58 research outputs found
Hierarchical Collective Agent Network (HCAN) for efficient 3 fusion and management of multiple networked sensors
Agent-based software systems and applications are constructed by integrating diverse sets of components that are intelligent, heterogeneous, distributed, and concurrent. This paper describes a multi-agent system to assure the operation efficiency and reliability in data fusion and management of a set of networked distributive sensors (NDS). We discuss the general concept and architecture of a Hierarchical Collective Agent Network (HCAN) and its functional components for learning and adaptive control of the NDS. Sophistication of a HCAN control environment and an anatomy of the agent modules for enabling intelligent data fusion and management are presented. An exemplar HCAN is configured to support dynamic data fusion and automated sensor management in a simulated distributive and collaborative military sensor network for Global Missile Defense (GMD) application
Dataflow-Oriented Provenance System for Multifusion Wireless Sensor Networks
We present a dataflow-oriented provenance system for data fusion sensor networks. This model works best with net- works sensing dynamic objects and although our system is generic, we model it on a proximity binary sensor network. We introduce a network-level fault-tolerance mechanism by using the cognitive strength of provenance models. Our provenance model reduce the limitations of a sensor’s capability and decrease the error-prone nature of wireless sen- sor networks. In addition provenance data is used in order to efficiently build the dynamic data fusion scenario and to adjust the network such as turning of some sensors. In a fault-tolerant, self-adjusting sensor network, sensor data produce more accurate results and with the improvements, tasks such as target localization is more precisely done. One other aspect of our network is that by having computation nodes spread to the network, the computation is done in a distributed manner and as nodes make decisions based on the provenance and fusion data available, the network has a distributed intelligence. Keywords: Multifusion, Wireless Sensor Networks, Open Provenance Mode
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
Dynamic Fusion of Web Data
Mashups exemplify a workflow-like approach to dynamically integrate data and services from multiple web sources. Such integration workflows can build on existing services for web search, entity search, database querying, and information extraction and thus complement other data integration approaches. A key challenge is the efficient execution of integration workflows and their query and matching steps at runtime. We relate mashup data integration with other approaches, list major challenges, and outline features of a first prototype design
Multimodal fusion of electromagnetic, ultrasound and MRI data for building an articulatory model
International audienceData fusion from multiple sensors is of significant interest to the speech research community, as it can potentially provide a better picture of speech production through the use of complementary sensor modalities. This paper deals with the practical aspects of this problem, such as acquisition and processing of the dynamic US and EM data of the tongue during speech production, static MRI images of the vocal tract using repetitions, and registration of the data from these different sources to a common reference frame. To the best of our knowledge, this is the first work that demonstrates the potential of static and dynamic data fusion in the construction of articulatory databases
Re-imagining health and well-being in low resource African settings using an augmented AI system and a 3D digital twin
In this paper, we discuss and explore the potential and relevance of recent
developments in artificial intelligence (AI) and digital twins for health and
well-being in low-resource African countries. Using an AI systems perspective,
we review emerging trends in AI systems and digital twins and propose an
initial augmented AI system architecture to illustrate how an AI system can
work in conjunction with a 3D digital twin. We highlight scientific knowledge
discovery, continual learning, pragmatic interoperability, and interactive
explanation and decision-making as important research challenges for AI systems
and digital twins.Comment: Submitted to Workshop on AI for Digital Twins and Cyber-physical
applications at IJCAI 2023, August 19--21, 2023, Macau, S.A.
From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability
Advances in Data Science permeate every field of Transportation Science and Engineering,
resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent
Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and
consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure,
vehicles or the travelers’ personal devices act as sources of data flows that are eventually
fed into software running on automatic devices, actuators or control systems producing, in turn,
complex information flows among users, traffic managers, data analysts, traffic modeling scientists,
etc. These information flows provide enormous opportunities to improve model development and
decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used
to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes;
in other words, for data-based models to fully become actionable. Grounded in this described data
modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic
to its three compounding stages, namely, data fusion, adaptive learning and model evaluation.
We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm
conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying
the majority of ITS applications. Finally, we provide a prospect of current research lines within
Data Science that can bring notable advances to data-based ITS modeling, which will eventually
bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government
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