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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

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Research on Autonomous Movement of Nursing Wheelchair Based on Multi-sensor Fusion

Research on Autonomous Movement of Nursing Wheelchair Based ...

引用

ieee international conference on Real-time computing and Robotics (RCAR)

作者： Yingbing Su Huarui Zhu Zhou Zhou Bingshan Hu Hongliu Yu Renren Bao Institute of Rehabilitation Engineering Technology University of Shanghai for Science and Technology Institute of Rehabilitation Engineering and Technology University of Shanghai for Science and Technology Engineer of Siasun Robot&Automation Co. Ltd.

Aiming at the mobility needs of semi-disabled patients, an intelligent nursing wheelchair was designed to realize the autonomous movement of the wheelchair. First, the Cartographer algorithm based on graph optimization was introduced to wheelchairs to locate them in unknown environments. Secondly, multiple sensor information is fused to improve the positioning accuracy. Finally, machine vision, SLAM, path planning and other technologies are applied to the wheelchair to improve its intelligence and interactivity. Finally, an intelligent nursing wheelchair prototype is built, and the autonomous mobility system of the wheelchair is proved to have good robustness through experiments.

关键词： Simultaneous localization and mapping Wheelchairs Machine vision Prototypes Medical services Robustness Real-time systems

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Deep Learning Platforms for High-Performance and distributed computing Object Detection 3

Deep Learning Platforms for High-Performance and Distributed...

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3rd international conference on High Technology for Sustainable Development, HiTech 2020

作者： Lazarova, Milena Nakov, Ognyan Mihaylova, Elizabet Technical University of Sofia Dept. Computer Systems Sofia Bulgaria

ISBN: (纸本)9781728186511

Deep learning developed in the last decade and has been established as recent, modern and very promising technique with large potential to be successfully applied in various domains. Despite deep learning outperforms alternative techniques in providing solution to many different problems there are several concerns and limitations of the its usage, one of them being the computational complexity of the training process. The paper discusses the possibilities of taking advantage of high-performance and distributed computing for efficiently and effectively training deep learning models for computer vision applications. A comparison of deep learning platforms and frameworks utilized on high-performance computer systems is presented and their application for computer vision problem of object detection is evaluated based on a suggested parallel computation architectural model and utilization of RetinaNet model. © 2020 ieee.

关键词： Computer vision

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IoT sensor and Deep Neural Network based Wildfire Prediction System

IoT Sensor and Deep Neural Network based Wildfire Prediction...

引用

international conference on Intelligent computing and Control systems (ICICCS)

作者： Zope, Vidya Dadlani, Tarun Matai, Ashutosh Tembhumikar, Pranjali Kalani, Richa Vivekanand Educ Soc Inst Technol Dept Comp Engn Mumbai Maharashtra India Vivekanand Educ Soc Inst Technol Comp Engn Mumbai Maharashtra India

ISBN: (纸本)9781728148762

Forests, one of the most valuable and necessary resources and protect earth's ecological balance, are a natural habitat to animals and forest products are vital in our lives in many direct and indirect ways. But wildfires can cause critical damage to grounds and many other resources like properties, human life, wildlife in superabundant amounts. Wildfires burn acres of land and destroy everything in their paths in mere minutes. Wildfire destroys homes, animals, trees and plants, wildlife as well as vegetation. The effects of wildfires are numerous and wide-ranging. it causes a hugely significant impact on the economy, environment, heritage and social fabric of rural areas. Naturally caused wildfires can be predicted using factors[3] like temperature, humidity, soil moisture, pressure and many more. In this paper, the prediction of forest fires by machine learning using some operational monitoring over a region and encountering changes in climate using different sensors are advocated. The Wildfire Prediction System (WiPreSy) monitors and records changes in climatic parameters and predicts the intensity of forest fire based on real-time data, thus avoiding the massive loss due to forest fires.

关键词： Forest fire prediction Temperature and humidity sensor DHT11 Soil moisture sensor YL-69 Pressure and Altitude sensor BMP-280 GPS sensor Wildfire prediction Complete system for forest fire prediction Bolt IoT and cloud

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Coupling Integration and Evaluation of Centralized Energy Station and distributed Energy Cascade Utilization System

Coupling Integration and Evaluation of Centralized Energy St...

