scholarly journals Prototype Design of a Ship Intelligent Integrated Platform

2020 ◽  
Author(s):  
Fuxin Wang ◽  
Hao Luo ◽  
Yuan Yu ◽  
Liyong Ma
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Network Architecture ◽  
Data Communication ◽  
Sensor Data ◽  
Hybrid Network ◽  
Integrated Platform ◽  
Hadoop Distributed File System ◽  
Point To Point ◽  
Prototype Design

Recently, more and more attention has been paid to ship intelligence. However, in the sensor data acquisition network represented by the integrated ship bridge system, sensor data is collected and transmitted point to point, and the data coupling is strong, which is not conducive to the hierarchical utilization of data. To provide more effective data communication methods and flexible support to applications, intelligent integrated platform is needed by modern ship. Prototype design and practice of a ship intelligent platform is proposed, and the key technologies of the platform is discussed. The overall architecture of the platform is described. A hybrid network architecture with fieldbus, real-time ethernet and ethernet information network is introduced. And data storage architecture using NoSQL and hadoop distributed file system is described. The system can meet the real-time performance requirement of the control and information communication. An energy efficiency application based on the designed platform is developed, machine learning based method is employed to predict the heavy oil fuel consumption for ship navigation.

Status Monitoring and Fault Diagnosis Network System Based on UPnP

2007 ◽  
Vol 353-358 ◽  
pp. 2632-2635
Author(s):  
Pei Yu Li ◽  
Da Peng Tan ◽  
Tao Qing Zhou ◽  
Bo Yu Lin
Keyword(s):  
Fault Diagnosis ◽  
Embedded System ◽  
Real Time ◽  
Data Storage ◽  
Network Architecture ◽  
Network System ◽  
Time Data ◽  
Status Monitoring ◽  
Self Recognition ◽  
Real Time Data

Aiming at some problems in the fields of industry monitoring technology (IMT) such as bad dynamic ability and poor versatility, this paper brought forward a kind of intelligent Status monitoring and Fault diagnosis Network System (SFNS) based on UPnP-Universal Plug and Play. The model for fault diagnosis network system was established according to characteristics and requirements of IMT network, and system network architecture was designed and realized by UPnP. Using embedded system technology, real-time data collection node, monitoring center node and data storage server were designed, and that supplies powerful real-time data support for SFNS. Industry fields experiments proved that this system can realize self recognition, seamless linkage and other self adapting ability, and can break through the limitation of real IP address to achieve real-time remote monitoring on line.

A Human-Inspired Method for Point-to-Point and Path-Following Navigation of Mobile Robots

2015 ◽  
Vol 7 (4) ◽  
Author(s):  
F. Heidari ◽  
R. Fotouhi
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Path Following ◽  
Obstacle Detection ◽  
Wheeled Mobile Robot ◽  
Sensor Data ◽  
Integrated Navigation ◽  
Fully Integrated ◽  
Point Motion ◽  
Point To Point

This paper describes a human-inspired method (HIM) and a fully integrated navigation strategy for a wheeled mobile robot in an outdoor farm setting. The proposed strategy is composed of four main actions: sensor data analysis, obstacle detection, obstacle avoidance, and goal seeking. Using these actions, the navigation approach is capable of autonomous row-detection, row-following, and path planning motion in outdoor settings. In order to drive the robot in off-road terrain, it must detect holes or ground depressions (negative obstacles) that are inherent parts of these environments, in real-time at a safe distance from the robot. Key originalities of the proposed approach are its capability to accurately detect both positive (over ground) and negative obstacles, and accurately identify the end of the rows of bushes (e.g., in a farm) and enter the next row. Experimental evaluations were carried out using a differential wheeled mobile robot in different settings. The robot, used for experiments, utilizes a tilting unit, which carries a laser range finder (LRF) to detect objects, and a real-time kinematics differential global positioning system (RTK-DGPS) unit for localization. Experiments demonstrate that the proposed technique is capable of successfully detecting and following rows (path following) as well as robust navigation of the robot for point-to-point motion control.

