Machine learning-enabled textile-based graphene gas sensing with energy harvesting-assisted IoT application

Zhu, Jianxiong; Cho, Minkyu; Li, Yutao; He, Ting; Ahn, Junseong; Park, Jaeho; Ren, Tian‐Ling; Lee, Chengkuo; Park, Inkyu

doi:10.1016/j.nanoen.2021.106035

Cited by 91 publications

(47 citation statements)

References 64 publications

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“…The framework of the proposed system is shown in Figure 1. First, the system analyzes the individual signals of the system to segment the signals into several semantic states by machine learning techniques [13,[22][23][24]. The states of all sensor signals combine into a full functional model of the system.…”

Section: Semantic Modeling Of Robotic Systemsmentioning

confidence: 99%

Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling

Sun

Yang

et al. 2022

Micromachines

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Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed. Considering the growing complexity of the manufacturing system, an automatic and intelligent health-monitoring system is required to detect abnormalities of robotics in real-time to promote quality and reduce safety risks. Therefore, in this study, we designed a novel semantic-based modeling method for multistage robotic systems. Experiments show that sole modeling is not sufficient for multiple stages. We propose a descriptor to conclude the stages of robotic systems by learning from operational data. The descriptors are akin to a vocabulary of the systems; hence, semantic checking can be carried out to monitor the correctness of operations. Furthermore, the stage classification and its semantics were used to apply various regression models to each stage to monitor the quality of each operation. The proposed method was applied to a photovoltaic manufacturing system. Benchmarks on production datasets from actual factories show the effectiveness of the proposed method to realize an AI-enabled real-time health-monitoring system of robotics.

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Section: Semantic Modeling Of Robotic Systemsmentioning

confidence: 99%

Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling

Sun

Yang

et al. 2022

Micromachines

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“…The feasibility of this wearable computational spectrometer is well proved by taking CO 2 and acetone sensing as demonstrations at the wavelength range of 4-5 µm and 5-6.5 µm, respectively. In 2021, Zhu et al leveraged the energy harvester and machine learning to enable a robust wearable hydrogen sensor for IoT applications [210]. The sensing mechanism is based on the catalytic effect of palladium nanoparticles on the wide bandgap-reduced graphene oxide (Figure 5d).…”

Section: Gas Sensingmentioning

confidence: 99%

mentioning

confidence: 99%

Recent Progress in the Energy Harvesting Technology—From Self-Powered Sensors to Self-Sustained IoT, and New Applications

et al. 2021

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With the fast development of energy harvesting technology, micro-nano or scale-up energy harvesters have been proposed to allow sensors or internet of things (IoT) applications with self-powered or self-sustained capabilities. Facilitation within smart homes, manipulators in industries and monitoring systems in natural settings are all moving toward intellectually adaptable and energy-saving advances by converting distributed energies across diverse situations. The updated developments of major applications powered by improved energy harvesters are highlighted in this review. To begin, we study the evolution of energy harvesting technologies from fundamentals to various materials. Secondly, self-powered sensors and self-sustained IoT applications are discussed regarding current strategies for energy harvesting and sensing. Third, subdivided classifications investigate typical and new applications for smart homes, gas sensing, human monitoring, robotics, transportation, blue energy, aircraft, and aerospace. Lastly, the prospects of smart cities in the 5G era are discussed and summarized, along with research and application directions that have emerged.

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“…[ 21–24 ] The combination of textiles and TENG is naturally considered to be an important solution for the energy sources of wearable electronics. [ 25–29 ]…”

Section: Introductionmentioning

confidence: 99%

Enhanced Output of On‐Body Direct‐Current Power Textiles by Efficient Energy Management for Sustainable Working of Mobile Electronics

Cheng

Ning

Chen

et al. 2022

Advanced Energy Materials

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Triboelectric power textile (TPT) that can harvest widespread but always neglected human biomechanical energy is considered a promising and reliable energy source for wearable electronics. However, the alternating current and high impedance of triboelectric nanogenerators as well as the meager electrical output caused by the properties of the textile itself greatly restrict its practical applications. Here, an autonomous power textile with practical application value, which consists of a high‐output direct‐current TPT (DC‐TPT) and a miniaturized energy management module (EMM), is achieved for continuous operation of wearable and mobile electronics. The home‐preparable multiarray DC‐TPT can harvest transferred charge of 5.5 µC per cycle with nine repeating units. Moreover, it is found that incorrect sewing positions of the polytetrafluoroethylene (PTFE) yarn change the working mechanism with a decreased DC output. The EMM with energy conversion efficiency of 82.6% can reduce the impedance of DC‐TPT from 200 to 1.6 MΩ. With its powerful assistance, the watch can work continuously for 172 s just by manually sliding the DC‐TPT 1.6 s on an arm, and wireless signals can be transmitted to 281 m away after sliding for 2 min. Various widely used electronics can also be easily and continuously driven.

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Machine learning-enabled textile-based graphene gas sensing with energy harvesting-assisted IoT application

Cited by 91 publications

References 64 publications

Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling

Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling

Recent Progress in the Energy Harvesting Technology—From Self-Powered Sensors to Self-Sustained IoT, and New Applications

Enhanced Output of On‐Body Direct‐Current Power Textiles by Efficient Energy Management for Sustainable Working of Mobile Electronics

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