Journals
  1. E. C. Kara, J. S. Macdonald, D. Black, M. Berges, G. Hug, and S. Kiliccote, "Estimating the Benefits of Electric Vehicle Smart Charging at Non-Residential Locations: A Data-Driven Approach," Applied Energy, 2015.
    bibtex
    @article{kara_estimating_2015, title = {Estimating the {Benefits} of {Electric} {Vehicle} {Smart} {Charging} at {Non}-{Residential} {Locations}: {A} {Data}-{Driven} {Approach}},
      journal = {Applied Energy},
      author = {Kara, Emre Can and Macdonald, Jason S. and Black, Douglas and Berges, Mario and Hug, Gabriela and Kiliccote, Sila},
      year = {2015},
      publisher = {Elsevier} },
     
  2. C. Liu, J. B. Harley, M. Bergés, D. W. Greve, and I. J. Oppenheim, "Robust ultrasonic damage detection under complex environmental conditions using singular value decomposition," Ultrasonics, 2015.
    bibtex
    @article{liu_robust_2015, title={Robust ultrasonic damage detection under complex environmental conditions using singular value decomposition},
      author={Liu, Chang and Harley, Joel B and Berg{\'e}s, Mario and Greve, David W and Oppenheim, Irving J},
      journal={Ultrasonics},
      year={2015},
      publisher={Elsevier} },
     
  3. S. Giri and M. Bergés, "An energy estimation framework for event-based methods in Non-Intrusive Load Monitoring," Energy Conversion and Management, vol. 90, pp. 488-498, 2015.
    bibtex
    @article{giri_energy_2015, title={An energy estimation framework for event-based methods in Non-Intrusive Load Monitoring},
      author={Giri, Suman and Berg{\'e}s, Mario},
      journal={Energy Conversion and Management},
      volume={90},
      pages={488--498},
      year={2015},
      publisher={Pergamon} },
     
  4. F. Jazizadeh, B. Becerik-Gerber, M. Berges, and L. Soibelman, "An unsupervised hierarchical clustering based heuristic algorithm for facilitated training of electricity consumption disaggregation systems," Advanced Engineering Informatics, vol. 28, iss. 4, pp. 311-326, 2014.
    bibtex
    @article{jazizadeh_unsupervised_2014b, title={An unsupervised hierarchical clustering based heuristic algorithm for facilitated training of electricity consumption disaggregation systems},
      author={Jazizadeh, Farrokh and Becerik-Gerber, Burcin and Berges, Mario and Soibelman, Lucio},
      journal={Advanced Engineering Informatics},
      volume={28},
      number={4},
      pages={311--326},
      year={2014},
      publisher={Elsevier} },
     
  5. N. Batra, O. Parson, M. Berges, A. Singh, and A. Rogers, "A comparison of non-intrusive load monitoring methods for commercial and residential buildings," arXiv preprint arXiv:1408.6595, 2014.
    bibtex
    @article{batra_comparison_2014, title={A comparison of non-intrusive load monitoring methods for commercial and residential buildings},
      author={Batra, Nipun and Parson, Oliver and Berges, Mario and Singh, Amarjeet and Rogers, Alex},
      journal={arXiv preprint arXiv:1408.6595},
      year={2014}
    }
  6. X. Liu, B. Akinci, M. Berges, and J. H. Garrett, "Domain-Specific Querying Formalisms for Retrieving Information of HVAC Systems," Journal of Computing in Civil Engineering, 2013.
    bibtex
    @article{liu_domain-specific_2013, title = {Domain-Specific Querying Formalisms for Retrieving Information of {HVAC} Systems},
      issn = {0887-3801},
      url = {dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000294},
      doi = {10.1061/(ASCE)CP.1943-5487.0000294},
      abstract = {In order to save energy and improve the control of indoor environments, researchers have developed hundreds of computer algorithms that can automatically and continuously analyze the conditions of Heating, Ventilation and Air-Conditioning ({HVAC)} systems. However, the complex information requirements of these algorithms inhibit deploying them in real-world facilities. We propose an integrated performance analysis framework that automatically collects, merges and provides the information required by them. In previous studies, we have identified a general set of information requirements for the computerized approaches and formalized a semi-automated approach that integrates multiple data models to support the required information. In order to automatically retrieve the information required by different approaches, the research discussed in this paper explored a query mechanism that can represent the required information in a formal way that can be reasoned about. We categorize the information items that are used to represent the information needs, formalize a domain-specific query language that can formally represent the query statements, and develop a library of mechanisms that can automatically reason about and retrieve the needed information. In order to validate the performance of the query language and mechanisms, we also developed a prototype, which includes a graphic user interface that helps users to define the queries, and the implementation of the reasoning mechanisms that process the queries. The precision and recall of the query language and mechanisms were tested using the queries identified from previous research.},
      journal = {Journal of Computing in Civil Engineering},
      author = {Liu, Xuesong and Akinci, Burcu and Berges, Mario and Garrett, James H.},
      month = feb, year = {2013} },
     
