A Hybrid Path Planning Method for an Unmanned Cruise Ship in Water Quality Sampling

Yu, Jiabin; Deng, Wei; Zhao, Zhiyao; Wang, Xiaoyi; Xu, Jiping; Wang, Li; Sun, Qian; Shen, Zhe

doi:10.1109/access.2019.2925894

Cited by 26 publications

(17 citation statements)

References 35 publications

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“…To guarantee performance, we will use the Bayesian SMBO method to select these hyperparameters. Not only can WD-GRU be used for air quality monitoring research but it also forms a new network by combining with other networks, which can be used in other research fields, such as the research on prediction and management control of water environment [65][66][67] and IoT intelligence [68].…”

Section: (T)mentioning

confidence: 99%

Modeling and Analysis of Data‐Driven Systems through Computational Neuroscience Wavelet‐Deep Optimized Model for Nonlinear Multicomponent Data Forecasting

Zhang

Bai

et al. 2021

Computational Intelligence and Neuroscience

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Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend. Firstly, the data is decomposed by wavelet transform to reduce the complexity of the time series data. The Gated Recurrent Unit (GRU) network is trained as a submodel for each decomposition component. The hyperparameters of wavelet decomposition and each submodel are optimized with Bayesian sequence model-based optimization (SMBO) to develop the modeling accuracy. Finally, the results of all submodels are added to obtain forecasting results. The PM2.5 data collected by the US Air Quality Monitoring Station is used for experiments. By comparing with other networks, it can be found that the proposed method outperforms well in the multisteps forecasting task for the complex time series.

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Section: (T)mentioning

confidence: 99%

Modeling and Analysis of Data‐Driven Systems through Computational Neuroscience Wavelet‐Deep Optimized Model for Nonlinear Multicomponent Data Forecasting

Zhang

Bai

et al. 2021

Computational Intelligence and Neuroscience

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show abstract

“…For medium-term prediction, S t takes the other three components and we set n as 24. This means we used the data from the historical 24 h to predict the data of the future 24 h. The method proposed in this paper can be combined with other system identification methods [30][31][32] to study the modeling and prediction of other dynamic time series and random systems [33,34] and can be applied to other fields [35][36][37] and other signal modeling and control systems [6,[38][39][40].…”

Section: Long-term Predictionmentioning

confidence: 99%

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Jin

Wang

et al. 2020

Sustainability

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Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.

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“…The GRU is trained by the gradient descent algorithm, and the parameters are continually updated until convergence. The proposed methods proposed in this paper can combine other identification approaches [50] to study the modeling and prediction problems applied to other fields [51,52] such as internet of things systems [53,54] and water environment prediction and management control [55,56].…”

Section: Deep Prediction Network For Combined Imfsmentioning

confidence: 99%

Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction

et al. 2020

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Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-term trend because the collected PM2.5 data have complex nonlinearity with multiple components of different frequency characteristics. This study proposes a hybrid deep learning predictor, in which the PM2.5 data are decomposed into components by empirical mode decomposition (EMD) firstly, and a convolutional neural network (CNN) is built to classify all the components into a fixed number of groups based on the frequency characteristics. Then, a gated-recurrent-unit (GRU) network is trained for each group as the sub-predictor, and the results from the three GRUs are fused to obtain the prediction result. Experiments based on the PM2.5 data from Beijing verify the proposed model, and the prediction results show that the decomposition and classification can develop the accuracy of the proposed predictor for air pollution prediction greatly.

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A Hybrid Path Planning Method for an Unmanned Cruise Ship in Water Quality Sampling

Cited by 26 publications

References 35 publications

Modeling and Analysis of Data‐Driven Systems through Computational Neuroscience Wavelet‐Deep Optimized Model for Nonlinear Multicomponent Data Forecasting

Modeling and Analysis of Data‐Driven Systems through Computational Neuroscience Wavelet‐Deep Optimized Model for Nonlinear Multicomponent Data Forecasting

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction

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