CMUT based chemical sensor for classification and quantification with machine learning in a real-world application

In a quest for further enhancing human senses, chemical sensors are developed. Chemical sensors are proved to diagnose diseases, classify and quantify chemical warfare agents as well as measuring air pollution down to parts per billion [1-3]. Connecting multiple devices in large networks can help authorities and governments respond faster and make better decisions considering the release of emissions and/or dangerous gases. In order to create such networks, an inexpensive, robust and portable sensor must be developed. The chemical capacitive micromachined ultrasonic transducer (CMUT) might be such a sensor. This thesis demonstrates a proof of concept for a CMUT based chemical sensor as a gas detecting unit that can classify and quantify chemicals with machine learning in a real-world application. The CMUT is a sensor consisting of an array of polymer coated cells adsorbing different gases. Adsorption causes a frequency shift in the sensor output. This shift can be correlated to chemicals and their concentrations through machine learning. Reference data collected for the machine learning models was identified as a time-consuming process. An autosampler was devised, reducing time and cost related to the data collection. The CMUT sensor was tested in a greenhouse for 4 weeks to measure CO2 concentration in a plant bed under varying conditions. Testing the following statement: If the sensor can detect low concentrations of CO2 in ambient air it can also detect other compounds. The machine learning models were trained on the collected samples, and later compared to find the best model. The results showed that the CMUT sensor successfully measured CO2 down to 120 ppm in ambient air, the machine learning models could classify between high and low concentrations. For classification purposes the neural network with relu activation showed the best results, with a 15% error for both high and low concentrations. Quantification of the data had poor performance due to sensor drift. Large RMSE scores was found for all quantification models. The drift is most likely caused by the breakdown of the polymer, causing a frequency shift. The dataset was unbalanced and had a higher distribution on lower concentrations. Which to some extent undermine the results from the machine learning, although giving an indication of sensor performance. Further research is recommended to assess the polymer coating on the CMUT as well as removing drift. Reducing the size of the sensor and equipment, as well as connecting the sensor to a cloud database, is recommended and identified as important steps for creating a sensor network.I søken etter å forbedre menneskets sanser ønsker man å utvikle kjemiske sensorer. Kjemiske sensorer har blitt brukt til å diagnostisere sykdommer, klassifisere og kvantifisere nervegass i tillegg til å måle luftforurensing som har svært lav oppløsning. Ved å sette sammen flere elektroniske neser i større nettverk vil det bidra med økt informasjon om utslipp i byer. Dette vil hjelpe myndigheter med å ta bedre og raskere beslutninger for å unngå spredning av farlige kjemikalier og/eller forurensning. For å lage slike nettverk må sensorene som benyttes være pålitelige, kostnadseffektive og robuste. En sensor som oppfyller disse kravene er den kjemiske kapasitive mikromaskinerte ultralyd transduceren (CMUT).M-MP

Dynamic Stability with Artificial Intelligence in Smart Grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Dynamic stability with artificial intelligence in smart grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

An Enhanced CNN-based ELM Classification for Disease Prediction in the Rice Crop

To meet the demands of a constantly expanding population, intensive farming is becoming more popular in the modern day. This strategy, meanwhile, increases the possibility of a wider range of plant illnesses. By reducing crop productivity in terms of both quantity and quality, these infections represent a threat to food production and ultimately result in a fall in the economy. Fortunately, new opportunities for early diagnosis of such epidemics have emerged because of technological improvements, which are advantageous for society as a whole. The difficulties created by technology and bio-mutations create a potential for additional breakthroughs, notwithstanding the significant contributions made by researchers in the field of agricultural disease diagnosis. The suggested framework comprises three key phases: preprocessing, feature extraction, and the classification of leaf diseases. To optimize computational resources and memory utilization, the input image undergoes pre-processing as a preliminary step. Afterward, a Convolutional Neural Network (CNN) is utilized on an extensive dataset of labeled images to capture pertinent features for the diagnosis of rice leaf diseases. The suggested model utilizes an Efficient Selective Pruning of Hidden Nodes (ELM) classifier based on the RBF kernel to classify the input data

Artificial intelligence approach for tomato detection and mass estimation in precision agriculture

Funding: This study was carried out with the support of “Research Program for Agricultural Science & Technology Development” (Project No: PJ013891012020), National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.Application of computer vision and robotics in agriculture requires sufficient knowledge and understanding of the physical properties of the object of interest. Yield monitoring is an example where these properties affect the quantified estimation of yield mass. In this study, we propose an image-processing and artificial intelligence-based system using multi-class detection with instance-wise segmentation of fruits in an image that can further estimate dimensions and mass. We analyze a tomato image dataset with mass and dimension values collected using a calibrated vision system and accurate measuring devices. After successful detection and instance-wise segmentation, we extract the real-world dimensions of the fruit. Our characterization results exhibited a significantly high correlation between dimensions and mass, indicating that artificial intelligence algorithms can effectively capture this complex physical relation to estimate the final mass. We also compare different artificial intelligence algorithms to show that the computed mass agrees well with the actual mass. Detection and segmentation results show an average mask intersection over union of 96.05%, mean average precision of 92.28%, detection accuracy of 99.02%, and precision of 99.7%. The mean absolute percentage error for mass estimation was 7.09 for 77 test samples using a bagged ensemble tree regressor. This approach could be applied to other computer vision and robotic applications such as sizing and packaging systems and automated harvesting or to other measuring instruments.Publisher PDFPeer reviewe

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

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Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable