基于多尺度空间注意力特征融合的人群计数网络

针对严重的尺度变化和遮挡导致在不同密集场景人群计数任务中性能差的问题,在密集场景识别网络(CSRNet)的基础上通过增加多尺度特征融合结构并引入空间注意力机制,提出了一种多尺度空间注意力特征融合网络(MAFNet).在MAFNet进行特征提取之前,需要对添加了人头标记的场景图进行高斯滤波生成真实密度图;此外,MAFNe...     

Marginal quality‐based long‐term incentive mechanisms for crowd sensing

Crowd sensing is a new paradigm that leverages pervasive sensor‐equipped mobile devices to provide sensing services like forensic analysis, documenting public spaces, and collaboratively constructing statistical models. Extensive user participation is indispensable for achieving good service quality. Nowadays, most of existing mechanisms focus on guaranteeing good service quality based on instantaneous extensive user participation for crowd sensing applications. Little attention has been dedicated to maximizing long‐term service quality for crowd sensing applications due to their heterogeneous interests, preferences, selfish behaviors, and so on. To fill these gaps, a closed‐form expression of the marginal sensing data quality based on the monopoly aggregation in economics is derived in this paper. Furthermore, we design marginal quality‐based incentive mechanisms for long‐term crowd sensing applications not only to enhance extensive user participation by maximizing the expected total profits of mobile users but also to stimulate mobile users to produce high‐quality contents by applying the marginal quality. Finally, simulation results show that our mechanisms outperform the existing solutions. Copyright © 2015 John Wiley & Sons, Ltd.     

Trajectory segment selection with limited budget in mobile crowd sensing

Mobile Crowd Sensing is an emerging paradigm, in which a large number of participants are involved to complete a sensing task under a certain incentive mechanism. Hence, when the budget used to pay participants is limited, how to choose the most appropriate participants becomes a critical problem. Most of existing works aim to select a subset of participants to maximize the coverage, without considering redundancy. There are two kinds of redundancy in the existing literature, one is brought by the incomplete coverage assessment, while the other one is brought by the traditional participant selection process. Since paying for redundant data leads to budget waste, existing works cannot solve the participant selection problem commendably under limited budget. To address such issues, we first propose a coverage assessment considering both uniform coverage and maximum coverage, then design a trajectory segment selection scheme. Rather than choosing the whole trajectory of a participant, our scheme selects certain segments. Both offline and online algorithms are proposed in this paper. Two benchmarks are implemented and we carry out extensive experiments based on a real dataset. The evaluation results prove the effectiveness and the advantage of our algorithms in terms of the coverage quality.     

A mobile crowd sensing ecosystem enabled by CUPUS: Cloud-based publish/subscribe middleware for the Internet of Things

Mobile crowd sensing (MCS) is a novel class of mobile Internet of Things (IoT) applications for community sensing where sensors and mobile devices jointly collect and share data of interest to observe phenomena over a large geographic area. The inherent device mobility and high sensing frequency has the capacity to produce dense and rich spatiotemporal information about our environment, but also creates new challenges due to device dynamicity and energy constraints, as well as large volumes of generated raw sensor data which need to be processed and analyzed to extract useful information for end users. The paper presents an ecosystem for mobile crowd sensing which relies on the CloUd-based PUblish/Subscribe middleware (CUPUS) to acquire sensor data from mobile devices in a flexible and energy-efficient manner and to perform near real-time processing of Big Data streams. CUPUS has unique features compared to other MCS platforms: It enables management of mobile sensor resources within the cloud, supports filtering and aggregation of sensor data on mobile devices prior to its transmission into the cloud based on global data requirements, and can push information of interest from the cloud to user devices in near real-time. We present our experience with implementation and deployment of an MCS application for air quality monitoring built on top of the CUPUS middleware. Our experimental evaluation shows that CUPUS offers scalable processing performance, both on mobile devices and within the cloud, while its data propagation delay is mainly affected by transmission delay on wireless links.     

