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"WEAKLY NONLINEAR SURFACE WAVE PREDICTION USING A DATA-DRIVEN METHOD WI" by Jialun Chen, Wenhua Zhao et al.

Accurate surface wave prediction can potentially improve the safety and efficiency of various offshore operations, such as heavy lifts and active control of wave energy converters and floating wind turbines. Prediction of surface waves, even if only for a few periods in advance, is of value for decision-making. This study aims to predict weakly nonlinear surface waves (up to the 2nd-order) in real-time using a data-driven model based on Artificial Neural Networks (ANN), where the application of physics is investigated to aid the development of a data-driven model. Based on numerically synthesized nonlinear wave records calculated using exact 2nd-order theory, ANN models were trained to separate the nonlinear bound components at an up-wave location, propagate the linear waves and reintroduce the nonlinear components as a correction to the prediction at a downwave location. The results show that the optimal approach is to predict each stage separately following the basic physical structu ....

Artificial Neural Networks , Artificial Neural Network , Machine Learning , Nonlinear Waves , Ave By Wave Prediction ,

Can ChatGPT predict the future? Training AI to figure out what happens next

Time series data is useful for things such as predicting weather, medical events, or financial changes, and generative AI can model such sequences if given a little help, NYU scholars find. ....

United Kingdom , Carnegie Mellon , Nate Gruver , York University , United Kingdom Lancaster University , Artificial Neural Network Computational Intelligence Forecasting Competition , Stanford University , New York , New York University , Artificial Neural Network ,

Machine learning for predictive maintenance in large-scale PV plants

Machine learning for predictive maintenance in large-scale PV plants
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University Of Technology Malaysia , Artificial Neural Network , Chiang Mai Rajabhat University , Technology Malaysia , Chiang Mai Rajabhat ,

"Quantitative Measurement and Assessment of Frailty in Elderly" by Yasmeen Naz Panhwar

Frailty assessment is a critical approach to assessing the health status of older people. There is interest in the nature and degree of frailty in various physical, environmental, social, psychological, and physiological domains. The physical domain has proved to be the most important as the degree of frailty can be assessed objectively. Various clinical tools deployed by geriatricians in assessing frailty can be grouped into two categories of questionnaire-based and physical performance analysis. In the former approach, answers vary from double (yes or no) to multiple choices and are marked accordingly. Whereas, in performance analysis, the time taken by a subject to complete a physical task such as walking for 3 m is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not identify the kinematic characteristics of motion and its root causes of it. However, motion characteristics are crucial in measuring the degree of frailty. Overall, r ....

Artificial Neural Network , Neural Network , Decision Tree , Random Forest , Support Vector Machine , Gaussian Mixture Model , Rockwood Frailty , Otion Sensors , Machine Learning , Ctivity Of Daily Living ,