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Liquid Level Intelligent Detection for Oil Tank Based on Empirical Mode Decomposition and Deep Belief Network

SENSORS AND MATERIALS 2022年第7期34卷 2897-2909页

作者： Zhao, Qiwei Ningbo Madelt Semicond Co Ltd Ningbo 315000 Peoples R China

The liquid level of different types of oil in an oil tank can be judged on the basis of the amplitude of time-domain signals, but the characteristic information obtained by this signal is relatively simple. In addition, the liquid level intelligent detection for oil tanks based on machine learning techniques, such decision tree algorithm, artificial neural network algorithm, and support vector machine, has stimulated considerable research interest. To obtain more characteristic information and improve the liquid level detection rate of different medium signals in an oil tank, a liquid level intelligent detection method based on empirical mode decomposition (EMD) and deep belief network (DBN) for a steel oil tank of 5 mm thickness is provided in this paper. Firstly, the ipsilateral phase detection method of the air-coupled ultrasonic Lamb wave was adopted to detect the oil tank with the help of the AO model. Then, the intrinsic mode function (IMF) of each order was obtained by analyzing the signals of different media in the oil tank by EMD, and the correlation characteristics of the time/frequency domain signals of each order IMF component were analyzed. Finally, the time/frequency domain signals of the IMF component served as the input signals of the DBN model. The liquid level is divided into 15 sections as the output of DBN. The experimental results of the combination of EMD and DBN show that the liquid levels of different media in the oil tank can be accurately identified and further classified within the range of 10 mm, the detection rate can reach 99%, and the detection range meets the actual testing requirements of the oil tank.

关键词： machine learning algorithm intelligent detection ultrasonic Lamb wave empirical mode decomposition (EMD) deep belief network (DBN)

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Smart doors - A machine learning Approach to monitor body temperature and detect face mask 3

Smart doors - A Machine Learning Approach to monitor body te...

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3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Karthikeyan, S. Moses, M. Leeban Swetha, M. Srinidhi, S. Thrisha, E. Bannari Amman Institute of Technology ECE Department Sathyamangalam India

ISBN: (纸本)9781665474979

In recent years Internet of Things(IoT) plays a vital role in automation. Nearly millions of people have been affected by the threatening disease COVID-19 (coronavirus), who are either sick or being killed due to the spread of the disease. The densely populated world possesses a threat of spreading such infectious diseases rapidly. So, there is a need for supervision of people's health status working in large organizations/institutions. This paper emphasizes the automation in monitoring the temperature of human beings and face mask detection so that spreading of infectious disease like COVID-19 can be brought down. The proposed solution aids the institutions/organizations to find out the infected person and take necessary precaution at an earlier stage to avoid spreading of the disease to the other healthy persons. This prototype overcomes the drawback of existing ideas in which affected individuals are frequently exposed to high radiation devices. The idea comes with the provision of ensuring the operation of the system only in the presence of human beings and also it paves the way to install a low cost set-up. The system makes use of sensor technology to spot the common symptoms of the disease and machine learning algorithm to ensure people are wearing masks. The obtained data gets stored on the cloud and analyzed by the organizations/institution's authorities. The lid present in the entrance is opened for the people with normal constraints. The whole scheme helps the larger organizations/institutions to avoid spreading of infectious diseases. © 2022 IEEE.

关键词： COVID-19 high radiation devices Internet of Things(IoT) machine learning algorithm temperature

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Mechanical ventilation intervention based on machine learning from vital signs monitoring: a scoping review

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第6期34卷 062001-062001页

作者： Baidillah, Marlin Ramadhan Busono, Pratondo Riyanto, Riyanto Natl Res & Innovat Agcy Res Ctr Elect Bandung 40135 Indonesia

Asynchronous breathing (AB) during mechanical ventilation (MV) may lead to a detrimental effect on the patient's condition. Due to the massive amount of data displayed in a large ICU, a machine learning algorithm (MLA) was proposed extensively to extract the patterns within the multiple continuous-in-time vital signs, to determine which are the variables that will predict the AB, to intervene in the MV as an early warning system, and finally to replace a highly demand of clinician's cognition. This study reviews the MLA for prediction and detection models from vital signs monitoring data for MV intervention. Publication on MLA development on MV intervention based on vital signs monitoring to support clinicians' decision-making process was extracted from the three electronic academic research databases Web of Science Core Collection (WoSCC), ScienceDirect, and PUBMED Central to February 2023. 838 papers from the electronic academic research databases are extracted. There are 14 review papers, while 25 related papers that pass with the quality assessments (QA). Few studies have been published that considered VS monitoring data along with the MV parameters waveforms for MV intervention. Vital signs monitoring data is not the only predictor in the developed MLA. Most studies suggested that developing the MLA for direct MV intervention requires more concern in the pre-processing of real-time data to avoid false positive and false detection than developing MLA itself.

