A Windows based graphical package for symmetrical components analysis

Micro-grids are becoming increasingly popular to consumers and as such in the future, a great number of them will be installed at consumer's sites. In this way, conventional distribution grid structures that accept distributed generation connections may face serious problems when their grid control and protection functions become more complicated. These problems have led to a new trend of generation power locally at distribution voltage level by using non-conventional sources like wind power, fuel cells, micro-turbines and biogas and their integration into the utility distribution system. Micro-grids can cause several fundamental problems in its system and grid control when operate as autonomous mode. This paper is a review of fundamental key issues on micro-grids with respect to voltage and frequency flow grid control and protection of micro-grids. Moreover, in the paper the symmetrical current components are analyzed on a larger system of distribution network. All of the studies are simulated using DIgSILENT PowerFactory to establish claims in this paper.

This research work estimates the effect of harmonics on symmetrical components of power network using least square method. Symmetrical component analysis of power line faults has been considered an effective tool for the design and control of power devices since their introduction. Harmonics present in power system disturb the operation of analogue protective devices. Moreover, the harmonics deteriorate the power quality, increase power loss, decrease appliances life and ultimately decrease the reliability of power system. A microprocessor based technique to estimate the value of symmetrical components under abnormal conditions in the presence of harmonics is investigated and simulated. The given technique deploys method of least squares in order to estimate the magnitude of symmetrical components. Simulation of proposed model is carried out in MATLAB/SIMULINK.

In order to extract useful information from a huge amount of biological data nowadays, simple and convenient tools are urgently needed for data analysis and modeling. In this paper, an automatic data mining tool, termed as ABCModeller (Automatic Binary Classification Modeller), with a user-friendly graphical interface was developed here, which includes automated functions as data preprocessing, significant feature extraction, classification modeling, model evaluation and prediction. In order to enhance the generalization ability of the final model, a consistent voting method was built here in this tool with the utilization of three popular machine-learning algorithms, as artificial neural network, support vector machine and random forest. Besides, Fibonacci search and orthogonal experimental design methods were also employed here to automatically select significant features in the data space and optimal hyperparameters of the three algorithms to achieve the best model. The reliability of this tool has been verified through multiple benchmark data sets. In addition, with the advantage of a user-friendly graphical interface of this tool, users without any programming skills can easily obtain reliable models directly from original data, which can reduce the complexity of modeling and data mining, and contribute to the development of related research including but not limited to biology. The excitable file of this tool can be downloaded from http://lishuyan.lzu.edu.cn/ABCModeller.rar.

The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.

1. Methods for measuring animal movement are critical for understanding numerous ecological and evolutionary processes. However, few methods are available for small organisms, and even fewer methods offer consistent individual-level resolution while remaining affordable, scalable and operable in the field. 2. We describe a low-cost animal movement tracking method with a user-friendly graphical interface, called GRAPHITE. Our automated software can quantify motions of insects by offline video analysis of inexpensive and lightweight human-readable tags attached to individual insects. The integrated graphical editor provides a full-featured environment for users to review the generated tracking data and make individual- or group-level edits. 3. GRAPHITE is a novel video analysis and graphical editing software (MATLAB v.9.0.0+) that identifies tags in image frames with a minimal false negative rate, links sequences of corresponding tags into "tracks" for each individual insect, infers the tag identifier, and provides a user-friendly graphical environment for editing tracking data. Users can either batch process raw video data using the full analysis pipeline or execute GRAPHITE modules independently for a tailored analysis. 4. We demonstrate the efficacy of the developed software with a specific application to the movement of honey bees at the entrance of hives. However, this system can be easily modified to track individually marked insects of 3 mm and larger. A notable advantage of this method is its ability to provide easy access to individual-level tracking data using human-readable tags.

Beginning in 1990, the University of Arizona, Arizona Remote Sensing Center (ARSC) has been involved in a collaborative effort with the Global Information and Early Warning System (GIEWS) and the Remote Sensing Center of the Food and Agriculture Organization (FAO) of the United Nations in designing and developing an integrated computer workstation for famine early warning. The goal of the project is to provide food security analysts with a set of computer tools to manage a very large and diverse set of data for predicting the onset of food security emergencies for every country on Earth. The initial stage of the project involved the conceptual definition of system elements and the development of overall system architecture. We are now developing an open, flexible, and portable system designed to significantly assist the work of the analysts. System architecture provides a task-specific and user-friendly graphic user interface (GUI) within a Windows environment that will link image processing, geographic information system (GIS), spreadsheet, text, and graphics software packages into a single operational environment. A relational database management system (RDBMS) is serving as the back-end of the workstation to facilitate data storage and retrieval and as a means to preserve analysis methodologies.

