scholarly journals Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1887 ◽  
Author(s):  
Sabina Licen ◽  
Alessia Di Gilio ◽  
Jolanda Palmisani ◽  
Stefania Petraccone ◽  
Gianluigi de Gennaro ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Anomaly Detection ◽  
Ambient Air ◽  
Dynamic Responses ◽  
Oxide Semiconductor ◽  
Monitoring Site ◽  
Industrial Emissions ◽  
Ancillary Data ◽  
Data Set ◽  
Self Organizing

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses.

PM 10 AND PM 2.5 IN THE AMBIENT AIR OF A LARGE INDUSTRIAL CITY

2021 ◽  
Author(s):  
L.M. Fatkhutdinova ◽  
◽  
G.A. Timerbulatova ◽  
G.F. Gabidinova ◽  
Keyword(s):  
Nitrogen Oxides ◽  
Organic Carbon ◽  
Retrospective Analysis ◽  
Health Risks ◽  
Fine Particles ◽  
Ambient Air ◽  
Industrial Emissions ◽  
Data Set ◽  
Pm 2.5 ◽  
Pm 10

Abstract: Introduction. Air pollution with particulate matter (PM) is a serious global problem. In the Russian Federation, regular field measurements of PMs in the ambient air are carried out only in a number of cities, and the data, as a rule, are not systematized. Aim of the study: retrospective analysis of the data set on concentrations of fine particles in the ambient air of the city of Kazan. Methods. Retrospective analysis of pollution by fine fractions of suspended solids in the ambient air of Kazan in the period from 2016 to 2020 has been carried out. To study the effect of individual factors (time period (year), measurement time during the day, climatic conditions, the presence of other pollutants) on the pollution levels PM 10 and PM 2.5, regression analysis was applied based on the method of mixed models. Results. The PM 10 concentrations remained stable over a 5-year period, while the PM 2.5 concentrations decreased. At the same time, an increase in the maximum annual concentrations of both fractions was observed. The concentrations of PM 10 and PM 2.5 significantly depended on weather factors. The presence of nitrogen oxides and organic carbon in the ambient air was associated with significantly higher concentrations of PM 10 and PM 2.5. Conclusion. A statistically significant upward trend in the dynamics of the years of the maximum annual values for both fractions of suspended particles can increase health risks. Secondary pollutants (nitrogen oxides, organic carbon) is an important factor for the formation of secondary particles in the ambient air. Implementation of preventive measures in relation to industrial emissions, regional urban planning policy and a scientifically based program of social and hygienic monitoring are the most important tools for effective management of health risks caused by exposure to fine particles.

scholarly journals No Neuron Left Behind: A genetic approach to higher precision topological mapping of self-organizing maps

2015 ◽  
Vol 5 (1) ◽  
pp. 1-12
Author(s):  
Chris Gorman ◽  
Clint Rogers ◽  
Iren Valova
Keyword(s):  
Pattern Recognition ◽  
Input Data ◽  
Topological Representation ◽  
Genetic Approach ◽  
Self Organizing Maps ◽  
Data Set ◽  
Running Time ◽  
Left Behind ◽  
Self Organizing

AbstractSelf-organizing maps are extremely useful in the field of pattern recognition. They become less useful, however, when neurons fail to activate during training. This phenomenon occurs when neurons are initialized in areas of non-input and are far enough away from the input data to never move toward the input. These neurons effectively misrepresent the data set. This results in, among other things, patterns becoming unrecognizable.We introduce an algorithm called No Neuron Left Behind to solve this problem.We show that our algorithm produces a more accurate topological representation of the input space.We also show that no neuron clusters form in areas of noninput and that mapping quality of the SOM increases drastically when our algorithm is implemented. Finally, the running time of NNLB is better or comparable to classic SOM without it.

scholarly journals Low levels of nitryl chloride: Nocturnal nitrogen oxides in the Lower Fraser Valley of British Columbia

