Dissipative and Damping Properties of Multi-layered Rubber-Metal Vibration Absorber

Rubber and rubber-metal (RM) elements are successfully used as bearing, joints, compensating devices, vibration and shock absorbers in civil engineering and machine building because of rubber and rubberlike materials (elastomers) have a capability of absorbing input energy much better than other construction materials. The elastic properties of rubber in such supports allows reverse backward to its original position under dynamic load action. Along with the instantaneous elastic deformation these materials exhibit a retarded elastic deformation, viscous flow (creep) and relaxation.The mechanical properties of rubber which are necessary for the optimal design of antivibration devices are next: bulk modulus of compression, dynamic and static shear modulus, energy dissipation factor. To describe the relationship between the compressive (or shear) stress σ(t) and strain ԑ(t) the creep and relaxation kernel, taking into account the viscoelastic properties of the rubber, is used. The kernels proposed by A. Rzhanitsin, Y. Rabotnov, M. Koltunov give satisfactory results for the mechanical properties of rubber in the mean frequency domain (10-3 ω 103 s-1). In this paper for the accounting of dissipative properties of the rubber Rabotnov’s kernel is used, the energy loss during one oscillation period is calculated. The flat-type RM absorber with kinematic excitation, which lower base oscillates harmonically is considered, oscillation parameters of the upper base on which the protected object is placed, are calculated. Damping properties are expressed by the ratio of the amplitude of the forced oscillations of the upper base (and object) to the amplitude of driving lower base.

Performance Analysis of MRC Receivers with Adaptive Modulation and Coding in Rayleigh Fading Correlated Channels with Imperfect CSIT

This paper addresses the performance analysis of an adaptive wireless link with one antenna transmitter and a multiple antenna maximum-ratio combining (MRC) receiver. Two main assumptions are used in this paper: (1) Rayleigh fading correlated channels (i.e., MRC branch correlation) and (2) imperfect (outdated) channel state information at the transmitter (CSIT) side. he main contribution of this work lies in the derivation of analytic expressions (in terms of a series expansion) of the statistics of correct packet reception conditional on the decisions made by the transmitter based on outdated CSIT. he novelty of this derivation is the joint modelling of spatially correlated branches, imperfect CSIT, and adaptive modulation based on threshold-trigger decision. Contrary to common belief, the results presented here suggest that spatial correlation not always afects the performance of the MRC receiver: at low signal-to-noise ratio (SNR), correlation can improve performance rather than degrading it. In contrast, at high SNR, correlation is found to always degrade performance. At high SNR, correlation tends to worse the degrading efects of imperfect CSIT, particularly when the number of antennas increases. Imperfect CSIT causes errors in the assignment of MCSs, thus reducing throughput performance. hese errors become more evident in the high SNR regime, particularly when the values of branch correlation and the number of antennas increase.info:eu-repo/semantics/publishedVersio

Multi-Agent Architecture for Intelligent Insurance Systems

Modern insurance information systems need intelligence to provide new functions that till now as a rule have been carried out by humans. Introduction of intelligent mechanisms into information systems allows the insurance companies to automate many processes in the insurance business and achieve two benefits. Firstly, the amount of work done by humans is reduced and secondly more services can be provided to customers electronically, that increases the level of customer service. Additionally, insurance information systems need to communicate with many other systems to get the needed data. These demands fit the characteristics of intelligent agents. Thus the paper proposes to implement insurance information system as a multi-agent system using intelligent agents to realize the modules of insurance information systems. A novel multi-agent architecture for insurance information system development is proposed

Distributed Intelligent Tutoring System Architectures

Traditionally Intelligent Tutoring Systems (ITSs) are built with modular architecture. Such ITSs consist of four traditional modules, namely tutoring module, expert module, student diagnosis module and communication module. Three of these modules correspond to the main types of knowledge (pedagogical knowledge, domain knowledge and knowledge about the learner) used in ITSs, so allowing to build components that are using only one type of knowledge. Still, modular architecture does not provide sufficient modularity for complex ITSs. To facilitate modularity and change management, lately distributed technologies like services and intelligent agents are used to develop ITSs. Agent based ITSs mainly use the same approach – they implement traditional modules as sets of agents. Customizable set of agents that can be used in various ITSs, has been defined. Implementation of modules as a set of distributed components allows to keep the traditional idea of modules. Different types of knowledge are separated from each other. At the same time, the system can be distributed and highly modular. A few multi-agent architectures for ITS development have been published. These architectures mainly consist of agents from the set of agents. The architectures are closed in the sense that system’s functionality can not be changed just by adding/removing components from the system. These architectures are closed – new agents can not be added to the system. As a consequence multi-agent architectures for ITS development disable one of the advantages of distributed technologies – possibility to change system’s functionality by just adding and/or removing distributed components from the system. As of author’s knowledge no specific service oriented architectures (SOA) for ITS development exist, except the ones presented further in the chapter. So there is a lack of open ITS architectures, despite ITS being a system that may need frequent changes due to the changes in the particular course or adaptation to new courses. To realize intelligent tutoring various types of learning materials and problems have to be presented to the learner, moreover it should be done intelligently enough. Thus each new type of problems may need corresponding code to handle it. Types of problems differ from course to course as well as might change in case the course is changed. So, the system should be open for corresponding new components. Actually, typical ITSs must be open for certain types of components handling new types of problems, materials, feedback, etc. Presence of specific distributed and open architectures would facilitate development of open ITSs. The aim of the chapter is to propose open ITS architectures, using two distributed paradigms – intelligent software agents and services

