Diagnosa COVID-19 Chest X-Ray Dengan Convolution Neural Network Arsitektur Resnet-152

The availability of medical aids in adequate quantities is very much needed to assist the work of the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Resnet Version-152 architecture was used in this study to train a dataset of 10.300 images, consisting of 4 classifications namely covid, normal, lung opacity with 3,000 images each and viral pneumonia 1,000 images. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 10.300 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (99%), Normal (98%) and Viral pneumonia (98%).

Short-term power prediction for renewable energy using hybrid graph convolutional network and long short-term memory approach

Accurate short-term solar and wind power predictions play an important role in the planning and operation of power systems. However, the short-term power prediction of renewable energy has always been considered a complex regression problem, owing to the fluctuation and intermittence of output powers and the law of dynamic change with time due to local weather conditions, i.e. spatio-temporal correlation. To capture the spatio-temporal features simultaneously, this paper proposes a new graph neural network-based short-term power forecasting approach, which combines the graph convolutional network (GCN) and long short-term memory (LSTM). Specifically, the GCN is employed to learn complex spatial correlations between adjacent renewable energies, and the LSTM is used to learn dynamic changes of power generation curves. The simulation results show that the proposed hybrid approach can model the spatio-temporal correlation of renewable energies, and its performance outperforms popular baselines on real-world datasets.Comment: This paper was accepted the 22nd Power Systems Computation Conference (PSCC 2022

Analisis Tren dan Perkiraan Pandemi COVID-19 di Indonesia Menggunakan Peramalan Metode Prophet :Sebelum dan Sesudah Aturan New Normal

Dalam menanggulangi penyebaran pandemi Covid-19 di Indonesia, pemerintah telah menetapkan PSBB dan aturan Normal Baru namun laju penyebaran pandemi terus meningkat dari waktu ke waktu. Selain itu, ketidakpastian akan berakhirnya pandemi ini berdampak pada perubahan kondisi sosial. Makalah ini bertujuan untuk memfasilitasi perbandingan antara PSBB dan regulasi New Normal tentang perkembangan jumlah kasus Covid-19 di Indonesia dengan memetakan jumlah kumulatif kasus (kasus aktif, sembuh, dikonfirmasi dan meninggal). Metode Prophet digunakan untuk memprediksi kasus kematian dan terkonfirmasi dalam 30 hari ke depan. Analisis data visual dengan pendekatan Exploratory Data Analysis (EDA) disajikan untuk memberikan pemahaman tentang perkembangan penyebaran pandemi di Indonesia. Pengujian kerangka analisis dilakukan dengan eksperimen untuk mengukur tingkat ketepatan prediksi metode Prophet dengan membagi kumpulan data historis periode 23 Maret 2020 - 31 Juli 2020, sedangkan data bulan terakhir dari kumpulan data periode 01 Agustus 2020 hingga 5 September 2020 digunakan sebagai target prediksi. Berdasarkan hasil pengujian metode Prophet memprediksi Indonesia akan mengalami peningkatan jumlah kasus terkonfirmasi sekitar 238.322 kasus dan kematian sekitar 9.609 hingga akhir September dengan tingkat kesalahan relatif dari estimasi yang dievaluasi dengan MAPE sekitar 23,9%. dan MAE sekitar 73,12 MAE. Hasil analisis visual penyebaran suatu pandemi juga disajikan dengan harapan dapat bermanfaat sebagai wawasan perkembangan jumlah kasus pandemi di Indonesia. Abstract In countering the spread of the Covid-19 pandemic in Indonesia, the government has set PSBB and New Normal rules but the rate of spread of the pandemic continues to increase from time to time. In addition, the uncertainty about the end of this pandemic has resulted in changing social conditions. This paper aims to facilitate a comparison between the PSBB and New Normal regulations on the development of the number of Covid-19 cases in Indonesia by mapping the cumulative number of cases (active, cured, confirmed and death cases). The Prophet method is used to predict confirmed cases and deaths within the next 30 days. Visual data analysis using the Exploratory Data Analysis (EDA) approach is presented to provide an understanding of the development of the pandemic spread in Indonesia. The testing analysis framework was carried out by experiments to measure the level of prediction accuracy of the prophet method by dividing the historical data set for the period 23 March 2020 - 31 July 2020, while the last month data from the data set for the period 01 August 2020 to 5 September 2020 were used as prediction targets. Based on the results of the Prophet method testing it is estimated that Indonesia will experience an increase in the number of confirmed cases around 238,322 and cases of death around 9,609 until the end of September with the relative error rate of estimates evaluated with MAPE around 23.9% and MAE around 73.12 MAE. The results of a visual analysis of the spread of a pandemic are also presented in the hope that they will be useful as an insight into the development of the number of pandemic cases in Indonesia

