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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

共 1248 条记录，以下是611-620 订阅

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An IoT Enabled Framework for Smart Buildings Empowered with Cloud Fog Infrastructures 3

An IoT Enabled Framework for Smart Buildings Empowered with ...

引用

3rd International Conference on Innovative computing, ICIC 2019

作者： Fayyaz, Hira Rehman, Zia Ul Abbas, Sagheer School of Systems and Technology University of Management and Technology Lahore Pakistan Bahria University Department of Computer Sciences Lahore Pakistan School of Computer Science NCBAE Lahore Pakistan

ISBN: (纸本)9781728146829

Modern Urban development of Internet of Things (IoT) are appropriate candidates for the initiative of Smart City;which aims at integrating physical environments into computing communication technologies leveraging maximum benefit in terms of resource utilization for optimal services. One substantial constituent of Smart City is Smart Building. A smart building is adequately intelligent to sense contextual environment and take smart decisions, competently employing available resource thus being more accurate, energy efficient and secure. Smart Building utilizes systems which are highly complex, dynamic natured and heterogeneous. Considering the functional requirements of Smart Building this research work emphasize that cloud has the potential of supporting complex computations, long term analytics and storage facilities for bulk amount of data being produced. It further specifies that cloud performance could be enhanced if innovative paradigm of Fog computing is introduced along with cloud solution. The proposed Internet of Things (IoT) framework of Smart Building is deliberated with all associated benefits and apprehension of cloud Fog paradigm. Application model of the framework along with it processing modules is also demonstrated in research effort. Performance metrics are evaluated with cloud deployments and with cloud fog deployments for further clarity. © 2019 ieee.

关键词： Internet of things

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Integration of SCADA services in cross-infrastructure holistic tests of cyber-physical energy systems 19

Integration of SCADA services in cross-infrastructure holist...

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19th ieee International Conference on Environment and Electrical Engineering (EEEIC) / 3rd ieee Industrial and Commercial Power systems Europe Conference (I and CPS Europe)

作者： Van Hoa Nguyen Tung Lam Nguyen Quoc Tuan Tran Besanger, Yvon Caire, Raphael Univ Grenoble Alpes INES CEA LITENDept Solar Technol F-73375 Le Bourget Du Lac France Univ Grenoble Alpes G2Elab CNRS F-38000 Grenoble France

ISBN: (纸本)9781728106526

Cyber-Physical Energy System, due to its multidomain nature, requires a holistic validation methodology, which may involve the integration of assets and expertise from various research infrastructures. In this paper, the integration of Supervisory Control and data acquisition services to cross-infrastructure experiment is proposed. The method requires a high degree of interoperability among the participating partners and can be applied to extend the capacity as well as the degree of realism of advanced validation method such as co-simulation, remote hardware-in-the-loop or hybrid simulation. The proposed method is applied to a case study of multi-agent system based control for islanded microgrid where real devices from one platform is integrated to real-time simulation and control platform in a distanced infrastructure, in a holistic experimental implementation.

关键词： Interoperability SCADA-as-a-service Cross-infrastructure Hardware-in-the-loop Co-simulation Holistic Testing

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Radio Regulatory Knowledge Graph Framework and Its Application

Radio Regulatory Knowledge Graph Framework and Its Applicati...

引用

ieee International Conference on Electronics and Communication Engineering (ICECE)

作者： Qiannan Lu Chunxin Li Jingjing Yang Peng Li Ming Huang Lin Li The School of Information Science and Engineering Yunnan University Kunming PR China Yunnan Radio Monitoring Station of the State Radio Monitoring Center Kunming PR China

ISBN: (数字)9781728192581

ISBN: (纸本)9781728192598

The explosive growth of data in radio monitoring and spectrum management brings an urgent demand for intelligent radio regulatory. However, due to the existence of a large amount of heterogeneous multi-dimensional data, the knowledge graph in radio regulatory domain has not been constructed. In this paper, an efficient radio regulatory knowledge graph framework was proposed. First, we extracted radio regulatory related work to ontologies, entities, relations, and built the knowledge graph. Then the radio transmission equipment type approval knowledge graph was constructed as an application. The rich relationships among entities, properties were revealed. It proves that the proposed framework has much theoretical and practical value.