  7. D. Carlson, S. H. Matthews, and M. Berges, "One Size Does Not Fit All: Averaged Data on Household Electricity is Inadequate for Residential Energy Policy and Decisions," Energy and Buildings, vol. 64, pp. 132-144, 2013.
    bibtex
    @article{carlson_one_2013, title = {One Size Does Not Fit All: Averaged Data on Household Electricity is Inadequate for Residential Energy Policy and Decisions},
      journal = {Energy and Buildings},
      author = {Carlson, Derrick and Matthews, H. Scott and Berges, Mario},
      year = {2013},
      month = {September},
      volume = {64},
      pages = {132--144},
      doi = {10.1016/j.enbuild.2013.04.005} },
     
  8. S. Giri, M. Berges, and A. Rowe, "Towards automated appliance recognition using an EMF sensor in NILM platforms," Advanced Engineering Informatics, vol. 27, iss. 4, pp. 477-485, 2013.
    bibtex
    @article{giri_towards_2013, title = {Towards automated appliance recognition using an {EMF} sensor in {NILM} platforms},
      volume = {27},
      issn = {1474-0346},
      url = {www.sciencedirect.com/science/article/pii/S1474034613000268},
      doi = {10.1016/j.aei.2013.03.004},
      abstract = {Abstract Non-Intrusive Load Monitoring ({NILM)} has been studied for a few decades now as a method of disaggregating information about appliance level power consumption in a building from aggregate measurements of voltage and/or current obtained at a centralized location in the electrical system. When such information is provided to the electricity consumer as feedback, they can then take the necessary steps to modify their behavior and conserve electricity. Research has shown potential for savings of up to 20\% through this kind of feedback. The training phase required to allow the algorithms to recognize appliances in the home at the beginning of a {NILM} setup is a big hindrance to wide adoption of the technique. One of the recent advances in this research area includes the addition of an Electro-Magnetic Field ({EMF)} sensor that measures the electric and magnetic field nearby an appliance to detect its operational state. This information, when coupled with the aggregate power consumption data for the home, can help to train a {NILM} system, which is a significant step forward in automating the training phase. This paper explores the theory behind the operation of the {EMF} sensor and discusses the feasibility of automating the training and classification process using these devices. A case study is presented, where magnetic field measurements of eight appliances are analyzed to determine the viability of using these signals alone to determine the type of appliance that the {EMF} sensor has been placed next to. Various dimensionality reduction techniques are applied to the collected data, and the resulting feature vectors are used to train a variety of common machine learning classifiers. A vector subspace obtained using Independent Component Analysis ({ICA)},
      along with a k-{NN} classifier, was found to perform best among the different alternatives explored. Possible reasons behind the findings are discussed and areas for further exploration are proposed.},
      number = {4},
      urldate = {2013-07-21},
      journal = {Advanced Engineering Informatics},
      author = {Giri, Suman and Berges, Mario and Rowe, Anthony},
      month = oct, year = {2013},
      keywords = {Automated training, classification, {EMF} detector, {NILM},
      Projections, Sensor-aided},
      pages = {477--485} },
     
