Visual interfaces for the digital simulation system of the IPR-R1 Triga nuclear research reactor

Nuclear Technology Development Center (CDTN) offers the Training Course for Research Reactor Operator (Ctorp). This course is offered since 1974 and about 250 nuclear professionals were certificated by CDTN. Thus, a digital simulation system for the IPR-R1 Triga research reactor was developed to be a tool for teaching, training and recycling professionals. The simulator was developed using the LabVIEW® (Laboratory Virtual Instruments Engineering Workbench), with support calculation software, where mathematical models and graphical interface configurations form a friendly platform, which allows the trainee to be identified with the physical systems of the research reactor. A simplified modeling of the main physical phenomena related to the operation of the reactor and the reactivity control systems, reactor cooling and reactor protection was used. The digital simulator allows an HMI (Human-Machine Interaction) by manipulating system variables and monitoring trends in quantities during the operation of the reactor, showing an interactive tool for teaching, training and recycling for professionals in the IPR-R1 Triga nuclear research, allowing simulations of the start, power and stop operations. This paper presents the design and results of the user visual interfaces developed for the reactor operation simulator. This is the equivalent part of structured text programming and, therefore, the most significant part of the developed simulator.

Deep ensemble neural networks for recognizing isolated Arabic handwritten characters

In recent years, handwritten character recognition has become an active research field. In particular, digitalization has triggered the interest of researchers from various computing disciplines to address several handwriting related challenges. Despite these efforts, there are still opportunities for the development and improvement of the recognition of the handwritten Arabic letters. In this paper, we designed and developed a deep ensemble architecture in which ResNet-18 architecture is exploited to model and classify character images. Specifically, we adapted ResNet-18 by adding a dropout layer after all convolutional layer and integrated it in multiple ensemble models to automatically recognize isolated handwritten Arabic characters. A standard Arabic Handwritten Character Dataset (AHCD) was used in the experiments to train and assess all the proposed models. Satisfactory results were obtained using all models. The best-attained accuracy was 98.30% using a typical ResNet-18 model. Similarly, 98.00% and 98.03% accuracies were obtained using an ensemble model with one fully connected layer (1 FC) and an ensemble with two fully connected layers (2 FC) coupled with a dropout layer, respectively.

A survey and analysis of big data management based on computational methodologies

n a business enterprise there is an enormous amount of data generated or processed daily through different data points. It is increasing day by day. It is tough to handle it through traditional applications like excel or any other tools. So, big data analytics and environment may be helpful in the current scenario and the situation discussed above. This paper discussed the big data management ways with the impact of computational methodologies. It also covers the applicability domains and areas. It explores the computational methods applicability scenario and their conceptual design based on the previous literature. Machine learning, artificial intelligence and data mining techniques have been discussed for the same environment based on the related study.

X-ray image based pneumonia classification using convolutional neural networks

Pneumonia is an infectious disease of the lungs. About one third to one half of pneumonia cases are caused by bacteria. Early diagnosis is a critical factor for a successful treatment process. Typically, the disease can be diagnosed by a radiologist using chest X-ray images. In fact, chest X-rays are currently the best available method for diagnosing pneumonia. However, the recognition of pneumonia symptoms is a challenging task that relies on the availability of expert radiologists. Such “human” diagnosis can be inaccurate and subjective due to lack of clarity and erroneous decision. Moreover, the error can increase more if the physician is requested to analyze tens of X-rays within a short period of time. Therefore, Computer-Aided Diagnosis (CAD) systems were introduced to support and assist physicians and make their efforts more productive. In this paper, we investigate, design, implement and assess customized Convolutional Neural Networks to overcome the image-based Pneumonia classification problem. Namely, ResNet-50 and DenseNet-161 models were inherited to design customized deep network architecture and improve the overall pneumonia classification accuracy. Moreover, data augmentation was deployed and associated with standard datasets to assess the proposed models. Besides, standard performance measures were used to validate and evaluate the proposed system.