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Full-Text Articles in Engineering
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 …
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 …
Lung Segmentation In Chest Radiographs Using Fully Convolutional Networks, Rahul Hooda, Ajay Mittal, Sanjeev Sofat
Lung Segmentation In Chest Radiographs Using Fully Convolutional Networks, Rahul Hooda, Ajay Mittal, Sanjeev Sofat
Turkish Journal of Electrical Engineering and Computer Sciences
Automated segmentation of medical images that aims at extracting anatomical boundaries is a fundamental step in any computer-aided diagnosis (CAD) system. Chest radiographic CAD systems, which are used to detect pulmonary diseases, first segment the lung field to precisely define the region-of-interest from which radiographic patterns are sought. In this paper, a deep learning-based method for segmenting lung fields from chest radiographs has been proposed. Several modifications in the fully convolutional network, which is used for segmenting natural images to date, have been attempted and evaluated to finally evolve a network fine-tuned for segmenting lung fields. The testing accuracy and …
Enhancing Face Pose Normalization With Deep Learning, Anil Çeli̇k, Nafi̇z Arica
Enhancing Face Pose Normalization With Deep Learning, Anil Çeli̇k, Nafi̇z Arica
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we propose a hybrid method for face pose normalization, which combines the 3-D model-based method with stacked denoising autoencoder (SDAE) deep network. Instead of applying a mirroring operation for the invisible face parts of the posed image, SDAE learns how to fill in those regions by a large set of training samples. In the performance evaluation, we compare the proposed method to four different pose normalization methods and investigate their effects on facial emotion recognition and verification problems in addition to visual quality tests. Methods evaluated in the experiments include 2-D alignment, 3-D model-based method, pure SDAE-based …
Elimination Of Useless Images From Raw Camera-Trap Data, Ulaş Tekeli̇, Yalin Baştanlar
Elimination Of Useless Images From Raw Camera-Trap Data, Ulaş Tekeli̇, Yalin Baştanlar
Turkish Journal of Electrical Engineering and Computer Sciences
Camera-traps are motion triggered cameras that are used to observe animals in nature. The number of images collected from camera-traps has increased significantly with the widening use of camera-traps thanks to advances in digital technology. A great workload is required for wild-life researchers to group and label these images. We propose a system to decrease the amount of time spent by the researchers by eliminating useless images from raw camera-trap data. These images are too bright, too dark, blurred, or they contain no animals. To eliminate bright, dark, and blurred images we employ techniques based on image histograms and fast …
Improving Word Embeddings Projection For Turkish Hypernym Extraction, Savaş Yildirim
Improving Word Embeddings Projection For Turkish Hypernym Extraction, Savaş Yildirim
Turkish Journal of Electrical Engineering and Computer Sciences
Corpus-driven approaches can automatically explore is-a relations between the word pairs from corpus. This problem is also called hypernym extraction. Formerly, lexico-syntactic patterns have been used to solve hypernym relations. The language-specific syntactic rules have been manually crafted to build the patterns. On the other hand, recent studies have applied distributional approaches to word semantics. They extracted the semantic relations relying on the idea that similar words share similar contexts. Former distributional approaches have applied one-hot bag-of-word (BOW) encoding. The dimensionality problem of BOW has been solved by various neural network approaches, which represent words in very short and dense …