  9. X. Liu, B. Akinci, M. Berges, and J. H. Garrett Jr., "Extending the information delivery manual approach to identify information requirements for performance analysis of HVAC systems," Advanced Engineering Informatics, 2013.
    bibtex
    @article{liu_extending_2012, title = {Extending the information delivery manual approach to identify information requirements for performance analysis of {HVAC} systems},
      issn = {1474-0346},
      url = {www.sciencedirect.com/science/article/pii/S1474034613000530},
      doi = {10.1016/j.aei.2013.05.003},
      year = 2013, urldate = {2013-07-23},
      journal = {Advanced Engineering Informatics},
      author = {Liu, Xuesong and Akinci, Burcu and Berges, Mario and Garrett Jr., James H.},
      keywords = {{HVAC} systems, Information delivery manual, Information requirement, performance analysis algorithms} },
     
  10. A. Rowe, M. Berges, G. Bhatia, E. Goldman, R. Rajkumar, J. H. Garrett, J. M. F. Moura, and L. Soibelman, "Sensor Andrew: Large-scale campus-wide sensing and actuation," IBM Journal of Research and Development, vol. 55, iss. 1.2, p. 6:1-6:14, 2011.
    bibtex
    @article{rowe_sensor_2011, title = {Sensor Andrew: Large-scale campus-wide sensing and actuation},
      volume = {55},
      issn = {0018-8646},
      doi = {10.1147/JRD.2010.2089662},
      number = {1.2},
      journal = {{IBM} Journal of Research and Development},
      author = {Rowe, Anthony and Berges, Mario and Bhatia, Gaurav and Goldman, Ethan and Rajkumar, Raj and Garrett, James H. and Moura, José M. F. and Soibelman, Lucio},
      month = jan, year = {2011},
      pages = {6:1 -- 6:14},
      url = {www.marioberges.com/pubs/2010_rowe_IBM.pdf} },
     
  11. M. Berges, E. Goldman, L. Soibelman, S. H. Matthews, and K. Anderson, "User-centered Non-Intrusive Electricity Load Monitoring for Residential Buildings," Journal of Computing in Civil Engineering, vol. 25, iss. 1, 2011.
    bibtex
    @article{berges_user-centered_2011, title = {User-centered {Non-Intrusive} Electricity Load Monitoring for Residential Buildings},
      volume = {25},
      number = {1},
      journal = {Journal of Computing in Civil Engineering},
      author = {Berges, Mario and Goldman, Ethan and Soibelman, Lucio and Matthews, H. Scott and Anderson, Kyle},
      year = {2011},
      url = {dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000108} },
     
  12. S. Taneja, B. Akinci, J. H. Garrett, L. Soibelman, M. Berges, G. Atasoy, X. Liu, S. M. Shahandashti, E. B. Anil, E. Ergen, A. Pradhan, and P. Tang, "CEC: Sensing and Field Data Capture for Construction and Facility Operations," Journal of Construction Engineering and Management, vol. 137, iss. 10, pp. 870-881, 2011.
    bibtex
    @article{taneja_sensing_2010, title = {{CEC:} Sensing and Field Data Capture for Construction and Facility Operations},
      volume = {137},
      abstract = {Collection of accurate, complete and reliable field data is not only essential for active management of construction projects involving various tasks, such as material tracking, progress monitoring and quality assurance, but also for facility/infrastructure management during the service lives of facilities/infrastructure systems. Limitations of current manual data collection approaches in terms of speed, completeness and accuracy render these approaches ineffective for decision support in highly dynamic environments, such as construction and facility operations. Hence, there is a need to leverage the advancements in automated field data capture technologies to support decisions during construction and facility operations. These technologies can be used not only for acquiring data about the various operations being carried out at construction and facility sites, but also for gathering information about the context surrounding these operations and monitoring the workflow of activities during these operations. With this, it is possible for project and facility managers to better understand the effect of environmental conditions on construction and facility operations, as well as to identify inefficient processes in these operations. This paper presents an overview of the various applications of automated field data capture technologies in construction and facility fieldwork. These technologies include image capture technologies such as laser scanners and video cameras, automated identification technologies such as barcodes and Radio Frequency Identification {(RFID)} tags, tracking technologies such as {GPS} and Wireless {LAN},
      and process monitoring technologies such as on-board instruments {(OBI).} The authors observe that though there exist applications for capturing construction and facility fieldwork data, these technologies have been underutilized for capturing the context at the fieldwork sites as well as for monitoring the workflow of construction and facility operations.},
      number = {10},
      journal = {Journal of Construction Engineering and Management},
      author = {Taneja, S. and Akinci, B. and Garrett, J. H and Soibelman, L. and Berges, Mario and Atasoy, G. and Liu, X. and Shahandashti, S. M. and Anil, E. B. and Ergen, E. and Pradhan, A. and Tang, P.},
      year = {2011},
      pages = {870-881},
      url = {dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000332} },
     
