Hybrid virtual metering points – a low-cost, near real-time energy and resource flow monitoring approach for production machines without PLC data connection

The H2020 innovation project digital-water.city (DWC) aims at boosting the integrated management of water systems in five major European cities &#8211;&#160;Berlin, Copenhagen, Milan, Paris and Sofia&#160;&#8211; by leveraging the potential of data and digital technologies. The goal is to quantify the benefits of a panel of 15 innovative digital solutions and achieve their long-term uptake and successful integration in the existing digital systems and governance processes. One of these promising technologies is a new generation of sensors for measuring combined sewer overflow occurrence, developed by ICRA and IoTsens.Recent EU regulations have correctly identified CSOs as an important source of contamination and promote appropriate monitoring of all CSO structures in order to control and avoid the detrimental effects on receiving waters. Traditionally there has been a lack of reliable data on the occurrence of CSOs, with the main limitations being: i) the high number of CSO structures per municipality or catchment and ii) the high cost of the flow-monitoring equipment available on the market to measure CSO events. These two factors and the technical constraints of accessing and installing monitoring equipment in some CSO structures have delayed the implementation of extensive monitoring of CSOs. As a result, utilities lack information about the behaviour of the network and potential impacts on the local water bodies.The new sensor technology developed by ICRA and IoTsens provides a simple yet robust method for CSO detection based on the deployment of a network of innovative low-cost temperature sensors. The technology reduces CAPEX and OPEX for CSO monitoring, compared to classical flow or water level measurements, and allows utilities to monitor their network extensively. The sensors are installed at the overflows crest and measure air temperature during dry-weather conditions and water temperature when the overflow crest is submerged in case of a CSO event. A CSO event and its duration can be detected by a shift in observed temperature, thanks to the temperature difference between the air and the water phase. Artificial intelligence algorithms further help to convert the continuous measurements into binary information on CSO occurrence. The sensors can quantify the CSO occurrence and duration and remotely provide real-time overflow information through LoRaWAN/2G communication protocols.The solution is being deployed since October 2020 in the cities of Sofia, Bulgaria, and Berlin, Germany, with 10 offline sensors installed in each city to improve knowledge on CSO emissions. Further 36 (Sofia) and 9 (Berlin) online sensors will follow this winter. Besides its main goal of improving knowledge on CSO emissions, data in Sofia will also be used to identify suspected dry-weather overflows due to blockages. In Berlin, data will be used to improve the accuracy of an existing hydrodynamic sewer model for resilience analysis, flood forecasting and efficient investment in stormwater management measures. First results show a good detection accuracy of CSO events with the offline version of the technology. As measurements are ongoing and further sensors will be added, an enhanced set of results will be presented at the conference.Visit us: https://www.digital-water.city/&#160;Follow us: Twitter (@digitalwater_eu); LinkedIn (digital-water.city)

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Smart Water Meter Using Electrical Resistance Tomography

Sensors ◽

10.3390/s19143043 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3043 ◽

Cited By ~ 2

Author(s):

Chenning Wu ◽

Martin Hutton ◽

Manuchehr Soleimani

Keyword(s):

Real Time ◽

Electrical Resistance ◽

Signal To Noise Ratio ◽

Low Cost ◽

Water Discharge ◽

Domain Size ◽

Good Resolution ◽

Electrical Resistance Tomography ◽

Flow Monitoring ◽

Smart Water

Smart flow monitoring is critical for sewer system management. Obstructions and restrictions to flow in discharge pipes are common and costly. We propose the use of electrical resistance tomography modality for the task of smart wastewater metering. This paper presents the electronics hardware design and bespoke signal processing to create an embedded sensor for measuring flow rates and flow properties, such as constituent materials in sewage or grey water discharge pipes of diameters larger than 250 mm. The dedicated analogue signal conditioning module, zero-cross switching scheme, and real-time operating system enable the system to perform low-cost serial measurements while still providing the capability of real-time capturing. The system performance was evaluated via both stationary and dynamic experiments. A data acquisition speed of 14 frames per second (fps) was achieved with an overall signal to noise ratio of at least 59.54 dB. The smallest sample size reported was 0.04% of the domain size in stationary tests, illustrating good resolution. Movements have been successfully captured in dynamic tests, with a clear definition being achieved of objects in each reconstructed image, as well as a fine overall visualization of movement.

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LOW COST REAL TIME ROCKET TELEMETRY SYSTEM DESIGN

10.26678/abcm.cobem2019.cob2019-1585 ◽

2019 ◽

Author(s):

Gabriel de Almeida Souza ◽

Larissa Barbosa ◽

Glênio Ramalho ◽

Alexandre Zuquete Guarato

Keyword(s):

System Design ◽

Real Time ◽

Low Cost ◽

Telemetry System

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Low-Cost High-Speed Techniques for Real-Time Simulation of Power Electronic Systems

10.21236/ada485330 ◽

2007 ◽

Author(s):

R. E. Crosbie ◽

J. J. Zenor ◽

R. Bednar ◽

D. Word ◽

N. G. Hingorani

Keyword(s):

Real Time ◽

High Speed ◽

Low Cost ◽

Electronic Systems ◽

Power Electronic ◽

Real Time Simulation ◽

Time Simulation

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Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

Mobile Information Systems ◽

10.1155/2019/4570808 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Yong He ◽

Hong Zeng ◽

Yangyang Fan ◽

Shuaisheng Ji ◽

Jianjian Wu

Keyword(s):

Deep Learning ◽

Integrated Pest Management ◽

Pest Management ◽

Real Time ◽

Oilseed Rape ◽

Data Augmentation ◽

Low Cost ◽

Response Speed ◽

Original Model ◽

Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

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Real-Time Low-Cost Drift Compensation for Chemical Sensors Using a Deep Neural Network with Hadamard Transform and Additive Layers

IEEE Sensors Journal ◽

10.1109/jsen.2021.3084220 ◽

2021 ◽

pp. 1-1

Author(s):

Diaa Badawi ◽

Agamyrat Agambayev ◽

Sule Ozev ◽

A. Enis Cetin

Keyword(s):

Neural Network ◽

Real Time ◽

Chemical Sensors ◽

Deep Neural Network ◽

Low Cost ◽

Hadamard Transform ◽

Drift Compensation

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A Low-Cost, Mobile Real-Time Kinematic Geolocation Service for Engineering and Research Applications

HardwareX ◽

10.1016/j.ohx.2021.e00203 ◽

2021 ◽

pp. e00203

Author(s):

André Broekman ◽

Petrus Johannes Gräbe

Keyword(s):

Real Time ◽

Low Cost ◽

Real Time Kinematic

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Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

Applied Sciences ◽

10.3390/app11114940 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4940

Author(s):

Jinsoo Kim ◽

Jeongho Cho

Keyword(s):

Embedded System ◽

Real Time ◽

Action Recognition ◽

Processing Speed ◽

Recognition Accuracy ◽

Low Cost ◽

Human Action Recognition ◽

Human Action ◽

Video Data ◽

Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

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