Method of Optimal Synthesis of Strongly Non-Linear (Impact) Systems

Stipri nelineāru (impulsu) sistēmu optimālās sintēzes metode Parādīts, ka (lai izgudrotu principiāli jaunas adaptīvas dinamiskas sistēmas) stipri nelineāru (triecienu) sistēmu optimālās sintēzes algoritmu var iedalīt šādos piecos formālos etapos: - pētniekam dotā pirmatnējā uzdevuma analīze; - galvenās fundamentālās sistēmas optimizācija; - ideālās vadības iedarbības analīze; - jaunu strukturālu shēmu sintēze; - optimālo parametru atrašana. Otrajā etapā iegūto vadības likumu var izlietot diviem mērķiem: -eksistējošo sistēmu novērtēšanai attiecībā pret ideālo likumu; - principiāli jaunu strukturālu sistēmu sintēzei attiecībā pret tuvinājumu ideālajam likumam. Šis etaps ir bāze jauniem izgudrojumiem. Te zinātniska pieeja dod iespēju izgudrot principiāli jaunas mašīnas un mehānismus. Jaunās strukturālās shēmas jāmeklē trīs stipri nelineāru (triecienu) sistēmu vadības principos: - sistēmās ar vadības laika ierosmi; - sistēmās ar fāzu koordinātu ierosmes vadību; -jauktās sistēmās

Distributed Intelligent Tutoring System Architectures

Traditionally Intelligent Tutoring Systems (ITSs) are built with modular architecture. Such ITSs consist of four traditional modules, namely tutoring module, expert module, student diagnosis module and communication module. Three of these modules correspond to the main types of knowledge (pedagogical knowledge, domain knowledge and knowledge about the learner) used in ITSs, so allowing to build components that are using only one type of knowledge. Still, modular architecture does not provide sufficient modularity for complex ITSs. To facilitate modularity and change management, lately distributed technologies like services and intelligent agents are used to develop ITSs. Previous research has concluded that agent based ITSs mainly use the same approach – they implement traditional modules as sets of agents. Customizable set of agents that can be used in various ITSs has been defined. Implementation of modules as a set of distributed components allows to keep the traditional idea of modules. Different types of knowledge are separated from each other. At the same time, the system can be distributed and highly modular. A few multi-agent architectures for ITS development have been published. These architectures mainly consist of agents from the abovementioned set of agents. The architectures are closed in the sense that system’s functionality can not be changed just by adding/removing components from the system. As a consequence multi-agent architectures for ITS development disable one of the advantages of distributed technologies – possibility to change system’s functionality by just adding and/or removing distributed components from the system. As of author’s knowledge no specific service oriented architectures (SOA) for ITS development exist, except the ones presented further in the chapter. So there is a lack of open ITS architectures, despite ITS being a system that may need frequent changes due to the changes in the particular course or adaptation to new courses. To realize intelligent tutoring various types of learning materials and problems have to be presented to the learner, moreover it should be done intelligently enough. For example, the system has to be capable to analyse each student’s action during the problem solving. Thus each new type of problems may need corresponding code to handle it. Types of problems differ from course to course as well as might change in case the course is changed. So, the system should be open for corresponding new components. Actually, typical ITSs must be open for certain types of components handling new types of problems, materials, feedback, etc. Presence of specific distributed and open architectures would facilitate development of open ITSs. The aim of the chapter is to propose open ITS architectures, using two distributed paradigms – intelligent software agents and services

MASITS Methodology Supported Development of Agent Based Intelligent Tutoring System MIPITS

Līdz šim ir izstrādātas daudzas intelektuālas mācību sistēmas (IMS-as), kas e-apmācības sistēmām pievieno adaptivitāti un intelektu. Intelektuāli aģenti ir visai plaši lietoti IMS-u izstrādē dēļ tādām savām īpašībām kā modularitāte un dabīga intelektuālu mehānismu implementēšana. Tajā pašā laikā IMS-u izstrāde ir sarežģīta un šim procesam ir nepieciešams metodoloģisks atbalsts, lai nodrošinātu to, ka aģentos sakņotas IMS-as tiek pieņemtas kā industriāls risinājums. Raksts atspoguļo specifisku aģentos sakņotu IMS-u izstrādes metodoloģiju MASITS un MIPITS sistēmu, kas izstrādāta ar šo metodoloģiju. Sistēma ir izstrādāta kursam „Mākslīgā intelekta pamati”. Tā piedāvā mācību materiālus un praktiskus uzdevumus, kā arī sniedz atgriezenisko saiti par apmācāmā risinājumu, novērtējot apmācāmā zināšanas. Galvenais uzsvars MIPITS sistēmā ir uz praktisku uzdevumu risināšanu. Uzdevumi tiek pielāgoti apmācāmā zināšanu līmenim un apmācāmā prioritātēm par uzdevumu apjomu un praktiskumu. Sistēma piedāvā trīs veidu problēmas: testus, pārmeklēšanas algoritmu un divpersonu spēļu algoritmu realizācijas uzdevumus