  13. M. Berges, E. Goldman, S. H. Matthews, and L. Soibelman, "Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring," Journal of Industrial Ecology, vol. 14, iss. 5, pp. 844-858, 2010.
    bibtex
    @article{berges_enhancing_2010, title = {Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring},
      volume = {14},
      issn = {10881980},
      doi = {10.1111/j.1530-9290.2010.00280.x},
      number = {5},
      journal = {Journal of Industrial Ecology},
      author = {Berges, Mario and Goldman, Ethan and Matthews, H. Scott and Soibelman, Lucio},
      month = oct, year = {2010},
      pages = {844--858},
      url = {onlinelibrary.wiley.com/doi/10.1111/j.1530-9290.2010.00280.x/pdf} },
     
  14. S. M. Shahandashti, S. N. Razavi, L. Soibelman, M. Berges, C. H. Caldas, I. Brilakis, J. Teizer, P. Vela, C. Haas, J. H. Garrett, B. Akinci, and Z. Zhu, "CEC: Data Fusion Approaches and Applications for Construction Engineering," Journal of Construction Engineering and Management, vol. 137, iss. 10, pp. 863-869, 2011.
    bibtex
    @article{shahandashti_data_2010, title = {{CEC:} Data Fusion Approaches and Applications for Construction Engineering},
      abstract = {Data fusion can be defined as the process of combining data or information for estimating the state of an entity. Data fusion is a multi-disciplinary field that has several benefits, such as enhancing the confidence, improving reliability and reducing ambiguity of measurements for estimating the state of entities in engineering systems. It can also enhance completeness of fused data that can be required for estimating the state of engineering systems. Data fusion has been applied to different fields, such as robotics, automation, and intelligent systems. This paper reviews some examples of recent applications of data fusion in civil engineering and presents some of the potential benefits of using data fusion in civil engineering.},
      journal = {Journal of Construction Engineering and Management},
      author = {Shahandashti, S. M. and Razavi, Saiedeh N. and Soibelman, Lucio and Berges, Mario and Caldas, Carlos H. and Brilakis, Ioannis and Teizer, Jochen and Vela, Patricio and Haas, Carl and Garrett, James H. and Akinci, Burcu and Zhu, Zhenhua},
      volume = {137},
      number = {10},
      pages = {863-869},
      year = {2011},
      url = {link.aip.org/link/doi/10.1061/(ASCE)CO.1943-7862.0000287} },
     
  15. M. Berges, E. Goldman, S. H. Matthews, and L. Soibelman, "Training Load Monitoring Algorithms on Highly Sub-Metered Home Electricity Consumption Data," Tsinghua Science \& Technology, vol. 13, p. 406, 2008.
    bibtex
    @article{berges_training_2008, title = {Training Load Monitoring Algorithms on Highly {Sub-Metered} Home Electricity Consumption Data},
      volume = {13},
      journal = {Tsinghua Science \& Technology},
      author = {Berges, Mario and Goldman, Ethan and Matthews, H. Scott and Soibelman, Lucio},
      year = {2008},
      pages = {406–411},
      url = {linkinghub.elsevier.com/retrieve/pii/S1007021408701822} },
     
Conferences and Workshops
  1. M. Eybpoosh, M. Berges, and H. Y. Noh, "Effects of damage location and size on sparse representation of guided-waves for damage diagnosis of pipelines under varying temperature." 2015, p. 94371x-94371x.
    bibtex
    @inproceedings{eybpoosh_effects_2015, title = {Effects of damage location and size on sparse representation of guided-waves for damage diagnosis of pipelines under varying temperature},
      volume = {9437},
      url = {dx.doi.org/10.1117/12.2084439},
      doi = {10.1117/12.2084439},
      abstract = {In spite of their many advantages, real-world application of guided-waves for structural health monitoring (SHM) of pipelines is still quite limited. The challenges can be discussed under three headings: (1) Multiple modes, (2) Multipath reflections, and (3) Sensitivity to environmental and operational conditions (EOCs). These challenges are reviewed in the authors’ previous work. This paper is part of a study whose objective is to overcome these challenges for damage diagnosis of pipes, while addressing the limitations of the current approaches. That is, develop methods that simplify signal while retaining damage information, perform well as EOCs vary, and minimize the use of transducers. In this paper, a supervised method is proposed to extract a sparse subset of the ultrasonic guided-wave signals that contain optimal damage information for detection purposes. That is, a discriminant vector is calculated so that the projections of undamaged and damaged pipes on this vector is separated. In the training stage, data is recorded from intact pipe, and from a pipe with an artificial structural abnormality (to simulate any variation from intact condition). During the monitoring stage, test signals are projected on the discriminant vector, and these projections are used as damage-sensitive features for detection purposes. Being a supervised method, factors such as EOC variations, and difference in the characteristics of the structural abnormality in training and test data, may affect the detection performance. This paper reports the experiments investigating the extent to which the differences in damage size and damage location, as well as temperatures, can influence the discriminatory power of the extracted damage-sensitive features. The results suggest that, for practical ranges of monitoring and damage sizes of interest, the proposed method has low sensitivity to such training factors. High detection performances are obtained for temperature differences up to 14°C. The findings reported in this paper suggest that although the proposed method is a supervised approach, labeling of the training data does not require prior knowledge about the damage characteristics (e.g., size, location). Moreover, the potential of the proposed method for online monitoring is illustrated, for wide range of temperature variations and different damage scenarios.},
      urldate = {2015-05-06},
      author = {Eybpoosh, Matineh and Berges, Mario and Noh, Hae Young},
      year = {2015},
      pages = {94371X--94371X--9} },
     
  2. M. Eybpoosh, M. Berges, and H. Y. Noh, "Nonlinear feature extraction methods for removing temperature effects in multi-mode guided-waves in pipes." 2015, p. 94371w-94371w.
    bibtex
    @inproceedings{eybpoosh_nonlinear_2015, title = {Nonlinear feature extraction methods for removing temperature effects in multi-mode guided-waves in pipes},
      volume = {9437},
      url = {dx.doi.org/10.1117/12.2084436},
      doi = {10.1117/12.2084436},
      abstract = {Ultrasonic guided-waves propagating in pipes with varying environmental and operational conditions (EOCs) are usually the results of complex superposition of multiple modes travelling in multiple paths. Among all of the components forming a complex guided-wave signal, the arrivals scattered by damage (so called scatter signal) are of importance for damage diagnosis purposes. This paper evaluates the potentials of nonlinear decomposition methods for extracting the scatter signal from a multi-modal signal recorded from a pipe under varying temperatures. Current approaches for extracting scatter signal can be categorized as (A) baseline subtraction methods, and (B) linear decomposition methods. In this paper, we first illustrate, experimentally, the challenges for applying these methods on multi-modal signals at varying temperatures. To better analyze the experimental results, the effects of temperature on multi-modal signals are simulated. The simulation results show that different wave modes may have significantly different sensitivities to temperature variations. This brings about challenges such as shape distortion and nonlinear relations between the signals recorded at different temperatures, which prevent the aforementi