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Scientific conference on Mechatronics Engineering and computer Science

作者： Kele Xu Yanrong Liu Electronic Information School Xianyang Vocational&Technical College School of Information Engineering Shaanxi International Business College

The growth of traditional crops relies mainly on fertilization, variety improvement and mechanized planting to increase production. Crop yield and fruit quality of crops are difficult to guarantee in the face of declining soil fertility and deteriorating environment. The direction of modern crop development is to develop a smart crop model, and to achieve efficient management and sustainable development of crops through precise sensing technology and in-depth intelligent technology. Smart agriculture based on Internet of Things and data mining has obvious effects on improving traditional crop planting patterns, and is an ideal model for efficient management of crops. The Internet of Things monitoring system combines a variety of wireless sensors, robots and drones to collect real-time growth data of crops in a timely manner, enabling visualization and online monitoring and management of crop growth environments. Therefore, this study is aimed at the problem of inaccurate collection of growth environment parameters of crops in greenhouses during the growth process of crops, and designed a greenhouse greenhouse intelligent monitoring system based on Internet of Things. This study used Internet network to realize human-computer communication interaction, and monitored the soil temperature and humidity, air temperature and humidity, and CO concentration in the greenhouse to complete the monitoring of the on-site crop growth environment. It was also accurate irrigation of crops to achieve precise collection of labour, environmental parameters for crop growth, and remote control of irrigation equipment. The experimental results of the system showed that the system is stable and has good robustness and can achieve the expected design goals.

关键词： the Growing Environment of Crops Online monitoring

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Motor Bearing Fault Diagnosis Based on Deep Learning

Motor Bearing Fault Diagnosis Based on Deep Learning

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/distributed Computing (SNPD)

作者： Wei Zhang Yong Hu Deliang Zeng Wei Luo Gengda Li Miao Liu School of Control and Computer Engineering North China Electric Power University Beijing China Intelligent Power Generation Technology Research Center Guodian New Energy Technology Research Institute Co.Ltd Beijing China

ISBN: (纸本)9781728116525

The effective fault monitoring of the motor bearings not only can ensure the smooth and efficient operation of equipment, but also can detect and eliminate the running fault in time to prevent major accidents. Based on deep learning algorithm, this paper constructs a stacked auto-encoder (SAE) network. The input data are compressed by introduced sparsity constraint, so that the network can accurately extract the fault characteristics of the input data. And the fault recognition ability of the network can be improved by introducing random noise to reconstruct input data. The simulation result shows that the SAE network can not only overcome the shortcomings of traditional fault diagnosis methods that requires personnel to distinguish fault samples and needs a large number of prior knowledge; but also realize the self-learning of fault feature information. The accuracy rates of fault identification reach 98%, 94%, 96% and 95.5% in four different working conditions. What's more, the network can adapt to the actual multi-load cases, demonstrate strong robustness under different working conditions.

关键词： Fault diagnosis Vibrations Feature extraction Neurons Deep learning Biological neural networks Training

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2018 4th international conference on computer and Technology Applications, ICCTA 2018

2018 4th International Conference on Computer and Technology...

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4th international conference on computer and Technology Applications, ICCTA 2018

ISBN: (纸本)9781538669952

The proceedings contain 16 papers. The topics discussed include: computer mathematical modeling of wave processes;green computing: awareness and practices;detection of Smurf flooding attacks using Kullback-Leibler-based scheme;urban activity summarization with geo-tagged social media data;personal data protection maturity model for the micro financial sector in Peru;data privacy in cloud computing;academic advising system using data mining method for decision making support;research on personalized referral service and big data mining for e-commerce with machine learning;a method of visual measurement based on QR code in navigation and positioning of closed-type orchard;an efficient two-stage B1C signal acquisition technique for engineering implementation of the modern Beidou receiver;conceptualization of an information system governance model dedicated to the governance of scientific research in the Moroccan university;pre-coding technique for adaptive threshold detectors in diffusion-based molecular communications;automatic music generation by true random numbers for Turkish Makams;responsive web design commitment by the web developers in Palestine;application of vision sensing technology in urban intelligent traffic control system;a method of verification of software based on CPN;software tools for environmental monitoring of the northern part of the Caspian sea;dependent task scheduling algorithm in distributed system;and the triple helix model: university-industry-governments linkage web-based application recommendation systems for emerging commercial-base research.

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Study on precise mushroom cultivation based on feedback perception 3

Study on precise mushroom cultivation based on feedback perc...