Small business computers: Creative Strategies International, USA (1979) $895

The Internet of Medical Things (IoMT) is an extended version of the Internet of Things (IoT). It mainly concentrates on the integration of medical things for servicing needy people who cannot get medical services easily, especially rural area people and aged peoples living alone. The main objective of this work is to design a real time interactive system for providing medical services to the needy who do not have a sufficient medical infrastructure. With the help of this system, people will get medical services at their end with minimal medical infrastructure and less treatment cost. However, the designed system could be upgraded to address the family of SARs viruses, and for experimentation, we have taken COVID-19 as a test case. The proposed system comprises of many modules, such as the user interface, analytics, cloud, etc. The proposed user interface is designed for interactive data collection. At the initial stage, it collects preliminary medical information, such as the pulse oxygen rate and RT-PCR results. With the help of a pulse oximeter, they could get the pulse oxygen level. With the help of swap test kit, they could find COVID-19 positivity. That information is uploaded as preliminary information to the designed proposed system via the designed UI. If the system identifies the COVID positivity, it requests that the person upload X-ray/CT images for ranking the severity of the disease. The system is designed for multi-model data. Hence, it can deal with X-ray, CT images, and textual data (RT-PCR results). Once X-ray/CT images are collected via the designed UI, those images are forwarded to the designed AI module for analytics. The proposed AI system is designed for multi-disease classification. It classifies the patients affected with COVID-19 or pneumonia or any other viral infection. It also measures the intensity level of lung infection for providing suitable treatment to the patients. Numerous deep convolution neural network (DCNN) architectures are available for medical image classification. We used ResNet-50, ResNet-100, ResNet-101, VGG 16, and VGG 19 for better classification. From the experimentation, it observed that ResNet101 and VGG 19 outperform, with an accuracy of 97% for CT images. ResNet101 outperforms with an accuracy of 98% for X-ray images. For obtaining enhanced accuracy, we used a major voting classifier. It combines all the classifiers result and presents the majority voted one. It results in reduced classifier bias. Finally, the proposed system presents an automatic test summary report textually. It can be accessed via user-friendly graphical user interface (GUI). It results in a reduced report generation time and individual bias.

In this paper, a Graphical User Interface (GUI) is designed and developed using Python programming language for detection of forest fires at early stages, which is efficient, cost-effective, flexible, secure and uses open-source technologies. The user-friendly Graphical User Interface is interfaced directly with the computer port to fetch the data with the help of Python modules, performs an analysis on the data and predict whether there is a situation of fire or not and displays the status on the Graphical User Interface. Data is also stored in an Excel file for further analysis. The possibility to detect forest fire situation at its early stages makes this GUI a valuable software in environmental disasters and research applications. Numerical results along with future work have been discussed in this paper.

Various numerical techniques have been developed for modeling electromagnetic field propagation in various novel complex media. The validity of these techniques is usually verified by comparison to the exact solutions of canonical problems. Recently, research has focused on chiral media, a subclass of materials known as bianisotropic materials, and numerical techniques have been developed in order to calculate the interaction of electromagnetic fields with chiral objects. One canonical problem for these techniques is plane-wave scattering from a chiral sphere. This work presents a software package that displays and saves the calculated data for the scattering from a chiral, dielectric, or perfectly conducting sphere using a friendly graphical user interface (GUI).

TheGESTsoftware has been developed for the solution of molecular crystal structures from powder diffraction data. In this package, a genetic algorithm is used for global optimization to search for the structural model. For more efficient calculation time, a modified BraggRfactor is used as the evaluation function for the genetic algorithm. Applications of this program for structure determination of molecular crystals from powder diffraction data are demonstrated with known and previously unknown structures. A friendly graphical user interface (GUI) is used to generate the control file and run the program. TheGESTprogram with GUI, for Linux and Windows platforms, is freely available from the authors upon request.

We developed a user-friendly numerical model, Shoreline Bioremediation Model (SBM), to simulate the biodegradation and bioremediation of oil entrapped within shorelines. The model takes oil properties and environmental conditions as input and produces variation of oil concentration with time, up to several years from the time of the spill. SBM is equipped with a user-friendly graphical user interface (GUI). The accessibility and easy-to-use interface allow the user to quickly produce several biodegradation and bioremediation scenarios before they are implemented at the contaminated shoreline. The model has been calibrated to predict the biodegradation rate of saturates and aromatics, but it can be also used to predict the biodegradation rates of individual oil components and to decide on bioremediation studies in shorelines based on the enhancement due to biostimulation (addition of nutrients) or bioaugmentation (addition of hydrocarbon degrading organisms). The GUI provides the oil concentration with time along with best case and worst case scenarios, which is commonly needed for decision making.

This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work.

Propagation prediction software for wireless communications is developed based on efficient ray-tracing simulation. The software has a friendly graphical user interface (GUI) and provides calculations for propagation parameters including path loss, delay spread, and angles of arrival/departure. Users can upload AutoCAD files of indoor or outdoor environments and assign wall geometries and electrical parameters easily. User defined radiation patterns can also be incorporated in the simulation. The software provides colored graphs for the simulated results.

Magnetoencephalography(MEG) is a functional, noninvasive technology for recording neuronal activity of human brain in real-time. MEG has been approved as an efficient and powerful functional detection method which has been used in clinical practices relate with epilepsy, depression, and other diseases that cannot be well-diagnosed by traditional structural methods, e.g., CT and MRI. By using MEG, very weak magnetic signals typically in the range of 10 femtotesla (fT)-1 picotesla (pT) that originate from electric currents flowing in neurons, can be recorded. Compared with scalp EEG, MEG provides much better spatial resolution. Meanwhile, MEG is a noninvasive technology for preoperative workup for epilepsy surgery, which means it may replace traditional invasive methods. As the development of high-sampling rate MEG systems as well as the discovery high-frequency oscillations (HFOs) in the brain, MEG is now typically digitally analyzed in very high-frequency range. Although MEG has been clinically used for two decades, it is not so widely-used as EEG. The lack of a powerful software platform is an important reason which hinders the development of MEG. A MEG-specific software, MEG processor, was released here. With the friendly graphical user interface (GUI) of MEG Processor, researcher can easily identify/mark HFOs and project the signal to MRI receiving a precise 3D reconstruction. We hope such a platform will help and encourage more clinicians to uncover the magnificent features of MEG.