The modular design of an electricity information acquisition device

2015 ◽  
pp. 209-215
Author(s):  
H Sun ◽  
J Ye ◽  
K Zheng ◽  
R Zhang
Keyword(s):  
Information Acquisition ◽  
Modular Design ◽  
Acquisition Device

An Information Acquisition Device for Driver Training Operation with MEMS Transduce

2012 ◽  
Vol 198-199 ◽  
pp. 1250-1255
Author(s):  
Xia Yang ◽  
Kai Yin ◽  
Hai Ying Liu ◽  
Hong Da Li
Keyword(s):  
Real Time ◽  
Information Acquisition ◽  
Three Dimensional ◽  
Characteristic Analysis ◽  
Driver Training ◽  
New Approach ◽  
Related Data ◽  
Existing Problems ◽  
Acquisition Device ◽  
Change Curve

In this paper, based on characteristic analysis of driver training operation, a new approach of information acquisition for driver training is found, which is through extracting acceleration information. The information acquisition device which is under MEMS three-axis acceleration transducer, based on ADXL335 and MCU, can achieve real-time and accurate measurement of vehicles’ three-dimensional acceleration. By comparing the change curve of measured acceleration with the related data of good driver, the existing problems will be found out. In this way, the driver training operation can be assessed in a timely way.

A Method to Create the Traffic Accident Scene Graphic Based on Electronic Maps and Information Acquisition Device

2014 ◽  
Vol 496-500 ◽  
pp. 2971-2974
Author(s):  
Wen Jun Liu ◽  
Sen Su ◽  
Fu Ping Wang ◽  
Kui Li ◽  
Zhi Yong Yin
Keyword(s):  
Image Processing ◽  
Information Acquisition ◽  
Traffic Accident ◽  
Satellite Images ◽  
Graph Drawing ◽  
Acquisition Device ◽  
Scientific Technical ◽  
Distance Data ◽  
Accident Site ◽  
Accident Scene

This paper presents a method to create the traffic accident scene graphic based on electronic maps and information acquisition device (IAD). The method uses global position system (GPS) and electronic maps to get the satellite images of accident site, and it uses an IAD to obtain the angle and distance data between the origin position determined by the IAD and the locations prepared to be measured in the accident scene. With the data operation and image processing, we can create an isometric traffic accident scene graphic. This method provides the traffic accident spot data collecting, graph drawing and reason analyzing a more convenient and scientific technical means.

The Application of Internet of Things in Crop Production Management Research

2013 ◽  
Vol 765-767 ◽  
pp. 889-893
Author(s):  
Kai Meng Sun
Keyword(s):  
Internet Of Things ◽  
Information Acquisition ◽  
Agricultural Production ◽  
Crop Production ◽  
Production Management ◽  
Solution Method ◽  
Management Research ◽  
Acquisition Device ◽  
Production Research ◽  
Design Idea

The composition, solution, method and design idea of crop information acquisition device applying internet of things were introduced. The applications of internet of things in crop production research were explained, and several technological difficulties and methods were discussed. Meanwhile, the effect and significance of applying internet of things in relevant disciplines of agricultural production were demonstrated.

Modular design

1999 ◽  
Vol 78 (3) ◽  
pp. 113-116 ◽  
Keyword(s):  
Modular Design

MONITORING AND PREDICTIVE ANALYTICS OF TECHNOLOGICAL EQUIPMENT ON THE BASED OF INDUSTRIAL INTERNET OF THINGS

2020 ◽  
pp. 7-12
Author(s):  
С.Л. Добрынин ◽  
В.Л. Бурковский
Keyword(s):  
Internet Of Things ◽  
Data Acquisition ◽  
Predictive Analytics ◽  
Actual State ◽  
Monitoring And Control ◽  
Industrial Internet Of Things ◽  
Machining Time ◽  
Industrial Internet ◽  
Acquisition Device ◽  
Data Acquisition Device

Произведен обзор технологий в рамках концепции четвертой промышленной революции, рассмотрены примеры реализации новых моделей управления технологическими процессами на базе промышленного интернета вещей. Описано техническое устройство основных подсистем системы мониторинга и контроля, служащей для повышения осведомленности о фактическом состоянии производственных ресурсов в особенности станков и аддитивного оборудования в режиме реального времени. Архитектура предлагаемой системы состоит из устройства сбора данных (УСД), реализующего быстрый и эффективный сбор данных от станков и шлюза, передающего ликвидную часть информации в облачное хранилище для дальнейшей обработки и анализа. Передача данных выполняется на двух уровнях: локально в цехе, с использованием беспроводной сенсорной сети (WSN) на базе стека протоколов ZigBee от устройства сбора данных к шлюзам и от шлюзов в облако с использованием интернет-протоколов. Разработан алгоритм инициализации протоколов связи между устройством сбора данных и шлюзом, а также алгоритм выявления неисправностей в сети. Расчет фактического времени обработки станочных подсистем позволяет более эффективно планировать профилактическое обслуживание вместо того, чтобы выполнять задачи обслуживания в фиксированные интервалы без учета времени использования оборудования We carried out a review of technologies within the framework of the concept of the fourth industrial revolution; we considered examples of the implementation of new models of process control based on the industrial Internet of things. We described the technical structure of the main subsystems of the monitoring and control system to increase awareness of the actual state of production resources in particular machine tools and additive equipment in real time. The architecture of the proposed system consists of a data acquisition device (DAD) that implements fast and efficient data collection from machines and a gateway that transfers the liquid part of information to the cloud storage for further processing and analysis. We carried out the data transmission at two levels, locally in the workshop, using a wireless sensor network (WSN) based on ZigBee protocol stack from the data acquisition device to the gateways and from the gateways to the cloud using Internet protocols. An algorithm was developed for initializing communication protocols between a data acquisition device and a gateway, as well as an algorithm for detecting network malfunctions. Calculating the actual machining time of machine subsystems allows us to more efficiently scheduling preventive maintenance rather than performing maintenance tasks at fixed intervals without considering equipment usage

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