Bio


Hae Young Noh is an associate professor in the Department of Civil and Environmental Engineering. Her research introduced the new concept of “structures as sensors” to enable physical structures (e.g., buildings and vehicle frames) to be user- and environment-aware. In particular, these structures indirectly sense humans and surrounding environments through their structural responses (i.e., vibrations) by inferring the desired information (e.g., human behaviors, environmental conditions, heating and cooling system performance), instead of directly measuring the sensing targets with additional dedicated sensors (e.g., cameras, motion sensors). This concept brought a paradigm shift in how we view these structures and how the structures interact with us.
Traditionally, structures that we inhabit (such as buildings or vehicles) are considered as passive and unchanging objects that we need to monitor and control, utilizing a dense set of sensors to collect information. This has often been complicated by “noise” caused by the occupants and environments. For example, building vibrations induced by indoor and outdoor environmental and operational conditions (e.g., people walking around, traffic outside, heating system running, etc.), have been often seen as noise that needs to be removed in traditional building science and structural engineering; however, they are a rich source of information about structure, users, environment, and resources. Similarly, in vehicle engineering, researchers and engineers have been investigating control and dynamics to reduce vehicle vibration for safety and comfort. However, vibrations measured inside vehicles contain information about transportation infrastructure, vehicle itself, and driver.
Noh's work utilizes this “noise” to empower the structures with the ability to perceive and understand the information about users and surroundings using their own responses, and actively adopt and/or interact to enhance their sustainability and the occupants’ quality of life. Since she utilizes the structure itself as a sensing medium, information collection involves a simpler set of hardware that can be easily maintained throughout the structural lifetime. However, the analysis of data to separate the desired information becomes more challenging. This challenge is addressed through high-rate dynamic sensing and multi-source inferencing. Ultimately, her work aims to allow structural systems to become general sensing platforms that are easier and more practical to deploy and maintain in a long-term.
At Stanford University, Noh received her PhD and MS degrees in the CEE department and her second MS degree in Electrical Engineering. Noh earned her BS in Mechanical and Aerospace Engineering at Cornell University.

Academic Appointments


Honors & Awards


  • Best Paper Award, ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI) (2020)
  • Best Student Paper Award, ASCE Engineering Mechanics Institute Dynamics Committee (ASCE EMI) (2020)
  • Best Student Paper Award, ASCE Engineering Mechanics Institute Dynamics Committee (ASCE EMI) (2019)
  • Best Demo Award, ACM Systems for Energy-Efficient Buildings, Cities, and Transportation (ACM BuildSys) (2019)
  • Best Poster Award, IEEE/ACM Information Processing and Sensor Network (IPSN) (2019)
  • Best Paper Award, IEEE International Conference on Machine Learning and Applications (ICMLA) (2018)
  • Best Student Paper Award, ASCE Engineering Mechanics Institute Dynamics Committee (ASCE EMI) (2018)
  • CIT Dean’s Early Career Fellow, Carnegie Mellon University (2018)
  • NSF CAREER Award, National Science Foundation (NSF) (2017)
  • Google Faculty Research Award, Google (2017)
  • People’s Choice Paper Award, ACM Systems for Energy-Efficient Built Environments (ACM BuildSys) (2017)
  • Best Poster Award & Best Poster Runner-Up, ACM Embedded Networked Sensor Systems (ACM SenSys) (2016)
  • MobiSys 2016 Junior Faculty/Postdoc Grants, ACM Mobile Systems, Applications, and Services (ACM MobiSys) (2016)
  • Best Poster Award, IEEE/ACM Information Processing and Sensor Network (IPSN) (2015)
  • Google Faculty Research Award, Google (2014)
  • Berkman Faculty Development Fund, Carnegie Mellon University (2013-2015)
  • John A. Blume Fellowship, - (2010-2011)
  • Samsung Scholarship Foundation Merit-Based Scholarship, Samsung Scholarship Foundation (2006-2010)
  • American Society of Civil Engineers Essay Contest Award, American Society of Civil Engineers (2010)

Professional Education


  • PhD, Stanford University, Civil and Environmental Engineering (2011)
  • MS, Stanford University, Electrical Engineering (2011)
  • MS, Stanford University, Civil and Environmental Engineering (2008)
  • BS, Cornell University, Mechanical and Aerospace Engineering (2005)

2023-24 Courses


Stanford Advisees


  • Doctoral Dissertation Advisor (AC)
    Yiwen Dong
  • Master's Program Advisor
    Andrés Arias Vásquez, Elyse Pollack, Bohan Wang, Hongyu Wu, Isabel Yamashita, Olivia Yamashita
  • Doctoral (Program)
    Jatin Aggarwal, Doyun Hwang, Yuyan Wu

