Search Results (2)

As the penetration rate of distributed power sources increases and distribution network structures grow increasingly complex, the uncertainty in switch action control during load transfer has become a critical issue affecting grid safety and reliability. Traditional control methods based on precise probability-based predictive control are susceptible to bias introduced by prior settings under small-sample conditions, making it difficult to meet the stringent requirements of time-synchronized control. To address this, this study proposes an imprecise probability-based synchronous load transfer control method for distribution networks. By integrating the Imprecise Dirichlet model (IDM) with a Naive Credal Classifier (NCC), it constructs an interval predictive control model for switching action timing. This approach effectively mitigates the prior dependency issue and enhances estimation robustness under small-sample conditions. Combined with a dynamic delay strategy, this approach strictly controls the interval between disconnection and reconnection actions within 20 ms, preventing circulating current risks and ensuring transfer reliability. The simulation and experimental results demonstrate that the proposed method outperforms traditional Bayesian classifiers in both time prediction control accuracy and model robustness, providing a theoretical foundation and a reference for engineering applications for secure action control in distribution networks. Full article

Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) achieve the best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew’s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB, and CDT achieve better results. Full article