A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy

• URL: http://arxiv.org/abs/2404.10401v2
• Date: Fri, 17 May 2024 11:38:56 GMT
• Title: A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy
• Authors: Dayin Chen, Xiaodan Shi, Haoran Zhang, Xuan Song, Dongxiao Zhang, Yuntian Chen, Jinyue Yan,
• Abstract summary: Enhancing the energy efficiency of buildings significantly relies on monitoring indoor ambient temperature. Existing phone-based ambient temperature estimation methods face challenges such as insufficient privacy protection, difficulty in adapting models to various phones, and hurdles in obtaining enough labeled training data. We propose a distributed phone-based ambient temperature estimation system which enables collaboration among multiple phones to accurately measure the ambient temperature.
• Score: 15.68699858395261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enhancing the energy efficiency of buildings significantly relies on monitoring indoor ambient temperature. The potential limitations of conventional temperature measurement techniques, together with the omnipresence of smartphones, have redirected researchers'attention towards the exploration of phone-based ambient temperature estimation methods. However, existing phone-based methods face challenges such as insufficient privacy protection, difficulty in adapting models to various phones, and hurdles in obtaining enough labeled training data. In this study, we propose a distributed phone-based ambient temperature estimation system which enables collaboration among multiple phones to accurately measure the ambient temperature in different areas of an indoor space. This system also provides an efficient, cost-effective approach with a few-shot meta-learning module and an automated label generation module. It shows that with just 5 new training data points, the temperature estimation model can adapt to a new phone and reach a good performance. Moreover, the system uses crowdsourcing to generate accurate labels for all newly collected training data, significantly reducing costs. Additionally, we highlight the potential of incorporating federated learning into our system to enhance privacy protection. We believe this study can advance the practical application of phone-based ambient temperature measurement, facilitating energy-saving efforts in buildings.

Related papers

• EnergyPlus Room Simulator [0.34263545581620375]
  We present the tool EnergyPlus Room Simulator, which enables the simulation of indoor climate in a specific room of a building. It allows to alter room models and simulate various factors such as temperature, humidity, and CO2 concentration. The tool is intended to support scientific, building-related tasks such as occupancy detection on a room level by facilitating fast access to simulation data.
  arXiv  Detail & Related papers  (2024-10-25T07:57:23Z)
• Hybrid Transformer-RNN Architecture for Household Occupancy Detection Using Low-Resolution Smart Meter Data [8.486902848941872]
  Digitalization of the energy system provides smart meter data that can be used for occupancy detection in a non-intrusive manner. Deep learning techniques make it possible to infer occupancy from low-resolution smart meter data. Our work is motivated to develop a privacy-aware and effective model for residential occupancy detection.
  arXiv  Detail & Related papers  (2023-08-27T14:13:29Z)
• Autonomous Payload Thermal Control [55.2480439325792]
  In small satellites there is less room for heat control equipment, scientific instruments, and electronic components. An autonomous thermal control tool that uses deep reinforcement learning is proposed for learning the thermal control policy onboard. The proposed framework is able to learn to control the payload processing power to maintain the temperature under operational ranges.
  arXiv  Detail & Related papers  (2023-07-28T09:40:19Z)
• Development, Optimization, and Deployment of Thermal Forward Vision Systems for Advance Vehicular Applications on Edge Devices [0.3058685580689604]
  We have proposed a thermal tiny-YOLO multi-class object detection (TTYMOD) system as a smart forward sensing system using an end-to-end YOLO deep learning framework. The system is trained on large-scale thermal public as well as newly gathered novel open-sourced dataset comprising of more than 35,000 distinct thermal frames. The efficacy of a thermally tuned nano network is quantified using various qualitative metrics which include mean precision, frames per second rate, and average inference time.
  arXiv  Detail & Related papers  (2023-01-18T15:45:33Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• Hybrid full-field thermal characterization of additive manufacturing processes using physics-informed neural networks with data [5.653328302363391]
  We develop a hybrid physics-based data-driven thermal modeling approach of AM processes using physics-informed neural networks. Partially observed temperature data measured from an infrared camera is combined with the physics laws to predict full-field temperature history. Results show that the hybrid thermal model can effectively identify unknown parameters and capture the full-field temperature accurately.
  arXiv  Detail & Related papers  (2022-06-15T18:27:10Z)
• Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning [64.92447072894055]
  Infrared (IR) cameras are robust under adverse illumination and lighting conditions. We propose an algorithm meta-learning framework to improve existing UDA methods. We produce a state-of-the-art thermal detector for the KAIST and DSIAC datasets.
  arXiv  Detail & Related papers  (2021-10-07T02:28:18Z)
• Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS) [0.5156484100374058]
  Object detection in thermal infrared spectrum provides more reliable data source in low-lighting conditions and different weather conditions. This paper is about exploring and adapting state-of-the-art object and vision framework on thermal vision with seven distinct classes for advanced driver-assistance systems (ADAS) The trained network variants on public datasets are validated on test data with three different test approaches. The efficacy of trained networks is tested on locally gathered novel test-data captured with an uncooled LWIR prototype thermal camera.
  arXiv  Detail & Related papers  (2021-09-20T21:38:55Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices [78.38046945665538]
  federated learning (FL) over massive mobile edge devices opens new horizons for numerous intelligent mobile applications. FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training. We develop a convergence-guaranteed FL algorithm enabling flexible communication compression.
  arXiv  Detail & Related papers  (2020-12-22T02:54:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.