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Proceedings of ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

Proceedings of ASME Turbo Expo 2023: Turbomachinery Technica...

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ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

ISBN: (纸本)9780791886953

The proceedings contain 912 papers. The topics discussed include: applicability of numerical simulation to the classification of fluid dynamic loss in aeroengine transmission gears;the development and use of a natural gas/oxygen burner rig for environmental barrier coating and ceramic matrix composite technology maturation;development of innovative low NOx hydrogen-fueled burner for aeronautic applications;analysis of different emission metrics for power generation machinery in flexible co-generation energy systems;development of a turbocharger speed sensor using fiber optic coupled optical method;advanced polytropic efficiency and H-S diagram application;compressor design for compact, highspeed electric motors;laser welding in steam turbine rotor recondition;structural characterization of a rapid-prototyped inlet particle separator model for aerodynamic lab testing;supercritical carbon dioxide shock behavior near the critical point;a characteristic-based 1D axial compressor model for stall and surge simulations;strategies for high-accuracy measurements of stage efficiency for a cooled turbine;and mineral composition effects on dust deposition at realistic engine conditions.

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Proceedings of ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

Proceedings of ASME Turbo Expo 2023: Turbomachinery Technica...

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ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

ISBN: (纸本)9780791887103

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Proceedings of ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

Proceedings of ASME Turbo Expo 2023: Turbomachinery Technica...

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ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

ISBN: (纸本)9780791886991

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BioGAP: A 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal Processing

BioGAP: A 10-Core FP-capable Ultra-Low Power IoT Processor, ...

引用

2023 IEEE International conference on Omni-Layer Intelligent systems, COINS 2023

作者： Frey, Sebastian Guermandi, Marco Benatti, Simone Kartsch, Victor Cossettini, Andrea Benini, Luca ETH Zurich Integrated Systems Laboratory Zurich Switzerland DEI University of Bologna Bologna Italy DIEF University of Modena and Reggio Emilia Reggio Emilia Italy Greenwaves Technologies Grenoble France

ISBN: (纸本)9798350346473

Wearable biosignal processing applications are driving significant progress toward miniaturized, energy-efficient Internet-of- Things solutions for both clinical and consumer applications. However, scaling toward high-density multi-channel front-ends is only feasible by performing data processing and machine Learning (ML) near-sensor through energy-efficient edge processing. To tackle these challenges, we introduce BioGAP, a novel, compact, modular, and lightweight (6g) medical-grade biosignal acquisition and processing platform powered by GAP9, a ten-core ultra-low-power SoC designed for efficient multi-precision (from FP to aggressively quantized integer) processing, as required for advanced ML and DSP. BioGAP's form factor is 16x21x14 mm3 and comprises two stacked PCBs: a baseboard integrating the GAP9 SoC, a wireless Bluetooth Low Energy (BLE) capable SoC, a power management circuit, and an accelerometer;and a shield including an analog front-end (AFE) for ExG acquisition. Finally, the system also includes a flexibly placeable photoplethysmogram (PPG) PCB with a size of 9x7x3 mm3 and a rechargeable battery (\phi 12x5 mm2), We demonstrate BioGAP on a Steady State Visually Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) application. We achieve 3.6 \mu J/sample in streaming and 2.2 \mu J/sample in onboard processing mode, thanks to an efficiency on the FFT computation task of 16.7 Mflops/s/mW with wireless bandwidth reduction of 97%, within a power budget of just 18.2 mW allowing for an operation time of 15 h. © 2023 IEEE.

关键词： System-on-chip

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Proceedings of ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

Proceedings of ASME Turbo Expo 2023: Turbomachinery Technica...

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ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

ISBN: (纸本)9780791887035

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Proceedings of ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

Proceedings of ASME Turbo Expo 2023: Turbomachinery Technica...

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ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

ISBN: (纸本)9780791886977

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Design and Simulation of Surface Acoustic Wave sensor in COMSOL Multiphysics for Detection of Acetone

Design and Simulation of Surface Acoustic Wave Sensor in COM...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Praneeth Borra Prasanti Korapati Vamsi Sravanam Shanmuk Bala Dikishitha Sree Tikkisetty Department of Electronics and Instrumentation Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The increasing demand for efficient and reliable gas sensors has propelled the exploration of novel materials and advanced simulation techniques. This study presents the design and development of a gas sensor that detects acetone using molybdenum disulfide $(\mathbf{MoS}_sensor)$ as the sensing material. Acetone is a major volatile organic compound that has applications in a variety of industries, including healthcare and environmental monitoring. Acetone gas is a respiratory symptom that indicates diabetes. The high sensitivity, stability and cost of $\mathbf{MoS}_{\mathbfsensor}$ make it a good sensor material. The sensor design involves the integration of $\mathbf{MoS}_{\mathbfsensor}$ thin films onto a substrate, with interdigitated electrodes facilitating the detection mechanism. COMSOL Multiphysics, a versatile simulation software, is employed to model the sensor's performance under different operating conditions and geometrical configurations. The simulation framework incorporates electrochemical processes, mass transport phenomena, and electrical properties to accurately represent the sensor's *** was found that the resistance of the sensor layer was reduced in the presence of acetone gas. The simulation was carried out using COMSOL Multiphysics.

关键词： Volatile organic compounds Transducers Surface resistance Surface acoustic waves Titanium Software Molybdenum Gas detectors Substrates Surface treatment

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Proceedings of ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

Proceedings of ASME Turbo Expo 2023: Turbomachinery Technica...

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ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

ISBN: (纸本)9780791887042

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Non-blocking Asynchronous Training for Reinforcement Learning in Real-World Environments

Non-blocking Asynchronous Training for Reinforcement Learnin...

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IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Bohm, Peter Pounds, Pauline Chapman, Archie C. Univ Queensland ITEE Brisbane Qld Australia Univ Queensland Brisbane Qld Australia

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Deep Reinforcement Learning (DRL) faces challenges bridging the sim-to-real gap to enable real-world applications. In contrast to the simulated environments used in conventional DRL training, real-world systems are non-linear and evolve in an asynchronous fashion;sensors and actuators have limited precision;communication channels are noisy;and many components introduce variable delays. While these issues are known to many researchers, published methods for systematically tackling the problem of DRL training under these conditions without using simulation are sparse in the field. To this end, this paper proposes a non-blocking and asynchronous DRL training architecture for non-linear, real-time dynamical systems tailored to handling variable delays. Compared to conventional DRL training, we: (i) decouple the RL loop into separate processes run independently at their own frequencies, (ii) further decouple collection of transition tuples (s(t);at(t);s(t+1)) via asynchronous and independent streaming of both actions and observations, and (iii) mitigate the effects of delays and increase sample efficiency by providing delay-length measurements to the training loop and regular retraining of the DRL network. This allows the action step time to be tuned to find an optimal control frequency for a given system, and handles streamed observations that arrive with random delays and independently of action timing. We demonstrate the efficacy of this architecture with a physical implementations of a commodity-grade swing-up pendulum and a quadrupedal robot. Our architecture achieves the best results balancing the pendulum for almost entire length of the episode, compared to conventional blocking approaches which fail to learn effective policies. Our results show that these techniques scale to more complex tasks such as quadrupedal locomotion.

关键词： Training Time-frequency analysis Reinforcement learning sensor systems Delays Frequency measurement sensors

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Proceedings of ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

Proceedings of ASME Turbo Expo 2023: Turbomachinery Technica...

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ASME Turbo Expo 2023: Turbomachinery Technical conference and Exposition, GT 2023

ISBN: (纸本)9780791886946

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