Informatica

Nowadays, there is a lack of smart marine monitoring systems, which have possibilities to integrate multi-dimensional components for monitoring and predicting marine water quality and making decisions for their optimal operations with minimal human intervention. This research aims to extend the smart coastal marine monitoring by proposing a solar energy planning and control component. The proposed approach involves the adaptive neuro-fuzzy inference system (ANFIS) for the wireless buoys, working online during the whole year in the Baltic Sea near the Lithuanian coast. The usage of our proposed fuzzy solar energy planning and control components allows us to prolong the lifespan of batteries in buoys, so it has a positive impact on sustainable development. The novelty and advantage of the proposed approach lie in establishing the ANFIS-based model to predict and control solar energy in a buoy for different lighting and temperature conditions depending on the four year seasons and to make a decision to transfer the collected data. The energy planning and consumption system for the wireless sensor network of buoys is carefully evaluated, and its prototype is developed. The proposed approach can be practically used for environmental monitoring, providing stakeholders with relevant and timely information for sound decision-making about hydro-meteorological situations in coastal marine water.

Our research is devoted to development of information infrastructure for e-service semi-automatic provision by using distributed data warehouses (DWs) of water protection domain. Development of software for semi-automatic service provision is based on artificial planner and structure of goals adapted for specialized needs of end users. Such e-service preparation mechanism can work under the unified coherent framework for solving the environment protection problems by evaluation of the processes of water consumption and contamination. The possibilities of integration of distributed DWs of water management sector into web portal meeting the requirements of conceptual interoperability are presented. Design approach is based on development of decision support system (DSS) that is designed as multilayered system with multi–componential, interoperable structure of databases (DBs), which are under responsibility of different public administration institutions such as European Environment Information and Observation Network (EIONET) and national environment protection agencies. The infrastructure of EIONET is used for supporting and improving data and information flows. The Water resource management information system (WRMIS) became the kernel component of DSS. WRMIS prototype facilitates data flows between the institutions and gives access to data for relevant institutions and the public providing e-services using proposed DSS. The research investigations are made according to the requirements of European Union Water Framework Directive, Sustainable Development Strategy and ReportNet as the EIONET infrastructure for supporting and improving data and information flows. Additional means are integrated in the structures of DSS as knowledge representation techniques based on conceptual schemas, data flow diagrams, and decision-making rules. The on-line management techniques are based on assurance of interoperability by using Open Web Platform W3C standards for web service development, such as XML, SOAP, HTTP, etc.

This paper analyses the possibilities of integrating different technological and knowledge representation techniques for the development of a framework for the remote control of multiple agents such as wheelchair-type robots. Large-scale multi-dimensional recognitions of emotional diagnoses of disabled persons often generate a large amount of multi-dimensional data with complex recognition mechanisms, based on the integration of different knowledge representation techniques and complex inference models. The problem is to reveal the main components of a diagnosis as well as to construct flexible decision making models. Sensors can help record primary data for monitoring objects. However the recognition of abnormal situations, clustering of emotional stages and resolutions for certain types of diagnoses is an oncoming issue for bio-robot constructors. The prediction criteria of the diagnosis of the emotional situations of disabled persons are described using knowledge based model of Petri nets. The research results present the development of multi-layered framework architecture with the integration of artificial agents for diagnosis recognition and control of further actions. The method of extension of Petri nets is introduced in the reasoning modules of robots that work in real time. The framework provides movement support for disabled individuals. The fuzzy reasoning is described by using fuzzy logical Petri nets in order to define the physiological state of disabled individuals through recognizing their emotions during their different activities.

This research work is aimed at the development of data analysis strategy in a complex, multidimensional, and dynamic domain. Our universe of discourse is concerned with the data mining techniques of data warehouses revealing the importance of multivariate structures of social‐economic data which influence criminality. Distinct tasks require different data structures and various data mining exercises in data warehouses. The proposed problem solution strategy allows choosing an appropriate method in recognition processes. The ensembles of diverse and accurate classifiers are constructed on the base of multidimensional classification and clusterisation methods. Factor analysis is introduced into data mining process for revealing influencing impacts of factors. The temporal nature and multidimensionality of the target object is revealed in dynamic model using multidimension regression estimates. The paper describes the strategy of integrating the methods of multiple statistical analysis in cases, where a great set of variables is observed in short time period. The demonstration of the data analysis strategy is performed using real social and economic development of data warehouses in different regions of Lithuania.

Some exceptional features of the dynamically changing environment require the additional means for qualitative knowledge representation, data verification and ensurance of operativity of decision making processes. The article considers the possibilities of integration the safequard methods for impartial multicriteria: decision making with the three levels of knowledge representation distinguished in the decision support system (DSS). The approach is to represent static and dynamic aspects of the target system and to reflect them in deep knowledge representation level. The means to formalize multiple objective decision making mechanisms is proposed. The examples developed during the designing stages of the ecological evaluation system for different enterprises demonstrate the results of using extended E-nets for modeling cognitive processes leading to decisions.