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1. i-lete: An IoT-based physical stress monitoring framework for athletes

   https://doi.org/10.1109/ISQED54688.2022.9806268  

   Ghosh, Prosenjit Kumar; Sundaravadivel, Prabha (April 2022, 23rd International Symposium on Quality Electronic Design (ISQED))

   Wearable devices are ubiquitous and Internet of Things (IoT) devices have made it possible by connecting real-time devices to virtual cloud. There are also a tremendous number of IoT-enabled consumer products for various healthcare applications. Mostly, IoT devices are used for health monitoring systems, though other business and service communities are customizing the IoT technology for greater opportunity and long-term benefit. Wearable health devices have been used for better health monitoring and exchanging more data with the physician to get the guidance of treatment or earlier diagnostic. Health monitoring in athletes is one of the multifaceted applications of wearable IoT devices whereas these devices collect and store data on their performance and progression. This technology can protect athletes by detecting any adverse health problem that occurs during the training period or at the time of the game. In this paper, we investigate the real-time monitoring of physiological parameters of the athlete during game time and performance analysis from the stored data. Continuous health monitoring during game time and off-days will reduce sports-related risks,stress and injuries of an athlete even sometimes it can save them from life-risk fatal accidents. This research integrates an IoT-based framework to develop a stress index for athletes that can be used as an indicator for monitoring athlete’s health. The proposed framework helps in monitoring the variability of the sensor information for the long-term analysis. 

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2. Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

   https://doi.org/10.3390/machines9010013  

   Yang, Yuhang; Dong, Zhiqiao; Meng, Yuquan; Shao, Chenhui (January 2021, Machines)

   null (Ed.)

   High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas. 

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3. Experimental and CFD Analysis of a Rack Manifold for High Power Density Liquid-Cooled Rack

   https://doi.org/10.1115/IPACK2023-114351  

   Shahi, Pardeep; Heydari, Ali; Bansode, Pratik; Siddarth, Ashwin; Chowdhury, Uschas; Miyamura, Harold; Modi, Himanshu; Agonafer, Dereje; Tradat, Mohammad; Rodriguez, Jeremy (October 2023, American Society of Mechanical Engineers)

   Abstract Direct Liquid Cooling (DLC) has emerged as a promising technology for thermal management of high-performance computing servers, enabling efficient heat dissipation and reliable operation. Thermal performance is governed by several factors, including the coolant physical properties and flow parameters such as coolant inlet temperature and flow rate. The design and development of the coolant distribution manifold to the Information Technology Equipment (ITE) can significantly impact the overall performance of the computing system. This paper aims to investigate the hydraulic characterization and design validation of a rack-level coolant distribution manifold or rack manifold. To achieve this goal, a custom-built high power-density liquid-cooled ITE rack was assembled, and various cooling loops were plugged into the rack manifold to validate its thermal performance. The rack manifold is responsible for distributing the coolant to each of these cooling loops, which is pumped by a CDU (Coolant Distribution Unit). In this study, pressure drop characteristics of the rack manifold were obtained for flow rates that effectively dissipate the heat loads from the ITE. The pressure drop is a critical parameter in the design of the coolant distribution manifold since it influences the flow rate and ultimately the thermal performance of the system. By measuring the pressure drop at various flow rates, the researchers can accurately determine the optimum flow rate for efficient heat dissipation. Furthermore, 1D flow network and CFD models of the rack-level coolant loop, including the rack manifold, were developed, and validated against experimental test data. The validated models provide a useful tool for the design of facility-level modeling of a liquid-cooled data center. The CFD models enable the researchers to simulate the fluid flow and heat transfer within the cooling system accurately. These models can help to design the coolant distribution manifold at facility level. The results of this study demonstrate the importance of the design and development of the coolant distribution manifold in the thermal performance of a liquid-cooled data center. The study also highlights the usefulness of 1D flow network and CFD models for designing and validating liquid-cooled data center cooling systems. In conclusion, the hydraulic characterization and design validation of a rack-level coolant distribution manifold is critical in achieving efficient thermal management of high-performance computing servers. This study presents a comprehensive approach for hydraulic characterization of the coolant distribution manifold, which can significantly impact the overall thermal performance and reliability of the system. The validated models also provide a useful tool for the design of facility-level modeling of a liquid-cooled data center. 

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4. Real-Time Distributed Cloud Computing Architecture for Structural Health Monitoring

   https://doi.org/10.1061/9780784482230.005  

   Searls, Owen; Zhao, Zhiyong; Couch, Alva L.; Sanayei, Masoud (April 2019, Structures Congress Conference 2019)

   Real-time fatigue health monitoring has the potential to serve as a valuable complement to structural health monitoring (SHM) for bridge inspections. SHM is an objective supplement to visual bridge inspections with a minimum interval between bridge inspections at 24 months. SHM can provide quantitative and objective data on a bridge’s fatigue condition for fracture-critical components, of which fatigue is a criterion. Current methods of continuous structural health monitoring for condition assessment are performed by collecting measured bridge response subjected to operational traffic from an array of sensors installed on fracture-critical members of a bridge. The measured responses are used to determine the remaining fatigue life of the bridge—the minimum time before repair. The large amount of data involved in this process complicates the design of a system that will automate the data collection process at a bridge, analyze that data, and display information about bridge health to researchers and engineers. Variations in bridge designs and condition assessment algorithms also necessitate that such a system be modular and adaptable to allow for expansion to additional structures. A new system has been developed that separates bridge SHM from the data storage and communication system. This architecture creates a reliable interface for sending data from one or more bridges to a cloud server where it can be processed using modular algorithms that can be adapted for different use cases. The cloud-based web service and data repository makes bridge structural health data available to researchers at all steps of the process. This system provides significant advantages over previous platforms for structural health monitoring and condition assessment, most notably in the areas of modularity, extensibility, and reliability. 

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5. Novel Approach of Determining and Predicting SiC MOSFET’s On Resistance From Device Case Temperature Using Machine Learning

   https://doi.org/10.1109/APEC48143.2025.10977206  

   Bradford, Paul; Deppe, Conner; Wang, Hongjie (March 2025, IEEE)

   Silicon-carbide (SiC) MOSFET devices are increasing in popularity in high-power converter applications. Device on-resistance (Rdson) is an important indicator for SiC MOSFET health status. Increments in Rdson over device lifetime indicate imminent device failure and result in decreased system efficiency. Direct and accurate measurement of SiC MOSFET device Rdson in high-power applications is difficult. Another approach is to estimate/predict the device Rdson from other, more easily measurable quantities, however, little work has been done on this approach in the literature. This leaves a significant technical gap in measuring/predicting device Rdson and slows down the device health status monitoring and power converter reliability research. To address the technical gaps, this work proposes a novel approach to predicting device Rdson from thermal cycle count and instantaneous temperature using machine learning regression models. The actual hardware data collected from accelerated lifetime tests of high-power SiC MOSFETs are used to train, test, and validate the proposed machine-learning regression models. The developed models, coupled with cycle counting algorithms, and device case thermal measurements, provide accurate live estimates of Rdson and can be used to predict changes in Rdson over expected mission profiles during power converter design. 

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