引用

2020 ieee international conference on Power, Intelligent computing and systems, ICPICS 2020

作者： Peng, Tao Li, Qifen Yang, Yongwen Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9781728198736

The integrated energy system (IES) has the characteristics of integrating and improving system performance and high reliability. Firstly, the paper analyses the problems existing in the original energy supply architecture and introduces the comprehensive energy supply architecture with deep gradient utilization. At the same time, the technical and economic model of IES is proposed as an index to evaluate the profitability of the system. An objective function of maximum return considering energy supply is proposed. This problem is a mixed integer linear programming (MILP) problem that is optimized to achieve IES operation optimization from an economic perspective. Finally, the method is applied to a numerical example to verify the economic foresight and practical usability of the proposed method. © 2020 ieee.

关键词： Integer programming

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Air Quality sensor Data Collection and Analytics With IoT for an Apartment With Mechanical Ventilation

Air Quality Sensor Data Collection and Analytics With IoT fo...

引用

ieee international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications

作者： Lukasz Scislo Nina Szczepanik-Scislo Faculty of Electrical and Computer Engineering Cracow University of Technology Cracow Faculty of Environmental and Power Engineering Cracow University of Technology Cracow

ISBN: (纸本)9781665426060

The aim of the research was to develop a concept of a remote measurement system for air quality management using IoT for an apartment with a mechanical exhaust system. The constant monitoring system with the possibility of manual control by the occupants allows teaching the occupants how to chose optimal settings with the biggest impact on the air quality. Additionally, using cloud data analysis, the measurements can be compared with simulations performed before the building construction. This allows choosing the proper apartment depending on the foreseen occupancy schedule and the family size.

关键词： Cloud computing Schedules Data analysis Memory Air quality Ventilation Mechanical variables measurement

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Dynamic Coordination in UAV Swarm Assisted MEC via Decentralized Deep Reinforcement Learning 12

Dynamic Coordination in UAV Swarm Assisted MEC via Decentral...

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12th international conference on Wireless Communications and Signal Processing (WCSP)

作者： Ye, Yuting Wei, Wenshu Geng, Dongqing He, Xiaofan Wuhan Univ Sch Elect Informat Wuhan Hubei Peoples R China

ISBN: (纸本)9781728172361

Driven by the exploding computing service demands from various intelligent mobile applications, an increasing amount of research efforts have been devoted to mobile edge computing (MEC). Meanwhile, unmanned aerial vehicles (UAVs) have found a great success in assisting existing wireless systems due to their flexibility and low cost. The advancements in these two closely related fields foster the development of the recently advocated UAV-assisted MEC paradigm, which is expected to bring unprecedented performance gain to the existing ground-based MEC systems. Nonetheless, existing works on UAV-assisted MEC mainly focus on the single-UAV scenarios and often assume static system states. In this paper, UAV swarm assisted MEC is considered where multiple collaborative UAVs are employed to help the terrestrial edge server to provide better edge computing services. However, the complexity of using existing methods to find the best dynamic coordination strategy in UAV swarm assisted MEC becomes intractable when the number of UAVs increases. To resolve this challenge, a novel decentralized deep reinforcement learning algorithm is proposed in this work, which can reduce the complexity by orders of magnitude. In addition, simulations are conducted to show that by using the proposed algorithm, the UAV swarm can efficiently learn a good dynamic coordination strategy and thus achieve a significantly better performance than the baseline scheme.

关键词： Mobile edge computing UAV distributed computing

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Dynamic probabilistic model checking for sensor validation in industry 4.0 applications 4

Dynamic probabilistic model checking for sensor validation i...