scholarly journals Design of Real-Time Database for the Vacuum Cold-Black Environment Simulator

2018 ◽  
Vol 232 ◽  
pp. 02034
Author(s):  
Jingyi Shao ◽  
Juan Ning ◽  
Yangyang Liu
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Network Architecture ◽  
Large Data ◽  
Space Environment ◽  
Time Data ◽  
Data Volume ◽  
Environment Simulation ◽  
Real Time Database ◽  
Interaction Efficiency

There is an urgent need to develop a real-time database (RTDB) to handle large amounts of real-time data, to realize process monitoring of vacuum cold-black environment simulation, cold-black environment data acquisition, fault processing, data storage, etc. This paper conducts real-time database design through network architecture, functional modules, communication interfaces, data management, and transaction scheduling. Simulation test and associated debugging test show that the real-time database can efficiently process large data volume, and the data interaction efficiency is greatly improved to meet the needs of space environment simulation equipment, laying a solid data foundation for subsequent applications.

scholarly journals An Alertness-Adjustable Cloud/Fog IoT Solution for Timely Environmental Monitoring Based on Wildfire Risk Forecasting

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3693
Author(s):  
Athanasios Tsipis ◽  
Asterios Papamichail ◽  
Ioannis Angelis ◽  
George Koufoudakis ◽  
Georgios Tsoumanis ◽  
...  
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Fog Computing ◽  
Fire Risk ◽  
Hybrid Network ◽  
Case Scenario ◽  
Wildfire Risk ◽  
Computing Paradigm ◽  
Risk Forecasting ◽  
Burning Index

Internet of Things (IoT) appliances, especially those realized through wireless sensor networks (WSNs), have been a dominant subject for heavy research in the environmental and agricultural sectors. To address the ever-increasing demands for real-time monitoring and sufficiently handle the growing volumes of raw data, the cloud/fog computing paradigm is deemed a highly promising solution. This paper presents a WSN-based IoT system that seamlessly integrates all aforementioned technologies, having at its core the cloud/fog hybrid network architecture. The system was intensively validated using a demo prototype in the Ionian University facilities, focusing on response time, an important metric of future smart applications. Further, the developed prototype is able to autonomously adjust its sensing behavior based on the criticality of the prevailing environmental conditions, regarding one of the most notable climate hazards, wildfires. Extensive experimentation verified its efficiency and reported on its alertness and highly conforming characteristics considering the use-case scenario of Corfu Island’s 2019 fire risk severity. In all presented cases, it is shown that through fog leveraging it is feasible to contrive significant delay reduction, with high precision and throughput, whilst controlling the energy consumption levels. Finally, a user-driven web interface is highlighted to accompany the system; it is capable of augmenting the data curation and visualization, and offering real-time wildfire risk forecasting based on Chandler’s burning index scoring.

scholarly journals Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0

2021 ◽  
Vol 11 (23) ◽  
pp. 11248
Author(s):  
Brecht De Beelde ◽  
David Plets ◽  
Wout Joseph
Keyword(s):  
Network Architecture ◽  
Network Planning ◽  
Sensor Data ◽  
Hybrid Network ◽  
Physical Parameters ◽  
Production Lines ◽  
Transmit Power ◽  
Coding Schemes ◽  
Data Rates ◽  
Minimal Data

With the deployment of data-driven assembly and production factories, challenges arise in sensor data acquisition and gathering. Different wireless technologies are currently used for transferring data, each with different advantages and constraints. In this paper, we present a hybrid network architecture for providing Quality of Service (QoS) in an industrial environment where guaranteed minimal data rates and maximal latency are of utmost importance for controlling devices and processes. The location of the access points (APs) is determined during the initial network-planning action, together with physical parameters such as frequency, transmit power, and modulation and coding schemes. Instead of performing network-planning just once before the network rollout, the network is monitored continuously by adding telemetry data to the frame header of all data streams, and the network is automatically reconfigured in real-time if the requirements are not met. By not using maximum transmit powers during the initial roll-out, more APs are needed, but coverage is guaranteed when new obstructions such as metallic racks or machinery are added. It is found that decreasing the transmit power by 6 dB gives the best trade-off between the number of required APs and network robustness. The proposed architecture is validated via simulations and via a proof-of-concept setup.