Space‐Time Warp Curve for Synthesizing Multi‐character Motions

This paper introduces a new motion‐synthesis technique for animating multiple characters. At a high level, we introduce a hub‐sub‐control‐point scheme that automatically generates many different spline curves from a user scribble. Then, each spline curve becomes a trajectory along which a 3D character moves. Based on the given curves, our algorithm synthesizes motions using a cyclic motion. In this process, space‐time warp curves, which are time‐warp curves, are embedded in the 3D environment to control the speed of the motions. Since the space‐time warp curve represents a trajectory over the time domain, it enables us to verify whether the trajectory causes any collisions between characters by simply checking whether two space‐time warp curves intersect. In addition, it is possible to edit space‐time warp curves at run time to change the speed of the characters. We use several experiments to demonstrate that the proposed algorithm can efficiently synthesize a group of character motions. Our method creates collision‐avoiding trajectories ten times faster than those created manually.     

Analysis of the Stakeholder Engagement in the Deployment of Renewables and Smart Grid Technologies

The implementation of higher shares of renewables in a global energy mix has to be accompanied by simultaneous deployment of enabling smart grid technologies (SGTs). This combination will inevitably lead to a revolutionary change in a conventional energy system, particularly, the shifting role of consumers to prosumers. But resistance may arise from such a dramatic shift, since it is associated with high uncertainty in conjunction with increasing responsibilities of all stakeholders, the urgent need of effective control, and the development of a process. To ensure the positive influence, coherent actions of all players, and appropriate treatment of the spots of resistance, the analysis of the interplay between key stakeholders has been done. The paper introduces the framework for stakeholders' analysis, applies it on the European Union (EU) example, and provides recommendations to reduce the resistance of SGTs deployment.     

Generating a multiplicity of policies for agent steering in crowd simulation

Pedestrian steering algorithms range from completely procedural to entirely data‐driven, but the former grossly generalize across possible human behaviors and suffer computationally, whereas the latter are limited by the burden of ever‐increasing data samples. Our approach seeks the balanced middle ground by deriving a collection of machine‐learned policies based on the behavior of a procedural steering algorithm through the decomposition of the space of possible steering scenarios into steering contexts. The resulting algorithm scales well in the number of contexts, the use of new data sets to create new policies, and in the number of controlled agents as the policies become a simple evaluation of the rules asserted by the machine‐learning process. We also explore the use of synthetic data from an “oracle algorithm” that serves as an as‐needed source of samples, which can be stochastically polled for effective coverage. We observe that our approach produces pedestrian steering similar to that of the oracle steering algorithm, but with a significant performance boost. Runtime was reduced from hours under the oracle algorithm with 10 agents to on the order of 10 frames per second (FPS) with 3000 agents. We also analyze the nature of collisions in such a framework with no explicit collision avoidance. Copyright © 2014 John Wiley & Sons, Ltd.     

基于 BIM 与 Pathfinder 的楼梯间堆积物对高层住宅建筑人群疏散影响研究

高层住宅建筑发生火灾时,楼梯间作为主要逃生通道,保证其畅通性尤为重要,但楼梯间堆积物却是人群疏散的主要阻碍。利用 BIM 与 Pathfinder 软件相结合,建立应急疏散仿真模型,设定 574 名不同年龄阶段的疏散人员参与模拟,在Steering 行为模式下进行仿真,分别研究堆积物摆放面积、摆放形状及摆放位置对高层住宅建筑人群疏散时间、疏散速度及拥堵情况产生的影响,并以此为高层住宅建筑楼梯间的规范管理提供理论依据。     

基于环约束的虚拟人动态LOD技术

针对虚拟人体模型及其运动序列提出一种简化方法．首先在人体骨架节点（关节）等基本模型表面附近添加环形约束,结合Qslim算法生成基本简化模型;其次利用顶点混合方法从基本简化模型生成简化序列．实验结果表明。该方法可生成逼真的简化运动序列,且在简化过程和LOD控制过程中均独立于具体运动序列,适合规模群体的实时驱动．     

半高式屏蔽门在模拟风压荷载及人群挤压荷载作用下结构安全性能测试

提出了半高式屏蔽门结构安全性能的测试方案.通过对屏蔽门样机施加等效风压荷载和人群挤压荷载,模拟屏蔽门受荷载作用下的不利工况.通过试验反映了半高式屏蔽门的实际工作状况.