Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning

• URL: http://arxiv.org/abs/2402.09442v3
• Date: Tue, 12 Mar 2024 11:14:15 GMT
• Title: Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning
• Authors: Weixiang Wan, Wenjian Sun, Qiang Zeng, Linying Pan, Jingyu Xu, Bo Liu
• Abstract summary: Wang Zhong lin and his team invented the triboelectric nanogenerator (TENG), which uses Maxwell displacement current as a driving force to directly convert mechanical stimuli into electrical signals. This paper is based on the intelligent sound monitoring and recognition system of TENG, which has good sound recognition capability.
• Score: 6.117706409613191
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of Internet of Things, how to develop a smart sensor system with sustainable power supply, easy deployment and flexible use has become a difficult problem to be solved. The traditional power supply has problems such as frequent replacement or charging when in use, which limits the development of wearable devices. The contact-to-separate friction nanogenerator (TENG) was prepared by using polychotomy thy lene (PTFE) and aluminum (AI) foils. Human motion energy was collected by human body arrangement, and human motion posture was monitored according to the changes of output electrical signals. In 2012, Academician Wang Zhong lin and his team invented the triboelectric nanogenerator (TENG), which uses Maxwell displacement current as a driving force to directly convert mechanical stimuli into electrical signals, so it can be used as a self-driven sensor. Teng-based sensors have the advantages of simple structure and high instantaneous power density, which provides an important means for building intelligent sensor systems. At the same time, machine learning, as a technology with low cost, short development cycle, strong data processing ability and prediction ability, has a significant effect on the processing of a large number of electrical signals generated by TENG, and the combination with TENG sensors will promote the rapid development of intelligent sensor networks in the future. Therefore, this paper is based on the intelligent sound monitoring and recognition system of TENG, which has good sound recognition capability, and aims to evaluate the feasibility of the sound perception module architecture in ubiquitous sensor networks.

Related papers

• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. mechanisms that leverage sensing Industrial Internet of Things (IIoT) devices to share data for the construction of DTs are susceptible to adverse selection problems.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• A Soft e-Textile Sensor for Enhanced Deep Learning-based Shape Sensing of Soft Continuum Robots [0.3495246564946556]
  The safety and accuracy of robotic navigation hold paramount importance, especially in the realm of soft continuum robotics. Traditional rigid sensors often fail to integrate well with the flexible nature of these robots, adding unwanted bulk and rigidity. This study presents a new approach to shape sensing in soft continuum robots through the use of soft e-textile resistive sensors.
  arXiv  Detail & Related papers  (2024-04-19T05:00:25Z)
• Physics-Enhanced Graph Neural Networks For Soft Sensing in Industrial Internet of Things [6.374763930914524]
  The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. achieving highly reliable IIoT can be hindered by factors such as the cost of installing large numbers of sensors, limitations in retrofitting existing systems with sensors, or harsh environmental conditions that may make sensor installation impractical. We propose physics-enhanced Graph Neural Networks (GNNs), which integrate principles of physics into graph-based methodologies.
  arXiv  Detail & Related papers  (2024-04-11T18:03:59Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission [10.174575604689391]
  We propose a novel sensing module to equip sensing frameworks with intelligent data transmission capabilities. We integrate a highly efficient machine learning model placed near the sensor. This model provides prompt feedback for the sensing system to transmit only valuable data while discarding irrelevant information.
  arXiv  Detail & Related papers  (2024-02-03T05:41:39Z)
• On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data [19.835810073852244]
  Instead of deploying soft sensors on the Cloud, this study shift towards employing on-device soft sensors, promising heightened efficiency and bolstering data security. Our approach substantially improves energy efficiency by deploying Artificial Intelligence (AI) directly on devices within a wireless sensor network.
  arXiv  Detail & Related papers  (2023-11-25T14:18:29Z)
• MultiIoT: Benchmarking Machine Learning for the Internet of Things [70.74131118309967]
  The next generation of machine learning systems must be adept at perceiving and interacting with the physical world. sensory data from motion, thermal, geolocation, depth, wireless signals, video, and audio are increasingly used to model the states of physical environments. Existing efforts are often specialized to a single sensory modality or prediction task. This paper proposes MultiIoT, the most expansive and unified IoT benchmark to date, encompassing over 1.15 million samples from 12 modalities and 8 real-world tasks.
  arXiv  Detail & Related papers  (2023-11-10T18:13:08Z)
• A Health Monitoring System Based on Flexible Triboelectric Sensors for Intelligence Medical Internet of Things and its Applications in Virtual Reality [4.522609963399036]
  The Internet of Medical Things (IoMT) is a platform that combines Internet of Things (IoT) technology with medical applications. In this study, we designed a robust and intelligent IoMT system through the synergistic integration of flexible wearable triboelectric sensors and deep learning-assisted data analytics. We embedded four triboelectric sensors into a wristband to detect and analyze limb movements in patients suffering from Parkinson's Disease (PD) This innovative approach enabled us to accurately capture and scrutinize the subtle movements and fine motor of PD patients, thus providing insightful feedback and comprehensive assessment of the patients conditions.
  arXiv  Detail & Related papers  (2023-09-13T01:01:16Z)
• Low-cost Efficient Wireless Intelligent Sensor (LEWIS) for Engineering, Research, and Education [72.2614468437919]
  The vision of smart cities equipped with sensors informing decisions has not been realized to date. Civil engineers lack of knowledge in sensor technology. The electrical components and computer knowledge associated with sensors are still a challenge for civil engineers.
  arXiv  Detail & Related papers  (2023-03-23T21:49:26Z)
• DensePose From WiFi [86.61881052177228]
  We develop a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. Our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches.
  arXiv  Detail & Related papers  (2022-12-31T16:48:43Z)
• EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications [65.32004302942218]
  Brain-Computer Interface (BCI) is a powerful communication tool between users and systems. Recent technological advances have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
  arXiv  Detail & Related papers  (2020-01-28T10:36:26Z)

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.