Caracterización de tensión y corriente a la salida de un convertidor dc-dc considerando series históricas de radiación de una estación meteorológica en la UTP

La necesidad de estudiar las células fotovoltaicas y modelar su comportamiento se deriva del aumento en la generación de energía renovable de tipo solar. Los valores de tensión a la salida de los módulos solares fotovoltaicos son bajos, por lo tanto, se hace necesario tratar el nivel de tensión a través de dispositivos convertidores electrónicos de potencia. Estos últimos son dispositivos que convierten un nivel de tensión no regulado en la entrada, a otro nivel de tensión regulado a la salida de este. Los convertidores brindan una interfaz entre el sistema de energía eléctrica y las fuentes renovables, destacando la importancia de estas últimas, se implementa un caso de estudio para predecir radiación solar a través de aprendizaje de máquina en lenguaje supervisado, específicamente utilizando modelos de regresión como los Regresores de Vectores de Soporte (SVR) y las Redes Neuronales Artificiales (ANN). La tesis provee un caso de estudio donde se predice la radiación para una fecha específica usando una técnica de aprendizaje de máquina, el valor máximo de radiación del día se ingresa a un arreglo de 6 módulos fotovoltaicos conectados en serie con el fin de determinar las características I-V y P-V ante dicho valor de radiación; a su vez, la tensión de salida del arreglo fotovoltaico es la tensión de entrada a un convertidor dc-dc que se diseña con el fin de elevar la tensión a un nivel superior que el de entrada.y photovoltaic cells and model their behavior stems from the increase in solar-type renewable energy generation. The voltage values at the output of the photovoltaic solar modules are low, therefore, it is necessary to treat the voltage level through electronic power converter devices. The latter are devices that convert an unregulated voltage level at the input to another regulated voltage level at its output. The converters provide an interface between the electrical energy system and renewable sources, highlighting the importance of the latter, a case study is implemented to predict solar radiation through machine learning in supervised language, specifically using regression models such as those Support Vector Regressors (SVR) and Artificial Neural Networks (ANN). The thesis provides a case study where radiation is predicted for a specific date using a machine learning technique, the maximum radiation value of the day is entered into an array of 6 photovoltaic modules connected in series in order to determine the characteristics I-V and P-V before said radiation value; in turn, the output voltage of the photovoltaic array is the input voltage to a dc-dc converter that is designed in order to raise the voltage to a higher level than the input.PregradoIngeniero(a) ElectricistaCONTENIDO AGRADECIMIENTOS .................................................................................................................... 6 INTRODUCCIÓN............................................................................................................................. 7 CAPÍTULO 1. ANÁLISIS DEL MODELO DE SIMULACIÓN DE CARACTERÍSTICAS I-V Y P-V OBTENIDOS DE UN MÓDULO FOTOVOLTAICO....................................................... 9 1.1 Modelamiento de Celda Fotovoltaica Simple ....................................................................... 9 1.2 Modelamiento de Módulo Fotovoltaico............................................................................... 10 1.3 Características I-V y P-V de un Módulo Fotovoltaico....................................................... 12 CAPÍTULO 2. MODELO DE SIMULACIÓN EN DIAGRAMA DE BLOQUES DE LOS CONVERTIDORES ELECTRÓNICOS DE POTENCIA DC-DC............................................ 15 2.1 Diseño de Convertidores DC-DC (Modelo Switcheado).................................................... 15 2.1.1 Convertidor Buck........................................................................................................... 15 2.1.2 Convertidor Boost .......................................................................................................... 20 2.1.3 Convertidor Buck-Boost................................................................................................ 22 CAPITULO 3. PREDICCIÓN DE RADIACIÓN SOLAR A CORTO PLAZO A PARTIR DE DATOS METEOROLÓGICOS UTILIZANDO TÉCNICAS DE APRENDIZAJE DE MÁQUINA....................................................................................................................................... 26 3.1 Regresión de Vectores de Soporte........................................................................................ 29 3.2 Redes Neuronales Artificiales (ANN) con arquitectura de Perceptrón Multicapa (MLP) ....................................................................................................................................................... 29 CAPÍTULO 4. SIMULACIONES Y ANÁLISIS DE RESULTADOS ....................................... 31 4.1 Formas de Onda de convertidores dc-dc............................................................................. 31 4.1.1 Convertidor Buck........................................................................................................... 31 4.1.2 Convertidor Boost .......................................................................................................... 32 4.1.3 Convertidor Buck-Boost................................................................................................ 34 4.2 Resultados de predicción de radiación solar....................................................................... 35 4.2.1 Regresión de Vectores de Soporte (SVR)..................................................................... 35 4.2.2 Redes Neuronales Artificiales (ANN) ........................................................................... 37 4.3 Caracterización de tensión y corriente a la salida de un convertidor dc-dc considerando series históricas de radiación de una estación meteorológica en la UTP................................ 39 CONLUSIONES.............................................................................................................................. 42 REFERENCIAS .............................................................................................................................. 4