All Publications


  • iLOCuS: Incentivizing Vehicle Mobility to Optimize Sensing Distribution in Crowd Sensing IEEE TRANSACTIONS ON MOBILE COMPUTING Xu, S., Chen, X., Pi, X., Joe-Wong, C., Zhang, P., Noh, H. 2020; 19 (8): 1831–47
  • Step-Level Occupant Detection across Different Structures through Footstep-Induced Floor Vibration Using Model Transfer JOURNAL OF ENGINEERING MECHANICS Mirshekari, M., Fagert, J., Pan, S., Zhang, P., Noh, H. 2020; 146 (3)
  • Diagnosis algorithms for indirect structural health monitoring of a bridge model via dimensionality reduction MECHANICAL SYSTEMS AND SIGNAL PROCESSING Liu, J., Chen, S., Berges, M., Bielak, J., Garrett, J. H., Kovacevic, J., Noh, H. 2020; 136
  • Occupant localization using footstep-induced structural vibration MECHANICAL SYSTEMS AND SIGNAL PROCESSING Mirshekari, M., Pan, S., Fagert, J., Schooler, E. M., Zhang, P., Noh, H. 2018; 112: 77–97
  • Characterizing human activity induced impulse and slip-pulse excitations through structural vibration JOURNAL OF SOUND AND VIBRATION Pan, S., Mirshekari, M., Fagert, J., Ramirez, C., Chung, A., Hu, C., Shen, J., Zhang, P., Noh, H. 2018; 414: 61–80
  • Track monitoring from the dynamic response of a passing train: A sparse approach MECHANICAL SYSTEMS AND SIGNAL PROCESSING Lederman, G., Chen, S., Garrett, J. H., Kovacevic, J., Noh, H., Bielak, J. 2017; 90: 141–53
  • Physics-Informed Machine Learning for Inverse Design of Optical Metamaterials ADVANCED PHOTONICS RESEARCH Sarkar, S., Ji, A., Jermain, Z., Lipton, R., Brongersma, M., Dayal, K., Noh, H. 2023
  • Characterizing the variability of footstep-induced structural vibrations for open-world person identification MECHANICAL SYSTEMS AND SIGNAL PROCESSING Dong, Y., Fagert, J., Noh, H. 2023; 204
  • TelecomTM: A Fine-Grained and Ubiquitous Traffic Monitoring System Using Pre-Existing Telecommunication Fiber-Optic Cables as Sensors PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT Liu, J., Yuan, S., Dong, Y., Biondi, B., Noh, H. 2023; 7 (2)

    View details for DOI 10.1145/3596262

    View details for Web of Science ID 001005382400019

  • IDIoT: Multimodal Framework for Ubiquitous Identification and Assignment of Human-carriedWearable Devices ACM TRANSACTIONS ON INTERNET OF THINGS Bannis, A., Pan, S., Ruiz, C., Shen, J., Noh, H., Zhang, P. 2023; 4 (2)

    View details for DOI 10.1145/3579832

    View details for Web of Science ID 000999148400003

  • Turning Telecommunication Fiber-Optic Cables into Distributed Acoustic Sensors for Vibration-Based Bridge Health Monitoring STRUCTURAL CONTROL & HEALTH MONITORING Liu, J., Yuan, S., Luo, B., Biondi, B., Noh, H. 2023; 2023
  • A hierarchical semantic segmentation framework for computer vision-based bridge damage detection SMART STRUCTURES AND SYSTEMS Liu, J., Wei, Y., Chen, B., Noh, H. 2023; 31 (4): 325-334
  • Stranger Detection and Occupant Identification Using Structural Vibrations Dong, Y., Fagert, J., Zhang, P., Noh, H., Rizzo, P., Milazzo, A. SPRINGER-VERLAG SINGAPORE PTE LTD. 2023: 905-914
  • The field of human building interaction for convergent research and innovation for intelligent built environments. Scientific reports Becerik-Gerber, B., Lucas, G., Aryal, A., Awada, M., Berges, M., Billington, S., Boric-Lubecke, O., Ghahramani, A., Heydarian, A., Hoelscher, C., Jazizadeh, F., Khan, A., Langevin, J., Liu, R., Marks, F., Mauriello, M. L., Murnane, E., Noh, H., Pritoni, M., Roll, S., Schaumann, D., Seyedrezaei, M., Taylor, J. E., Zhao, J., Zhu, R. 2022; 12 (1): 22092

    Abstract

    Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

    View details for DOI 10.1038/s41598-022-25047-y

    View details for PubMedID 36543830

  • Seismic multi-hazard and impact estimation via causal inference from satellite imagery. Nature communications Xu, S., Dimasaka, J., Wald, D. J., Noh, H. Y. 2022; 13 (1): 7793

    Abstract

    Rapid post-earthquake reconnaissance is important for emergency responses and rehabilitation by providing accurate and timely information about secondary hazards and impacts, including landslide, liquefaction, and building damage. Despite the extensive collection of geospatial data and satellite images, existing physics-based and data-driven methods suffer from low estimation performance due to the complex and event-specific causal dependencies underlying the cascading processes of earthquake-triggered hazards and impacts. Herein, we present a rapid seismic multi-hazard and impact estimation system that leverages advanced statistical causal inference and remote sensing techniques. The unique feature of this system is that it provides accurate and high-resolution estimations on a regional scale by jointly inferring multiple hazards and building damage from satellite images through modeling their causal dependencies. We evaluate our system on multiple seismic events from diverse countries around the globe. Our results corroborate that incorporating causal dependencies significantly improves large-scale estimation accuracy for multiple hazards and impacts compared to existing systems. The results also reveal quantitative causal mechanisms among earthquake-triggered multi-hazard and impact for multiple seismic events. Our system establishes a new way to extract and utilize the complex interactions of multiple hazards and impacts for effective disaster responses and advancing understanding of seismic geological processes.

