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Schindler, M., Schaffernicht, E. & Lilienthal, A. J. (2019). Differences in Quantity Recognition Between Students with and without Mathematical Difficulties Analyzed Through Eye: Analysis Through Eye-Tracking and AI. In: M. Graven, H. Venkat, A. Essien & P. Vale (Ed.), Proceedings of the 43rd Conference of the International Group for the Psychology of Mathematics Education: . Paper presented at 43rd Conference of the International Group for the Psychology of Mathematics Education, Pretoria, South Africa, 7-12 July, 2019 (pp. 281-288). PME, 3
Open this publication in new window or tab >>Differences in Quantity Recognition Between Students with and without Mathematical Difficulties Analyzed Through Eye: Analysis Through Eye-Tracking and AI
2019 (English)In: Proceedings of the 43rd Conference of the International Group for the Psychology of Mathematics Education / [ed] M. Graven, H. Venkat, A. Essien & P. Vale, PME , 2019, Vol. 3, p. 281-288Conference paper, Published paper (Refereed)
Abstract [en]

Difficulties in mathematics learning are an important topic in practice and research. In particular, researchers and practitioners need to identify students’ needs for support to teach and help them adequately. However, empirical research about group differences of students with and without mathematical difficulties (MD) is still scarce. Previous research suggests that students with MD may differ in their quantity recognition strategies in structured whole number representations from students without MD. This study uses eye-tracking (ET), combined with Artificial Intelligence (AI), in particular pattern recognition methods, to analyze group differences in gaze patterns in quantity recognition of N=164 fifth grade students.

Place, publisher, year, edition, pages
PME, 2019
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Mathematics
Identifiers
urn:nbn:se:oru:diva-79730 (URN)
Conference
43rd Conference of the International Group for the Psychology of Mathematics Education, Pretoria, South Africa, 7-12 July, 2019
Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-06Bibliographically approved
Xing, Y., Vincent, T. A., Fan, H., Schaffernicht, E., Hernandez Bennetts, V., Lilienthal, A. J., . . . Gardner, J. W. (2019). FireNose on Mobile Robot in Harsh Environments. IEEE Sensors Journal, 19(24), 12418-12431
Open this publication in new window or tab >>FireNose on Mobile Robot in Harsh Environments
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2019 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 19, no 24, p. 12418-12431Article in journal (Refereed) Published
Abstract [en]

In this work we present a novel multi-sensor unit, a.k.a. FireNose, to detect and discriminate both known and unknown gases in uncontrolled conditions to aid firefighters under harsh conditions. The unit includes three metal oxide (MOX) gas sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infrared (NDIR) sensor optimized for the detection of CO2, a commercial temperature humidity sensor, and a flow sensor. We developed custom film coatings for the MOX sensors (SnO2, WO3 and NiO) which greatly improved the gas sensitivity, response time and lifetime of the miniature devices. Our proposed system exhibits promising performance for gas sensing in harsh environments, in terms of power consumption (∼ 35 mW at 350°C per MOX sensor), response time (<10 s), robustness and physical size. The sensing unit was evaluated with plumes of gases in both, a laboratory setup on a gas testing rig and on-board a mobile robot operating indoors. These high sensitivity, high-bandwidth sensors, together with online unsupervised gas discrimination algorithms, are able to detect and generate their spatial distribution maps accordingly. In the robotic experiments, the resulting gas distribution maps corresponded well to the actual location of the sources. Therefore, we verified its ability to differentiate gases and generate gas maps in real-world experiments.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
FireNose, mobile robot, MOX sensor, gas map, harsh environments
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-77784 (URN)10.1109/JSEN.2019.2939039 (DOI)000506895500081 ()2-s2.0-85076340302 (PubMedID)2-s2.0-85076340302 (Scopus ID)
Funder
EU, Horizon 2020
Available from: 2019-11-06 Created: 2019-11-06 Last updated: 2020-02-05Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. J. (2019). Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers. In: 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN): . Paper presented at 2019 IEEE 18th International Symposium on Olfaction and Electronic Nose (ISOEN), Fukoka, Japan, May 26-29, 2019. IEEE, Article ID 151773.
Open this publication in new window or tab >>Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers
2019 (English)In: 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE, 2019, article id 151773Conference paper, Published paper (Refereed)
Abstract [en]

Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of one-class classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a one-class nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an open environment.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Metal oxide semiconductor sensor, electronic nose, gas detection, gas sensing, open sampling systems
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-77210 (URN)10.1109/ISOEN.2019.8823148 (DOI)2-s2.0-85072989108 (Scopus ID)
Conference
2019 IEEE 18th International Symposium on Olfaction and Electronic Nose (ISOEN), Fukoka, Japan, May 26-29, 2019
Projects
SmokeBot
Available from: 2019-10-13 Created: 2019-10-13 Last updated: 2020-02-06Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. J. (2019). Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose. Sensors, 19(3), Article ID E685.
Open this publication in new window or tab >>Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id E685Article in journal (Refereed) Published
Abstract [en]

Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Emergency response, gas discrimination, gas distribution mapping, mobile robotics olfaction, search and rescue robot, unsupervised learning
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-72366 (URN)10.3390/s19030685 (DOI)000459941200248 ()30736489 (PubMedID)2-s2.0-85061226919 (Scopus ID)
Note

Funding Agency:

European Commission  645101

Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2020-02-06Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. (2018). A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments. Sensors and actuators. B, Chemical, 259, 183-203
Open this publication in new window or tab >>A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments
2018 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 259, p. 183-203Article in journal (Refereed) Published
Abstract [en]

Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.

Place, publisher, year, edition, pages
Amsterda, Netherlands: Elsevier, 2018
Keywords
Gas discrimination, environmental monitoring, metal oxide sensors, cluster analysis, unsupervised learning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-63468 (URN)10.1016/j.snb.2017.10.063 (DOI)000424877600023 ()2-s2.0-85038032167 (Scopus ID)
Projects
SmokBot
Funder
EU, Horizon 2020, 645101
Available from: 2017-12-19 Created: 2017-12-19 Last updated: 2019-02-12Bibliographically approved
Banaee, H., Schaffernicht, E. & Loutfi, A. (2018). Data-Driven Conceptual Spaces: Creating Semantic Representations for Linguistic Descriptions of Numerical Data. The journal of artificial intelligence research, 63, 691-742
Open this publication in new window or tab >>Data-Driven Conceptual Spaces: Creating Semantic Representations for Linguistic Descriptions of Numerical Data
2018 (English)In: The journal of artificial intelligence research, ISSN 1076-9757, E-ISSN 1943-5037, Vol. 63, p. 691-742Article in journal (Refereed) Published
Abstract [en]

There is an increasing need to derive semantics from real-world observations to facilitate natural information sharing between machine and human. Conceptual spaces theory is a possible approach and has been proposed as mid-level representation between symbolic and sub-symbolic representations, whereby concepts are represented in a geometrical space that is characterised by a number of quality dimensions. Currently, much of the work has demonstrated how conceptual spaces are created in a knowledge-driven manner, relying on prior knowledge to form concepts and identify quality dimensions. This paper presents a method to create semantic representations using data-driven conceptual spaces which are then used to derive linguistic descriptions of numerical data. Our contribution is a principled approach to automatically construct a conceptual space from a set of known observations wherein the quality dimensions and domains are not known a priori. This novelty of the approach is the ability to select and group semantic features to discriminate between concepts in a data-driven manner while preserving the semantic interpretation that is needed to infer linguistic descriptions for interaction with humans. Two data sets representing leaf images and time series signals are used to evaluate the method. An empirical evaluation for each case study assesses how well linguistic descriptions generated from the conceptual spaces identify unknown observations. Furthermore,  comparisons are made with descriptions derived on alternative approaches for generating semantic models.