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3rd IEEE international conference on Cloud Computing and Big Data Analysis, ICCCBDA 2018

作者： Hongpo, Wang Hong, Zhou School of Computer Science and Information Engineering Tianjin Agricultural University Tianjin300384 China

ISBN: (纸本)9781538643006

In order to precise control the planting process and monitor mushroom growth in real time online, the design scheme of the environment monitoring system based on Internet of things was put forward on the main environmental parameters that affect the growth of mushroom. And according to the mushroom environment characteristics, the appropriate sensor and sensor placement were chose. By using RS-485 bus technology the information acquisition module, display module, communication interface and control interface monitoring terminal were built up. For the wiring is not conducive to the place in the mushroom room, its environmental information preclude the use of wireless sensor networks. On the server side is a web house environmental monitoring system was build based on the MySQL database» On the network communication interface the ENC28J60 Ethernet controller was used. In order to obtain the perceived data more accurately and effectively, loss-based retransmission detection based on the ACK scheme is adopted in the wireless sensor network. Experimental results show that this method can effectively improve the reliability of the data. © 2018 IEEE.

关键词： Wireless sensor networks

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Structural design of operational analysis and state modeling for power secondary system 4

Structural design of operational analysis and state modeling...

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4th IEEE international conference on computer and Communications, ICCC 2018

作者： Wu, Lingyun Liu, Chengjiang Yang, Peng Wang, Yan Zhou, Yuan Southwest Branch of State Grid Corporation of China Chengdu610041 China NARI Group Corporation State Grid Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9781538683392

With the continuous expansion of the monitoring scope of power secondary systems and the continuous improvement of network security requirements, many safety equipment and business systems are operating in the system. They produce a large amount of raw security data and operation monitoring data. At present, these data are isolated from each other. it makes control of the system's overall operational status and security risks a challenge. In order to realize the monitoring of the overall operating status of power secondary systems and cross-asset collaborative security data mining analysis. This paper presents a non-supervised machine learning based power system analysis and state modeling. First, design a set of asset model rules for secondary power systems. It builds the host equipment, network equipment, security equipment, and business system itself running in the existing business system into an organic whole. Afterwards, we used intelligent machine learning techniques to 'draw' the 'contours' of normal operation of each business. It provides a contour model for overall system operation analysis and threat warning. Finally, based on this model and real-time analysis technology, we aggregate the data distributed in various business systems in real time. It provides comprehensive support for operation monitoring and safety protection of power secondary systems. © 2018 IEEE.

关键词： monitoring

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A Real-Time distributed Deep Learning Approach for intelligent Event Recognition in Long Distance Pipeline monitoring with DOFS 10

A Real-Time Distributed Deep Learning Approach for Intellige...

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10th international conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC 2018

作者： Chen, Jiping Wu, Huijuan Liu, Xiangrong Xiao, Yao Wang, Mengjiao Yang, Mingru Rao, Yunjiang Key Laboratory of Optical Fiber Sensing and Communications Ministry of Education University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728109749

ISBN: (纸本)9781728109756;9781728109749

intelligent event recognition along the fiber is still a challenging problem in long distance pipeline monitoring with distributed optical fiber sensors (DOFS), because the complicated burying environments are changing from time to time, and the interference sources are unpredictable at different fiber locations. The fixed hand crafted feature extraction is always time-consuming and laborious, and the update of algorithm always lags behind the environmental change, which restricts its practical scale applications. Thus in this paper, we propose a real-time distributed deep learning model by using the efficient 1-D convolutional neural network (1-D CNN), to learn the distinguishable features of different disturbances and identify them automatically by training the raw event data (signal), which also can be updated easily. The experimental results from the real field data for the safety monitoring of oil pipelines demonstrate the effectiveness of the proposed method, which performs better than the 2-D CNN in terms of both recognition metrics and speed. © 2018 IEEE.

关键词： 1-D CNN Deep learning DOFS Pipeline monitoring Real time recognition

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Development of distributed Low Voltage Distribution Remote monitoring System

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Journal of Physics: conference Series 2020年第5期1550卷

作者： Hong Hu Xie Qiong Chen School of Information Engineering Nanchang Hangkong University China

intelligent power distribution monitoring technology is an important technology to improve the intelligence and automation level of the power distribution system. However, because the current power distribution monitoring methods in low-voltage power distribution rooms are not very developed, regular manual inspections and manual data recording are needed, leading to efficiency very low, so the research and development of intelligent distribution monitoring system in low-voltage distribution room is very *** system is mainly composed of intelligent electrical parameter collection terminal, 4G data transmission module, Alibaba Cloud Internet of Things platform, and remote power host computer, and each device can perform effective data communication. The system is assigned to each low-voltage power distribution room for unified management and viewing. The low-voltage power distribution real-time monitoring system can realize the real-time monitoring of transformer parameters and wireless transmission, and control the 4G communication module to receive or send data to the Alibaba Cloud Internet of Things platform. The remote cloud can monitor and query electrical parameters in real time, and can also send control instructions to the intelligent collection terminal. The terminal host receives various data (such as the three-phase voltage and current of the transformer, active and reactive power, power factor, frequency, harmonics, environmental temperature and humidity, etc.) from the data collector in real time and analyzes it. Once the data exceeds The set threshold will cause an alarm to prevent potential safety hazards in the low-voltage power distribution room. The experimental results prove that the system can monitor the working status of various electrical equipment in the power distribution room in real time, accurately and efficiently, thereby ensuring the safety and stability of the entire power distribution system, and has certain research value.