    View details for DOI 10.1038/s41467-022-35418-8

    View details for PubMedID 36526641

  • Ten questions concerning human-building interaction research for improving the quality of life BUILDING AND ENVIRONMENT Becerik-Gerber, B., Lucas, G., Aryal, A., Awada, M., Berges, M., Billington, S. L., Boric-Lubecke, O., Ghahramani, A., Heydarian, A., Jazizadeh, F., Liu, R., Zhu, R., Marks, F., Roll, S., Seyedrezaei, M., Taylor, J. E., Hoelscher, C., Khan, A., Langevin, J., Mauriello, M., Murnane, E., Noh, H., Pritoni, M., Schaumann, D., Zhao, J. 2022; 226
  • HierMUD: Hierarchical multi-task unsupervised domain adaptation between bridges for drive-by damage diagnosis STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL Liu, J., Xu, S., Berges, M., Noh, H. 2022
  • Adaptive Hybrid Model-Enabled Sensing System (HMSS) for Mobile Fine-Grained Air Pollution Estimation IEEE TRANSACTIONS ON MOBILE COMPUTING Chen, X., Xu, S., Liu, X., Xu, X., Noh, H., Zhang, L., Zhang, P. 2022; 21 (6): 1927-1944
  • Recursive Sparse Representation for Identifying Multiple Concurrent Occupants Using Floor Vibration Sensing PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT Fagert, J., Mirshekari, M., Zhang, P., Noh, H. 2022; 6 (1)

    View details for DOI 10.1145/3517229

    View details for Web of Science ID 000904877500010

  • Predicting peak stresses in microstructured materials using convolutional encoder-decoder learning MATHEMATICS AND MECHANICS OF SOLIDS Shrivastava, A., Liu, J., Dayal, K., Noh, H. 2022
  • Poster Abstract: SeatBeats Heart Rate Monitoring System using Structural Seat Vibrations Codling, J. R., Cohen, L. F., Kalivarapu, V., Noh, H., Zhang, P., IEEE COMP SOC IEEE COMPUTER SOC. 2022: 511-512
  • Re-Vibe: Vibration-based Indoor Person Re-Identification through Cross-Structure Optimal Transport Dong, Y., Zhu, J., Noh, H., ACM ASSOC COMPUTING MACHINERY. 2022: 348-352
  • Poster Abstract: Integration of Physics-Based Building Model and Sensor Data to Develop an Adaptive Digital Twin Miao, B. H., Dong, Y., Wu, Z. Y., Alemdar, B. N., Zhang, P., Kohler, M. D., Noh, H., ACM ASSOC COMPUTING MACHINERY. 2022: 282-283
  • A Neural-Based Bandit Approach to Mobile Crowdsourcing Lin, S., Yao, Y., Zhang, P., Noh, H., Joe-Wong, C., ACM ASSOC COMPUTING MACHINERY. 2022: 15-21
  • Editorial: Understanding Human-Infrastructure Interactions: Context-Aware Structures and Interfaces FRONTIERS IN BUILT ENVIRONMENT Moreu, F., Noh, H., Zhang, P., Mascarenas, D. 2021; 7
  • Obstruction-invariant occupant localization using footstep-induced structural vibrations MECHANICAL SYSTEMS AND SIGNAL PROCESSING Mirshekari, M., Fagert, J., Pan, S., Zhang, P., Noh, H. 2021; 153
  • PhyMDAN: Physics-informed knowledge transfer between buildings for seismic damage diagnosis through adversarial learning MECHANICAL SYSTEMS AND SIGNAL PROCESSING Xu, S., Noh, H. 2021; 151
  • Structure- and Sampling-Adaptive Gait Balance Symmetry Estimation Using Footstep-Induced Structural Floor Vibrations JOURNAL OF ENGINEERING MECHANICS Fagert, J., Mirshekari, M., Pan, S., Lowes, L., Iammarino, M., Zhang, P., Noh, H. 2021; 147 (2)
  • PIWIMS: Physics Informed Warehouse Inventory Monitory via Synthetic Data Generation Falcao, J., Baweja, P., Wang, Y., Sangpetch, A., Noh, H., Sangpetch, O., Zhang, P., ASSOC COMP MACHINERY ASSOC COMPUTING MACHINERY. 2021: 613-618
  • MassHog: Weight-Sensitive Occupant Monitoring for Pig Pens using Actuated Structural Vibrations Codling, J. R., Bonde, A., Dong, Y., Cao, S., Sangpetch, A., Sangpetch, O., Noh, H., Zhang, P., ASSOC COMP MACHINERY ASSOC COMPUTING MACHINERY. 2021: 600-605
  • An efficient Bayesian framework for updating PAGER loss estimates EARTHQUAKE SPECTRA Noh, H., Jaiswal, K. S., Engler, D., Wald, D. J. 2020; 36 (4): 1719–42
  • FAIM: Vision and Weight Sensing Fusion Framework for Autonomous Inventory Monitoring in Convenience Stores FRONTIERS IN BUILT ENVIRONMENT Falcao, J., Ruiz, C., Pan, S., Noh, H., Zhang, P. 2020; 6
  • Fine-Grained Activity of Daily Living (ADL) Recognition Through Heterogeneous Sensing Systems With Complementary Spatiotemporal Characteristics FRONTIERS IN BUILT ENVIRONMENT Pan, S., Berges, M., Rodakowski, J., Zhang, P., Noh, H. 2020; 6
  • PAS: Prediction-Based Actuation System for City-Scale Ridesharing Vehicular Mobile Crowdsensing IEEE INTERNET OF THINGS JOURNAL Chen, X., Xu, S., Han, J., Fu, H., Pi, X., Joe-Wong, C., Li, Y., Zhang, L., Noh, H., Zhang, P. 2020; 7 (5): 3719–34
  • O-MedAL: Online active deep learning for medical image analysis WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY Smailagic, A., Costa, P., Gaudio, A., Khandelwal, K., Mirshekari, M., Fagert, J., Walawalkar, D., Xu, S., Galdran, A., Zhang, P., Campilho, A., Noh, H. 2020; 10 (4)

