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Deep learning

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Articles 91 - 96 of 96

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Traffic Flow Prediction Based On Deep Learning, Mingyu Liu, Jianping Wu, Yubo Wang, He Lei Jan 2019

Traffic Flow Prediction Based On Deep Learning, Mingyu Liu, Jianping Wu, Yubo Wang, He Lei

Journal of System Simulation

Abstract: Traffic flow prediction is an important component of urban intelligent transportation system. With the development of machine learning and artificial intelligence, deep learning has been applied in traffic engineering area. Gated recurrent unit (GRU) neural network is selected to predict urban traffic flow. Cross-validation method is used to explore the optimal number of gated recurrent units. The GRU model is compared with other three predictors such as support vector regression and evaluated in different performance measurements. The results show that GRU model has better performance in traffic flow prediction than the other three models.

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Research On Evaluation Framework Of Coa Based On Wargaming, Haiyang Liu, Yubo Tang, Xiaofeng Hu, Guangpeng Qiao Jan 2019

Research On Evaluation Framework Of Coa Based On Wargaming, Haiyang Liu, Yubo Tang, Xiaofeng Hu, Guangpeng Qiao

Journal of System Simulation

Abstract: Aiming at the evaluation problem of COA (course of action) level indicators in joint operation, an evaluation framework of COA based on wargaming was proposed. By multidimensional analysis of wargaming data, an evaluation feature space was constructed from basic features generated by data cube models and SoS (system of systems) features based on complex network. Wargaming experiments were used to generate small batch metrics results of COA level indicators, and the corresponding result labels of evaluation feature space data were generated by data fitting method. Two-phase correlation analysis was used for dimensionality reduction of high dimensional evaluation features. …

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Visual Object Tracking Algorithm Based On Deep Denoising Autoencoder Over Rgb-D Data, Mingxin Jiang, Zhigeng Pan, Lanfang Wang, Taoxin Hu Jan 2019

Visual Object Tracking Algorithm Based On Deep Denoising Autoencoder Over Rgb-D Data, Mingxin Jiang, Zhigeng Pan, Lanfang Wang, Taoxin Hu

Journal of System Simulation

Abstract: A visual object tracking algorithm based on cross-modality features deep learning over RGB-D data is proposed. A sparse denoising autoencoder deep learning network is constructed, which can extract cross-modal features of the samples in RGB-D video data. The cross-modal features of the samples are input to the logistic regression classifier, the observation likelihood model is established according to the confidence score of the classifier, and the reasonable state transition model is established. The object tracking results over RGB-D data are obtained using particle filtering algorithm. Experimental results show that the proposed method has strong robustness to abnormal changes. …

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A Model For Battlefield Situation Change Rate Prediction Based On Deep Learning, Jiuyang Tao, Wu Lin, Wang Chi, Junda Chu, Liao Ying, Zhu Feng Jan 2019

A Model For Battlefield Situation Change Rate Prediction Based On Deep Learning, Jiuyang Tao, Wu Lin, Wang Chi, Junda Chu, Liao Ying, Zhu Feng

Journal of System Simulation

Abstract: To measure and estimate the uncertainty of the battlefield situation is of great significance for the commanders to plan the reconnaissance mission and reduce the risk of decision-making. Based on Shannon's information theory, firstly, methods and a model on measurement of situation change rate are proposed. Secondly, a scene with two-dimensional grid elements maneuvering is established, based on deep learning, the prediction method for maneuvering trend is explored. It is proved that cross entropy is equivalent to situation change rate. Finally, with the increase of the objective uncertainty, situation change rate and the accuracy of the forecast is …

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A Web Page Classifier Library Based On Random Image Content Analysis Using Deep Learning, Leonardo Espinosa Leal, Amaury Lendasse, Kaj Mikael Björk, Anton Akusok Jun 2018

A Web Page Classifier Library Based On Random Image Content Analysis Using Deep Learning, Leonardo Espinosa Leal, Amaury Lendasse, Kaj Mikael Björk, Anton Akusok

Engineering Management and Systems Engineering Faculty Research & Creative Works

In This Paper We Present a Methodology and the Corresponding Python Library1 for the Classification of Webpages. the Method Retrieves a Fixed Number of Images from a Given Webpage, and based on Them Classifies the Webpage into a Set of Established Classes with a Given Probability. the Library Trains a Random Forest Model Built Upon the Features Extracted from Images by a Pre-Trained Neural Network. the Implementation is Tested by Recognizing Weapon Class Webpages in a Curated List of 3859 Websites. the Results Show that the Best Method of Classifying a Webpage among the Classes of Interest is to Assign …

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Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos Oct 2017

Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper, …

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