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International Conference on computer Supported Cooperative Work in Design

作者： Yu, Hanchao Liu, Hong Yang, Xiaopeng School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan City 250014 China

ISBN: (纸本)9781457703874

In order to generate novel 3D animation modelings automatically, an interactive genetic algorithm HAIGA based on C/S mode was proposed. In HAIGA, HSF synergy technology and ACIS rule were brought in. The ACIS rule expression was expressed by three binary trees, which were used to scale 3D entities unevenly in the x-axis, y-axis and z-axis direction separately. New rule expressions, which were used to generate new modelings by evolving automatically based on the existing 3D animation madelings, were generated by selection, crossover, mutation on binary trees. A prototype system in which 3D animation modelings could evolve automatically was developed. Experiments in the system show that the proposed method can support cooperative design effectively to generate a series of novel 3D animation modelings. © 2011 IEEE.

关键词： computer aided design

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Research on distributed data stream mining in Internet of things

Research on distributed data stream mining in Internet of th...

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2014 International Conference on Logistics Engineering, Management and computer Science, LEMCS 2014

作者： Xu, Liancheng Xun, Jiao School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9789462520103

For mining useful data from mass data generated by Internet of things, analyses shortages of the traditional Apriori algorithm which has a lower mining efficiency and occupies the larger memory space. So, MapReduce model of cloud computing is introduced. In the mechanism of MapReduce, combine the architecture characteristics and key technology of Internet of Things, conduct distributedmining on data and information in environment of the Internet of Things, and the calculation model of distributed data stream mining is drawn. Performance analysis proves that the new data stream mining model overcomes difficulty of traditional data mining, and each link reflects the idea of distributed structure and improves mining efficiency obviously. © 2014. The authors - Published by Atlantis Press.

关键词： Cloud computing

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Data-Driven Crowd Motion Modeling With Group Properties Analysis 25

Data-Driven Crowd Motion Modeling With Group Properties Anal...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Jia, Chuanhua Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

Traditional crowd evacuation simulation methods focus on the analysis of individual behavior in the crowd, but ignore the analysis of potential group properties in the process of crowd motion, which reduces the visual realism of the crowd evacuation simulation process. During crowd motion, crowds cause unconscious self-organization due to common destinations and social relationships, thus forming groups. Considering the influence of group properties on crowd motion is important to improve the visual realism of crowd motion simulation. To address this problem, we propose a crowd motion modeling method for group properties analysis (GPA-CMM). First, we build a data-driven group properties quantification (DGPQ) model to describe the characteristics of group motion accurately. In the model, we divide the crowd into several groups, extract motion properties of the crowd, and quantify the features of intra-group stability and inter-group conflict. Then, in order to analyze their influences of group properties, we build a stability and conflict based crowd motion analysis (SC-CMA) model. Finally, we implement a crowd simulation system based on SC-CMA, visualize the results of the theoretical analysis in a graphical way. The experimental results show that the method can simulate crowd motion more realistically. © 2022 IEEE.

关键词： Behavioral research

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Application of fractals in architectural shape design

Application of fractals in architectural shape design

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IEEE Symposium on Web Society, SWS

作者： Wang, Wen Liu, Hong Ma, Xueqiang Shandong Normal University Jinan 250001 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology China Shandong JianzhuUniversity Jinan 250001 China

ISBN: (纸本)9781424463572

A computer-aided method for architectural shape design was developed based on fractal algorithm. This method is creative, intelligent and automatic. Fractal algorithm applied here generates fractal dust as the data of unorganized points. Then the idea of surface reconstruction is borrowed in the construction of surface walls. In the process of surface reconstruction, the Power Crust algorithm has been improved and KD tree structure has been applied in the separation of inner- poles and outer-poles to accelerate the search for nearest neighbor and raise the efficiency of surface reconstruction. © 2010 IEEE.

关键词： Fractals

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Federated Learning based Path Planning Method for Crowd Evacuation 25

Federated Learning based Path Planning Method for Crowd Evac...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yuan, Zihao Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

The surveillance videos captured by cameras deployed in large-scale places can provide help for path planning of crowd evacuation in emergencies. However, most surveillance videos are not allowed to be shared in order to avoid privacy disclosure. Thus, it is difficult to obtain the video data of the whole scenario for path planning which greatly reduces effectiveness of path planning. How to provide global path planning for crowd evacuation while ensuring privacy protection is a challenging problem. To solve this problem, we propose a federated learning based path planning method for crowd evacuation. In this method, the potential field model are introduced into the federated learning framework to provide the privacy protection since only the potential field information is aggregated between the camera terminal and the central server, instead of the surveillance videos. First, we construct the local potential field for the surveillance scene of each camera by extracting the scene information and the crowds information. Second, the central server periodically performs the global potential field information aggregation and integrates these information to plan the optimal global path dynamically for crowd evacuation. Finally, we implemente a simulation platform to simulate the process of crowd movement and visualize the dynamic change of the potential energy field. Experimental results prove that the proposed method can provide privacy-preserving path planning for multi-camera evacuation scenarios. © 2022 IEEE.

关键词： Motion planning

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A sensor data processing and storage method for wireless body area networks 10

A sensor data processing and storage method for wireless bod...