    View details for DOI 10.1002/widm.1353

    View details for Web of Science ID 000509415300001

  • OAC: Overlapping Office Activity Classification through IoT-Sensed Structural Vibration Bonde, A., Pan, S., Mirshekari, M., Ruiz, C., Noh, H., Zhang, P., IEEE IEEE COMPUTER SOC. 2020: 216–22
  • Structural Property Guided Gait Parameter Estimation Using Footstep-Induced Floor Vibrations Fagert, J., Mirshekari, M., Pan, S., Zhang, P., Noh, H., Pakzad, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2020: 191–94
  • Enhancing the Data Learning With Physical Knowledge in Fine-Grained Air Pollution Inference IEEE ACCESS Ma, R., Liu, N., Xu, X., Wang, Y., Noh, H., Zhang, P., Zhang, L. 2020; 8: 88372–84
  • Demo Abstract: Active Structural Occupant Detector Codling, J. R., Mirshekari, M., Noh, H., Zhang, P., IEEE IEEE. 2020: 353–54
  • Poster Abstract: Using Deep Learning to Classify The Acceleration Measurement Devices Wu, Y., Ruiz, C., Pan, S., Noh, H., Hassan, M., Zhang, P., Hu, W., IEEE IEEE. 2020: 351–52
  • DAMAGE-SENSITIVE AND DOMAIN-INVARIANT FEATURE EXTRACTION FOR VEHICLE-VIBRATION-BASED BRIDGE HEALTH MONITORING Liu, J., Chen, B., Chen, S., Berges, M., Bielak, J., Noh, H., IEEE IEEE. 2020: 3007–11
  • IDIoT: Towards Ubiquitous Identification of IoT Devices through Visual and Inertial Orientation Matching During Human Activity Ruiz, C., Pan, S., Bannis, A., Chang, M., Noh, H., Zhang, P., IEEE IEEE COMPUTER SOC. 2020: 40–52
  • Structures as Sensors: Indirect Sensing for Inferring Users and Environments COMPUTER Zhang, P., Pan, S., Mirshekari, M., Fagert, J., Noh, H. 2019; 52 (10): 84–88
  • Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh SCIENTIFIC DATA Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H., Bielak, J., Garrett, J. H., Kovacevic, J., Berges, M. 2019; 6: 146

    Abstract

    We present DR-Train, the first long-term open-access dataset recording dynamic responses from in-service light rail vehicles. Specifically, the dataset contains measurements from multiple sensor channels mounted on two in-service light rail vehicles that run on a 42.2-km light rail network in the city of Pittsburgh, Pennsylvania. This dataset provides dynamic responses of in-service trains via vibration data collected by accelerometers, which enables a low-cost way of monitoring rail tracks more frequently. Such an approach will result in more reliable and economical ways to monitor rail infrastructure. The dataset also includes corresponding GPS positions of the trains, environmental conditions (including temperature, wind, weather, and precipitation), and track maintenance logs. The data, which is stored in a MAT-file format, can be conveniently loaded for various potential uses, such as validating anomaly detection and data fusion as well as investigating environmental influences on train responses.

    View details for DOI 10.1038/s41597-019-0148-9

    View details for Web of Science ID 000481667300002

    View details for PubMedID 31406119

    View details for PubMedCentralID PMC6690915

  • Empirical investigation of regression models for predicting system behavior in air handling units SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT Velibeyoglu, I., Noh, H., Pozzi, M. 2019; 25 (3): 247–60
  • A graphical approach to assess the detectability of multiple simultaneous faults in air handling units ENERGY AND BUILDINGS Velibeyoglu, I., Noh, H., Pozzi, M. 2019; 184: 275–88
  • Characterizing Structural Changes to Estimate Walking Gait Balance Fagert, J., Mirshekari, M., Pan, S., Zhang, P., Noh, H., Pakzad, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 333–35
  • Detecting Anomalies in Longitudinal Elevation of Track Geometry Using Train Dynamic Responses via a Variational Autoencoder Liu, J., Wei, Y., Berges, M., Bielak, J., Garrett, J. H., Noh, H., Lynch, J. P., Huang, H., Sohn, H., Wang, K. W. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2513711

    View details for Web of Science ID 000483016400039

  • A Damage Localization and Quantification Algorithm for Indirect Structural Health Monitoring of Bridges Using Multi-Task Learning Liu, J., Berges, M., Bielak, J., Garrett, J. H., Kovacevic, J., Noh, H., Bond, L. J., Holland, S., Laflamme, S. AMER INST PHYSICS. 2019

