7,236 research outputs found
Predictive Collision Management for Time and Risk Dependent Path Planning
Autonomous agents such as self-driving cars or parcel robots need to
recognize and avoid possible collisions with obstacles in order to move
successfully in their environment. Humans, however, have learned to predict
movements intuitively and to avoid obstacles in a forward-looking way. The task
of collision avoidance can be divided into a global and a local level.
Regarding the global level, we propose an approach called "Predictive Collision
Management Path Planning" (PCMP). At the local level, solutions for collision
avoidance are used that prevent an inevitable collision. Therefore, the aim of
PCMP is to avoid unnecessary local collision scenarios using predictive
collision management. PCMP is a graph-based algorithm with a focus on the time
dimension consisting of three parts: (1) movement prediction, (2) integration
of movement prediction into a time-dependent graph, and (3) time and
risk-dependent path planning. The algorithm combines the search for a shortest
path with the question: is the detour worth avoiding a possible collision
scenario? We evaluate the evasion behavior in different simulation scenarios
and the results show that a risk-sensitive agent can avoid 47.3% of the
collision scenarios while making a detour of 1.3%. A risk-averse agent avoids
up to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent's
evasive behavior can be controlled actively and risk-dependent using PCMP.Comment: Extended version of the SIGSPATIAL '20 pape
Defending against Sybil Devices in Crowdsourced Mapping Services
Real-time crowdsourced maps such as Waze provide timely updates on traffic,
congestion, accidents and points of interest. In this paper, we demonstrate how
lack of strong location authentication allows creation of software-based {\em
Sybil devices} that expose crowdsourced map systems to a variety of security
and privacy attacks. Our experiments show that a single Sybil device with
limited resources can cause havoc on Waze, reporting false congestion and
accidents and automatically rerouting user traffic. More importantly, we
describe techniques to generate Sybil devices at scale, creating armies of
virtual vehicles capable of remotely tracking precise movements for large user
populations while avoiding detection. We propose a new approach to defend
against Sybil devices based on {\em co-location edges}, authenticated records
that attest to the one-time physical co-location of a pair of devices. Over
time, co-location edges combine to form large {\em proximity graphs} that
attest to physical interactions between devices, allowing scalable detection of
virtual vehicles. We demonstrate the efficacy of this approach using
large-scale simulations, and discuss how they can be used to dramatically
reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio
Observation Scheduling System
Software has been designed to schedule remote sensing with the Earth Observing One spacecraft. The software attempts to satisfy as many observation requests as possible considering each against spacecraft operation constraints such as data volume, thermal, pointing maneuvers, and others. More complex constraints such as temperature are approximated to enable efficient reasoning while keeping the spacecraft within safe limits. Other constraints are checked using an external software library. For example, an attitude control library is used to determine the feasibility of maneuvering between pairs of observations. This innovation can deal with a wide range of spacecraft constraints and solve large scale scheduling problems like hundreds of observations and thousands of combinations of observation sequences
Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique
Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues
- …