Using Appearance to Predict Pedestrian Trajectories Through Disparity-Guided Attention and Convolutional LSTM

Feng, Yan; Zhang, Tingsheng; Sah, Abhishek Pratap; Han, Lei; Zhang, Zutao

doi:10.1109/tvt.2021.3094678

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…Two categories of soft sensor models are mechanism and data-driven models [ 11 ]. The former uses the law of conservation of mass, kinetics, thermodynamics, material and heat balance, and chemical reactions to derive mathematical models [ 12 ]. The latter utilizes data samples to establish mathematical models [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network

Zheng,

Bao,

Zhao

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network

Zheng,

Bao,

Zhao

et al. 2024

Heliyon

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“…RF is a supervised machine learning algorithm and first presented by Breiman [36]. The long short-term memory (LSTM) model (which represents the state-of-the-art recurrent cell in many fields) was first proposed in 1997 [37][38][39][40][41][42][43]. CNNs were originally designed to resolve image classification problems and they have been applied effectively for remote-sensing-based image classification [44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China

2022

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Precipitation monitoring is important for earth system modeling and environmental management. Low spatial representativeness limits gauge measurements of rainfall and low spatial resolution limits satellite-derived rainfall. SM2RAIN-based products, which exploit the inversion of the water balance equation to derive rainfall from soil moisture (SM) observations, can be an alternative. However, the quality of SM data limits the accuracy of rainfall. The goal of this work was to improve the accuracy of rainfall estimation through merging multiple soil moisture (SM) datasets. This study proposed an integration framework, which consists of multiple machine learning methods, to use satellite and ground-based soil moisture observations to derive a precipitation product. First, three machine learning (ML) methods (random forest (RF), long short-term memory (LSTM), and convolutional neural network (CNN)) were used, respectively to generate three SM datasets (RF-SM, LSTM-SM, and CNN-SM) by merging satellite (SMOS, SMAP, and ASCAT) and ground-based SM observations. Then, these SM datasets were merged using the Bayesian model averaging method and validated by wireless sensor network (WSN) observations. Finally, the merged SM data were used to produce a rainfall dataset (SM2R) using SM2RAIN. The SM2R dataset was validated using automatic meteorological station (AMS) rainfall observations recorded throughout the Upper Heihe River Basin (China) during 2014–2015 and compared with other rainfall datasets. Our results revealed that the quality of the SM2R data outperforms that of GPM-SM2RAIN, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ERA5-Land (ERA5) and multi-source weighted-ensemble Precipitation (MSWEP). Triple-collocation analysis revealed that SM2R outperformed China Meteorological Data and the China Meteorological Forcing Dataset. Ultimately, the SM2R rainfall product was considered successful with acceptably low spatiotemporal errors (RMSE = 3.5 mm, R = 0.59, and bias = −1.6 mm).

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“…Due to its advantages to solve the vanishing gradient issues, LSTM is more effective than a recurrent neural network to deal with industrial problems that are highly related to time series [15][16][17]. A LSTM neural network has been successfully deployed in many practical applications; it can learn longer-term dependencies due to the associated memory units [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network

et al. 2021

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Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data.

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Using Appearance to Predict Pedestrian Trajectories Through Disparity-Guided Attention and Convolutional LSTM

Cited by 12 publications

References 43 publications

Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network

Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network

Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China

Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network

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