    View details for DOI 10.1063/1.5099821

    View details for Web of Science ID 000479309100117

  • Area Occupancy Counting Through Sparse Structural Vibration Sensing IEEE PERVASIVE COMPUTING Pan, S., Mirshekari, M., Fagert, J., Ruiz, C., Noh, H., Zhang, P. 2019; 18 (1): 28–37
  • Gait Health Monitoring Through Footstep-Induced Floor Vibrations Fagert, J., Mirshekari, M., Pan, S., Zhang, P., Noh, H., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 319–20
  • Vehicle Dispatching for Sensing Coverage Optimization in Mobile Crowdsensing Systems Xu, S., Chen, X., Pi, X., Joe-Wong, C., Zhang, P., Noh, H., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 311–12
  • Secure Pairing via Video and IMU Verification Ruiz, C., Pan, S., Noh, H., Zhang, P., Han, J., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 333–34
  • Deskbuddy: an Office Activity Detection System Bonde, A., Pan, S., Noh, H., Zhang, P., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 352–53
  • Incentivizing Large-scale Vehicular Crowdsensing System For Smart City Applications Xu, S., Chen, X., Pi, X., Joe-Wong, C., Zhang, P., Noh, H., Lynch, J. P., Huang, H., Sohn, H., Wang, K. W. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2514021

    View details for Web of Science ID 000483016400040

  • A Deep Autoencoder Model for Pollution Map Recovery with Mobile Sensing Networks Ma, R., Liu, N., Xu, X., Wang, Y., Noh, H., Zhang, P., Zhang, L., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 577–83
  • Device-free Multiple People Localization through Floor Vibration Shi, L., Mirshekari, M., Fagert, J., Chi, Y., Noh, H., Zhang, P., Pan, S., ACM ASSOC COMPUTING MACHINERY. 2019: 57–61
  • Device-free Sleep Stage Recognition through Bed Frame Vibration Sensing Hu, Z., Sezgin, E., Lin, S., Zhang, P., Noh, H., Pan, S., ACM ASSOC COMPUTING MACHINERY. 2019: 39–43
  • A Signal Quality Assessment Metrics for Vibration-based Human Sensing Data Acquisition Zhang, Y., Zhang, L., Noh, H., Zhang, P., Pan, S., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 29–33
  • WhereWear: Calibration-free Wearable Device Identification through Ambient Sensing Ruiz, C., Pan, S., Noh, H., Zhang, P., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 29–34
  • P-Loc: A Device-free Indoor Localization System Utilizing Building Power-line Network Zhou, T., Zhang, Y., Chen, X., Mosalam, K. M., Noh, H., Zhang, P., Zhang, L., ACM ASSOC COMPUTING MACHINERY. 2019: 611–15
  • Demo Abstract: Autonomous Inventory Monitoring through Multi-Modal Sensing (AIM3S) for Cashier-Less Stores Ruiz, C., Falcao, J., Pan, S., Noh, H., Zhang, P., Zhang, M. ASSOC COMPUTING MACHINERY. 2019: 395–96
  • Fine-Grained Recognition of Activities of Daily Living through Structural Vibration and Electrical Sensing Pan, S., Berges, M., Rodakowski, J., Zhang, P., Noh, H., Zhang, M. ASSOC COMPUTING MACHINERY. 2019: 149–58
  • AIM3S: Autonomous Inventory Monitoring through Multi-Modal Sensing for Cashier-Less Convenience Stores Ruiz, C., Falcao, J., Pan, S., Noh, H., Zhang, P., Zhang, M. ASSOC COMPUTING MACHINERY. 2019: 135–44
  • Smart Home Occupant Identification via Sensor Fusion Across On-Object Devices ACM TRANSACTIONS ON SENSOR NETWORKS Han, J., Pan, S., Sinha, M., Noh, H., Zhang, P., Tague, P. 2018; 14 (3-4)

    View details for DOI 10.1145/3218584

    View details for Web of Science ID 000457137600008

  • Conductive Thread-Based Textile Sensor for Continuous Perspiration Level Monitoring SENSORS Jia, J., Xu, C., Pan, S., Xia, S., Wei, P., Noh, H., Zhang, P., Jiang, X. 2018; 18 (11)

    Abstract

    Individual perspiration level indicates a person's physical status as well as their comfort level. Therefore, continuous perspiration level measurement enables people to monitor these conditions for applications including fitness assessment, athlete physical status monitoring, and patient/elderly care. Prior work on perspiration (sweat) sensing required the user either to be static or to wear the adhesive sensor directly on the skin, which limits users' mobility and comfort. In this paper, we present a novel conductive thread-based textile sensor that measures an individual's on-cloth sweat quantity. The sensor consists of three conductive threads. Each conductive thread is surrounded by a braided cotton cover. An additional braided cotton cover is placed outside the three conductive threads, holding them in a position that is stable for measurement. the sensor can be embedded at various locations on a person's clothing. When the person sweats, the cotton braids absorb the sweat and change the conductivity (resistance) between conductive threads. We used a voltage dividing circuit to measure this resistance as the sensor output (DC). We then conducted a sensor calibration to map this measured voltage to the quantity of electrolyte solution (with the same density as sweat) applied to the sensor. We used this sensor to measure individuals' perspiration quantity and infer their perceived perspiration levels. The system is able to limit the average prediction error to 0.4 levels when compared to five pre-defined perceived perspiration levels.