Efficient Big Data-Based Storage and Processing Model in Internet of Things for Improving Accuracy Fault Detection in Industrial Processes

2020 ◽  
pp. 215-230 ◽  
Author(s):  
Mamoon Rashid ◽  
Harjeet Singh ◽  
Vishal Goyal ◽  
Nazir Ahmad ◽  
Neeraj Mogla
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Fault Detection ◽  
Real Time ◽  
Sensor Data ◽  
Industrial Processes ◽  
Time Data ◽  
Real Time Data ◽  
Hadoop Distributed File System ◽  
Improving Accuracy

As the lot of data is getting generated and captured in Internet of Things (IoT)—based industrial devices which is real time and unstructured in nature. The IoT technology—based sensors are the effective solution for monitoring these industrial processes in an efficient way. However, the real—time data storage and its processing in IoT applications is still a big challenge. This chapter proposes a new big data pipeline solution for storing and processing IoT sensor data. The proposed big data processing platform uses Apache Flume for efficiently collecting and transferring large amounts of IoT data from Cloud—based server into Hadoop Distributed File System for storage of IoT—based sensor data. Apache Storm is to be used for processing this real—time data. Next, the authors propose the use of hybrid prediction model of Density-based spatial clustering of applications with noise (DBSCAN) to remove sensor data outliers and provide better accuracy fault detection in IoT Industrial processes by using Support Vector Machine (SVM) machine learning classification technique.

scholarly journals Detection and Prevention of DoS Attacks in VANET with RSU’s Cooperative Message Temporal Signature

2019 ◽  
Vol 8 (2) ◽  
pp. 6371-6377
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Computer Network ◽  
Data Communication ◽  
Attack Detection ◽  
Dos Attacks ◽  
Wireless Data ◽  
Hoc Networks

Vehicular Ad Hoc Networks (VANETs) is an emerging wireless data communication technology in computer network, which communicates dynamically to exchange and share real time information between vehicles on roads. This network architecture considers vehicles as nodes and information as packets for communication. Vehicles create an autonomous network with assistance of RSU (Road Side Units). This technology supports real time alert systems to broadcast emergency messages to the police, ambulance and drivers of the vehicles in some unexpected situations like traffic emergency, accident, road conditions, vehicle tracking, whether conditions and message monitoring. Since these nodes operate in a physically insecure environment in the range of 100 to 300 meters circumference. Security is a challenging issue for the technology to provide secured prominent approach for routing. Like other networks VANET network is also prone to various attacks. Timing and DoS is done by capturing the messages in VANET and replaying them at later point of time in bulk, such that RSU and vehicle resources are wasted in processing those messages. We need to have a technology by virtue of which network nodes (vehicles) should be smart enough to manage road safety at their own. In this paper RSU assisted DoS attack detection and prevention technique is proposed for VANET. The proposed method is based on RSU message temporal signature with detection and prevention, build over the RSU message attestation technique

Design of Portable Electrocardiograph Monitoring System

2020 ◽  
Author(s):  
Jia Hua-Ping ◽  
Zhao Jun-Long ◽  
Liu Jun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Storage ◽  
P Wave ◽  
Ecg Monitoring ◽  
The Public ◽  
Ecg Signals ◽  
Early Warning Mechanism ◽  
Timely Treatment ◽  
Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

scholarly journals Real-Time Instance Segmentation of Traffic Videos for Embedded Devices

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 275
Author(s):  
Ruben Panero Martinez ◽  
Ionut Schiopu ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Training Procedure ◽  
Segmentation Method ◽  
Embedded Devices ◽  
Network Training ◽  
Assignment Algorithm ◽  
Ablation Study ◽  
Reduced Rate ◽  
Instance Segmentation

The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with 31.57 average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.

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