Machine Learning in Confocal Laser Microscopy and Spectroscopy

Confocal laser scanning microscopy (CLSM) is a preferred method for obtaining optical images with submicron resolution. Replacing the pinhole and detector of a CLSM with a digital camera (CCD or CMOS) has the potential to simplify the design and reduce cost. However, the relatively slow speed of a typical camera results in long scans. To address this issue, in the present investigation a microlens array (MLA) was used to split the laser beam into 48 beamlets that are focused onto the sample. In essence, 48 pinhole-detector measurements were performed in parallel. Images obtained from the 48 laser spots were stitched together into a final image.Photoluminescence (PL) spectroscopy is a non-destructive optical method that is widely used to characterize semiconductors. In the PL process, a substance absorbs photons and emits light with longer wavelengths. This paper discusses a method for identifying substances from their PL spectra using machine learning, a technique that is efficient in making classifications. Neural networks were constructed by taking simulated PL spectra as the input and the identity of the substance as the output. Six different semiconductors were chosen as categories: gallium oxide (Ga2O3), zinc oxide (ZnO), gallium nitride (GaN), cadmium sulfide (CdS), tungsten disulfide (WS2) and cesium lead bromide (CsPbBr3). The developed algorithm has a high accuracy (>90%) for assigning a substance to one of these six categories from its PL spectrum.With an XY stage, a CLSM can scan a large area on a sample. Adjusting the height of the objective is necessary which made the laser beam could focus on the sample surface. However, if the surface of the sample is not flat, the laser spot will go in and out of focus, causing bad scanning results. Deep learning especially convolutional neural networks is an efficient way to treat images. It shows its success in the field of object detection, image classification, face recognition, etc. The deep learning techniques were used to design a model that predicts the out-of-focus distance with the image of laser spot. The model can develop to a system that could automatically focusing the CLSM in real time

Determination of significant variables to particulate matter (PM10) variations in northern region, Malaysia during haze episodes (2006-2015)

The most substantial air pollutant variables during haze episode in Northern region for 10-consecutive years (2006-2015) were analyzed and highlighted. ANN together with SAPCR were integrated to identify the variables contributed to fluctuation of particulate matter (PM10) during haze period. 13 variables including air pollutant and meteorological factor were included as explorable variables. The humidity, wind speed and ozone were recognized as determinant to PM10 variation during haze from 2006-2015. Three artificial neural models were created based on all parameters, leave-out method and PCR-factor loading. The best model will be selected based on a few criterions like determination of coefficient, R2, root-mean-square-error and squared sum of all errors. ANN-HM-LO was a better model than ANN-HM-PCR in overall prediction performance with R2 result for ANN-HM-LO was 0.839, whilst ANN-HM-PCR was just 0.801.Keywords: haze studies; sensitivity analysis; artificial neural network; principal component regressio

Higher-order Network Analysis of Fine Particulate Matter (PM 2.5) Transport in China at City Level

abstract: Specification of PM[subscript 2.5] transmission characteristics is important for pollution control and policymaking. We apply higher-order organization of complex networks to identify major potential PM[subscript 2.5] contributors and PM[subscript 2.5] transport pathways of a network of 189 cities in China. The network we create in this paper consists of major cities in China and contains information on meteorological conditions of wind speed and wind direction, data on geographic distance, mountains, and PM[subscript 2.5] concentrations. We aim to reveal PM[subscript 2.5] mobility between cities in China. Two major conclusions are revealed through motif analysis of complex networks. First, major potential PM[subscript 2.5] pollution contributors are identified for each cluster by one motif, which reflects movements from source to target. Second, transport pathways of PM[subscript 2.5] are revealed by another motif, which reflects transmission routes. To our knowledge, this is the first work to apply higher-order network analysis to study PM[subscript 2.5] transport.The final version of this article, as published in Scientific Reports, can be viewed online at: http://www.nature.com/articles/s41598-017-13614-

