Cloud4NFICA-Nearness Factor-Based Incremental Clustering Algorithm Using Microsoft Azure for the Analysis of Intelligent Meter Data

Intelligent electricity meters (IEMs) form a key infrastructure necessary for the growth of smart grids. IEMs generate a considerable amount of electricity data incrementally. However, on an influx of new data, traditional clustering task re-cluster all of the data from scratch. The incremental clustering method is an essential way to solve the problem of clustering with dynamic data. Given the volume of IEM data and the number of data types involved, an incremental clustering method is highly complex. Microsoft Azure provide the processing power necessary to handle incremental clustering analytics. The proposed Cloud4NFICA is a scalable platform of a nearness factor-based incremental clustering algorithm. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data. Cloud4NFICA is incremental in nature, hence accommodates the influx of new data. Cloud4NFICA was designed as an infrastructure as a service. It is visible from the study that the developed system performs well on the scalability aspect.

Regularized ridge regression models to estimate static elastic moduli from wireline measurements: case study from Southern Iraq

Elastic moduli such as Young’s modulus (E), Poisson’s ratio (v), and bulk modulus (K) are vital to creating geomechanical models for wellbore stability, hydraulic fracturing, sand production, etc. Due to the difficulty of obtaining core samples and performing rock testing, alternatively, wireline measurements can be used to estimate dynamic moduli. However, dynamic moduli are significantly different from elastic moduli due to many factors. In this paper, correlations for three zones (Nahr Umr shale, Zubair shale, and Zubair sandstone) located in southern Iraq were created to estimate static E, K, and ν from dynamic data. Core plugs from the aforementioned three zones alongside wireline measurements for the same sections were acquired. Single-stage triaxial (SST) tests with CT scans were executed for the core plugs. The data were separated into two parts; training (70%), and testing (30%) to ensure the models can be generalized to new data. Regularized ridge regression models were created to estimate static E, K, and ν from dynamic data (wireline measurements). The shrinkage parameter (α) was selected for each model based on an iterative process, where the goal is to ensure having the smallest error. The results showed that all models had testing R2 ranging between 0.92 and 0.997 and consistent with the training results. All models of E, K, and ν were linear besides ν for the Zubair sandstone and shale which were second-degree polynomial. Furthermore, root means squared error (RMSE) and mean absolute error (MAE) were utilized to assess the error of the models. Both RMSE and MAE were consistently low in training and testing without a large discrepancy. Thus, with the regularization of ridge regression and consistent low error during the training and testing, it can be concluded that the proposed models can be generalized to new data and no overfitting can be observed. The proposed models for Nahr Umr shale, Zubair shale, and Zubair sandstone can be utilized to estimate E, K, and ν based on readily available dynamic data which can contribute to creating robust geomechanical models for hydraulic fracturing, sand production, wellbore stability, etc.

Automatic Identification System (AIS) Dynamic Data Integrity Monitoring and Trajectory Tracking Based on the Simultaneous Localization and Mapping (SLAM) Process Model

To enhance the safety of marine navigation, one needs to consider the involvement of the automatic identification system (AIS), an existing system designed for ship-to-ship and ship-to-shore communication. Previous research on the quality of AIS parameters revealed problems that the system experiences with sensor data exchange. In coastal areas, littoral AIS does not meet the expectations of operational continuity and system availability, and there are areas not covered by the system. Therefore, in this study, process models were designed to simulate the tracking of vessel trajectories, enabling system failure detection based on integrity monitoring. Three methods for system integrity monitoring, through hypotheses testing with regard to differences between model output and actual simulated vessel positions, were implemented, i.e., a Global Positioning System (GPS) ship position model, Dead Reckoning and RADAR Extended Kalman Filter (EKF)—Simultaneous localization and mapping (SLAM) based on distance and bearing to navigational aid. The designed process models were validated on simulated AIS dynamic data, i.e., in a simulated experiment in the area of Gdańsk Bay. The integrity of AIS information was determined using stochastic methods based on Markov chains. The research outcomes confirmed the usefulness of the proposed methods. The results of the research prove the high level (~99%) of integrity of the dynamic information of the AIS system for Dead Reckoning and the GPS process model, while the level of accuracy and integrity of the position varied depending on the distance to the navigation aid for the RADAR EKF-SLAM process model.