23 research outputs found

    Internet of Things in Agricultural Innovation and Security

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    The agricultural Internet of Things (Ag-IoT) paradigm has tremendous potential in transparent integration of underground soil sensing, farm machinery, and sensor-guided irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. The aim of the IoT in agricultural innovation and security chapter is to present agricultural IoT research and paradigm to promote sustainable production of safe, healthy, and profitable crop and animal agricultural products. This chapter covers the IoT platform to test optimized management strategies, engage farmer and industry groups, and investigate new and traditional technology drivers that will enhance resilience of the farmers to the socio-environmental changes. A review of state-of-the-art communication architectures and underlying sensing technologies and communication mechanisms is presented with coverage of recent advances in the theory and applications of wireless underground communications. Major challenges in Ag-IoT design and implementation are also discussed

    Data-driven online monitoring of wind turbines

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    \u3cp\u3eCondition based maintenance is a modern approach to maintenance which has been successfully used in several industrial sectors. A specific problem in wind turbine maintenance is that failures of certain parts may be caused by the malperformance or failure of other parts. This mandates for approaches that can produce timely warnings by combining sensor data from different sources. More concretely, in this paper, we present a hybrid statistical approach to condition based maintenance by combining regression analysis with tools from statistical process control. Our approach improves the wind turbine maintenance practice by using adaptive alarm thresholds for the monitored parameters, whilst correcting for environmental factors or for other relevant parameters. We illustrate our approach with a case study demonstrating that we are able to predict upcoming failures much earlier than the current practice.\u3c/p\u3
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