A critical review of wind power forecasting methods - past, present and future

The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature

BIG DATA RANKING SYSTEM AS AN EFFECTIVE METHOD OF VISUALIZING THE QUALITY OF URBAN STRUCTURAL UNITS

Proceedings of the XXV ISUF International Conference “Urban Form and Social Context: from Traditions to Newest Demands” (Krasnoyarsk, July 5–9, 2018)Big data is the basis for new technological changes. Constantly growing volumes of arrays greatly complicate data processing and understanding. Big data analysis extracts knowledge and meaningful information from large and complex data sets. The extraction of information displays regularities hidden in the data. Modern cities use the latest technologies to support sustainable development and a high standard of living. The indicator of a high standard of living of the urban population and, consequently, an indicator of a quality city is the quality of the urban environment. To evaluate the structural units of a city, the most common method is ranking. Ranking systems based on big data are the most effective method of visualizing the quality of structural elements of a city. Innovative ways of collecting and analyzing data are gradually replacing obsolete mechanisms of city management. Unlike statistical data, which are out of date by the time of their analysis, big data can be processed in real time that increases the quality and speed of decision making. The complexity of big data methods implementing in ranking systems is caused by problems of staff shortages, technical equipment, legal rights, security problems and openness of data. Ranking quality systems of the urban environment can be used by the city administration, designers, civil communities to assess the current state and management of the urban environment. The creation of such ranking systems is the first step towards the formation of smart open data-driven cities. The introduction of big data into cities can be divided into three levels as the influence of data on urban governance increases: applied (open data city); semi-autonomous (data-driven city); autonomous (smart city)

Profiling and forecasting air pollutant index for Malaysia

Detection of poor air quality is important to provide an early warning system for air quality control and management. Thus, air pollutant index (API) is designed as a referential parameter in describing air pollution levels to provide information to enhance public awareness. This study aims to study API trend, time series forecasting methods, their performance evaluations and missing values effect for accurate early warning system using several approaches. First, a calendar grid visualization is introduced to effectively display API daily profiling for the whole of Malaysia in identifying the exact point of poor air quality. Second, comparisons between classical and modern forecasting methods, artificial neural network (ANN), fuzzy time series (FTS) and hybrid are carried out to identify the best model in Johor sampling stations; industrial, urban and suburban. Third, due to the issue of different perfect score in existing index measurement to evaluate forecast performance, a combination index measures is proposed alongside error magnitude measurement. Fourth, decomposition and spatial techniques are compared to find the effect of high accuracy imputations in API missing values. The finding presented that the air quality trend across the day, week, month and year are more significant due to the daily arrangement in the calendar grid visualization. The ANN model gives the best forecasting model of API for industrial and urban area while the hybrid model provide the best forecasting for suburban area. The forecasting performance for industrial and urban areas improve between 14% to 20% and 20% to 55% in error magnitude and index measurements, respectively when high accuracy missing values imputation is conducted. In conclusion, the profiling using calendar grid visualization is useful to guide the control actions of early warning system. Forecasting using modern methods give promising result in API and the improvements in measurements will assist in choosing the best forecasting method. Missing values imputation in data series can enhance the forecasting performance

