally invariant. The model of the nano-sphere plasmons is developed (RPA, analytical version, adjusted to description of large metallic clusters, with radius of 10 − 60 nm) including surface and volume modes. Damping of plasmons is analyzed via Lorentz friction, and irradiation losses in far- and near-field regimes. Resulting resonance shifts are verified experimentally for Au and Ag colloidal water solutions with respect to particle size. Probability of the electron interband transition (within the Fermi golden rule) in substrate semiconductor induced by coupling to plasmons in near-field regime turns out to be significantly larger than for coupling of electrons to planarwave photons. This is of practical importance for enhancement of thin-film solar cell efficiency, both for semiconductor type (like III-V semiconductor based cells) and for Mechanism of plasmon-mediated enhancement of PV efficiency 2 conjugate-polymer-based or dye organic plastic cells, intensively developed at present. We have described also a non-dissipative collective mode of surface plasmons in a chain of near-field-coupled metallic nanospheres, for particular size, separation parameters and wave-lengths. This would find an application in sub-diffraction electro-photonic circuit arrangement and for possible energy transport in solar cells, in particular in organic materials with low mobility of carriers.