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4th ieee international conference on Smart Internet of Things, SmartIoT 2020

作者： Xin, Xin Keoh, Sye Loong Sevegnani, Michele Saerbeck, Martin University of Glasgow School of Computing Science Glasgow United Kingdom Tüv Süd Asia Pacific Digital Service Centre of Excellence Singapore Singapore

ISBN: (纸本)9781728165141

Industry 4.0 adopts Internet of Things (IoT) and service-oriented architectures to integrate Cyber-Physical systems and Enterprise Planning into manufacturing operations. This kind of integration consists of a combination of connected sensors and run-Time control algorithms. Consequential control decisions are driven by sensor-generated data. Hence, the trustworthiness of the sensor network readings is increasingly crucial to guarantee the performance and the quality of a manufacturing task. However, existing methodologies to test such systems often do not scale to the complexity and dynamic nature of today's sensor networks. This paper proposes a novel run-Time verification framework combining sensor-level fault detection and system-level probabilistic model checking. This framework can rigorously quantify the trustworthiness of sensor readings, hence enabling formal reasoning for system failure prediction. We evaluated our approach on an industrial turn-mill machine equipped with a sensor network to monitor its main components continuously. The results indicate that the proposed verification framework involving the quantified sensor's trustworthiness enhances the accuracy of the system failure prediction. © 2020 ieee.

关键词： Industry 4.0

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WSN for Food Product Quality Control

WSN for Food Product Quality Control

引用

ieee international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications

作者： Volodymyr Romanov Igor Galelyuka Oleksandr Voronenko V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9781665426060

Quality control of food products, including beverages, during the production process is an important task that can be successfully solved by information technology. Currently, quality control of wine, beer, juices and other beverages is carried out using biochemical control, which takes a time and requires the application of complex analytical devices under the control of qualified personnel. The development of information technologies, biosensors, sensor networks makes it possible to solve the problem of quality control of various beverages in express mode. The main contribution of this paper is the design and prototyping of wireless amperometric biosensors and wireless sensor network (WSN) on their base to monitor the concentration of various components in beverages. The WSN architecture was modified for technological process of winemaking. Wireless biosensors were tested with different types of enzymes on different concentration of solutions. Such tests confirm the possibility to use the wireless biosensors on some stages of technological process of winemaking. The upgrading wireless nodes by sensing unit with particular enzymes will allow covering all technological process.

关键词： Wireless communication Wireless sensor networks Protocols Quality control Biochemistry Food products sensors

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Energy-Efficient systems for Smart sensor Communications 30

Energy-Efficient Systems for Smart Sensor Communications

引用

30th international Telecommunication Networks and Applications conference (ITNAC)

作者： Hussain, Zahir M. Univ Kufa Comp Sci & Math Najaf Iraq Edith Cowan Univ Sch Engn Joondalup Australia

ISBN: (纸本)9781728188270

A wireless sensor network ( WSN) is a communication network with ad hoc configuration consisting of a number of tiny, low-power, low-cost sensors which are normally distributed in a decentralized fashion and have limited processing capability. WSNs have found a wide range of applications such as industrial process control, healthcare monitoring, surveillance, forest fire detection, natural disaster detection, target tracking, among many other applications. It is known that WSNs are resource-constrained, hence, energy efficiency is crucial for all applications of WSNs to extend the life span of the sensors' batteries. The most energy consuming operation in WSN is data communication, hence, it is important to reduce amount of data transmission through WSNs without significantly affecting the transferred information. In this paper we will focus on two directions of data-efficient signal representations that are expected to provide WSNs with sufficient energy control. The first direction is the use of intelligent short word-length (SWL) systems via embedded sigma-delta modulation, and the second direction is to use compressive sensing (CS) with chaotic sequences. If security is a factor, then CS via chaos can support secure communication in addition to its main function as a technique for data compression.

关键词： Wireless sensor Network WSN Short Word-length SWL Compressive Sensing Network Security

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Circuit design and waveform analysis of partial discharge ultrasonic sensor based on parallel systems

Circuit design and waveform analysis of partial discharge ul...

引用

Digital Twins and Parallel Intelligence (DTPI), ieee international conference on

作者： Yuhang Liu Yonglin Tian Fei-Yue Wang Tsinghua University Beijing China University of Science and Technology of China Hefei China Institute of Automation Chinese Academy of Sciences Beijing China

Partial discharge is a common harmful phenomenon in industry, it is necessary to establish a distributed low-cost online monitoring system. In this essay, we use ACP theory of parallel systems [1] to design the measurement conversion circuit for the ultrasonic sensor, moreover, the experimental verification is carried out in the actual system. Finally, the algorithm is designed on the PC to extract the characteristic parameters effectively, which has obtained good results.

关键词： Partial discharges Partial discharge measurement Industries Ultrasonic variables measurement Digital twin conferences Acoustics

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