Smart models to improve agrometeorological estimations and predictions

La población mundial, en continuo crecimiento, alcanzará de forma estimada los 9,7 mil millones de habitantes en el 2050. Este incremento, combinado con el aumento en los estándares de vida y la situación de emergencia climática (aumento de la temperatura, intensificación del ciclo del agua, etc.) nos enfrentan al enorme desafío de gestionar de forma sostenible los cada vez más escasos recursos disponibles. El sector agrícola tiene que afrontar retos tan importantes como la mejora en la gestión de los recursos naturales, la reducción de la degradación medioambiental o la seguridad alimentaria y nutricional. Todo ello condicionado por la escasez de agua y las condiciones de aridez: factores limitantes en la producción de cultivos. Para garantizar una producción agrícola sostenible bajo estas condiciones, es necesario que todas las decisiones que se tomen estén basadas en el conocimiento, la innovación y la digitalización de la agricultura de forma que se garantice la resiliencia de los agroecosistemas, especialmente en entornos áridos, semi-áridos y secos sub-húmedos en los que el déficit de agua es estructural. Por todo esto, el presente trabajo se centra en la mejora de la precisión de los actuales modelos agrometeorológicos, aplicando técnicas de inteligencia artificial. Estos modelos pueden proporcionar estimaciones y predicciones precisas de variables clave como la precipitación, la radiación solar y la evapotranspiración de referencia. A partir de ellas, es posible favorecer estrategias agrícolas más sostenibles, gracias a la posibilidad de reducir el consumo de agua y energía, por ejemplo. Además, se han reducido el número de mediciones requeridas como parámetros de entrada para estos modelos, haciéndolos más accesibles y aplicables en áreas rurales y países en desarrollo que no pueden permitirse el alto costo de la instalación, calibración y mantenimiento de estaciones meteorológicas automáticas completas. Este enfoque puede ayudar a proporcionar información valiosa a los técnicos, agricultores, gestores y responsables políticos de la planificación hídrica y agraria en zonas clave. Esta tesis doctoral ha desarrollado y validado nuevas metodologías basadas en inteligencia artificial que han ser vido para mejorar la precision de variables cruciales en al ámbito agrometeorológico: precipitación, radiación solar y evapotranspiración de referencia. En particular, se han modelado sistemas de predicción y rellenado de huecos de precipitación a diferentes escalas utilizando redes neuronales. También se han desarrollado modelos de estimación de radiación solar utilizando exclusivamente parámetros térmicos y validados en zonas con características climáticas similares a lugar de entrenamiento, sin necesidad de estar geográficamente en la misma región o país. Analógamente, se han desarrollado modelos de estimación y predicción de evapotranspiración de referencia a nivel local y regional utilizando también solamente datos de temperatura para todo el proceso: regionalización, entrenamiento y validación. Y finalmente, se ha creado una librería de Python de código abierto a nivel internacional (AgroML) que facilita el proceso de desarrollo y aplicación de modelos de inteligencia artificial, no solo enfocadas al sector agrometeorológico, sino también a cualquier modelo supervisado que mejore la toma de decisiones en otras áreas de interés.The world population, which is constantly growing, is estimated to reach 9.7 billion people in 2050. This increase, combined with the rise in living standards and the climate emergency situation (increase in temperature, intensification of the water cycle, etc.), presents us with the enormous challenge of managing increasingly scarce resources in a sustainable way. The agricultural sector must face important challenges such as improving natural resource management, reducing environmental degradation, and ensuring food and nutritional security. All of this is conditioned by water scarcity and aridity, limiting factors in crop production. To guarantee sustainable agricultural production under these conditions, it is necessary to based all the decision made on knowledge, innovation, and the digitization of agriculture to ensure the resilience of agroecosystems, especially in arid, semi-arid, and sub-humid dry environments where water deficit is structural. Therefore, this work focuses on improving the precision of current agrometeorological models by applying artificial intelligence techniques. These models can provide accurate estimates and predictions of key variables such as precipitation, solar radiation, and reference evapotranspiration. This way, it is possible to promote more sustainable agricultural strategies by reducing water and energy consumption, for example. In addition, the number of measurements required as input parameters for these models has been reduced, making them more accessible and applicable in rural areas and developing countries that cannot afford the high cost of installing, calibrating, and maintaining complete automatic weather stations. This approach can help provide valuable information to technicians, farmers, managers, and policy makers in key wáter and agricultural planning areas. This doctoral thesis has developed and validated new methodologies based on artificial intelligence that have been used to improve the precision of crucial variables in the agrometeorological field: precipitation, solar radiation, and reference evapotranspiration. Specifically, prediction systems and gap-filling models for precipitation at different scales have been modeled using neural networks. Models for estimating solar radiation using only thermal parameters have also been developed and validated in areas with similar climatic characteristics to the training location, without the need to be geographically in the same region or country. Similarly, models for estimating and predicting reference evapotranspiration at the local and regional level have been developed using only temperature data for the entire process: regionalization, training, and validation. Finally, an internationally open-source Python library (AgroML) has been created to facilitate the development and application of artificial intelligence models, not only focused on the agrometeorological sector but also on any supervised model that improves decision-making in other areas of interest

Mean-Field-Type Games in Engineering

A mean-field-type game is a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile but also the joint distributions of state-action pairs. This article presents some engineering applications of mean-field-type games including road traffic networks, multi-level building evacuation, millimeter wave wireless communications, distributed power networks, virus spread over networks, virtual machine resource management in cloud networks, synchronization of oscillators, energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201