    View details for DOI 10.3390/s18113775

    View details for Web of Science ID 000451598900185

    View details for PubMedID 30400608

    View details for PubMedCentralID PMC6263898

  • Robust Building Energy Load Forecasting Using Physically-Based Kernel Models ENERGIES Prakash, A., Xu, S., Rajagopal, R., Noh, H. 2018; 11 (4)

    View details for DOI 10.3390/en11040862

    View details for Web of Science ID 000434703400173

  • MyoVibe: Enabling Inertial Sensor-Based Muscle Activation Detection In High-Mobility Exercise Environments ACM TRANSACTIONS ON SENSOR NETWORKS Mokaya, F., Noh, H., Lucas, R., Zhang, P. 2018; 14 (1)

    View details for DOI 10.1145/3149127

    View details for Web of Science ID 000433515800006

  • Occupant-Induced Office Floor Vibration Dataset for Activity Level Monitoring Zhang, Y., Pan, S., Fagert, J., Mirshekari, M., Noh, H., Zhang, P., Zhang, L., ACM ASSOC COMPUTING MACHINERY. 2018: 5–6
  • Do You Peel What I Hear? Enabling Autonomous IoT Device Pairing using Different Sensor Types Han, J., Chung, A., Sinha, M., Harishankar, M., Pan, S., Noh, H., Zhang, P., Tague, P., IEEE IEEE. 2018: 836–52
  • Demo Abstract: PosePair: Pairing loT Devices Through Visual Human Pose Analysis Ruiz, C., Pan, S., Sadde, A., Noh, H., Zhang, P., IEEE IEEE. 2018: 144–45
  • VVRRM: Vehicular Vibration-based Heart RR-Interval Monitoring System Bonde, A., Pan, S., Jia, Z., Zhang, Y., Noh, H., Zhang, P., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 37–42
  • UniverSense: IoT Device Pairing through Heterogeneous Sensing Signals Pan, S., Ruiz, C., Han, J., Bannis, A., Tague, P., Noh, H., Zhang, P., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 55–60
  • MedAL: Accurate and Robust Deep Active Learning for Medical Image Analysis Smailagic, A., Costa, P., Noh, H., Walawalkar, D., Khandelwal, K., Galdran, A., Mirshekari, M., Fagert, J., Xu, S., Zhang, P., Campilho, A., Wani, M. A., Kantardzic, M., Sayedmouchaweh, M., Gama, J., Lughofer, E. IEEE. 2018: 481–88
  • Guiding the Data Learning Process with Physical Model in Air Pollution Inference Ma, R., Xu, X., Wang, Y., Noh, H., Zhang, P., Zhang, L., Abe, N., Liu, H., Pu, C., Hu, Ahmed, N., Qiao, M., Song, Y., Kossmann, D., Liu, B., Lee, K., Tang, J., He, J., Saltz, J. IEEE. 2018: 4475–83
  • Human Gait Monitoring Using Footstep-Induced Floor Vibrations Across Different Structures Mirshekari, M., Fagert, J., Bonde, A., Zhang, P., Noh, H., ACM ASSOC COMPUTING MACHINERY. 2018: 1382–91
  • Occupant Activity Level Estimation Using Floor Vibration Zhang, Y., Pan, S., Fagert, J., Mirshekari, M., Noh, H., Zhang, P., Zhang, L., ACM ASSOC COMPUTING MACHINERY. 2018: 1355–63
  • Moisture Based Perspiration Level Estimation Jia, J., Xu, C., Pan, S., Xia, S., Wei, P., Noh, H., Zhang, P., Jiang, X., ACM ASSOC COMPUTING MACHINERY. 2018: 1301–8
  • PGA: Physics Guided and Adaptive Approach for Mobile Fine-Grained Air Pollution Estimation Chen, X., Xu, X., Liu, X., Pan, S., He, J., Noh, H., Zhang, L., Zhang, P., ACM ASSOC COMPUTING MACHINERY. 2018: 1321–30
  • Poster Abstract: Generative Model Based Fine-Grained Air Pollution Inference for Mobile Sensing Systems Ma, R., Xu, X., Noh, H., Zhang, P., Zhang, L., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 426–27
  • Demo Abstract: Vibration-Based Occupant Activity Level Monitoring System Zhang, Y., Pan, S., Fagert, J., Mirshekari, M., Noh, H., Zhang, P., Zhang, L., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 349–50
  • Poster Abstract: Robust Detection of Motor-Produced Audio Signals Bannis, A., Noh, H., Zhang, P., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 412–13
  • Structural Vibration Sensing to Evaluate Animal Activity on a Pig Farm Bonde, A., Pan, S., Sangpetch, O., Sangpetch, A., Woramontri, W., Noh, H., Zhang, P., ACM ASSOC COMPUTING MACHINERY. 2018: 25–26
  • Seat Vibration for Heart Monitoring in a Moving Automobile Bonde, A., Mirshekari, M., Fagert, J., Pan, S., Noh, H., Zhang, P., ACM ASSOC COMPUTING MACHINERY. 2018: 7–8
  • A data fusion approach for track monitoring from multiple in-service trains MECHANICAL SYSTEMS AND SIGNAL PROCESSING Lederman, G., Chen, S., Garrett, J. H., Kovacevic, J., Noh, H., Bielak, J. 2017; 95: 363–79
  • Bayesian Updating of Earthquake Vulnerability Functions with Application to Mortality Rates EARTHQUAKE SPECTRA Noh, H., Kiremidjian, A., Ceferino, L., So, E. 2017; 33 (3): 1173–89
  • Updating Structural Parameters with Spatially Incomplete Measurements Using Subspace System Identification JOURNAL OF ENGINEERING MECHANICS Park, S., Noh, H. 2017; 143 (7)
  • Track-monitoring from the dynamic response of an operational train MECHANICAL SYSTEMS AND SIGNAL PROCESSING Lederman, G., Chen, S., Garrett, J., Kovacevic, J., Noh, H., Bielak, J. 2017; 87: 1–16
  • SenseTribute: Smart Home Occupant Identification via Fusion Across On-Object Sensing Devices Han, J., Pan, S., Sinha, M., Noh, H., Zhang, P., Tague, P., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2017
  • An energy-based sparse representation of ultrasonic guided-waves for online damage detection of pipelines under varying environmental and operational conditions MECHANICAL SYSTEMS AND SIGNAL PROCESSING Eybpoosh, M., Berges, M., Noh, H. 2017; 82: 260–78
  • SurfaceVibe: Vibration-Based Tap & Swipe Tracking on Ubiquitous Surfaces Pan, S., Ramirez, C., Mirshekari, M., Fagert, J., Chung, A., Hu, C., Shen, J., Noh, H., Zhang, P., IEEE IEEE. 2017: 197–208
  • Characterizing Left-Right Gait Balance Using Footstep-Induced Structural Vibrations Fagert, J., Mirshekari, M., Pan, S., Zhang, P., Noh, H., Lynch, J. P. SPIE-INT SOC OPTICAL ENGINEERING. 2017