2017 ◽  
Author(s):  
Hans D. Osthoff ◽  
Charles A. Odame-Ankrah ◽  
Youssef M. Taha ◽  
Travis W. Tokarek ◽  
Corinne L. Schiller ◽  
...  
Keyword(s):  
British Columbia ◽  
Nitrogen Oxides ◽  
Measurement Data ◽  
Ambient Air ◽  
Monitoring Site ◽  
Data Set ◽  
Mixing Ratios ◽  
Nitryl Chloride ◽  
Lower Fraser Valley ◽  
Fraser Valley

Abstract. The nocturnal nitrogen oxides, which include the nitrate radical (NO3), dinitrogen pentoxide (N2O5), and its uptake product on chloride containing aerosol, nitryl chloride (ClNO2), can have profound impacts on the lifetime of NOx (= NO + NO2), radical budgets, and next-day photochemical ozone (O3) production, yet their abundances and chemistry are only sparsely constrained by ambient air measurements. Here, we present a measurement data set collected at a routine monitoring site near the Abbotsford International Airport (YXX) located approximately 30 km from the Pacific Ocean in the Lower Fraser Valley (LFV) on the West coast of British Columbia. Measurements were made from July 20 to August 4, 2012, and included mixing ratios of ClNO2, N2O5, NO, NO2, total odd nitrogen (NOy), O3, photolysis frequencies, and size distribution and composition of non-refractory submicron aerosol. At night, O3 was rapidly and often completely removed by dry deposition and by titration with NO of anthropogenic origin and unsaturated biogenic hydrocarbons in a shallow nocturnal inversion surface layer. The low nocturnal O3 mixing ratios and presence of strong chemical sinks for NO3 limited the extent of nocturnal nitrogen oxide chemistry at ground level. Consequently, mixing ratios of N2O5 and ClNO2 were low (

Self Organizing Maps, Pattern Recognition and Financial Crises

2011 ◽  
Author(s):  
Sylvain Barthelemy ◽  
Pascal Devaux ◽  
Francois Faure ◽  
Matthieu Pautonnier
Keyword(s):  
Pattern Recognition ◽  
Financial Crises ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Outlier Detection Transilience-Probabilistic Model for Wind Tunnels Based on Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2532
Author(s):  
Encarna Quesada ◽  
Juan J. Cuadrado-Gallego ◽  
Miguel Ángel Patricio ◽  
Luis Usero
Keyword(s):  
Anomaly Detection ◽  
Outlier Detection ◽  
Research Work ◽  
Industrial Sector ◽  
Sensor Data ◽  
Correct Selection ◽  
Wind Tunnels ◽  
Data Set ◽  
First Case ◽  
Anomaly Analysis

Anomaly Detection research is focused on the development and application of methods that allow for the identification of data that are different enough—compared with the rest of the data set that is being analyzed—and considered anomalies (or, as they are more commonly called, outliers). These values mainly originate from two sources: they may be errors introduced during the collection or handling of the data, or they can be correct, but very different from the rest of the values. It is essential to correctly identify each type as, in the first case, they must be removed from the data set but, in the second case, they must be carefully analyzed and taken into account. The correct selection and use of the model to be applied to a specific problem is fundamental for the success of the anomaly detection study and, in many cases, the use of only one model cannot provide sufficient results, which can be only reached by using a mixture model resulting from the integration of existing and/or ad hoc-developed models. This is the kind of model that is developed and applied to solve the problem presented in this paper. This study deals with the definition and application of an anomaly detection model that combines statistical models and a new method defined by the authors, the Local Transilience Outlier Identification Method, in order to improve the identification of outliers in the sensor-obtained values of variables that affect the operations of wind tunnels. The correct detection of outliers for the variables involved in wind tunnel operations is very important for the industrial ventilation systems industry, especially for vertical wind tunnels, which are used as training facilities for indoor skydiving, as the incorrect performance of such devices may put human lives at risk. In consequence, the use of the presented model for outlier detection may have a high impact in this industrial sector. In this research work, a proof-of-concept is carried out using data from a real installation, in order to test the proposed anomaly analysis method and its application to control the correct performance of wind tunnels.