Place, publisher, year, edition, pages
AAAI Press, 2018
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-70433 (URN)10.1613/jair.1.11258 (DOI)000455091500015 ()2-s2.0-85057746407 (Scopus ID)
Available from: 2018-12-04 Created: 2018-12-04 Last updated: 2019-01-23Bibliographically approved
Lundell, J., Krug, R., Schaffernicht, E., Stoyanov, T. & Kyrki, V. (2018). Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization. In: Asfour, T (Ed.), IEEE-RAS Conference on Humanoid Robots: . Paper presented at IEEE-RAS 18th Conference on Humanoid Robots (Humanoids 2018), Beijing, China, November 6-9, 2018 (pp. 132-138). IEEE
Open this publication in new window or tab >>Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
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2018 (English)In: IEEE-RAS Conference on Humanoid Robots / [ed] Asfour, T, IEEE, 2018, p. 132-138Conference paper, Published paper (Refereed)
Abstract [en]

Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE-RAS International Conference on Humanoid Robots, ISSN 2164-0572
Keywords
Sensorimotor learning, Grasping and Manipulation, Concept and strategy learning
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-71311 (URN)000458689700019 ()
Conference
IEEE-RAS 18th Conference on Humanoid Robots (Humanoids 2018), Beijing, China, November 6-9, 2018
Funder
Swedish Foundation for Strategic Research
Note

Funding Agency:

Academy of Finland  314180

Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2019-03-01Bibliographically approved
Wiedemann, T., Shutin, D., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. (2017). Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling. In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings. Paper presented at International Symposium on Olfaction and Electronic Nose (ISOEN 2017), Montreal, Canada, May 28-31, 2017 (pp. 122-124). IEEE
Open this publication in new window or tab >>Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling
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2017 (English)In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings, IEEE, 2017, p. 122-124Conference paper, Published paper (Refereed)
Abstract [en]

Here we report on active water sampling devices forunderwater chemical sensing robots. Crayfish generate jetlikewater currents during food search by waving theflagella of their maxillipeds. The jets generated toward theirsides induce an inflow from the surroundings to the jets.Odor sample collection from the surroundings to theirolfactory organs is promoted by the generated inflow.Devices that model the jet discharge of crayfish have beendeveloped to investigate the effectiveness of the activechemical sampling. Experimental results are presented toconfirm that water samples are drawn to the chemicalsensors from the surroundings more rapidly by using theaxisymmetric flow field generated by the jet discharge thanby centrosymmetric flow field generated by simple watersuction. Results are also presented to show that there is atradeoff between the angular range of chemical samplecollection and the sample collection time.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-60688 (URN)10.1109/ISOEN.2017.7968884 (DOI)978-1-5090-2393-6 (ISBN)978-1-5090-2392-9 (ISBN)
Conference
International Symposium on Olfaction and Electronic Nose (ISOEN 2017), Montreal, Canada, May 28-31, 2017
Available from: 2017-09-08 Created: 2017-09-08 Last updated: 2019-03-29Bibliographically approved
Canelhas, D. R., Schaffernicht, E., Stoyanov, T., Lilienthal, A. & Davison, A. J. (2017). Compressed Voxel-Based Mapping Using Unsupervised Learning. Robotics, 6(3), Article ID 15.
Open this publication in new window or tab >>Compressed Voxel-Based Mapping Using Unsupervised Learning
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2017 (English)In: Robotics, E-ISSN 2218-6581, Vol. 6, no 3, article id 15Article in journal (Refereed) Published
Abstract [en]

In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI AG, 2017
Keywords
3D mapping, TSDF, compression, dictionary learning, auto-encoder, denoising
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-64420 (URN)10.3390/robotics6030015 (DOI)000419218300002 ()2-s2.0-85030989493 (Scopus ID)
Note

Funding Agencies:

European Commission  FP7-ICT-270350 

H-ICT  732737 

Available from: 2018-01-19 Created: 2018-01-19 Last updated: 2018-01-19Bibliographically approved
Kucner, T. P., Magnusson, M., Schaffernicht, E., Hernandez Bennetts, V. M. & Lilienthal, A. (2017). Enabling Flow Awareness for Mobile Robots in Partially Observable Environments. IEEE Robotics and Automation Letters, 2(2), 1093-1100
Open this publication in new window or tab >>Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
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2017 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 1093-1100Article in journal (Refereed) Published
Abstract [en]

Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Field robots; mapping; social human-robot interaction
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-55174 (URN)10.1109/LRA.2017.2660060 (DOI)000413736600094 ()
Projects
ILIAD
Funder
Knowledge Foundation, 20140220 20130196
Note

Funding Agencies:

EU project SPENCER  ICT-2011-600877 

H2020-ICT project SmokeBot  645101 

H2020-ICT project ILIAD  732737 

Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2017-11-23Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0804-8637

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