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed

Predicting diarrhoea outbreak with climate change

Climate change is expected to exacerbate diarrhoea outbreak in South Africa, a leading cause of morbidity and mortality in the region. In this study, we modelled the impacts of climate change on diarrhoea with machine learning methods. We applied two deep learning techniques, convolutional neural networks (CNNs) and long-short term memory networks (LSTMs); and a support vector machine to predict daily diarrhoea cases over the different South African provinces by incorporating climate information. Generative Adversarial Networks (GANs) was used to generate synthetic data which was used to augment the available dataset. Furthermore, relevance estimation and value calibration (REVAC) was used to tune the parameters of the machine learning algorithms to optimize the accuracy of their predictions. Sensitivity analysis was also performed to investigate the contribution of the different climate factors to the diarrhoea prediction model. The results of the study showed that all three ML methods were appropriate for predicting daily diarrhoea cases with respect to the selected climate variables in each South African province. The ML methods were all able to yield low and similar RMSE. However, the level of accuracy for each model varied across different experiments, with the deep learning models outperforming the SVM model. Among the deep learning techniques, the CNN model performed best when only real-world dataset was used, while the LSTM model outperformed the other models when the real dataset was augmented with synthetic data. Across the provinces, the accuracy of all three ML algorithms improved by at least 30% when data augmentation was implemented. In addition, REVAC improved the accuracy of the CNN model by more than 12% in KwaZulu Natal province. However, the percentage increase in accuracy of the LSTM model was less than 4% in Western Cape province when REVAC was used. Our sensitivity analysis revealed that the most influential climate variables to be considered when predicting outbreak of diarrhoea in South Africa are precipitation, humidity, evaporation and temperature conditions. The result of this study is important for the development of an early warning system for diarrhoea outbreak over South Africa

Intra-hour solar forecasting for photovoltaic systems integration in weak electric grids

La tesis "Intra-hour solar forecasting for photovoltaic systems integration in weak electric grids" estudia la problemática de la variabilidad del recurso solar en la integración de sistemas fotovoltaicos en redes eléctricas débiles, que es el principal obstáculo que enfrenta esta tecnología para un despliegue masivo. Por un lado, se desarrolla un sistema de predicción de energía fotovoltaica intra-horario basado en dos cámaras de cielo capaz de predecir las rampas de producción causadas por el efecto de las nubes. El sistema hace uso de técnicas de procesamiento de imágenes y deep learning para identificar las nubes y predecir cuando éstas afectaran a la producción de las plantas fotovoltaicas cercanas. Por otro lado, se evalúa el potencial fotovoltaico de las Islas Canarias haciendo uso de técnicas de Big Data. También se estudian los problemas de integración derivados de la inclusión de energía fotovoltaica en las redes eléctricas de distribución, proponiendo un algoritmo para la optimización del control de los inversores fotovoltaicos