RNN-Based Path Prediction of Obstacle Vehicles With Deep Ensemble

Min, Kyushik; Kim, Dongchan; Park, Jong-Won; Huh, Kunsoo

doi:10.1109/tvt.2019.2933232

Cited by 51 publications

(15 citation statements)

References 19 publications

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“…× 100 (13) where | | is the number of test datasets; ( , ) is the identity operator that returns 1 when and are identical; otherwise, it returns 0. Next, "mode_accuracy_2" (MA2) is defined as in (14), and shows how close the RNN output mode is derived to the true label.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this paper, we propose a novel recurrent neural network (RNN)-based optimal duty cycle control method for WSNs, in which the optimal sensing schedule is dynamically determined for each sensor node. We use an RNN learning structure because it can capture the sequential and temporal dynamic behavior well [14]. Based on the neighbor node's object detection results, in the proposed method, the server determines the node that must change its duty cycle and defines the optimal duty cycle that can meet the detection requirements with minimum energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

Choi

Yoo

2021

IEEE Access

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With the development of Internet of Things (IoT) technologies, environmental monitoring systems using wireless sensor networks (WSNs) have received considerable attention. Reliable object detection and tracking is an important research issue in various WSN applications, such as environment and disaster monitoring, disaster propagation tracking, and intruder monitoring and tracking. Generally, because batteries are used as energy sources for sensors in WSNs, a highly energy-efficient operation is needed to prolong the life of the sensors and networks. To save energy, sensors usually manage multi-mode sensing operations, in which they periodically switch between active and inactive periods. A tradeoff exists between object detection accuracy and energy efficiency when we select a sensing schedule. Depending on the object speed, direction, and sensor deployment topology, different sensing schedules should be dynamically applied to individual sensors. In this paper, we propose a novel recurrent neural network (RNN)-based dynamic duty cycle control method for sensor nodes. For RNN training, a target optimal duty cycle for a given network condition is derived from the proposed digital twin-space analytic solution. Simulation results show that the proposed model provides accurate object detection performance and achieves high energy efficiency.INDEX TERMS Duty cycle control, machine learning, object tracking, recurrent neural network, wireless sensor networks.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

Choi

Yoo

2021

IEEE Access

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“…With the development of the depth of the neural network, the hidden information in the massive data can be extracted to make more accurate predictions. The recurrent neural network (RNN) [ 26 ] and its improved versions, such as long short-term memory (LSTM) [ 27 ], etc., is widely used for regression prediction problems, demonstrating its superior nonlinear modeling capabilities. For example, a gated recurrent unit (GRU) network [ 28 ] and Bi-LSTM [ 29 ] were proposed to improve LSTM.…”

Section: Related Workmentioning

confidence: 99%

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Jin

et al. 2021

Entropy

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Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.

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“…Therefore, a recurrent neural network (RNN) is proposed to solve this problem, which is characterized by adding a recurrent structure to the neural unit of the hidden layer. In [15], a path forecasting method is proposed and got good forecasting results. However, there is only one processing function in the neural unit of RNN, which will cause the problem of gradient disappearance or explosion after recurrent training, and will adversely affect the forecasting results [16].…”

Section: Introductionmentioning

confidence: 99%

Load Forecasting Based on LSTM Neural Network and Applicable to Loads of “Replacement of Coal with Electricity”

Chen

Zhang

Jiang

et al. 2021

J. Electr. Eng. Technol.

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With the complete implementation of the “Replacement of Coal with Electricity” policy, electric loads borne by urban power systems have achieved explosive growth. The traditional load forecasting method based on “similar days” only applies to the power systems with stable load levels and fails to show adequate accuracy. Therefore, a novel load forecasting approach based on long short-term memory (LSTM) was proposed in this paper. The structure of LSTM and the procedure are introduced firstly. The following factors have been fully considered in this model: time-series characteristics of electric loads; weather, temperature, and wind force. In addition, an experimental verification was performed for “Replacement of Coal with Electricity” data. The accuracy of load forecasting was elevated from 83.2 to 95%. The results indicate that the model promptly and accurately reveals the load capacity of grid power systems in the real application, which has proved instrumental to early warning and emergency management of power system faults.

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RNN-Based Path Prediction of Obstacle Vehicles With Deep Ensemble

Cited by 51 publications

References 19 publications

Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Load Forecasting Based on LSTM Neural Network and Applicable to Loads of “Replacement of Coal with Electricity”

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