34,862 research outputs found

    GM-Net: Learning Features with More Efficiency

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    Deep Convolutional Neural Networks (CNNs) are capable of learning unprecedentedly effective features from images. Some researchers have struggled to enhance the parameters' efficiency using grouped convolution. However, the relation between the optimal number of convolutional groups and the recognition performance remains an open problem. In this paper, we propose a series of Basic Units (BUs) and a two-level merging strategy to construct deep CNNs, referred to as a joint Grouped Merging Net (GM-Net), which can produce joint grouped and reused deep features while maintaining the feature discriminability for classification tasks. Our GM-Net architectures with the proposed BU_A (dense connection) and BU_B (straight mapping) lead to significant reduction in the number of network parameters and obtain performance improvement in image classification tasks. Extensive experiments are conducted to validate the superior performance of the GM-Net than the state-of-the-arts on the benchmark datasets, e.g., MNIST, CIFAR-10, CIFAR-100 and SVHN.Comment: 6 Pages, 5 figure

    Exact controllability of multiplex networks

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    Date of Acceptance: 11/09/2014Peer reviewedPublisher PD

    DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection

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    Although YOLOv2 approach is extremely fast on object detection; its backbone network has the low ability on feature extraction and fails to make full use of multi-scale local region features, which restricts the improvement of object detection accuracy. Therefore, this paper proposed a DC-SPP-YOLO (Dense Connection and Spatial Pyramid Pooling Based YOLO) approach for ameliorating the object detection accuracy of YOLOv2. Specifically, the dense connection of convolution layers is employed in the backbone network of YOLOv2 to strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an improved spatial pyramid pooling is introduced to pool and concatenate the multi-scale local region features, so that the network can learn the object features more comprehensively. The DC-SPP-YOLO model is established and trained based on a new loss function composed of mean square error and cross entropy, and the object detection is realized. Experiments demonstrate that the mAP (mean Average Precision) of DC-SPP-YOLO proposed on PASCAL VOC datasets and UA-DETRAC datasets is higher than that of YOLOv2; the object detection accuracy of DC-SPP-YOLO is superior to YOLOv2 by strengthening feature extraction and using the multi-scale local region features.Comment: 23 pages, 9 figures, 9 table

    FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

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    In this paper, we develop deep spatio-temporal neural networks to sequentially count vehicles from low quality videos captured by city cameras (citycams). Citycam videos have low resolution, low frame rate, high occlusion and large perspective, making most existing methods lose their efficacy. To overcome limitations of existing methods and incorporate the temporal information of traffic video, we design a novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with long short term memory networks (LSTM) in a residual learning fashion. Such design leverages the strengths of FCN for pixel-level prediction and the strengths of LSTM for learning complex temporal dynamics. The residual learning connection reformulates the vehicle count regression as learning residual functions with reference to the sum of densities in each frame, which significantly accelerates the training of networks. To preserve feature map resolution, we propose a Hyper-Atrous combination to integrate atrous convolution in FCN and combine feature maps of different convolution layers. FCN-rLSTM enables refined feature representation and a novel end-to-end trainable mapping from pixels to vehicle count. We extensively evaluated the proposed method on different counting tasks with three datasets, with experimental results demonstrating their effectiveness and robustness. In particular, FCN-rLSTM reduces the mean absolute error (MAE) from 5.31 to 4.21 on TRANCOS, and reduces the MAE from 2.74 to 1.53 on WebCamT. Training process is accelerated by 5 times on average.Comment: Accepted by International Conference on Computer Vision (ICCV), 201

    Mobility and Equity for New York's Transit-Starved Neighborhoods: The Case for Full-Featured Bus Rapid Transit

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    New York City's public transportation system moves millions of people every day. But an increasing number who live in outer borough neighborhoods are stuck with unreliable transit options and long travel times tracked in hours, not minutes.It does not have to be this way.Developed by the Pratt Center for Community Development and funded by the Rockefeller Foundation, this report highlights the limitations of New York City's current public transit system, the adverse effects those limitations have on our economy and quality of life, and the role Bus Rapid Transit (BRT) can play in remedying these transit inequities.BRT has transformed cities across the world from Mexico City to Barcelona to Cleveland. At a fraction of the cost to build just a mile of subway rail, BRT gives riders a reliable way to get where they need to go.BRT is effective. It is innovative. And it could be the solution for New York's transit-starved neighborhoods

    Application of multiple resistive superconducting fault-current limiters for fast fault detection in highly interconnected distribution systems

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    Superconducting fault-current limiters (SFCLs) offer several benefits for electrical distribution systems, especially with increasing distributed generation and the requirements for better network reliability and efficiency. This paper examines the use of multiple SFCLs in a protection scheme to locate faulted circuits, using an approach which is radically different from typical proposed applications of fault current limitation, and also which does not require communications. The technique, referred to as “current division discrimination” (CDD), is based upon the intrinsic inverse current-time characteristics of resistive SFCLs, which ensures that only the SFCLs closest to a fault operate. CDD is especially suited to meshed networks and particularly when the network topology may change over time. Meshed networks are expensive and complex to protect using conventional methods. Simulation results with multiple SFCLs, using a thermal-electric superconductor model, confirm that CDD operates as expected. Nevertheless, CDD has limitations, which are examined in this paper. The SFCLs must be appropriately rated for the maximum system fault level, although some variation in actual fault level can be tolerated. For correct coordination between SFCLs, each bus must have at least three circuits that can supply fault current, and the SFCLs should have identical current-time characteristics
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