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在“互联网+”的时代背景下,传统出版行业的弊端逐渐显露了出来,主要表现为出版成本过高。在此背景下,图书的众筹出版就成为了图书新的出版方式,不过目前这种出版方式还存在着一些问题。其中图书前期规划、众筹平台,以及监督管治和众筹融资之间的关系就成为其解决出版行业的弊端,改变出版行业现状的关键。当然,消费者的反响,以及版权和众筹团体的关系也至关重要。 相似文献
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众创空间是指为满足大众创新创业需求,提供工作空间、网络空间、社交空间和资源共享空间,积极利用众筹、众扶、众包等新手段,以社会化、专业化、市场化、网络化为服务特色,实现低成本、便利化、全要素、开放式运营的创新创业平台.文章介绍了湖南省众创空间高质量发展的现状,并针对存在的问题提出发展建议. 相似文献
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基于"Web 2.0"、"City 2.0"、"政府2.0"等概念,提出了"交通2.0"概念,并建立了系统模型。该系统模型包括众筹数据和开放数据两个维度,众筹数据通过收集公众从智能手机、车载终端、Web 2.0网站上传的交通数据,获取现有检测系统不能提供的实时数据,并及时解决公众使用交通系统过程中发现的问题;开放数据通过开放公共数据,催化公众把开放的数据做出各种相互补充相互竞争的应用和服务,形成可持续的全面解决方案和创新的生态环境。该系统模型通过云计算、大数据处理、手机应用等技术做到了聚合众筹数据,提供开放数据以催化产生创新的交通应用和服务以完善现有交通设施和管理制度,并给将来的交通规划提供数据支撑和科学决策支持。智慧交通项目集成"交通2.0"后,实践表明奠定众筹数据和开放数据两个渠道,使公众能够积极地参与到交通生态链的完善和智慧化过程中来,是真正实现从"智能"到"智慧"的关键,是智慧交通系统产生活力的源泉。 相似文献
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互联网众筹数据在山东联通无线网络优化中进行探索性的应用,通过搭建运营商独立的数据采集和分析平台,实时反馈用户的网络状况,综合分析海量数据查找网络中的不足及与友商的差距,有效提升用户体验,节约测试成本,提升网络质量。 相似文献
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在移动网络时代,石家庄联通基于"互联网+"的众筹优化模式,通过自主研发"i"测试应用系统,实现对用户网络体验质量的深度挖掘和精准定位,并对如何深度评估网络质量、挖掘网络问题、提升用户体验做了深层次的研究,应用前景极其广泛。 相似文献
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面向公共安全图像监控的人群行为分析 总被引:1,自引:0,他引:1
从城市公共安全人群监控和分析的应用需求出发,首先分析上海图像监控系统建设和应用现状,然后归纳总结当前国内外在人群行为分析领域的研究框架和思路,进而分别从群体建模、群体状态分析和群体行为三方面展开讨论,分析给出各种方法的优缺点和适用性,最后总结群体行为分析的应用前景. 相似文献
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随着经济的发展,在一些公共场合人流量越来越大。传统的人工监控因为其局限性,已不能满足实际需要,于是出现了基于图像处理技术的智能视频场景监控系统。针对高人流密度场景的现状,在基于像素数的人群密度估计算法的基础上,提出了基于可变矩形框的人群密度估计算法。该算法主要由最小二乘法线性拟合和检测算法2部分组成。实验结果表明该算法不仅能减小视频中行人大小变化所造成的像素数非线性变化误差,而且能较有效地消除噪声点的影响,具有较高的人群密度估计准确度和鲁棒性。 相似文献
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Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In mo-nocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately esti-mated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd’s density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method. 相似文献
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The human visual system has the ability to rapidly identify and redirect attention to important visual information in high complexity scenes such as the human crowd. Saliency prediction in the human crowd scene is the process using computer vision techniques to imitate the human visual system, predicting which areas in a human crowd scene may attract human attention. However, it is a challenging task to identify which factors may attract human attention due to the high complexity of the human crowd scene. In this work, we propose Multiscale DenseNet — Dilated and Attention (MSDense-DAt), a convolutional neural network (CNN) using self-attention to integrate the result of knowledge-driven gaze in the human visual system to identify salient areas in the human crowd scene. Our method combines various state-of-the-art deep learning architectures to deal with the high complexity in human crowd image, such as multiscale DenseNet for multiscale deep features extraction, self-attention, and dilated convolution. Then the effectiveness of each component in our CNN architecture is evaluated by comparing different components combinations. Finally, the proposed method is further evaluated in different crowd density levels to appraise the effect of crowd density on model performance. 相似文献
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针对人群密集公共场所的视频监控,传统的人工监控因为其局限性,已不能满足实际需要,人群智能监控应运而生,而人群密度成为监控的重要对象。基于像素点统计的人群密度估计方法简单直观,但仅适用于人群密度较低场合,密度较高时误差较大。对中高密度人群,本文给出了一种基于灰度共生矩阵和分形的人群特征提取方法,进而利用支持向量机实现人群密度分类。对基于视频的人群密度估计实验结果表明本文提出的方法是有效的。 相似文献
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提出了一种人群密度估计算法,将像素统计和纹理特征两种基本方法进行有效结合。前景提取使用改进的Vibe算法,设定感兴趣区域(ROI)来减少运算量。同时,引入形态学处理和透视矫正消除了因人物远近所造成的误差。并设定了一套人群密度等级划分的标准,克服了因人群密度高低频繁变化造成的误差。最终,实验结果显示运算速度和正确率均较为可观,证明了本算法的可靠性。 相似文献
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Nan Zhang Xiaolong Yang Min Zhang Yan Sun Keping Long 《International Journal of Communication Systems》2018,31(1)
With the rapid development of cloud computing, the number of cloud users is growing exponentially. Data centers have come under great pressure, and the problem of power consumption has become increasingly prominent. However, many idle resources that are geographically distributed in the network can be used as resource providers for cloud tasks. These distributed resources may not be able to support the resource‐intensive applications alone because of their limited capacity; however, the capacity will be considerably increased if they can cooperate with each other and share resources. Therefore, in this paper, a new resource‐providing model called “crowd‐funding” is proposed. In the crowd‐funding model, idle resources can be collected to form a virtual resource pool for providing cloud services. Based on this model, a new task scheduling algorithm is proposed, RC‐GA (genetic algorithm for task scheduling based on a resource crowd‐funding model). For crowd‐funding, the resources come from different heterogeneous devices, so the resource stability should be considered different. The scheduling targets of the RC‐GA are designed to increase the stability of task execution and reduce power consumption at the same time. In addition, to reduce random errors in the evolution process, the roulette wheel selection operator of the genetic algorithm is improved. The experiment shows that the RC‐GA can achieve good results. 相似文献
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分析了人员疏散理论的研究现状,在总结现有的各种仿真模型的基础上,提出了基于多智能体的微观离散疏散仿真模型。并且在该模型的基础上开发了相应的人员疏散计算机仿真系统,根据系统模拟人员疏散,对仿真疏散得到的结果进行了分析研究,为实际的疏散工作提供了有价值的参考建议。 相似文献