О распознавании ситуации на основе технологии искусственного интеллекта

This paper considers the situation awareness technique based on artificial intelligence technology. Certain meta–class of situation awareness problems comprising three major classes like environment and two moving objects. In the presented study the situation awareness (SA) is considered as some vector whose parameters contain information about the interrelationship and state of at least three separate classes at a given time instant. Over the SA set the function is being formed that allows to identify SA based on measurements of three classes’ parameters (properties) taken in the space and time. Mechanism of defining the inherence function and SA repository (set) forming is realized through the inference algorithm Rete and immunocomputing method.В настоящей статье рассматривается методика распознавания ситуации, основанная на технологии искусственного интеллекта. В представленном исследовании ситуация рассматривается как некоторый вектор, параметры которого содержат информацию о взаимосвязи и состоянии отдельных классов в определенный момент времени. На множестве ситуаций определяется функция, позволяющая идентифицировать ситуацию на основе измерений свойств (параметров) классов, взятых в пространстве и времени. Определение функции принадлежности и формирование репозитория (множества) выполняется с использованием алгоритма вывода Rete и метода иммунокомпьютинга

Интеллектуальная ГИС в системах мониторинга

The development of Intelligent Geographic Information Systems (IGIS) for monitoring systems in the broad sense including urban and regional applications is considered. The paper is focused on the development of the IGIS method based on a Service Oriented Architecture (SOA) concept.Рассматривается развитие интеллектуальных геоинформационных систем для различных систем мониторинга. Статья представляет метод построения интеллектуальных геоинформационных систем, основанный на SOA концепции

AN APPROACH FOR ANALYZING THE EFFECTS OF RISKS ON BUSINESS PROCESSES USING SEMANTIC ANNOTATIONS

The management of risks has gained a lot of attention in the last years. Among the current challenges in this domain are the integration of risk management in the strategic planning and performance management across business units and organizational structures, the assessment of a company’s risk bearing capacity, and the improvement of the methods of risk measurement. In order to support the elicitation of risks in business processes, measure their impact on a company’s return and provide reports for regulatory authorities, it can be reverted to technology-oriented knowledge management. In this context we propose an approach that uses semantically annotated models to represent the influence of risks on business activities based on the concepts provided by a risk knowledge base. By transferring the annotated model information to the knowledge base, inference rules can be applied to analyze the effects of risks on the business processes during subsequent capacity simulations. For a first evaluation the approach has been implemented using the ADOxx meta modeling platform, the Protégé ontology management toolkit and the Jess rule engine. Finally, the use of the implementation is shortly illustrated by reverting to a sample business process from the domain of banking

Metadata behind the Interoperability of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) produce changes of status that are frequent, dynamic and unpredictable, and cannot be represented using a linear cause-effect approach. Consequently, a new approach is needed to handle these changes in order to support dynamic interoperability. Our approach is to introduce the notion of context as an explicit representation of changes of a WSN status inferred from metadata elements, which in turn, leads towards a decision-making process about how to maintain dynamic interoperability. This paper describes the developed context model to represent and reason over different WSN status based on four types of contexts, which have been identified as sensing, node, network and organisational contexts. The reasoning has been addressed by developing contextualising and bridges rules. As a result, we were able to demonstrate how contextualising rules have been used to reason on changes of WSN status as a first step towards maintaining dynamic interoperability

Ontology creation for wireless capsule endoscopy videos

In this paper we study multimedia ontology for Wireless Capsule Endoscopy (WCE) videos by enhancing its existing data structure. The ‘wireless capsule’ is a tiny disposable video camera that transmits 2 ~ 3 frames per second for a period of 8 ~ 11 hours. There are open problems in WCE, such as bleeding detection, as it is hard to identify accurately, using low-level features, i.e., color values. In addition, the physicians have to examine the videos continuously for two hours or more, which becomes restrictive. There have been research attempts to reduce this review time. However, they suffer from low accuracy and sensitivity, and do not process WCE videos with an efficient information data structure. To address this problem, we propose a new data structure named ‘multimedia ontology for WCE videos’ formed by combining medical and multimedia domain knowledge. Ontology represents a structure to describe the concepts and relationships in a specific domain with relevant data and its terminology. We define two types of ontology, i.e., generic and specific ontology. Generic ontology represents the broad concepts in WCE videos, such as medical terms, anatomic information, video format, etc., while specific ontology is a data-driven one including color, location, and region of images. The process of creating multimedia ontology consists of three steps: (1)collection of raw data from WCE videos, such as video data format, feature values, meta-data information and anomalies, (2) classification of the raw data into concepts including generic and specific ontology, and (3) identification of relationship between two concepts such as ‘Is-A’, ‘Part-Of’, and ‘Has-A’. This WCE Ontology structure can be used to better address the open problems by providing 'relevant area focus' from the formed structure and can also be extended to other problems like detection of lesions and polyps

Aplicação de ontologia e sistema especialista para diagnóstico de falhas em transformadores de potência

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Neste trabalho foi desenvolvido o protótipo de um Sistema Especialista (SE) que tem como objetivo principal auxiliar os especialistas em transformadores de potência, de uma forma ágil e confiável, na emissão de diagnósticos de falhas incipientes (defeitos) nos transformadores principais dos geradores da Usina Hidrelétrica da Itaipu Binacional. Este protótipo foi desenvolvido utilizando o arcabouço Jess, e a base de conhecimento sobre transformadores de potência foi classificada e modelada na forma de ontologia utilizando o Protégé-2000. Nesta proposta, a integração do Jess e o Protégé possibilitou a transferência dos dados obtidos nos ensaios de cromatografía de gases dissolvidos no óleo mineral isolante e outras informações armazenadas na base de conhecimento, utilizadas pelo SE para a geração dos diagnósticos correspondentes. Os testes realizados com os protótipos demonstraram a eficiência da metodologia, validando a proposta