    View details for DOI 10.1117/12.2260376

    View details for Web of Science ID 000410169000038

  • Poster Abstract: Interdependent Component Framework for Simulating Indoor Internet-of-Things Systems (Intercom) Bannis, A., Noh, H., Zhang, P., IEEE IEEE. 2017: 315–16
  • Calibration-Free Footstep Frequency Estimation Using Structural Vibration Mirshekari, M., Zhang, P., Noh, H., Caicedo, J., Pakzad, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2017: 287–89
  • Individualized Calibration of Industrial-Grade Gas Sensors in Air Quality Sensing System Liu, X., Xu, X., Chen, X., Mai, E., Noh, H., Zhang, P., Zhang, L., ACM ASSOC COMPUTING MACHINERY. 2017
  • Delay Effect in Mobile Sensing System for Urban Air Pollution Monitoring Liu, X., Chen, X., Xu, X., Mai, E., Noh, H., Zhang, P., Zhang, L., ACM ASSOC COMPUTING MACHINERY. 2017
  • Automated synchronization of driving data using vibration and steering events PATTERN RECOGNITION LETTERS Fridman, L., Brown, D. E., Angell, W., Abdic, I., Reimer, B., Noh, H. 2016; 75: 9–15
  • Sparse representation of ultrasonic guided-waves for robust damage detection in pipelines under varying environmental and operational conditions STRUCTURAL CONTROL & HEALTH MONITORING Eybpoosh, M., Berges, M., Noh, H. 2016; 23 (2): 369–91

    View details for DOI 10.1002/stc.1776

    View details for Web of Science ID 000368033700012

  • Burnout: A Wearable System for Unobtrusive Skeletal Muscle Fatigue Estimation Mokaya, F., Lucas, R., Noh, H., Zhang, P., IEEE IEEE. 2016
  • Occupant Traffic Estimation through Structural Vibration Sensing Pan, S., Mirshekari, M., Zhang, P., Noh, H., Lynch, J. P. SPIE-INT SOC OPTICAL ENGINEERING. 2016

    View details for DOI 10.1117/12.2222024

    View details for Web of Science ID 000382319400005

  • Characterizing Wave Propagation to Improve Indoor Step-Level Person Localization using Floor Vibration Mirshekari, M., Pan, S., Zhang, P., Noh, H., Lynch, J. P. SPIE-INT SOC OPTICAL ENGINEERING. 2016

    View details for DOI 10.1117/12.2222136

    View details for Web of Science ID 000382319400004

  • Robust Occupant Detection Through Step-Induced Floor Vibration by Incorporating Structural Characteristics Lam, M., Mirshekari, M., Pan, S., Zhang, P., Noh, H., Allen, M., Mayes, R. L., Rixen, D. SPRINGER. 2016: 357–67
  • Development of empirical and analytical fragility functions using kernel smoothing methods EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS Noh, H. Y., Lallemant, D., Kiremidjian, A. S. 2015; 44 (8): 1163-1180

    View details for DOI 10.1002/eqe.2505

    View details for Web of Science ID 000354730500001

  • STIM: Smart Train Infrastructure Monitoring Lederman, G., Bielak, J., Noh, H., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2015: 330–31
  • Mitigating the effects of variable speed on drive-by infrastructure monitoring Thorsen, A., Lederman, G., Oshima, Y., Bielak, J., Noh, H., Lynch, J. P., Wang, K. W., Sohn, H. SPIE-INT SOC OPTICAL ENGINEERING. 2015

    View details for DOI 10.1117/12.2084435

    View details for Web of Science ID 000355726100007

  • Effects of damage location and size on sparse representation of guided-waves for damage diagnosis of pipelines under varying temperature Eybpoosh, M., Berges, M., Noh, H., Shull, P. J. SPIE-INT SOC OPTICAL ENGINEERING. 2015

    View details for DOI 10.1117/12.2084439

    View details for Web of Science ID 000355725000052

  • Nonlinear feature extraction methods for removing temperature effects in multi-mode guided-waves in pipes Eybpoosh, M., Berges, M., Noh, H., Shull, P. J. SPIE-INT SOC OPTICAL ENGINEERING. 2015

    View details for DOI 10.1117/12.2084436

    View details for Web of Science ID 000355725000051

  • Temperature variation effects on sparse representation of guided-waves for damage diagnosis in pipelines Eybpoosh, M., Berges, M., Noh, H., Shull, P. J. SPIE-INT SOC OPTICAL ENGINEERING. 2015

    View details for DOI 10.1117/12.2084434

    View details for Web of Science ID 000355725000045

  • Rail-infrastructure Monitoring through the Dynamic Response of a Passing Train Lederman, G., Noh, H., Bielak, J., Chang, F. K., Kopsaftopoulos, F. DESTECH PUBLICATIONS, INC. 2015: 1451–58
  • Indoor Person Identification through Footstep Induced Structural Vibration Pan, S., Wang, N., Qian, Y., Velibeyoglu, I., Noh, H., Zhang, P., ACM ASSOC COMPUTING MACHINERY. 2015: 81–86
  • Step-Level Person Localization Through Sparse Sensing Of Structural Vibration Mirshekari, M., Pan, S., Bannis, A., Lam, Y., Zhang, P., Noh, H., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2015: 376–77
  • MyoVibe: Vibration Based Wearable Muscle Activation Detection In High Mobility Exercises Mokaya, F., Lucas, R., Noh, H., Zhang, P., ACM ASSOC COMPUTING MACHINERY. 2015: 27–38
  • Structural Sensing System with Networked Dynamic Sensing Configuration Pan, S., Mirshekari, M., Noh, H., Zhang, P., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2015: 344–45
  • BOES: Building Occupancy Estimation System Using Sparse Ambient Vibration Monitoring Pan, S., Bonde, A., Jing, J., Zhang, L., Zhang, P., Noh, H., Lynch, J. P., Wang, K. W., Sohn, H. SPIE-INT SOC OPTICAL ENGINEERING. 2014

    View details for DOI 10.1117/12.2046510

    View details for Web of Science ID 000344110800045

  • Toward characterizing the effects of environmental and operational conditions on diffuse-field ultrasonic guided-waves in pipes Eybpoosh, M., Berges, M., Noh, H., Lynch, J. P., Wang, K. W., Sohn, H. SPIE-INT SOC OPTICAL ENGINEERING. 2014

    View details for DOI 10.1117/12.2046347

    View details for Web of Science ID 000344110800044

  • Data-Driven Forecasting Algorithms for Building Energy Consumption Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems Noh, H. Y., Rajagopal, R. SPIE-INT SOC OPTICAL ENGINEERING. 2013

    View details for DOI 10.1117/12.2009894

    View details for Web of Science ID 000323283300022

  • Development of fragility functions as a damage classification/prediction method for steel moment-resisting frames using a wavelet-based damage sensitive feature EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS Noh, H. Y., Lignos, D. G., Nair, K. K., Kiremidjian, A. S. 2012; 41 (4): 681-696

    View details for DOI 10.1002/eqe.1151

    View details for Web of Science ID 000301430900006

  • Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems Noh, H. Y., Rajagopal, R., Kiremidjian, A. S. SPIE-INT SOC OPTICAL ENGINEERING. 2012

    View details for DOI 10.1117/12.915409

    View details for Web of Science ID 000304192100005

  • Use of Wavelet-Based Damage-Sensitive Features for Structural Damage Diagnosis Using Strong Motion Data JOURNAL OF STRUCTURAL ENGINEERING-ASCE Noh, H. Y., Nair, K. K., Lignos, D. G., Kiremidjian, A. S. 2011; 137 (10): 1215-1228
  • Application of a sparse representation method using K-SVD to data compression of experimental ambient vibration data for SHM Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011 Noh, H. Y., Kiremidjian, A. S. SPIE-INT SOC OPTICAL ENGINEERING. 2011

    View details for DOI 10.1117/12.881887

    View details for Web of Science ID 000294447800132

  • Application of time series based damage detection algorithms to the benchmark experiment at the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan SMART STRUCTURES AND SYSTEMS Noh, H. Y., Nair, K. K., Kirernidjian, A. S., Loh, C. 2009; 5 (1): 95-117
  • Application of a time series based damage detection algorithm to the Taiwanese benchmark experiment 10th International Conference on Application of Statistics and Probability in Civil Engineering Noh, H., Nair, K. K., Kiremidjian, A. S., Loh, C. TAYLOR & FRANCIS LTD. 2007: 551–552