关键词： Knowledge engineering Ontologies Real-time systems Explosives Monitoring Radio spectrum management Edge computing

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Pose-graph based Crowdsourced Mapping Framework

Pose-graph based Crowdsourced Mapping Framework

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Connected and Automated Vehicles Symposium (CAVS)

作者： Anweshan Das Joris IJsselmuiden Gijs Dubbelman Faculty of Electrical Engineering Mobile Perception Systems lab of SPS/VCA Eindhoven University of Technology Eindhoven The Netherlands Track32. Nieuwe Kazernelaan 2 D86 Ede The Netherlands

ISBN: (数字)9781728190013

ISBN: (纸本)9781728190020

Autonomous vehicles are dependent on High Definition (HD) maps. The process of generating and updating these maps is slow, expensive, and not scalable for the whole world. Crowdsourcing vehicle sensor data to generate and update maps is a solution to the problem. In this paper, we propose and evaluate an end-to-end pose-graph optimization-based mapping framework using crowdsourced vehicle data. The in-vehicle data acquisition framework and the cloud-based mapping framework that fuses data from a consumer-grade Global Navigation Satellite System (GNSS) receiver, an odometry sensor, and a stereo camera is described in detail. We focus on using stereo image pairs for loop-closure detection to combine crowdsourced data from different sessions that are affected by GNSS biases. We evaluate our framework on a data-set of more than 180 km recorded around the Eindhoven area. After the map generation process, the results exhibit a 56.23% improvement in maximum offset error and a 24.39% improvement in precision around the loop-closure area.

关键词： Crowdsourcing Global navigation satellite system Cloud computing Fuses Image edge detection data acquisition Receivers

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Application Research of NB-IoT Technology Based on Fog computing in intelligent Parking System

Application Research of NB-IoT Technology Based on Fog Compu...

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ieee advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Xiaowei Lin Baoling Qin Fenglin Zheng Xinhong Chen Chenglin Huang Riqiang Pan College of Electronic Information Engineering Foshan University Foshan China

In view of the intelligent demand of modern large-scale parking lot and based on the enormous application advantages of fog computing on the Internet of things and the unique characteristics of NB-IoT technology, the design and application of NB-IoT technology which is based on fog computing in parking system model architecture is proposed, to promote parking systems to be more intelligent, increase efficiency, reduce expenses, and increase efficiency. The system model design adopts four-layer structure: Perception Layer, Network Layer, Cloud-Fog Platform Layer and User Layer, and uses the technical characteristics and advantages such as Fog computing and NB-IoT to make data analysis, storage, calculation, exchange and communication in the system more integrated, fluent, and real-time. Meanwhile, the model is built into a low-latency, wide-coverage, highly intelligent parking system, and its research results provide totally new ideas and solutions to the drawbacks of the existing parking systems.

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Prune Strategy to YOLO-Based Model

Prune Strategy to YOLO-Based Model

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ieee International Conference of Safety Produce Informatization (IICSPI)

作者： Boyu Zhao Zhicheng Dong Xuerui Zhang Jie Li School of Information Science and Technology Tibet University Lhasa China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Chongqing University of Science and Technology Chongqing China

ISBN: (数字)9781728177380

ISBN: (纸本)9781728177397

One-stage networks have been widely used in target detection systems that need to learn from the big data. Most of them have good real-time performance and accuracy. However, due to their convolution structure, in actual applications, they still require more memory space and stronger computing power, thereby we choose to apply a pruning strategy on the target detection network to reduce the size of the network model and the number of parameters. In order to prove the practicability of the pruning method, we select the YOLOv3 model for experiments and provide a crop data set collected in the orchard to show the model effect. In this data set, in the best case, the volume of the network model is reduced by 52.7% compared with the original model, and the reasoning time is reduced by 57.5%. At the same time, it also uses data processing methods to compensate for the decrease in accuracy caused by pruning.

关键词： Training Solid modeling Computational modeling data preprocessing Object detection data models Agriculture

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Latency Reduction Techniques for NB-IoT Networks

Latency Reduction Techniques for NB-IoT Networks

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ieee International Conference on intelligent data acquisition and advanced computing systems

作者： Kofi Atta Nsiah Zubair Amjad Axel Sikora Benoit Hilt Jean-Philippe Lauffenburger Hahn-Schickard Gesellschaft fur Angewandte Forschung e.V. Institute of Reliable Embedded Systems and Communication Electronics (ivESK) Offenburg University of Applied Sciences Institut de Recherche en Informatique Mathematiques Automatique et Signal (IRIMAS) Universite de Haute Alsace

ISBN: (纸本)9781728140704

Enabling ultra-low latency is one of the major drivers for the development of future cellular networks to support delay sensitive applications including factory automation, autonomous vehicles and tactile internet. Narrowband Internet of Things (NB-IoT) is a 3rd Generation Partnership Project (3GPP) Release 13 standardized cellular network currently optimized for massive Machine Type Communication (mMTC). To reduce the latency in cellular networks, 3GPP has proposed some latency reduction techniques that include Semi Persistent Scheduling (SPS) and short Transmission Time Interval (sTTI). In this paper, we investigate the potential of adopting both techniques in NB-IoT networks and provide a comprehensive performance evaluation. We firstly analyze these techniques and then implement them in an open-source network simulator (NS3). Simulations are performed with a focus on Cat-NB1 User Equipment (UE) category to evaluate the uplink user-plane latency. Our results show that SPS and sTTI have the potential to greatly reduce the latency in NB-IoT systems. We believe that both techniques can be integrated into NB-IoT systems to position NB-IoT as a preferred technology for low data rate Ultra-Reliable Low-Latency Communication (URLLC) applications before 5G has been fully rolled out.

关键词： NB-IoT Semi-persistent scheduling Short TTI Machine-type communication Low latency Latency Cellular networks Performance Evaluation SAFETY PRECEDENCE SEQUENCE solar power satellites Special purpose ships 3GPP

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intelligent system for office environment using internet of things 3

Intelligent system for office environment using internet of ...

引用

3rd International Conference on Trends in Electronics and Informatics, ICOEI 2019

作者： Sunchu, Rahul Palli, Srichandrahaas Datta, V.V. Sai Rama Shanmugasundaram, M. Dept. of Embedded Technology Vellore Institute of Technology India Vellore Institute of Technology India

ISBN: (纸本)9781538694398

This Internet of Things is the interconnection of everyday objects with each other via the Internet of computing devices embedded in them, enabling them to send and receive data. This paper presents the design of smart office systems for controlling, monitoring and automation of electrical appliances depending on the entry and exit of the individual employee into the workspace. This design also simultaneously maintains and monitors the information in the cloud about the entry and exit timings, number of cabins electrified and electricity usage of the employees using the RFID tags scans at the entry point of the office, It also allows and assists the employees to control and monitor his cabin electricity status through a smartphone. The intelligent system for office environment project mainly focuses on the use of convenience for the users and to save electricity. To optimize office administrative management, a webpage is created which gives the administrative management the information about how many cabins. The equipment used for this is RC522 reader which is a Radio Frequency Identification system (RFID) which will send the data to the raspberry pi which controls the office environment using IoT. A mobile phone application is designed which works by integrating the concoction of both IoT and the RFID system to monitor and control the devices through the application 'MQTT' which remotely gives the flexibility to the users. © 2019 ieee.

关键词： intelligent systems

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LiDAR-Based Road Extraction for UGV in High Definition Map

LiDAR-Based Road Extraction for UGV in High Definition Map

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ieee International Conference on Unmanned systems (ICUS)

作者： Shengguo Hu Huivan Chen Boyang Wang Jianwei Gong Yuedong Ma School of Mechanical Engineering Beijing Institute of Technology Beijing China Intelligent Vehicle Research Center Beijing Institute of Technology Beijing China Institute of Advanced Technology Beijing Institute of Technology Ji’nan China North Automatic Congtrol Technology Institute Taiyuan China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

Extracting road information from the high-density point cloud collected by the vehicle-borne laser scanning system is essential for making High Definition (HD) maps. This paper proposes a three-dimensional (3D) laser point cloudbased pavement extraction method applicable to roads of different structure types. First, the filtering method based on cloth simulation is used to extract all ground point sets from the original point cloud data. The partial surface points in front of the vehicle are selected from the ground point to estimate the normal vector of the road surface model. Then, based on the uniformity of the point cloud density distribution of the road surface, all the road points are finally filtered out from the plane point concentration by calculating the continuity of the onedimensional and two-dimensional point density. After that, according to the characteristics that the road points are positively distributed, the method based on the skewness balance is used to estimate the separable road sign. We show that this method can accurately extract road surface information from point cloud data with an extraction rate of 96.1%.

关键词： Roads Mechanical engineering Three-dimensional displays Lasers intelligent vehicles Fitting Filtering

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Learning to Represent and Generalize Lateral Control Deviation Based on Driving data

Learning to Represent and Generalize Lateral Control Deviati...

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ieee International Conference on Unmanned systems (ICUS)

作者： Junbo Liao Shaobin Wu Jianwei Gong Haijie Guan Kunhong Ye Shixing Li School of Mechanical Engineering Beijing Institute of Technology Beijing China Intelligent Vehicle Research Center Beijing Institute of Technology Beijing Institute of Advanced Technology Beijing Institute of Technology Ji’nan China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

The lateral control deviation caused by insufficient control algorithm and defective actuator response Will lead to the deviation between the actual driving trajectory and planned trajectory. How to use the driving data to represent lateral control deviations and then generalize them to the correction of planned trajectories will further improve the driving safety of intelligent vehicles. In this paper, a statistical model is established to predict the lateral control deviation. The lateral control deviation between the planned trajectory and the actual driving trajectory is represented by velocity, curvature, and heading deviation. We select three kinds of features to train the statistical models: the previous, current, and future state of path point sequences. The Gaussian Mixture Model (GMM) is used to train the statistical model of lateral control deviation, and Gaussian Mixture Regression (GMR) is used to predict the lateral control deviation. Meanwhile, the actual driving trajectory of the vehicle can be generated directly according to the planned trajectory and the vehicle state. Experiment results show that the proposed method in this paper can predict the future lateral control deviation value With higher accuracy by comparing it with the K-means clustering method.

关键词： Training Training data Predictive models Prediction algorithms data models Trajectory Standards

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