关键词： mechanical ventilation multiple continuous-in-time vital signs machine learning algorithm asynchronous breathing

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Co-pyrolytic interactions and products of brominated epoxy resin and polyethylene terephthalate: TG-FTIR analysis and machine learning prediction

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JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS 2023年 175卷

作者： Xu, Ziming Cheng, Can Zhong, Jian Gao, Wenhao Li, Jinping Liu, Jingxin Wuhan Text Univ Sch Environm Engn Wuhan 430073 Peoples R China Wuhan Text Univ Engn Res Ctr Clean Prod Text Dyeing & Printing Minist Educ Wuhan 430073 Peoples R China

Pyrolysis is an effective approach for organic waste utilization, and the co-pyrolysis of multiple materials holds potential for both environmental and economic benefits. Brominated epoxy resin (BER) and polyethylene terephthalate (PET) are widely used organic materials in modern society, and the productions of their corresponding wastes are increasing annually. However, limited research has been conducted on the co-pyrolysis of BER and PET. In this study, mixtures of BER and PET were prepared in various proportions, and thermogravimetry (TG) and thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR) analyses were employed to investigate the reaction characteristics, volatile product compositions, and evolution during co-pyrolysis. Synergistic effects were observed at lower temperatures in the major reaction stage for mixtures with PET content below 40%, while inhibitory effects were noted at higher temperatures. For mixtures with PET content over 40%, the antagonistic effect was more prominent. The main volatile products during co-pyrolysis were identified as CO2, benzoic acid, and alcohols, and their evolutions exhibited complex changes due to interactions. In addition, machine learning algorithms successfully predicted real-time volatile emissions, with the random forest model proving accurate. This study enhanced understanding of organics interactions during thermal treatment and would contribute to the reduction of environmental pollution.

关键词： Brominated epoxy resin (BER) Polyethylene terephthalate (PET) Co-pyrolysis characteristics Product identification machine learning algorithm

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Electric Vehicle Battery Temperature Prediction Using LSTM algorithm 4

Electric Vehicle Battery Temperature Prediction Using LSTM A...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Sridhar, B. Allirani, S. Reshmin Shafi, R.S. Rithvik Abinav, K.R. Siddharth, A. Sri Ramakrishna Engineering College Department of Eee Coimbatore India

ISBN: (纸本)9798350383997

In a rapidly evolving world, characterized by a growing emphasis on sustainable transportation, the pursuit of enhanced Electric Vehicle (EV) efficiency and reliability has become paramount. This abstract presents an innovative and intelligent system poised to redefine the management of EV batteries, prioritizing optimal performance and user safety. Our notable framework uses the capacities of cutting-edge LSTM machine learning algorithm to precisely anticipate, screen, and control EV battery temperatures. Its core objectives are clear: maintain battery temperatures within the optimal range and pre-emptively identify critical anomalies before they escalate. At the heart of this system lies its machine learning capabilities, continuously forecasting battery temperature trends. When optimal temperature is not within the stipulated range, the system swiftly takes action with dual purposes: heating or cooling the battery to safeguard its health and extend its lifespan. Yet, this system goes beyond mere reactivity to temperature extremes;it excels in predictive prowess, identifying irregularities in battery temperature patterns before they become critical issues. This proactive approach equips the system to issue timely alerts and intervene to prevent potential hazards. Statistics, enabling them to stay well-informed, take preventive actions, and experience worry-free journeys. © 2024 IEEE.

关键词： Electric Vehicle (EV) Long Short Term algorithm (LSTM) machine learning algorithm

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Automatic Train Overpass Using Camera and Load Sensors and Obstacle Detection 2

Automatic Train Overpass Using Camera and Load Sensors and O...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Aravendra Kumar Dubey, Ratnesh Kumar Mishra, Shubha Sharma, Suraj Itm University Csa Department Gwalior India Mits Center for Ai Department Gwalior India

ISBN: (纸本)9798350371055

In India, the most used means of transportation is rail. Enhancements are required to modern technology in order to make it safer for the general population. In this context, the research offers suggestions for automating railway gate operations. The level crossing responds to the arrival and departure of trains nearby by opening and closing on its own using Internet of Things load sensors and warning signals (buzzers or sirens). During the buffer closure period, an automobile may become stuck on the track in between crossing gates. It will be recognized by camera sensors, and Internet of Things sensors will alert the gate closing mechanism. This might significantly reduce the number of labor-intensive tasks and incidents at level crossings. © 2024 IEEE.