Mobile sensor networks for environmental monitoring

Vulnerability to natural disasters and the human pressure on natural resources have increased the need for environmental monitoring. Proper decisions, based on real-time information gathered from the environment, are critical to protecting human lives and natural resources. To this end, mobile sensor networks, such as wireless sensor networks, are promising sensing systems for flexible and autonomous gathering of such information. Mobile sensor networks consist of geographically deployed sensors very close to a phenomenon of interest. The sensors are autonomous, self-configured, small, lightweight and low powered, and they become mobile when they are attached to mobile objects such as robots, people or bikes. Research on mobile sensor networks has focused primarily on using sensor mobility to reduce the main sensor network limitations in terms of network topology, connectivity and energy conservation. However, how sensor mobility could improve environmental monitoring still remains largely unexplored. Addressing this requires the consideration of two main mobility aspects: sampling and mobility constraints. Sampling is about where mobile sensors should be moved, while mobility constraints are about how such movements should be handled, considering the context in which the monitoring is carried out. This thesis explores approaches for sensor mobility within a wireless sensor network for use in environmental monitoring. To achieve this goal, four sub-objectives were defined: Explore the use of metadata to describe the dynamic status of sensor networks. Develop a mobility constraint model to infer mobile sensor behaviour. Develop a method to adapt spatial sampling using mobile, constrained sensors. Extend the developed adaptive sampling method to monitoring highly dynamic environmental phenomena. Chapter 2 explores the use of metadata to describe the dynamic status of sensor networks. A context model was proposed to describe the general situation in which a sensor network is monitoring. The model consists of four types of contexts: sensor, network, sensing and organisation, where each of the contexts describes the sensor network from a different perspective. Metadata, which are descriptors of observed data, sensor configurations and functionalities, are used as parameters to describe what is happening in the different contexts. The results reveal that metadata are suitable for describing sensor network status within different contexts and reporting the status back to other components, systems or users. Chapter 3 develops a model which describes mobility constraints for inferring mobile sensor behaviour. The proposed mobility constraint model consists of three components: first, the context typology proposed in Chapter 2 to describe mobility constraints within the different contexts; second, a context graph, modelled as a Bayesian network, to encode dependencies of mobility constraints within the same or different contexts, as well as among mobility constraints and sensor behaviour; and third, contextual rules to encode how dependent mobility constraints are expected to constrain sensor behaviour. Metadata values for the monitored phenomenon and sensor properties are used to feed the context graph. They are propagated through the graph structure, and the contextual rules are used to infer the most suitable behaviour. The model was used to simulate the behaviour of a mobile sensor network to obtain a suitable spatial coverage in low and high fire risk scenarios. It was shown that the mobility constraint model successfully inferred behaviour, such as sleeping sensors, moving sensors and deploying more sensors to enhance spatial coverage. Chapter 4 develops a spatial sampling strategy for use with mobile, constrained sensors according to the expected value of information (EVoI) and mobility constraints. EVoI allows decisions to be made about the location to observe. It minimises the expected costs of wrong predictions about a phenomenon using a spatially aggregated EVoI criterion. Mobility constraints allow decisions to be made about which sensor to move. A cost-distance criterion is used to minimise unwanted effects of sensor mobility on the sensor network itself, such as energy depletion. The method was assessed by comparing it with a random selection of sample locations combined with sensor selection based on a minimum Euclidian distance criterion. The results demonstrate that EVoI enables selection of the most informative locations, while mobility constraints provide the needed context for sensor selection. Chapter 5 extends the method developed in Chapter 4 for the case of highly dynamic phenomena. It develops a method for deciding when and where to sample a dynamic phenomenon using mobile sensors. The optimisation criterion is to maximise the EVoI from a new sensor deployment at each time step. The method was demonstrated in a scenario in which a simulated fire in a chemical factory released polluted smoke into the open air. The plume varied in space and time because of variations in atmospheric conditions and could be only partially predicted by a deterministic dispersion model. In-situ observations acquired by mobile sensors were considered to improve predictions. A comparison with random sensor movements and the previous sensor deployment without performing sensor movements shows that the optimised sensor mobility successfully reduced risk caused by poor model predictions. Chapter 6 synthesises the main findings and presents my reflections on the implications of such findings. Mobile sensors for environmental monitoring are relevant to improving monitoring by selecting sampling locations that deliver the information that most improves the quality of decisions for protecting human lives and natural resources. Mobility constraints are relevant to managing sensor mobility within sampling strategies. The traditional consideration of mobility constraints within the field of computer sciences mainly leads to sensor self-protection rather than to the protection of human beings and natural resources. By contrast, when used for environmental monitoring, mobile sensors should above all improve monitoring performance, even thought this might produce negative effects on coverage, connectivity or energy consumption. Thus, mobility constraints are useful for reducing such negative effects without constraining the sampling strategy. Although sensor networks are now a mature technology, they are not yet widely used by surveyors and environmental scientists. The operational use of sensor networks in geo-information and environmental sciences therefore needs to be further stimulated. Although this thesis focuses on wireless sensor network, other types of informal sensor networks could be also relevant for environmental monitoring, such as smart phones, volunteer citizens and sensor web. Finally, the following recommendations are given for further research: extend the sampling strategy for dynamic phenomena to take account of mobility constraints; develop sampling strategies that take a decentralised approach; focus on mobility constraints related to connectivity and data transmission; elicit expert knowledge to reveal preferences for sensor mobility under mobility constraints within different types of environmental applications; and validate the proposed strategies in operational implementations. </p