Machines Learning Trends, Perspectives and Prospects in Education Sector

Jalil, Nasir Abdul; Hwang, Ha Jin; Dawi, Norazryana Mat

doi:10.1145/3345120.3345147

Cited by 25 publications

(10 citation statements)

References 14 publications

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“…This method can improve how well our model matches the actual latent control function. Because we hope that our model can pre-dict factors' changing and interaction instead of learning the temporal relationship of flight segments [19,20], one batch consists of 100 continuous steps as input values and 10 uninterrupted steps as output values. In the experiment section, we will randomly sample 64 batches as the input to a round of training.…”

Section: Interpolation Of Missing Datamentioning

confidence: 99%

Research on Forecasting of the Compressor Geometric Variable System Based on the MAE Model

Xia

Zhan

Tan

et al. 2023

International Journal of Aerospace Engineering

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The compressor geometric variable system is vital for aeroengines, as it affects their performance and design. To monitor the compressor geometric variable system states and detect anomalies in real time, a t -step forecasting method based on the MAE (masked autoencoders) model was proposed in this article. Unlike previous studies that used simulated or lab-generated data, we use actual flight data recorded by the aircraft data acquisition system to make our results more realistic. Through our experimental efforts, the feasibility of forecasting the compressor geometric variable system based on the MAE model is verified. That is not only the first application of transformer models with a masked pretraining mechanism in time series forecasts but also taking the lead in exploring the possibility of this key system forecast. We also test the generalizability of our method across different types of aeroengines. Finally, to make our theories more reasonable and convincing, experiments on different aeroengine states, including the transition state and the steady state, are carried out.

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Section: Interpolation Of Missing Datamentioning

confidence: 99%

Research on Forecasting of the Compressor Geometric Variable System Based on the MAE Model

Xia

Zhan

Tan

et al. 2023

International Journal of Aerospace Engineering

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“…By using this method, the forecasting task would be more challenging and the model might learn actual latent control features more effectively. Because the model is expected to learn the relationship of parameters changing and interacting, instead of the temporal link between flight segments [ 15 , 16 ]. In the experiment section, 64 batches will be randomly sampled as inputs to a round of training.…”

Section: Data Preprocessingmentioning

confidence: 99%

A highly accurate method for forecasting the compressor geometric variable system based on the data-driven method

Xia¹,

Zhan²,

Tan³

et al. 2023

PLoS ONE

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To make the puzzle of aero-engines complete, understanding the law of the compressor geometric variable system is a vital part. Modeling all aspects of aero-engines quickly has been a continuous area of research since the advent of artificial intelligence (AI). However, diagnosing or predicting faults is an ancient adage, and it is vital to explore key system forecast research, particularly since traditional forecasting techniques do not account for future information of non-target parameters. In this article, based on the feasibility of forecasting the compressor geometric variable system, an enhanced ConvNeXt model utilizing the Sliding Window Algorithm mechanism is proposed. And this method takes into account the future information of non-target parameters. With the novel strategy, the issue of the forecast’s error increasing with forecast length is alleviated. As a result, in a particular condition, the error we obtained only accounts for 20.07% of that of the standard forecast approach. Additionally, it is verified that this method can be applied to various aero-engines. Finally, experiments on several aero-engine states involving the transition state and the steady state are conducted to strengthen the plausibility and credibility of our theories. It should be noted that the foundation of each experiment is data from actual flights.

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“…ML techniques utilised by domains including social media, medical analysis, computer vision and gaming are applied in cyber defence measures in smart city sectors such as transportation, healthcare, buildings and ICS [1,[26][27][28][29][30]. For example, ML techniques are utilised as cyber defence measures for anomalous behaviour detection.…”

Section: Application Of the Learning Techniquesmentioning

confidence: 99%

Super Learner Ensemble for Anomaly Detection and Cyber-Risk Quantification in Industrial Control Systems

Ahmadi-Assalemi

Al-Khateeb

Epiphaniou

et al. 2022

IEEE Internet Things J.

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Machines Learning Trends, Perspectives and Prospects in Education Sector

Cited by 25 publications

References 14 publications

Research on Forecasting of the Compressor Geometric Variable System Based on the MAE Model

Research on Forecasting of the Compressor Geometric Variable System Based on the MAE Model

A highly accurate method for forecasting the compressor geometric variable system based on the data-driven method

Super Learner Ensemble for Anomaly Detection and Cyber-Risk Quantification in Industrial Control Systems

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