Property:References

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Pages using the property "References"

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"TROLL": a regenerating robot +(self-detection) K. Gold, B. Scassellati, Using probabilistic reasoning over time to self-recognize, Robotics and Autonomous Systems (2008), doi:10.1016/j.robot.2008.07.006 (body schema) Mai Hikita, Sawa Fuke, Masaki Ogino, Takashi Minato and Minoru Asada. Visual attention by saliency leads cross-modal body representation. IROS - 2008. (anomaly detection) Takahiro Suzuki, Fumihiro Bessho, Tatsuya Harada and Yasuo Kuniyoshi. Visual Anomaly Detection under Temporal and Spatial Non-uniformity for News Finding Robot. IROS 2011. (self-augmentation) Luzius Brodbeck and Fumiya Iida. Enhanced Robotic Body Extension with Modular Units, IROS 2012.

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A decision support system for reducing false alarms in ICU +1. Liu C, Zhao L, Tang H, Li Q, Wei S, Li J. Life-threatening false alarm rejection in ICU: using the rule-based and multi-channel information fusion method. Physiological Measurement. 2016;37(8):1298. 2. Konkani A, Oakley B, Bauld TJ. Reducing hospital noise: a review of medical device alarm management. Biomedical Instrumentation & Technology. 2012;46(6):478-87. 3. Cvach M. Monitor alarm fatigue: an integrative review. Biomedical Instrumentation & Technology. 2012;46(4):268-77. 4. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet. Circulation. 2000;101(23):e215.
Activity monitoring for AAL +Beth Logan et al. A Long-Term Evaluation of Sensing Modalities for Activity Recognition. Ubiquitous Computing. Lecture Notes in Computer Science vol. 4717, pp. 483-50, 2007. Juan Carlos Augusto, Hideyuki Nakashima, Hamid Aghajan. Ambient Intelligence and Smart Environments: A State of the Art. Handbook of Ambient Intelligence and Smart Environments, pp 3-31, 2010.
Acumen Robot Model Series +http://www.acumen-language.org/ http://en.wikipedia.org/wiki/SCARA SCARA
Adaptive warning field system +SAS2-project, http://islab.hh.se/mediawiki/SAS2 ROS - Robot Operating System, http://www.ros.org/ OpenCv - http://opencv.org/ Nemati, Hassan, Åstrand, Björn (2014). Tracking of People in Paper Mill Warehouse Using Laser Range Sensor. 2014 UKSim-AMSS 8th European Modelling Symposium, EMS 2014, Pisa, Italy, 21-23 October, 2014. Power, P. Wayne, and Johann A. Schoonees. "Understanding background mixture models for foreground segmentation." Proceedings image and vision computing New Zealand. Vol. 2002. 2002.
Agent and object detection and classification in a warehouse setting +SAS2-project, http://islab.hh.se/mediawiki/SAS2 ROS - Robot Operating System, http://www.ros.org/ OpenCv - http://opencv.org/ Lalonde, Jean-Francois; Vandapel, Nicolas; Huber, Daniel; Hebert, Martial; Natural terrain classification using three-dimensional ladar data for ground robot mobility, Journal of Field Robotics, Vol. 23, No. 10, pp. 839 - 861, November, 2006 Mosberger, Rafael; Vision-based human detection from mobile machinery in industrial environments, Thesis, Örebro University, Sweden, 2016 Saarinen, Jari P.; Andreasson, Henrik; Stoyanov, Todor; Lilienthal, Achim J.; 3D normal distributions transform occupancy maps: An efficient representation for mapping in dynamic environments, The International Journal of Robotics Research, Vol 32, Issue 14, pp. 1627 – 1644, 2013
Analyzing Human Motion using Inertial Sensors +J. Rueterbories, E. G. Spaich, B. Larsen, and O. K. Andersen, “Methods for gait event detection and analysis in ambulatory systems,” Med. Eng. & Phys., vol. 32, no. 6, pp. 545–552, 2010. J. J. Kavanagh and H. B. Menz, “Accelerometry: A technique for quantifying movement patterns during walking,” Gait & Posture, vol. 28, no. 1, pp. 1–15, 2008. D. Lai, R. Begg, and M. Palaniswami, “Computational intelligence in gait research: A perspective on current app. And future challenges,” Info. Tech. in Biomed., IEEE Trans. on, vol. 13, no. 5, pp. 687–702, 2009.
Anomaly Detection on Truck Histograms +Learning Low-Dimensional Representation of Bivariate Histogram Data https://ieeexplore.ieee.org/abstract/document/8464276
Anomaly ranking of District Heating Substations +M. Goldstein and S. Uchida, "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data", PLOS ONE, vol. 11, no. 4, p. e0152173, 2016. P. Arjunan, H. Khadilkar, T. Ganu, Z. Charbiwala, A. Singh and P. Singh, "Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information", Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments - BuildSys '15, 2015. D. Araya, K. Grolinger, H. ElYamany, M. Capretz and G. Bitsuamlak, "An ensemble learning framework for anomaly detection in building energy consumption", Energy and Buildings, vol. 144, pp. 191-206, 2017. S. Rayana and L. Akoglu, "Less is More", ACM Transactions on Knowledge Discovery from Data, vol. 10, no. 4, pp. 1-33, 2016. Huang, Huaming, "Rank Based Anomaly Detection Algorithms" (2013). Electrical Engineering and Computer Science - Dissertations.Paper 331.
Automatic Machine Learning (AUTO-AUTO-ENCODER!) +The following paper summarises the algorithm configuration in the different domain : http://aad.informatik.uni-freiburg.de/papers/16-AUTOML-AutoNet.pdf This paper presents the initial idea behind Bayesian optimization for estimating parameter: https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf Previous master thesis on applying autoencoder for histogram data: Robin Ng, “Efficient Implementation of Histogram Dimension Reduction using Deep Learning”, 2017.

