scholarly journals Development of a WSN based real time energy monitoring platform for industrial applications

Author(s):  
Xin Lu ◽  
Sheng Wang ◽  
Weidong Li ◽  
Pingyu Jiang ◽  
Chaoyang Zhang
2013 ◽  
Vol 7 ◽  
pp. 238-247 ◽  
Author(s):  
M. Trejo-Perea ◽  
G.J. Ríos Moreno ◽  
A. Castañeda-Miranda ◽  
D. Vargas-Vázquez ◽  
R.V. Carrillo-Serrano ◽  
...  

2011 ◽  
Vol 33 (1) ◽  
pp. 137-146 ◽  
Author(s):  
Ramazan Bayindir ◽  
Erdal Irmak ◽  
İlhami Colak ◽  
Askin Bektas

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Liang Zhao

This paper presents a novel abnormal data detecting algorithm based on the first order difference method, which could be used to find out outlier in building energy consumption platform real time. The principle and criterion of methodology are discussed in detail. The results show that outlier in cumulative power consumption could be detected by our method.


Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1803
Author(s):  
Nasser Hosseinzadeh ◽  
Ahmed Al Maashri ◽  
Naser Tarhuni ◽  
Abdelsalam Elhaffar ◽  
Amer Al-Hinai

This article presents the development of a platform for real-time monitoring of multi-microgrids. A small-scale platform has been developed and implemented as a prototype, which takes data from various types of devices located at a distance from each other. The monitoring platform is interoperable, as it allows several protocols to coexist. While the developed prototype is tested on small-scale distributed energy resources (DERs), it is done in a way to extend the concept for monitoring several microgrids in real scales. Monitoring strategies were developed for DERs by making a customized two-way communication channel between the microgrids and the monitoring center using a long-range bridged wireless local area network (WLAN). In addition, an informative and easy-to-use software dashboard was developed. The dashboard shows real-time information and measurements from the DERs—providing the user with a holistic view of the status of the DERs. The proposed system is scalable, modular, facilitates the interoperability of various types of inverters, and communicates data over a secure communication channel. All these features along with its relatively low cost make the developed real-time monitoring platform very useful for online monitoring of smart microgrids.


Sensor Review ◽  
2015 ◽  
Vol 35 (2) ◽  
pp. 141-145 ◽  
Author(s):  
Richard Bloss

Purpose – The purpose of this paper is to review the recent advancements in the development of wearable sensors which can continuously monitor critical medical, assess athletic activity, watch babies and serve industrial applications. Design/methodology/approach – The paper presents an in-depth review of a number of developments in wearable sensing and monitoring technologies for medical, athletic and industrial applications. Researchers and companies around the world were contacted to discuss their direction and progress in this field of medical condition and industrial monitoring, as well as discussions with medical personnel on the perceived benefits of such technology. Findings – Dramatic progress is being made in continuous monitoring of many important body functions that indicate critical medical conditions that can be life-threatening, contribute to blindness or access activity. In the industrial arena, wearable devices bring remote monitoring to a new level. Practical implications – Doctors will be able to replace one-off tests with continuous monitoring that provides a much better continuous real-time “view” into the patient’s conditions. Wearable monitors will help provide much better medical care in the future. Industrial managers and others will be able to monitor and supervise remotely. Originality/value – An expert insight into advancements in medical condition monitoring that replaces the one-time “finger prick” type testing only performed in the doctor’s office. It is also a look at how wearable monitoring is greatly improved and serving athletics, the industry and parents.


2021 ◽  
Author(s):  
He Zhang ◽  
Jianxun Zhang ◽  
Rui Wang ◽  
Yazhe Huang ◽  
Mengxiao Zhang ◽  
...  

AbstractWith the rapid development of the Internet of Things (IoT) in the 5G age, the construction of smart cities around the world consequents on the exploration of carbon reduction path based on IoT technology is an important direction for global low carbon city research. Carbon dioxide emissions in small cities are usually higher than that in large and medium cities. However, due to the huge difference in data environment between small cities and Medium-large sized cities, the weak hardware foundation of the IoT, and the high input cost, the construction of a small city smart carbon monitoring platform has not yet been carried out. This paper proposes a real-time estimate model of carbon emissions at the block and street scale and designs a smart carbon monitoring platform that combines traditional carbon control methods with IoT technology. It can exist long-term data by using real-time data acquired with the sensing device. Therefore, the dynamic monitoring and management of low-carbon development in small cities can be achieved. The contributions are summarized as follows: (1) Intelligent thermoelectric systems, industrial energy monitoring systems, and intelligent transportation systems are three core systems of the monitoring platform. Carbon emission measurement methods based on sample monitoring, long-term data, and real-time data have been established, they can solve the problem of the high cost of IoT equipment in small cities. (2) Combined with long-term data, the real-time correction technology, they can dispose of the matter of differences in carbon emission measurement under diverse scales.


Author(s):  
Kufre Esenowo Jack ◽  
Nsikak John Affia ◽  
Uchenna Godswill Onu ◽  
Emmanuel Okekenwa ◽  
Ernest Ozoemela Ezugwu ◽  
...  

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiawei Lian ◽  
Junhong He ◽  
Yun Niu ◽  
Tianze Wang

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.


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