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10th International Conference on Information Technology in Medicine and Education, ITME 2019

作者： Han, Lu Xun, Lulu Liu, Jinfa Yin, Wenjun Zheng, Xiangwei Zheng, Zhuoran Shandong Normal University Ji'nan China Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Ji'nan China

ISBN: (纸本)9781728139173

This paper proposes an adaptive processing method and a dynamic storage method of pulsed wave signal based on wireless body area network. Firstly, the ZigBee star network model is adopted as the basic network architecture of the system. Secondly, an adaptive Gaussian filter model is designed to eliminate the influence of environmental and man-made factors. Finally, the dynamic storage algorithm is used to map the pulsed wave signal to a new stored sample, and the regression model is used to restore the new sample to the original signal. Simulation experiments show that: 1) The adaptive Gaussian model minimizes the signal noise and better maintains the characteristics of the pulsed wave signal. 2) On the premise of using ARIMA regression model of neural network, dynamic storage algorithm improves the regression accuracy compared with fixed storage algorithm. © 2019 IEEE.

关键词： Zigbee

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Blockchain-based Crowd-sensing Trust Management Mechanism for Crowd Evacuation 25

Blockchain-based Crowd-sensing Trust Management Mechanism fo...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Jiawen Zhang, Guijuan Lu, Dianjie Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

With the continuous urban expansion, the safety of public places has attracted more and more attention. Once an accident occurs and people distrust their surroundings, it will cause congestion, trampling and other events. Individual trust value is particularly important in crowd evacuation. However, it is very difficult to evaluate the individual credibility with existing methods. We propose a blockchain-based crowd-sensing trust management mechanism (B-CSTM) to address this problem. First, we provide a message credibility evaluation mechanism that can evaluate the credibility of messages sent by individuals on spot. Second, we developed a trust calculation method that uses a distributed Evacuation Perception Unit (EPU) to calculate individuals' trust values by perception scores. Finally, we build a blockchain-based trust management mechanism model that uses blockchain to store and query trust values. Through experimental analysis, we visualize the trust results in the evacuation scenario. It is shown that the mechanism is feasible for the management of trust values (collection, calculation and storage, etc.) in crowd evacuation. © 2022 IEEE.

关键词： Blockchain

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Crowd intelligence in AI 2.0 era

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Frontiers of Information Technology & Electronic Engineering 2017年第1期18卷 15-43页

作者： Wei LI Wen-jun WU Huai-min WANG Xue-qi CHENG Hua-jun CHEN Zhi-hua ZHOU Rong DING State Key Laboratory of Software Development Beihang University National Laboratory for Parallel and Distributed Processing College of ComputerNational University of Defense Technology Institute of Computing Technology Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University National Key Laboratory for Novel Software Technology Nanjing University

The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence(AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-tooffline(O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.

关键词： Crowd intelligence Artificial intelligence 2.0 Crowdsourcing Human computation

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A real-time information perceived vehicle opportunity route: RIPVO 1

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7th China Conference of Wireless Sensor Networks, CWSN 2013

作者： Li, Kelei Wang, Xinhua Lv, Mianmian Wang, Zhen Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 250014 China

ISBN: (数字)9783642541742

ISBN: (纸本)9783642541735

Considering that VANET may reflect the characters of opportunity network, a routing scenario with real-time information perceiving is proposed in this paper. First, a node executes limited and directed data flooding and then carries out feedback with modified greedy algorithm using hot area. In this strategy, nodes dynamically adjust parameters by self-learning properly. The experiment result under ONE simulator shows its promotion comparing to classic Epidemic, etc. © 2014 Springer-Verlag Berlin Heidelberg.

关键词： Delay tolerant networks

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Collaborative Teams Recruitment Based on Dual Constraints of Willingness and Trust for Crowd Sensing 25

Collaborative Teams Recruitment Based on Dual Constraints of...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Song, Nianyun Lu, Dianjie Shi, Yepeng Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

As crowd sensing tasks become more complex, it is increasingly needed for participants to form teams to collaborate interactively in order to complete tasks more efficiently. It is valuable to fully understand user behavior, the organizational principles of crowd collaboration teams and incorporate them into the user recruitment process. In fact, there is a two-way process of subjective and objective selection for building a team in crowd sensing. From the participant's perspective, the willingness of users to participate in the task is a subjective factor;from the platform's perspective, the trust relationship between users is an objective factor. However, existing work has not well recognized the value of including collaborative teams in the crowd sensing recruitment process and has not considered the willingness and trust relationships between participants simultaneously, which results in poor quality of service (QoS) among recruited users. To address this problem, we propose a willingness and trust-based collaborative team recruitment method (WT-CTRM). First, we construct a willingness network (WN) and a trust network (TN) to describe the interaction of willingness and trust among the collaborated users respectively. Based on these networks, we then predict the unknown willingness and trust relationships between users for collaboration by using graph convolutional networks (GCNs). Finally, we build collaborative teams for the user recruitment process of crowd sensing based on the consensus constraints of the predicted willingness network and trust network. Simulation results show that the recruitment scheme significantly improves the QoS in task scenarios with collaborative requirements. © 2022 IEEE.

关键词： Quality of service

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