713 research outputs found
Cavaliere di Santo Stefano
No full textSi studiano i rapporti fra Fossombroni e l'Ordine di Santo Stefano, da quando fu nominato visitatore dei beni di Valdichiana a quando vestì come cavaliere collatario di commenda di grazia
Towards an agent-driven scenario awareness in remote sensing environments
No full textIn dynamic environments, autonomous and unmanned vehicle systems (UVSs) represent a reliable solution, especially when the request of high performance is a stringent constraint for complex and risky tasks, such as searching survival points, multiple target monitoring, and tracking, etc. In these cases, cooperative activities among all the involved UVSs are strategic for the achievement of a collective goal. When UVS teams work collaboratively, they collect heterogeneous data from multiple sources and bring benefits through an enhanced situational awareness (SA). Multi-UVS scenarios are, by their nature, easy to be modeled as multi-agent systems. This paper presents an agent-based modeling, governing different types of unmanned vehicles that are sent ahead in an area of interest to gather environmental, sensing, image data in order to provide a complete multi-view scenario understanding. The agent model is instantiated in each vehicle, and depending on the vehicle features, encapsulates a semantic mental modeler, customized for the specific vehicle features. The agents collect raw data from the environment and translate them into high-level knowledge, i.e., a conceptualization of the data semantics (i.e., a set of pixels assumes the meaning of a car). The proposed agent-based modeling lays on a synergy between Semantic Web technologies and Fuzzy Cognitive Map (FCM) models, producing a high-level description of the evolving scenes, and then a comprehensive scenario situational awareness
Exploiting a multi-device knowledge meshing to agent-based activity tracking
No full textNowadays, systems of systems, composed of multiple cooperative smart devices, reached popularity in many areas, including surveillance, digital forensics, agriculture and more. The analysis of great amounts of data coming from different sources could be very time consuming for humans, so they require automatic tools that help them to monitor vast and complex environments to detect anomalous situations. To this purpose, this paper introduces an agent-based model to allow IoT systems to monitor outdoor environments and detect suspicious or critical situations. The agent-modeling allows the accomplishment and coordination of various tasks, including object detection and data collection achieved through tracking, environmental context detection by using frame classification and semantic segmentation, contextual knowledge generation and activity detection through ontology reasoning. Finally, the agents report to humans about what happened by estimating the scene criticality through a fuzzy controller. A case study shows the potential of the whole framework and experiences evaluate the skills of the framework for activity detection
A UAV-Driven Surveillance System to Support Rescue Intervention
No full textIn recent years, the intelligent surveillance systems have attracted many application domains, due to the increasing demand on security and safety. Unmanned Areal Vehicles (AUVs) represent the reliable, low-cost solution for mobile sensor node deployment, localization, and collection of measurements.
This paper presents a surveillance UAV-based system, aimed at understanding the scene situation by collecting raw data from the environment (by exploiting some possible sensor modalities: CCTV camera, infrared camera, thermal camera, radar, etc.), processing their fusion and yielding a semantic, high-level scenario description. UAV is able to recognize objects and the spatio-temporal relations with other objects and the environment. Moreover, UAV is able to individuate alerting situations and suggest a recommended intervention to humans. A Fuzzy cognitive map model is indeed, injected in the UAV: from the semantic description of the scenario, the UAV is able to deduct casual effect of occurring situations, that enhances the scenario understanding, especially when alarming situations are discovered
Proactive UAVs for Cognitive Contextual Awareness
No full textUnmanned vehicle systems are often teleoperated, semiautonomous, and strictly dependent on human operators. In complex and dynamic environments, unmanned vehicles should be autonomous in a stricter sense, which means they should exhibit a human-like behavior to be capable of accurately perceiving the environment; understanding the situation, locating and interacting with environmental elements; and reporting solutions to humans. In order to address these desiderata, a modeling of a proactive, context-aware unmanned system is presented. Precisely, the system framework is designed for an unmanned aerial vehicle (UAV) that flies over an area, and collects data in the form of video frames, sensor values, etc. It recognizes situations, senses scene object and environment data, acquires the awareness about the evolving scenes, and, finally, takes a decision based on the perception of the overall scenario. The system design is based on two primary building blocks: 1) the semantic web technologies that provide the high-level object description in the tracked scenario, and 2) the fuzzy cognitive map model that provides the cognitive accumulation of spatial knowledge in order to discern specific situations that need a decision. Although the paper presents a UAV-based surveillance system model, its applicability is shown based on a realistic case study (viz., broken car on the highway); moreover, several possible scenario configurations have been simulated to assess the criticality level perceived by the system (UAV) in a given situation and to validate the effective response/decision in the case of critical situations
Data-Information-Concept Continuum From a Text Mining Perspective
No full textThe recent Web panorama reveals a tangible proliferation of “social” data, in form of posts, opinions, feelings, experiences. Most of the available data is unstructured text, unsuitable to be processed by computers, especially due to ambiguity and vagueness of the natural language. Research developments highlight the difficulty in capturing semantics of terms, linguistic expressions, and sentences and their consequent representation as a finite concept. This article presents an open-minded overview of the Text Mining approaches, targeted at transforming unstructured textual data into explicit knowledge, with a special focus on the conceptualization, i.e., the concept identification by analysing syntactic and semantic relations among terms as well as the contextual surrounding information. Different knowledge granulation is described in a layered knowledge model, where the term, the information and the concept represent the basic knowledge granules that cover most Text Mining approaches, in an evolving knowledge continuum
