scholarly journals Press Start to Play: Classifying Multi-Robot Operators and Predicting Their Strategies through a Videogame

Robotics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 53
Author(s):  
Juan Jesús Roldán ◽  
Víctor Díaz-Maroto ◽  
Javier Real ◽  
Pablo R. Palafox ◽  
João Valente ◽  
...  
Keyword(s):  
Information Sources ◽  
Situational Awareness ◽  
Clustering Algorithms ◽  
Modeling Methods ◽  
Probability Of Success ◽  
Operator Behavior ◽  
Near Future ◽  
Inside Out ◽  
Operator Workload ◽  
Multi Robot

One of the active challenges in multi-robot missions is related to managing operator workload and situational awareness. Currently, the operators are trained to use interfaces, but in the near future this can be turned inside out: the interfaces will adapt to operators so as to facilitate their tasks. To this end, the interfaces should manage models of operators and adapt the information to their states and preferences. This work proposes a videogame-based approach to classify operator behavior and predict their actions in order to improve teleoperated multi-robot missions. First, groups of operators are generated according to their strategies by means of clustering algorithms. Second, the operators’ strategies are predicted, taking into account their models. Multiple information sources and modeling methods are used to determine the approach that maximizes the mission goal. The results demonstrate that predictions based on previous data from single operators increase the probability of success in teleoperated multi-robot missions by 19%, whereas predictions based on operator clusters increase this probability of success by 28%.

The Role of Schema and Document Matchings in XML Source Clustering

2011 ◽  
pp. 125-153
Author(s):  
Pasquale De Meo ◽  
Giacomo Fiumara ◽  
Antonino Nocera ◽  
Domenico Ursino
Keyword(s):  
Information Sources ◽  
Structural Information ◽  
Clustering Algorithms ◽  
Data Sources ◽  
Xml Data ◽  
Xml Documents ◽  
Data Level

In recent years, there has been an increase in the volume and heterogeneity of XML data sources. Moreover, these information sources are often comprised of both schemas and instances of XML data. In this context, the need of grouping similar XML documents together has led to an increasing research on clustering algorithms for XML data. In this chapter, we present an overview of the most popular methods for clustering XML data sources, distinguishing between the intensional data level and the extensional data level, depending whether the sources to cluster are DTDs and XML schemas, or XML documents; in the latter case, we focus on the structural information of the documents. We classify and describe techniques for computing similarities among XML data sources, and discuss methods for clustering DTDs/XML schemas and XML documents.

scholarly journals Inferring the Presence of Freezing Drizzle Using Archived Data from the Automated Surface Observing System (ASOS)

2020 ◽  
Vol 37 (12) ◽  
pp. 2239-2250
Author(s):  
Scott D. Landolt ◽  
Andrew Gaydos ◽  
Daniel Porter ◽  
Stephanie DiVito ◽  
Darcy Jacobson ◽  
...  
Keyword(s):  
United States ◽  
Numerical Modeling ◽  
Situational Awareness ◽  
Doppler Radar ◽  
Transportation Safety ◽  
Classification Algorithms ◽  
Current Form ◽  
Modeling Methods ◽  
Awareness Tool ◽  
Precipitation Type

AbstractIn its current form, the Automated Surface Observing System (ASOS) provides automated precipitation type reports of rain, snow, and freezing rain. Unknown precipitation can also be reported when the system recognizes precipitation is occurring but cannot classify it. A new method has been developed that can reprocess the raw ASOS 1-min-observation (OMO) data to infer the presence of freezing drizzle. This freezing drizzle derivation algorithm (FDDA) was designed to identify past freezing drizzle events that could be used for aviation product development and evaluation (e.g., Doppler radar hydrometeor classification algorithms, and improved numerical modeling methods) and impact studies that utilize archived datasets [e.g., National Transportation Safety Board (NTSB) investigations of transportation accidents in which freezing drizzle may have played a role]. Ten years of archived OMO data (2005–14) from all ASOS sites across the conterminous United States were reprocessed using the FDDA. Aviation routine weather reports (METARs) from human-augmented ASOS observations were used to evaluate and quantify the FDDA’s ability to infer freezing drizzle conditions. Advantages and drawbacks to the method are discussed. This method is not intended to be used as a real-time situational awareness tool for detecting freezing drizzle conditions at the ASOS but rather to determine periods for which freezing drizzle may have impacted transportation, with an emphasis on aviation, and to highlight the need for improved observations from the ASOS.

Modeling fog of war effects in AFSIM

2021 ◽  
pp. 154851292110419
Author(s):  
Dillon Tryhorn ◽  
Richard Dill ◽  
Douglas D Hodson ◽  
Michael R Grimaila ◽  
Christopher W Myers
Keyword(s):  
Information Sources ◽  
Situational Awareness ◽  
Command And Control ◽  
Decision Makers ◽  
Analysis Tool ◽  
Military Leaders ◽  
Control Actions ◽  
Communication Links ◽  
Decision Making Processes ◽  
And Control

