Electrical equipment of industrial machines. Serial data link for real-time communication between controls and drives

2015 ◽  
Keyword(s):  
Real Time ◽  
Electrical Equipment ◽  
Data Link ◽  
Serial Data

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

scholarly journals 84 Gbit/s SiGe BiCMOS duobinary serial data link including Serialiser/Deserialiser (SERDES) and 5‐tap FFE

2015 ◽  
Vol 51 (4) ◽  
pp. 343-345 ◽  
Author(s):  
T. De Keulenaer ◽  
G. Torfs ◽  
Y. Ban ◽  
R. Pierco ◽  
R. Vaernewyck ◽  
...  
Keyword(s):  
Data Link ◽  
Serial Data ◽  
Sige Bicmos

Implementation of a Design for Oxygen Saturation Measurement System

2014 ◽  
Vol 644-650 ◽  
pp. 1079-1084
Author(s):  
Jia Liu ◽  
Han Zhou Chen ◽  
Xi’an Zhu
Keyword(s):  
Oxygen Saturation ◽  
Real Time ◽  
Measurement System ◽  
Interface Design ◽  
Serial Communication ◽  
Embedded Software ◽  
Windows Ce ◽  
Sensor Module ◽  
Serial Data ◽  
Saturation Measurement

This paper describes an implementation and validation result of a interface design for wearable oxygen saturation sensor systems. The system mainly consists of medical sensor module, RS232 module and ARM-based embedded software, the medical sensor module used for receiving, amplifying, filtering the signals of oxygen saturation and pulse in real time,RS232 module implementing serial communication driver and serial data receiver. The part of software is developed by Visual Studio 2005 VC#, adopts Windows CE as its platform which supports multi-threaded, database, communication and instantaneity. The typical characteristics of this system are its portable interface, easy operating way and software scalability.

Wave Combining Driver Based Serial Data Link Transceiver Design for Multi-Standard Applications

2019 ◽  
Vol 14 (5) ◽  
pp. 675-679
Author(s):  
Mahesh Kumawat ◽  
Abhishek Dalal ◽  
Mohit S. Choudhary ◽  
Ravi Kumar ◽  
Gaurav Singh ◽  
...  
Keyword(s):  
Data Link ◽  
Transceiver Design ◽  
Serial Data
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