Smart sensors in smart grids provide real-time data and status of bidirectional flows of energy for monitoring, protection, and control of grid operations to improve reliability and resilience. Smart sensor data interoperability is a major challenge for smart grids. This paper proposes a methodology for modeling interoperability of smart sensors in terms of interactions using labeled transition systems and finite state processes in order to quantitatively and automatically measure and assess the interoperability, identify and resolve interoperability issues, and improve interoperability. A generic interoperability model of synchronous message passing from a sender to a receiver is built based on the proposed methodology. A case study is provided to apply this methodology for modeling interoperability between the Institute of Electrical and Electronics Engineers C37.118 phasor measurement unit-based smart sensors and phasor data concentrators. The interoperability model can be used for the quantitative and automated measurement and assessment of the interoperability of phasor measurement unit-based smart sensors and phasor data concentrators to address interoperability issues. This methodology can also be applied to modeling interoperability of smart sensors based on other standard communication protocols in order to achieve and assure sensor data interoperability in smart grids.
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http://dx.doi.org/10.1109/tsg.2021.3124490 | DOI Listing |
Front Plant Sci
January 2025
Research Center for Agricultural Monitoring and Early Warning, Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China.
As the source of data acquisition, sensors provide basic data support for crop planting decision management and play a foundational role in developing smart planting. Accurate, stable, and deployable on-site sensors make intelligent monitoring of various planting scenarios possible. Recent breakthroughs in plant advanced sensors and the rapid development of intelligent manufacturing and artificial intelligence (AI) have driven sensors towards miniaturization, intelligence, and multi-modality.
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January 2025
Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea.
Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production.
View Article and Find Full Text PDFAdv Mater
January 2025
Department of Robotics and Mechatronics, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, 42988, South Korea.
Triboelectric nanogenerators (TENGs) have gained significant attention for ability to convert mechanical energy into electrical energy. As the applications of TENG devices expand, their safety and reliability becomes priority, particularly where there is risk of fire or spontaneous combustion. Flame-retardant materials can be employed to address these safety concerns without compromising the performance and efficiency of TENGs.
View Article and Find Full Text PDFAdv Mater
January 2025
College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China.
New types of metal-organic framework (MOF) materials have great potential in solving the current global dilemma on energy, environment, and medical care. Herein, based on two kinds of biomolecule-MOFs (Bio-MOFs) with favorable biocompatibility and degradation-reconstruction characteristics, we have established a self-powered muti-functional device to achieve an efficient and broad-spectrum environmental energy collection and biomedical applications. Combining Zn(II) and carnosine-based Zn-Car_MOF possessing a high piezoelectric response (d = 11.
View Article and Find Full Text PDFBiosens Bioelectron
January 2025
College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China; Affiliated Dongyang Hospital of Wenzhou Medical University, Jinhua, China. Electronic address:
Pathological conditions in organisms often arise from various cellular or tissue abnormalities, including dysregulation of cell numbers, infections, aberrant differentiation, and tissue pathologies such as lung tumors and skin tumors. Thus, developing methods for analyzing and identifying these biological abnormalities presents a significant challenge. While traditional bioanalytical methods such as flow cytometry and magnetic resonance imaging are well-established, they suffer from inefficiencies, high costs, complexity, and potential hazards.
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