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- Journal of System Simulation (8)
- Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research (1)
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- Engineering Management and Systems Engineering Faculty Research & Creative Works (1)
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Articles 1 - 12 of 12
Full-Text Articles in Engineering
Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian
Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian
Electronic Thesis and Dissertation Repository
Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building/group to predict future consumption for that same building/group. With hundreds of thousands of smart meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Cluster-Based Chained Transfer Learning (CBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through …
Research And Implementation Of Driving Concern Area Detection Based On Deep Learning, Jihua Ye, Shuxia Shi, Hanxi Li, Shimin Wang, Siyu Yang
Research And Implementation Of Driving Concern Area Detection Based On Deep Learning, Jihua Ye, Shuxia Shi, Hanxi Li, Shimin Wang, Siyu Yang
Journal of System Simulation
Abstract: As a key technology of intelligent driving, driving concern area detection method has an important impact on the performance of intelligent driving or intelligent early warning system. In view of the shortcomings of the existing methods, this paper proposes an effective method for driving concern area detection based on the deep learning. We obtain the camera internal and external parameters by using camera self-calibration method based on camera model, use the Canny edge detection and Bisecting K-means clustering to realize the vanishing point estimation, and establish the road detection model based on the obtained estimates. We obtain the depth …
Extracting Patterns In Medical Claims Data For Predicting Opioid Overdose, Ryan Sanders
Extracting Patterns In Medical Claims Data For Predicting Opioid Overdose, Ryan Sanders
Graduate Theses and Dissertations
The goal of this project is to develop an efficient methodology for extracting features from time-dependent variables in transaction data. Transaction data is collected at varying time intervals making feature extraction more difficult. Unsupervised representational learning techniques are investigated, and the results compared with those from other feature engineering techniques. A successful methodology provides features that improve the accuracy of any machine learning technique. This methodology is then applied to insurance claims data in order to find features to predict whether a patient is at risk of overdosing on opioids. This data covers prescription, inpatient, and outpatient transactions. Features created …
Flood Management Deep Learning Model Inputs: A Review Of Necessary Data And Predictive Tools, Jacob Hale, Suzanna Long, Steven Corns, Tom Shoberg
Flood Management Deep Learning Model Inputs: A Review Of Necessary Data And Predictive Tools, Jacob Hale, Suzanna Long, Steven Corns, Tom Shoberg
Engineering Management and Systems Engineering Faculty Research & Creative Works
Current flood management models are often hampered by the lack of robust predictive analytics, as well as incomplete datasets for river basins prone to heavy flooding. This research uses a State-of-the-Art matrix (SAM) analysis and integrative literature review to categorize existing models by method and scope, then determines opportunities for integrating deep learning techniques to expand predictive capability. Trends in the SAM analysis are then used to determine geospatial characteristics of the region that can contribute to flash flood scenarios, as well as develop inputs for future modeling efforts. Preliminary progress on the selection of one urban and one rural …
Mid To Late Season Weed Detection In Soybean Production Fields Using Unmanned Aerial Vehicle And Machine Learning, Arun Narenthiran Veeranampalayam Sivakumar
Mid To Late Season Weed Detection In Soybean Production Fields Using Unmanned Aerial Vehicle And Machine Learning, Arun Narenthiran Veeranampalayam Sivakumar
Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
Mid-late season weeds are those that escape the early season herbicide applications and those that emerge late in the season. They might not affect the crop yield, but if uncontrolled, will produce a large number of seeds causing problems in the subsequent years. In this study, high-resolution aerial imagery of mid-season weeds in soybean fields was captured using an unmanned aerial vehicle (UAV) and the performance of two different automated weed detection approaches – patch-based classification and object detection was studied for site-specific weed management. For the patch-based classification approach, several conventional machine learning models on Haralick texture features were …
A Horizon Detection Method Based On Deep Learning And Random Forest, Jihua Ye, Shuxia Shi, Hanxi Li, Jiali Zuo, Shimin Wang
A Horizon Detection Method Based On Deep Learning And Random Forest, Jihua Ye, Shuxia Shi, Hanxi Li, Jiali Zuo, Shimin Wang
Journal of System Simulation
Abstract: The detection effect of existing horizon line detection methods is greatly affected by the environment, and the computational complexity is high. Aiming at the problem of horizon line detection in complex road scene in real-life, a horizon line detection method based on deep learning and random forest is proposed. The deep learning model is used to extract the depth features, then the obtained depth features are used for random forest training. The results of horizon line detection are obtained by random forest regression-voting. The simulation results show that this method has good detection effect. The detection results are not …
Deep Learning Method For Hyperspectral Remote Sensing Images With Small Samples, Xiangbin Shi, Zhong Jian, Cuiwei Liu, Liu Fang, Deyuan Zhang
Deep Learning Method For Hyperspectral Remote Sensing Images With Small Samples, Xiangbin Shi, Zhong Jian, Cuiwei Liu, Liu Fang, Deyuan Zhang
Journal of System Simulation
Abstract: In order to solve the problem of large information dimension and fewer labeled training samples of hyperspectral remote sensing images, this paper proposes a hyperspectral remote sensing image classification framework HSI-CNN, which reduces the number of model parameters while maintaining the depth of neural network. Image pattern invariance and spectral channel contribution rate are analyzed, and the spectral redundancy information is reduced by principal component analysis. A full convolution neural network structure suitable for small sample hyperspectral remote sensing images is designed and the amount of network parameters is effectively reduced. Three kinds of HSI-CNN structures are proposed …
Video Action Recognition Based On Key-Frame, Mingxiao Li, Qichuan Geng, Mo Hong, Wu Wei, Zhou Zhong
Video Action Recognition Based On Key-Frame, Mingxiao Li, Qichuan Geng, Mo Hong, Wu Wei, Zhou Zhong
Journal of System Simulation
Abstract: Video action recognition is an important part of intelligent video analysis. In recent years, deep learning methods, especially the two-stream convolutional neural network achieved the state-of-the-art performance. However, most methods simply use uniform sampling to get frames, which may cause the loss of information in sampling interval. We propose a segmentation method and a key-frame extraction method for video action recognition, and combine them with a multi-temporal-scale two-stream network. Our framework achieves a 94.2% accuracy at UCF101 split1, which is the same as the state-of-the-art method’s performance.
Traffic Flow Prediction Based On Deep Learning, Mingyu Liu, Jianping Wu, Yubo Wang, He Lei
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.
Research On Evaluation Framework Of Coa Based On Wargaming, Haiyang Liu, Yubo Tang, Xiaofeng Hu, Guangpeng Qiao
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. …
Visual Object Tracking Algorithm Based On Deep Denoising Autoencoder Over Rgb-D Data, Mingxin Jiang, Zhigeng Pan, Lanfang Wang, Taoxin Hu
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. …
A Model For Battlefield Situation Change Rate Prediction Based On Deep Learning, Jiuyang Tao, Wu Lin, Wang Chi, Junda Chu, Liao Ying, Zhu Feng
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 …