B

Barcode mapping in warehouses +AIMS-project, http://islab.hh.se/mediawiki/AIMS ROS - Robot Operating System, http://www.ros.org/ ZBar bar code reader, http://zbar.sourceforge.net/ Stampfer, D.; Lutz, M.; Schlegel, C., "Information driven sensor placement for robust active object recognition based on multiple views," Technologies for Practical Robot Applications (TePRA), 2012 IEEE International Conference on , vol., no., pp.133,138, 23-24 April 2012, doi: 10.1109/TePRA.2012.6215667 Karpischek, S., Michahelles, F., Fleisch, E., “my2cents: enabling research on consumer-product interaction”, Pers Ubiquit Comput (2012) 16:613–622, DOI 10.1007/s00779-011-0426-9 Han, Y., Sumi, Y., Matsumoto, Y., and And, N, “.Acquisition of Object Pose from Barcode for Robot Manipulation”, I. Noda et al. (Eds.): SIMPAR 2012, LNAI 7628, pp. 299–310, 2012. G Meng, S Darman, “Label and Barcode Detection in Wide Angle Image”, Master Thesis, Halmstad University, Sweden, http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-23979
Biases in electronic health records +1. Verheij, Robert A., et al. "Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse." Journal of medical Internet research 20.5 (2018). 2. Gianfrancesco, Milena A., et al. "Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data." JAMA internal medicine (2018). 3. Johnson, Alistair EW, et al. "MIMIC-III, a freely accessible critical care database." Scientific data 3 (2016): 160035.

C

Chess playing humanoid robot by vision +https://www.youtube.com/watch?v=gXOkWuSCkRI
Comprehending low-dimensional manifolds of temporal data from the home +Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579-2605. Lundström, J., Järpe, E., & Verikas, A. (2016). Detecting and exploring deviating behaviour of smart home residents. Expert Systems with Applications, 55, 429-440. Rauber, P. E., Falcão, A. X., & Telea, A. C. (2016). Visualizing time-dependent data using dynamic t-SNE. Proc. EuroVis Short Papers, 2(5). Cheng, J., Liu, H., Wang, F., Li, H., & Zhu, C. (2015). Silhouette analysis for human action recognition based on supervised temporal t-sne and incremental learning. IEEE Transactions on Image Processing, 24(10), 3203-3217.
Consensus clustering for categorizing orthogonal vehicle operations +- Some slides: https://www.siam.org/meetings/sdm11/clustering.pdf - Muller, E., Gunnemann, S., Farber, I., & Seidl, T. (2012, April). Discovering multiple clustering solutions: Grouping objects in different views of the data. In Data Engineering (ICDE), 2012 IEEE 28th International Conference on (pp. 1207-1210). IEEE. - Hu, J., & Pei, J. (2017). Subspace multi-clustering: a review. Knowledge and Information Systems, 1-28. - Yang, S., & Zhang, L. (2017). Non-redundant multiple clustering by nonnegative matrix factorization. Machine Learning, 106(5), 695-712. - Dang, X. H., & Bailey, J. (2015). A framework to uncover multiple alternative clusterings. Machine Learning, 98(1-2), 7-30. - Gionis, A., Mannila, H., & Tsaparas, P. (2007). Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 4. - Qi, Z., & Davidson, I. (2009, June). A principled and flexible framework for finding alternative clusterings. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 717-726). ACM. - Muller, E., Gunnemann, S., Farber, I., & Seidl, T. (2012). Discovering multiple clustering solutions: Grouping objects in different views of the data. In IEEE 28th International Conference on Data Engineering (ICDE), (pp. 1207-1210). - Cui, Y., Fern, X. Z., & Dy, J. G. (2007). Non-redundant multi-view clustering via orthogonalization. In IEEE International Conference on Data Mining (ICDM), (pp. 133-142). - Strehl, A., & Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, pp. 583-617.
Constrained dynamic path planning for truck and trailer +R. Siegwart and I. R. Nourbakhsh,Introduction to Autonomous Mobile Robots. Scituate, MA, USA: Bradford Company, 2004 A. Nozad, Heavy vehicle path stability control for collision avoidance applications," Master's thesis, Chalmers university of technology, 2011 J. G. Fernandez, A vehicle dynamics model for driving simulators," Master's thesis, Chalmers university of technology, 2012
Courteous robot guide for visitors to an intelligent home +Yusuke Kato, Takayuki Kanda, Hiroshi Ishiguro. May I help you? Design of Human-like Polite Approaching Behavior. HRI 2015: 35-42 Tomoko Yonezawa, Hirotake Yamazoe, Akira Utsumi, Shinji Abe. Anthropomorphic awareness of partner robot to user’s situation based on gaze and speech detection. International Journal of Autonomous and Adaptive Communications Systems. Volume 5, Issue 1. DOI: 10.1504/IJAACS.2012.044782
Cross-Spectrum Ocular Identity Recognition via Deep Learning +R. Jillela and A. Ross, "Matching face against iris images using periocular information," 2014 IEEE International Conference on Image Processing (ICIP), Paris, 2014, pp. 4997-5001. doi: 10.1109/ICIP.2014.7026012: https://ieeexplore.ieee.org/document/7026012 P. R. Nalla and A. Kumar, "Toward More Accurate Iris Recognition Using Cross-Spectral Matching," in IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 208-221, Jan. 2017. doi: 10.1109/TIP.2016.2616281: https://ieeexplore.ieee.org/document/7587438