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Social Vehicle-To-Everything (V2X) communication model for intelligent Transportation Systems based on 5G scenario 18

Social Vehicle-To-Everything (V2X) communication model for I...

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2nd international conference on Future Networks and distributed Systems (ICFNDS)

作者： Raza, Naeem Jabbar, Sohail Han, Jihun Han, Kijun Natl Text Univ Dept Comp Sci Faisalabad Pakistan Kyungpook Natl Univ Dept Comp Sci & Engn Daegu South Korea

ISBN: (纸本)9781450364287

Vehicular-to-Everything (V2X) communications are the emerging paradigm for the intelligent Transportation System (ITS) used to enhance the traffic efficiency and reliability of timely data delivery by implementing a complete set of communication mechanism in all the devices and infrastructure involved in traffic control, monitoring, and management. This research work aims to propose a Social V2X Communication Model to improve the traffic efficiency in ITS environment by timely delivery of a required set of data with ultra-high speed integrated cellular 5G technologies. This is achieved by introducing the social behavior in V2X communication and with the provision of automated information, and surveillance. Along with this, triggering of required actions based on ultra-high speed and ultra-low latency 5G scenario of integrated networking technologies (Mobile Edge Computing/Fog Computing, Software Defined Networking and Cloud Computing) of integrated entities such as road users (Vehicles such as Cars, Buses, Trucks are parts of it. Motorcycles, and even the pedestrians), roadside infrastructure (Traffic lights, traffic signs, barriers and gates), Road Side Unit (RSU) and all the other network units (MEC /Fog Server, V2X Application Server (AS), SDN switches, and SDN controllers). The proposed model will support all kinds of communications such as a vehicle to vehicle (V2V) communications, vehicle to pedestrian communications (V2P), a vehicle to infrastructure (V2I) communications and vehicle to network (V2N) communications in a social internet of vehicles (SIOV) environment.

关键词： intelligent Transportation Systems Vehicle-to-Everything Communications Social Internet of Things Social Internet of Vehicles 5G

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Procedia computer Science

Procedia Computer Science

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6th international conference on Smart Computing and Communications, ICSCC 2017

The proceedings contain 117 papers. The topics discussed include: dynamic reconfiguration of robot software component in real time distributed system using clustering techniques;a novel methodology for identifying cross-country faults in series-compensated double circuit transmission lines;Mist Data: leveraging mist computing for secure and scalable architecture for smart and connected health;an investigation of two phase multi-relay S-DF cooperative wireless network over time-variant fading channels with incorrect CSI;tags and item features as a bridge for cross-domain recommender systems;an emotion recognition model based on facial recognition in virtual learning environment;deep neural network based place and manner of articulation detection and classification for Bengali continuous speech;leach based hierarchical routing protocol for monitoring of overground pipelines using linear wireless sensor networks;modeling of human power flywheel motor through artificial neural network- a novel approach;design of intelligent hybrid force and position control of robot manipulator;RBF neural control design for SISO nonaffine nonlinear systems;multi-criteria decision making with triangular intuitionistic fuzzy number based on distance measure & parametric entropy approach;KNN and PCA classifier with autoregressive modelling during different ECG signal interpretation;and intelligent tracking control of redundant robot manipulators including actuator dynamics.

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2nd international conference on Information and Communication Technology for intelligent Systems, ICTIS 2017

2nd International Conference on Information and Communicatio...

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2nd international conference on Information and Communication Technology for intelligent Systems, ICTIS 2017

ISBN: (纸本)9783319636443

The proceedings contain 147 papers. The special focus in this conference is on Information and Communication Technology for intelligent Systems. The topics include: Design of high-speed LVDS data communication link using FPGA;face super resolution by tangential and exponential kernel weighted regression model;monitoring of distributed resources based on client-server (single-hop) mobile agents in homogeneous network of interconnected nodes;energy balanced clustering protocol using particle swarm optimization for wireless sensor networks;routing protocol for device-to-device communication in softnet towards 5G;a survey of computer vision based corrosion detection approaches;word sense ambiguity in question sentence translation;implementing a hybrid crypto-coding algorithm for an image on FPGA;realization of FPGA based PID controller for speed control of DC motor using xilinx sysgen;emerging trends in ADR;natural language interface for multilingual database;a simple and efficient text-based CAPTCHA verification scheme using virtual keyboard;outcome fusion-based approaches for user-based and item-based collaborative filtering;a practical perspective;financial time series clustering;financial time series clustering;challenges and research issues;simplified process of obstructive sleep apnea detection using ECG signal based analysis with data flow programming;smart two level k-means algorithm to generate dynamic user pattern cluster;notification of data congestion intimation [NDCI] for IEEE 802.11 adhoc network with power save mode;a case study on level control system and deep neural network based classification of tumourous and non-tumorous medical images.

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