scholarly journals Field Study of Metal Oxide Semiconductor Gas Sensors in Temperature Cycled Operation for Selective VOC Monitoring in Indoor Air

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 647
Author(s):  
Tobias Baur ◽  
Johannes Amann ◽  
Caroline Schultealbert ◽  
Andreas Schütze
Keyword(s):  
Air Quality ◽  
Metal Oxide ◽  
Gas Sensor ◽  
Gas Sensors ◽  
Indoor Air ◽  
Ambient Air ◽  
Quantitative Agreement ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
High Temporal Resolution

More and more metal oxide semiconductor (MOS) gas sensors with digital interfaces are entering the market for indoor air quality (IAQ) monitoring. These sensors are intended to measure volatile organic compounds (VOCs) in indoor air, an important air quality factor. However, their standard operating mode often does not make full use of their true capabilities. More sophisticated operation modes, extensive calibration and advanced data evaluation can significantly improve VOC measurements and, furthermore, achieve selective measurements of single gases or at least types of VOCs. This study provides an overview of the potential and limits of MOS gas sensors for IAQ monitoring using temperature cycled operation (TCO), calibration with randomized exposure and data-based models trained with advanced machine learning. After lab calibration, a commercial digital gas sensor with four different gas-sensitive layers was tested in the field over several weeks. In addition to monitoring normal ambient air, release tests were performed with compounds that were included in the lab calibration, but also with additional VOCs. The tests were accompanied by different analytical systems (GC-MS with Tenax sampling, mobile GC-PID and GC-RCP). The results show quantitative agreement between analytical systems and the MOS gas sensor system. The study shows that MOS sensors are highly suitable for determining the overall VOC concentrations with high temporal resolution and, with some restrictions, also for selective measurements of individual components.

scholarly journals An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3366-3371 ◽  
Author(s):  
Ruo Bo Xin ◽  
Zhi Fang Jiang ◽  
Ning Li ◽  
Lu Jian Hou
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Ambient Air ◽  
Monitoring Site ◽  
Ambient Air Quality ◽  
Bp Network ◽  
Air Quality Forecast ◽  
Qingdao City

In order to obtain high precision results of urban air quality forecast, we propose a short-term predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into BP network, and use a reasonable method to extract the historical data of two years as the training samples, which are the main reasons why the prediction results are better both in speed and in accuracy. And when predicting within a certain period of hours, we also adopt an average and equivalent idea to reduce the error accuracy, which brings us good results.

scholarly journals Short-Time Fourier Transform and Decision Tree-Based Pattern Recognition for Gas Identification Using Temperature Modulated Microhotplate Gas Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Aixiang He ◽  
Jun Yu ◽  
Guangfen Wei ◽  
Yi Chen ◽  
Hao Wu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Fourier Transform ◽  
Decision Tree ◽  
Gas Sensors ◽  
Operating Temperature ◽  
Ambient Air ◽  
Sensor Response ◽  
Short Time Fourier Transform ◽  
Short Time ◽  
Different Gases

Because the sensor response is dependent on its operating temperature, modulated temperature operation is usually applied in gas sensors for the identification of different gases. In this paper, the modulated operating temperature of microhotplate gas sensors combined with a feature extraction method based on Short-Time Fourier Transform (STFT) is introduced. Because the gas concentration in the ambient air usually has high fluctuation, STFT is applied to extract transient features from time-frequency domain, and the relationship between the STFT spectrum and sensor response is further explored. Because of the low thermal time constant, the sufficient discriminatory information of different gases is preserved in the envelope of the response curve. Feature information tends to be contained in the lower frequencies, but not at higher frequencies. Therefore, features are extracted from the STFT amplitude values at the frequencies ranging from 0 Hz to the fundamental frequency to accomplish the identification task. These lower frequency features are extracted and further processed by decision tree-based pattern recognition. The proposed method shows high classification capability by the analysis of different concentration of carbon monoxide, methane, and ethanol.

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