This research identifies specific communication sensor features vulnerable to fog and provides a method to introduce them into an Advanced Framework for Simulation, Integration, and Modeling (AFSIM) wargame scenario. Military leaders use multiple information sources about the battlespace to make timely decisions that advance their operational objectives while attempting to deny their opponent’s actions. Unfortunately, the complexities of battle combined with uncertainty in situational awareness of the battlespace, too much or too little intelligence, and the opponent’s intentional interference with friendly command and control actions yield an abstract layer of battlespace fog. Decision-makers must understand, characterize and overcome this “battlespace fog” to accomplish operational objectives. This research proposes a novel tool, the Fog Analysis Tool (FAT), to automatically compile a list of communication and sensor objects within a scenario and list options that may impact decision-making processes. FAT improves wargame realism by introducing and standardizing fog levels across communication links and sensor feeds in an AFSIM scenario. Research results confirm that FAT provides significant benefits and enables the measurement of fog impacts to tactical command and control decisions within AFSIM scenarios.

scholarly journals Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1720 ◽  
Author(s):  
Juan Roldán ◽  
Elena Peña-Tapia ◽  
Andrés Martín-Barrio ◽  
Miguel Olivares-Méndez ◽  
Jaime Del Cerro ◽  
...  
Keyword(s):  
Situational Awareness ◽  
The Impact ◽  
Multi Robot

scholarly journals The Method of Cyber Awareness Analysis of an Energy Facility

2021 ◽  
Vol 19 (2) ◽  
pp. 17-28
Author(s):  
D. A. Gaskova ◽  
A. G. Massel
Keyword(s):  
Energy Security ◽  
Situational Awareness ◽  
Formal Description ◽  
Semantic Modeling ◽  
Energy Infrastructure ◽  
Modeling Methods ◽  
Cyber Threats ◽  
Energy Facility ◽  
Three Stages ◽  
The Impact

The article proposes to analyze cyber-situational awareness of an energy facility in three stages. There are i) analysis of cyber threats to the energy infrastructure; ii) modeling of extreme situations scenarios in the energy sector caused by the implementation of the cyber threats; iii) risk assessment of the cybersecurity disruption to energy infrastructure. Three methods are presented, corresponding to each stage. The authors propose to apply semantic modeling methods to analyze the impact of cyber threats to energy facilities, taking into account energy security within the presented approach. Such methods show their effectiveness in the absence or incompleteness of data for modeling the behavior of systems, which defies formal description or accurate forecasting. The presented approach to the cyber situational awareness analysis of energy facilities considered as a synthesis of cybersecurity and situational awareness studies, characterized by the use of semantic modeling methods.

scholarly journals A Novel Motion Blur Resistant vSLAM Framework for Micro/Nano-UAVs

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 121
Author(s):  
Buğra ŞİMŞEK ◽  
Hasan Şakir BİLGE
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Situational Awareness ◽  
Low Cost ◽  
Experimental Studies ◽  
Motion Blur ◽  
Aerial Vehicles ◽  
Motion Deblurring ◽  
Localization And Mapping ◽  
Near Future ◽  
Nano Size

Localization and mapping technologies are of great importance for all varieties of Unmanned Aerial Vehicles (UAVs) to perform their operations. In the near future, it is planned to increase the use of micro/nano-size UAVs. Such vehicles are sometimes expendable platforms, and reuse may not be possible. Compact, mounted and low-cost cameras are preferred in these UAVs due to weight, cost and size limitations. Visual simultaneous localization and mapping (vSLAM) methods are used for providing situational awareness of micro/nano-size UAVs. Fast rotational movements that occur during flight with gimbal-free, mounted cameras cause motion blur. Above a certain level of motion blur, tracking losses exist, which causes vSLAM algorithms not to operate effectively. In this study, a novel vSLAM framework is proposed that prevents the occurrence of tracking losses in micro/nano-UAVs due to the motion blur. In the proposed framework, the blur level of the frames obtained from the platform camera is determined and the frames whose focus measure score is below the threshold are restored by specific motion-deblurring methods. The major reasons of tracking losses have been analyzed with experimental studies, and vSLAM algorithms have been made durable by our studied framework. It has been observed that our framework can prevent tracking losses at 5, 10 and 20 fps processing speeds. vSLAM algorithms continue to normal operations at those processing speeds that have not been succeeded before using standard vSLAM algorithms, which can be considered as a superiority of our study.

scholarly journals Applying Clustering and Topic Modeling to Automatic Analysis of Citizens’ Comments in EGovernment

2020 ◽  
Vol 12 (6) ◽  
pp. 1-10
Author(s):  
Gunay Y. Iskandarli ◽  
Keyword(s):  
Semantic Similarity ◽  
Topic Modeling ◽  
Clustering Algorithms ◽  
Automatic Analysis ◽  
Large Set ◽  
Modeling Methods

The paper proposes an approach to analyze citizens' comments in e-government using topic modeling and clustering algorithms. The main purpose of the proposed approach is to determine what topics are the citizens' commentaries about written in the e-government environment and to improve the quality of e-services. One of the methods used to determine this is topic modeling methods. In the proposed approach, first citizens' comments are clustered and then the topics are extracted from each cluster. Thus, we can determine which topics are discussed by citizens. However, in the usage of clustering and topic modeling methods appear some problems. These problems include the size of the vectors and the collection of semantically related of documents in different clusters. Considering this, the semantic similarity of words is used in the approach to reduce measure. Therefore, we only save one of the words that are semantically similar to each other and throw the others away. So, the size of the vector is reduced. Then the documents are clustered and topics are extracted from each cluster. The proposed method can significantly reduce the size of a large set of documents, save time spent on the analysis of this data, and improve the quality of clustering and LDA algorithm.

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