Context-aware profiling of concepts from a semantic topological space
No full textAbstract In the era of Internet of “everything”, the natural language text is still the undiscussed medium of representing information, as evidenced by the pervasiveness of tweets, instant messages, posts, and documents. There is an increasing need of innovative technologies targeted at a more machine-oriented communication. Many keyword-based and statistical approaches have supported information retrieval, data mining, and natural language processing systems, but a deeper understanding of text is still an urgent challenge: concepts, semantic relationships among them, contextual information needed for the concept disambiguation require further progress in the textual-information management. This work introduces a novel technique of extracting the main concepts from the text. Concepts are described by word-based connections disposed in a semantic topological space, built by the formal model, the simplicial complex. It links the points, i.e., the words appearing in the text and incrementally creates a geometrical structure, describing concepts that are more or less specialized, depending on the aggregation distance of words. The conceptual network is context-aware, since it reveals unambiguous concepts, specialized by the analysis of the surrounding text. The framework that implements the approach, discovers basic concepts, composed of minimal number of words useful to describe a finite sense concept, and richer extended concepts built adding further relations among terms. The final topological space provides a multi-granule concept representation: from a local, word-closeness view to a highly refined description. Experiments and comparative analysis validate the effectiveness of the approach, evidencing satisfactory performance in the concept identification, with precision values greater than 80% in the most of the experiments and the recall is on average, around 60–70% with peaks of 90% for some specific concept categories
Crop health assessment through hierarchical fuzzy rule-based status maps
No full textPrecision agriculture is evolving toward a contemporary approach that involves multiple sensing techniques to monitor and enhance crop quality while minimizing losses and waste of no longer considered inexhaustible resources, such as soil and water supplies. To understand crop status, it is necessary to integrate data from heterogeneous sensors and employ advanced sensing devices that can assess crop and water status. This study presents a smart monitoring approach in agriculture, involving sensors that can be both stationary (such as soil moisture sensors) and mobile (such as sensor-equipped unmanned aerial vehicles). These sensors collect information from visual maps of crop production and water conditions, to comprehensively understand the crop area and spot any potential vegetation problems. A modular fuzzy control scheme has been designed to interpret spectral indices and vegetative parameters and, by applying fuzzy rules, return status maps about vegetation status. The rules are applied incrementally per a hierarchical design to correlate lower-level data (e.g., temperature, vegetation indices) with higher-level data (e.g., vapor pressure deficit) to robustly determine the vegetation status and the main parameters that have led to it. A case study was conducted, involving the collection of satellite images from artichoke crops in Salerno, Italy, to demonstrate the potential of incremental design and information integration in crop health monitoring. Subsequently, tests were conducted on vineyard regions of interest in Teano, Italy, to assess the efficacy of the framework in the assessment of plant status and water stress. Indeed, comparing the outcomes of our maps with those of cutting-edge machine learning (ML) semantic segmentation has indeed revealed a promising level of accuracy. Specifically, classification performance was compared to the output of conventional ML methods, demonstrating that our approach is consistent and achieves an accuracy of over 90% throughout various seasons of the year
Towards an Ontology Design Pattern for UAV Video Content Analysis
No full textVideo scene understanding is leading to an increased research investment in developing artificial intelligence technologies, pattern recognition, and computer vision, especially with the advance in sensor technologies. Developing autonomous unmanned vehicles, able to recognize not just targets appearing in a scene but a complete scene the targets are involved in (describing events, actions, situations, etc.) is becoming crucial in the recent advanced intelligent surveillance systems. At the same time, besides these consolidated technologies, the Semantic Web Technologies are also emerging, yielding seamless support to the high-level understanding of the scenes. To this purpose, the paper proposes a systematic ontology modeling to support and improve video content analysis, by generating a comprehensive high-level scene description, achieved by semantic reasoning and querying. The ontology schema comes from as an integration of new and existing ontologies and provides some design pattern guideline to get a high-level description of a whole scenario. It starts from the description of basic targets in the video scenario, thanks to the support of video tracking algorithms and target classification; then provides a higher level interpretation, compounding event-driven target interactions (for local activity comprehension), to reach gradually an abstraction high level that enables a concise and complete scenario description
Emotional Concept Extraction Through Ontology-Enhanced Classification
No full textCapturing emotions affecting human behavior in social media bears strategic importance in many decision-making fields, such as business and public policy, health care, and financial services, or just social events. This paper introduces an emotion-based classification model to analyze the human behavior in reaction to some event described by a tweet trend. From tweets analysis, the model extracts terms expressing emotions, and then, it builds a topological space of emotion-based concepts. These concepts enable the training of the multi-class SVM classifier to identify emotions expressed in the tweets. Classifier results are “softly” interpreted as a blending of several emotional nuances which thoroughly depicts people’s feeling. An ontology model captures the emotional concepts returned by classification, with respect to the tweet trends. The associated knowledge base provides human behavior analysis, in response to an event, by a tweet trend, by SPARQL queries. © 2019, Springer Nature Switzerland AG
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