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Data Mining In a Warehouse Inventory +Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid. Good Practice in Large-Scale Learning for Image Classi cation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2014, 36 (3), pp.507-520.<10.1109/TPAMI.2013.146>.<hal-00835810> Florent Perronnin, Zeynep Akata, Zaid Harchaoui, Cordelia Schmid. Towards Good Practice in Large-Scale Learning for Image Classification. CVPR 2012 - IEEE Computer Vision and Pattern Recognition, Jun 2012, Providence (RI), United States. IEEE, pp.3482-3489, 2012,<10.1109/CVPR.2012.6248090>.<hal-00690014> Raphael Puget, Nicolas Baskiotis, Patrick Gallinari. Sequential Dynamic Classi cation for Large Scale Multi-class Problems. Extreme Classi cation Workshop at ICML, Jul 2015, Lille,France. 2015.<hal-01207428>
Deep feature analysis and extraction on Logged Vehicle data for the task of predictive maintenance +• Doquet, Guillaume, and Michele Sebag. "Agnostic feature selection." The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2019 • Prytz, Rune, et al. "Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data." Engineering applications of artificial intelligence 41 (2015): 139-150.
Deep stacked ensemble +1- David H.Wolpert, "Stacked generalisation" https://doi.org/10.1016/S0893-6080(05)80023-1 2- Jason Brownle, "How to Develop a Stacking Ensemble for Deep Learning Neural Networks in Python With Keras", https://machinelearningmastery.com/stacking-ensemble-for-deep-learning-neural-networks/
Detecting Faults and Estimating Missing Values in Smart Meter Data +http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5524054 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1425550
Detecting Points of Interest for Robotic First Aid +-pose recognition Jamie Shotton, Ross Girshick, Andrew Fitzgibbon, Toby Sharp, Mat Cook, Mark Finocchio, Richard Moore, Pushmeet Kohli, Antonio Criminisi, Alex Kipman, Andrew Blake, "Efficient Human Pose Estimation from Single Depth Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 12, pp. 2821-2840, Dec. 2013, doi:10.1109/TPAMI.2012.241 -first aid Travers, A. H., Rea, T. D., Bobrow, B. J., et al. (2010). Part 4: CPR overview 2010 American Heart Association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation, 122(18 suppl 3), S676-S684.
Detecting changes in causal relations +Structural causal discovery techniques: https://arxiv.org/pdf/1211.3295.pdf Change detection in Granger causality: http://cowles.yale.edu/sites/default/files/files/pub/d20/d2059.pdf
Detecting different types of machines based on usage +1.- Bengio Y, Courville A, P Vincent P. Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence. Volume: 35, Issue: 8, Aug. 2013. 2.- Kotsiantis S. Supervised Machine Learning: A Review of Classification Techniques. Informatica 31 (2007) 249-268 3.- Grira N, Crucianu M, Boujemaa N. Unsupervised and Semi-supervised Clustering: a Brief Survey. 4.- Taskar B, Segal E, Koller D. Probabilistic Classification and Clustering in Relational Data.
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