关键词： Automatic railway crossing Internet of things (IoT) machine learning algorithm unmanned crossing

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Microheater Integrated Nanotube Array Gas Sensor for Parts-Per-Trillion Level Gas Detection and Single Sensor-Based Gas Discrimination

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ACS NANO 2022年第7期16卷 10968-10978页

作者： Tang, Wenying Chen, Zhesi Song, Zhilong Wang, Chen Wan, Zhu'an Chan, Chak Lam Jonathan Chen, Zhuo Ye, Wenhao Fan, Zhiyong Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong Peoples R China Hong Kong Univ Sci & Technol Shenzhen Res Inst Shenzhen 518057 Peoples R China

Real-time monitoring of health threatening gases for chemical safety and human health protection requires detection and discrimination of trace gases with proper gas sensors. In many applications, costly, bulky, and power-hungry devices, normally employing optical gas sensors and electro-chemical gas sensors, are used for this purpose. Using a single miniature low-power semiconductor gas sensor to achieve this goal is hardly possible, mostly due to its selectivity issue. Herein, we report a dual-mode microheater integrated nanotube array gas sensor (MINA sensor). The MINA sensor can detect hydrogen, acetone, toluene, and formaldehyde with the lowest measured limits of detection (LODs) as 40 parts-per-trillion (ppt) and the theoretical LODs of ~7 ppt, under the continuous heating (CH) mode, owing to the nanotubular architecture with large sensing area and excellent surface catalytic activity. Intriguingly, unlike the conventional electronic noses that use arrays of gas sensors for gas discrimination, we discovered that when driven by the pulse heating (PH) mode, a single MINA sensor possesses discrimination capability of multiple gases through a transient feature extraction method. These above features of our MINA sensors make them highly attractive for distributed low-power sensor networks and battery-powered mobile sensing systems for chemical/environmental safety and healthcare applications.

关键词： gas sensor pulse-heating trace gas detection machine learning algorithm single-sensor gas discrimination

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The stories we tell ourselves: Local newspaper reporting and support for the radical right

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POLITICAL GEOGRAPHY 2023年 100卷

作者： Jambrina-Canseco, Beatriz London Sch Econ & Polit Sci LSE Dept Geog & Environm London England London Sch Econ & Polit Sci LSE Int Inequal Inst London England

Rising support for the radical right has become a hallmark of the current political landscape. A lot of attention has been devoted to the reasons influencing individual voting decisions, with some progress in understanding within-country variation in the vote. But these studies usually assume that perceptions coincide with objective reality. This article addresses this shortcoming, using quantitative text analysis and spatial econometrics to show that local narratives - sometimes more than contextual statistics - can drive spatial differences in the populist vote. Taking Spain as an example, I train a machine learning algorithm to determine the prevalence of given news topics across the national territory based on how many related articles local newspapers published on Twitter in the year before the last national election. I then use spatial econometric techniques to link these results to local divergences in support for the radical right party VOX. The analysis sheds some light onto the economic anxiety -cultural backlash -geography of discontent debate. The empirical evidence supports the notion that narratives about economic anxiety and regional gaps matter, but also shows that narratives about separatism played a key role in the rise of the radical right in Spain.

关键词： Radical right support Local newspapers machine learning algorithm Twitter Spain

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Performance Analysis of Support Vector machine (SVM) for Diabetes Disease Detection 5

Performance Analysis of Support Vector Machine (SVM) for Dia...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Krisdianto, Ricky Apriani, Ivana Masada, Hossey Hidayaturrahman School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331528553

Diabetes is an important health problem and has a significant mortality rate in Indonesia, so it is one of the diseases that need further attention. In medical diagnosis, classification methods are extensively utilized to build models using machine learning algorithms for predicting medical outcomes based on patient data. Support Vector machine (SVM) is one of the machine learning algorithms used in predicting diseases including diabetes. The purpose of this study was to discover the optimal performance achieved by the SVM kernel model in predicting diabetes. Two datasets containing 9 and 22 features are being utilized in this research to identify the relationships influencing the model's accuracy performance. Also applying preprocessing methods based on the needs of each dataset to improve the accuracy of the model. The result of this research shows that the Radial Basis Function is the best kernel, achieving 97% accuracy in the first dataset and 87% accuracy in the second dataset. © 2024 IEEE.

关键词： Diabetes Disease Kernel Functions machine learning algorithm Radial Basis Function Support Vector machine (SVM)

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Intelligent LoRa-Based Positioning System

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2022年第9期16卷 2961-2975页

作者： Chen, Jiann-Liang Chen, Hsin-Yun Ma, Yi-Wei Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (***) revealed that AI@LBS system can reduce average distance error to 0.48m.

关键词： Location-Based Service LoRa Fingerprint Mechanism machine learning algorithm Synthetic Minority Over-sampling Technique (SMOTE) Density-Based Spatial Clustering of Applications with Noise (DBSCAB)

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