2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)最新文献_第4页

Map Reduce by K-Nearest Neighbor Joins 通过k近邻连接映射约简

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00050

Srikanth Bethu, B. Babu, S. G. Rao, R. Florence

引用次数: 1

Asymmetric Cryptography Based Unidirectional Authentication Method for RFID 基于非对称密码的RFID单向认证方法

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00073

Zhongke Li, H. Zhao, Xiaoping Su, C. Wan

引用次数: 2

Drone Detection Based on FD-HOG Descriptor 基于FD-HOG描述符的无人机检测

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00084

Zizhe Wang, L. Qi, Tie Yun, Yi Ding, Yang Bai

引用次数: 9

Analysis of the Impact of Denial of Service Attacks on the Group of Robots 拒绝服务攻击对机器人群的影响分析

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00023

E. Basan, M. Medvedev, Stanislav Teterevyatnikov

引用次数: 7

Research on Vehicle Taillight Detection and Semantic Recognition Based on Internet of Vehicle 基于车联网的汽车尾灯检测与语义识别研究

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00038

Zhenzhou Wang, Wei Huo, Pingping Yu, Lin Qi, Ning Cao

{"title":"Research on Vehicle Taillight Detection and Semantic Recognition Based on Internet of Vehicle","authors":"Zhenzhou Wang, Wei Huo, Pingping Yu, Lin Qi, Ning Cao","doi":"10.1109/CYBERC.2018.00038","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00038","url":null,"abstract":"Internet of Things(IOT) technology provides sufficient information technology and necessary decision-making basis for intelligent transportation. Internet of vehicles(IOV) is based on the architecture of the perception layer, network layer, and application layer. This article focuses on the tail light status in the perception layer of automotive active safety systems and proposes a method of vehicle taillamp detection and recognition based on active contour extraction and color feature analysis to solve the problem of information perception against the front vehicle. First, the front vehicles are detected and extracted by convolutional neural network and active contour models. Second, it analyzes the characteristics of taillamp pair and then segment the correct taillamp pair by color space conversion and location correlation principle. On this basis, the histograms distribution of taillamp are analyzed in different states and complete the recognition of taillamp. The experimental results show that the method can accurately identify the taillamp signal of the front vehicle in the daytime and effectively reduce the rate of false judgement caused by light interference.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129350196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Usage Demand Forecast and Quantity Recommendation for Urban Shared Bicycles 城市共享单车使用需求预测及数量建议

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00052

Yifeng Cui, Weifeng Lv, Qing Wang, Bowen Du

{"title":"Usage Demand Forecast and Quantity Recommendation for Urban Shared Bicycles","authors":"Yifeng Cui, Weifeng Lv, Qing Wang, Bowen Du","doi":"10.1109/CYBERC.2018.00052","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00052","url":null,"abstract":"The rise and wide use of urban shared bicycles brings great convenience to residents' daily trips. However, as the number of shared bicycles continues to increase in the city, more and more shared bicycles are being allocated unreasonably, which has a great impact on the public transportation and city appearance. To solve this problem, in this paper, we focus on the recommendation of the number of shared bicycles near the subway station and take the volume of the station's outbound passenger flow as the potential demand to study. Along this line, we first collect and investigate the subway traffic data which are gathered by automated fare collection(AFC) systems and some related feature data as the basic data source for the demand prediction. Second, upon these data, we develop a novel passenger flow forecast model based on an advanced Xgboost method and the idea of sliding window. Combined with the predicted results, we then design a method to transform the passenger flow results into an usage demand index and recommend a suitable number of shared bicycles for a specific subway station by it. Finally, we conduct a series of experiments with a variety of real-world data, and the results demonstrate our model can achieve a better performance and give us a reasonable quantity recommendation.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129358481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

3D Indoor Localization Mechanism Based on Multiple Sensors 基于多传感器的室内三维定位机制

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00097

Shijun Chen, Dawei Chen, Yuanyuan Wang, Xinxin Liu

{"title":"3D Indoor Localization Mechanism Based on Multiple Sensors","authors":"Shijun Chen, Dawei Chen, Yuanyuan Wang, Xinxin Liu","doi":"10.1109/CYBERC.2018.00097","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00097","url":null,"abstract":"In unfamiliar complicated high-rise building, users not only have to understand the 2D plane location information of floor, but also need to obtain the Z axis information of floor. According to this requirement, we proposed a 3D indoor localization mechanism based on multi-sensor, which integrates the air pressure examination and geomagnetic examination information to assist inertial navigation for calibration, and conduct integrated processing of examined information. The mechanism is based on the dynamic air pressure floor discrimination model, and according to the actual current floor, average time spent by passing each floor and the height of each floor, determine the floor of user in a real-time manner; according to the standard geomagnetic information data of floor, compute the geomagnetic coordinates of current location based on the geomagnetic realtime localization model, and obtain the floor of personnel and the plane location on the floor in a real-time manner. During the localization process of mechanism, it does not require additional hardware equipment or network signal, but only needs the air pressure gauge sensor and geomagnetic sensor to complete the indoor 3D real-time localization of building, which is convenient in practical application with broad adaptability.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127287091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural Exploration of Chinese Character Combination Networks 汉字组合网络的结构探索

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00040

Deshun Zhao, Kun Zhao, Jun Yang, Wei-yi Ma, Bo Yu

引用次数: 0

Ground Robot Path Planning Based on Simulated Annealing Genetic Algorithm 基于模拟退火遗传算法的地面机器人路径规划

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00081

Lanfei Wang, Jun Guo, Qu Wang (王曲）, Jiangming Kan

引用次数: 7

Spark Load Balancing Strategy Optimization Based on Internet of Things 基于物联网的Spark负载均衡策略优化

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00025

Suzhen Wang, Lu Zhang, Yanpiao Zhang, Ning Cao

{"title":"Spark Load Balancing Strategy Optimization Based on Internet of Things","authors":"Suzhen Wang, Lu Zhang, Yanpiao Zhang, Ning Cao","doi":"10.1109/CYBERC.2018.00025","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00025","url":null,"abstract":"The data collected by the Internet of Things (IOT) technology is becoming larger and larger, and the traditional data processing methods have encountered tremendous challenges. Spark, as a memory-based distributed computing framework, provides support for the data processing of the IOT. Load balancing is an important indicator to measure the performance of Spark computing. The load balancing strategy of Spark cluster only takes into account the locality of data, and neglects the computing capability and resource utilization of each node, which is prone to load unbalance and affecting the IOT data processing efficiency. Aiming at this issue, this paper optimizes and improves the current load balancing strategy of Spark based on the computing performance of each node in the Spark cluster, and proposes a task execution node assignment algorithm based on genetic algorithm and particle swarm optimization (TENAA). Experiments show that, compared with the Spark load balancing strategy, the load balancing strategy proposed in this paper has a significant increase both in load deviation and task completion time.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129655850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2