Multiwavelength fiber laser based on bidirectional lyot filter in conjunction with intensity dependent loss mechanism

We experimentally demonstrate a multiwavelength fiber laser (MWFL) based on bidirectional Lyot filter. A semiconductor optical amplifier (SOA) is used as the gain medium, while its combination with polarization controllers (PCs) and polarization beam combiner (PBC) induces intensity dependent loss (IDL) mechanism. The IDL mechanism acts as an intensity equalizer to flatten the multiwavelength spectrum, which can be obtained at a certain polarization state. Using different ratio of optical splitter has affected to multiwavelength flatness degradation. Subsequently, when we removed a polarizer in the setup, the extinction ratio (ER) is decreased. Ultimately, with two segments of polarization maintaining fiber (PMF), two channel spacings can be achieved due to splicing shift of 0° and 90°

Artificial Neural Networks applied to improve low-cost air quality monitoring precision

It is a fact that air pollution is a major environmental health problem that affects everyone, especially in urban areas. Furthermore, the cost of high-end air pollution monitoring sensors is considerably high, so public administrations are unable to afford to place an elevated number of measuring stations, leading to the loss of information that could be very helpful. Over the last few years, a large number of low-cost sensors have been released, but its use is often problematic, due to their selectivity and precision problems. A calibration process is needed in order to solve an issue with many parameters with no clear relationship among them, which is a field of application of Machine Learning. The objectives of this project are first, integrating three low-cost air quality sensors into a Raspberry Pi and then, training an Artificial Neural Network model that improves precision in the readings made by the sensors.Es un hecho que la contaminación del aire es un gran problema para la salud a nivel mundial, especialmente en zonas urbanas. Además, el coste de los sensores de contaminación de gama alta es considerablemente alto, por lo que los organismos públicos no pueden permitirse emplazar un gran número de estaciones de medida, perdiendo información que podría ser muy útil. A lo largo de los últimos años, han surgido muchos sensores de contaminación de bajo coste, pero su uso suele ser complicado, ya que tienen problemas de selectividad y precisión. Los objetivos de este proyecto son primero integrar tres sensores de contaminación de bajo coste en una Raspberry Pi y sobre todo, entrenar un modelo basado en una red neuronal artificial que mejore la precisión de las lecturas realizadas por los sensores.Està demostrat que la contaminació de l'aire és un gran problema per a la salut a nivell mundial, especialment en zones urbanes. A més, el cost dels sensors de contaminació de gama alta és considerablement alt, motiu pel qual els organismes públics no es poden permetre emplaçar una gran quantitat d'estacions de mesura, perdent informació que podria resultar molt útil. Al llarg dels últims anys, han sorgit molts sensors de contaminació de baix cost, però el seu ús és sovint complicat, ja que tenen problemes de selectivitat i precisió. Els objectius d'aquest projecte són primer de tot integrar tres sensors de contaminació de baix cost en una Raspberry Pi i sobretot, entrenar un model basat en una xarxa neuronal artificial que millori la precisió de les lectures realitzades pels sensors

Artificial neural networks model for air quality in the region of İzmir

Thesis (Master)--Izmir Institute of Technology, Environmental Engineering, Izmir, 2002Includes bibliographical references (leaves: 68-72)Text in English; Abstract: Turkish and Englishxiv, 88 leavesIn this study, a systematic approach to the development of the artificial neural networks based forecasting model is presented. S02, and dust values are predicted with different topologies, inputs and transfer functions. Temperature and wind speed values are used as input parameters for the models. The back-propagation learning algorithm is used to train the networks. R 2 (correlation coefficient), and daily average errors are employed to investigate the accuracy of the networks. MATLAB 6 neural network toolbox is used for this study. The study results indicate that the neural networks are able to make accurate predictions even with the limited number of parameters. Results also show that increasing the topology of the network and number of the inputs, increases the accuracy of the network. Best results for the S02 forecasting are obtained with the network with two hidden layers, hyperbolic tangent function as transfer function and three input variables (R2 was found as 0,94 and daily average error was found as 3,6 j..lg/m3).The most accurate results for the dust forecasting are also obtained with the network with two hidden layer, hyperbolic tangent function as transfer function and three input variables (R2 was found as 0,92 and daily average error was found as 3,64 j..lg/m3).S02 and dust predictions using their last seven days values as an input are also studied, and R2 is calculated as 0,94 and daily average error is calculated as 4,03 Jlg/m3 for S02 prediction and R2 is calculated as 0,93 and daily average error is calculated as 4,32 Jlg/m3 for dust prediction and these results show that the neural network can make accurate predictions

A Machine Learning-Based Method for Modelling a Proprietary SO2 Removal System in the Oil and Gas Sector

The aim of this study is to develop a model for a proprietary SO2 removal technology by using machine learning techniques and, more specifically, by exploiting the potentialities of artificial neural networks (ANNs). This technology is employed at the Eni oil and gas treatment plant in southern Italy. The amine circulating in this unit, that allows for a reduction in the SO2 concentration in the flue gases and to be compliant with the required specifications, is a proprietary solvent; thus, its composition is not publicly available. This has led to the idea of developing a machine learning (ML) algorithm for the unit description, with the objective of becoming independent from the licensor and more flexible in unit modelling. The model was developed in MatLab® by implementing ANNs and the aim was to predict three targets, namely the flow rate of SO2 that goes to the Claus unit, the emissions of SO2, and the flow rate of steam sent to the regenerator reboiler. These represent, respectively, the two physical outputs of the unit and a proxy variable of the amine quality. Three different models were developed, one for each target, that employed the Levenberg–Marquardt optimization algorithm. In addition, the ANN topology was optimized case by case. From the analysis of the results, it emerged that with a purely data-driven technique, the targets can be predicted with good accuracy. Therefore, this model can be employed to better manage the SO2 removal system, since it allows for the definition of an optimal control strategy and the maximization of the plant’s productivity by not exceeding the process constraints

AN ANFIS – BASED AIR QUALITY MODEL FOR PREDICTION OF SO2 CONCENTRATION IN URBAN AREA

This paper presents the results of attempt to perform modeling of SO2concentration in urban area in vicinity of copper smelter in Bor (Serbia), using ANFIS methodological approach. The aim of obtained model was to develop a prediction tool that will be used to calculate potential SO2 concentration, above prescribed limitation, based on input parameters. As predictors, both technogenic and meteorological input parameters were considered. Accordingly, the dependence of SO2concentration was modeled as the function of wind speed, wind direction, air temperature, humidity and amount sulfur emitted from the pyrometallurgical process of sulfidic copper concentration treatment