Journal of Modeling & Simulation in Electrical & Electronics Engineering
Year:2023 | Volume:3 | Issue:3|Papers Count:7

This article discusses about coordination of directional overcurrent relays (DOCRs) in the power system transmission lines. In these relays, 4 characteristics are used as variables to calculate coordination between of them. These 4 variables include PS, TMS, A and B, which are used to obtain optimal coordination between relays. In this article, to obtain optimal coordination, two fault locations are considered. One of them is near the relay (at the 10%-line beginning) and another is fault far from the relay (at the 90%-line end). The problem of coordination of relays is solved and optimized using the PSO optimization algorithm. The proposed method has been tested in the standard distribution 6-Buse IEEE System, and its results are stated. The results of the PSO optimization method that was obtained have been compared with traditional methods such as GA-NLP. Comparing the results of this method with the traditional methods presented in the other articles, found that the presented method obtains more optimal results than other traditional methods. Since the proposed method minimizes the operation time of DOCRs relays. This method is a reliable and effective method for calculating coordination between relays.

Channel coding is a vital component within digital telecommunications, helping to deal with unwanted factors like noise and enabling the establishment of a more robust communication link. Among the most renowned coding schemes are linear block codes, for which a variety of decoding methods have been proposed in recent years. This paper demonstrates how a linear block coding problem can be expressed as a Probabilistic Graphical Model (PGM). We then explain how Probabilistic Programming Languages (PPLs), which are tools for solving such PGMs, can be used to decode this type of coding. Employing the Figaro programming language, as a PPL, we have simulated the decoding of several famous linear block codes and found that the results of our proposed method closely match those of existing techniques. Our approach offers several advantages, such as the flexibility to utilize diverse inference methods, the ability to choose between hard and soft decoding dynamically, and the implementation of a wide range of coding techniques. PPLs also enable the adjustment of decoding algorithm parameters and the estimation of channel conditions, ultimately enhancing the receiver's adaptability to varying channel conditions. Finally, we discuss the advantages and disadvantages of our proposed method.

Self-organizing communication networks are a vital pillar in 5G and B5G technology, which operate automatically without human intervention in self-healing, self-configuring, and self-optimizing. Self-healing in these networks predicts and resolves network problems and improves performance with the following three methods in the research conducted: rule-based, algorithmic, and machine-learning approaches. This research used the TOPSIS technique as a multi-criteria decision-making method to rank and score cells after data preprocessing. Then, based on the rank of each cell, it is divided into two classes: normal and abnormal. Then, with three algorithms, decision tree, New Bayes and Random Fars, normal and abnormal cell prediction was performed independently. In the last step, using the combined method of maximum voting, the algorithm was completed and the results showed an improvement in the parameters Precisio=0.939, Recall=0.962, F-Measure=0.968, Accuracy=94.0717.

Dynamic circuits offer a promising solution due to their low power consumption and high performance compared to static ones. However, dynamic circuits have their limitations, particularly in terms of robustness. This article presents a new dynamic circuit that reduces power consumption and delays for high-fan-in OR gates without significant loss of robustness.  In the new dynamic circuit, the pull-down network (PDN) is split to increase the speed. Furthermore, employing a reference circuit decreases the conflict current that occurs between the PDN and keeper transistors. For this purpose, the reference circuit replicates the leakage current of the PDN. Therefore, the power and delay of the presented circuit are reduced. In addition, the sub-threshold leakage current and hence the leakage power are decreased in the PDN because of the body effect. The results of simulating high fan-in OR gates in a 90nm CMOS technology show 45% and 53% reduction in delay and power consumption, respectively while maintaining the same level of robustness as the conventional circuit for 64 inputs OR gates. Moreover, the tag comparator designed with the presented circuit shows a 2.65 times improvement in the figure of merit compared to the conventional design.

Many analysts believe that the future of the banking industry depends on the generalization and growth of fintechs. The growth and expansion of fintechs in the world indicate their importance in the banking industry. Today, it is important to know more about fintechs and its different parts [1]. Process mining is a new approach based on information technology that seeks to identify and improve the actual process model. Process mining is a chain of events encompassing the beginning and ending stages of a specific activity. Process mining aims to discover, monitor, and improve real-world processes by knowledge extraction from data stored in information systems. Process mining is on the list of new research disciplines, something between data mining and process modeling. In this method, the main ideas are very important, so discovering, monitoring, and enhancing business processes are three important factors in process mining science. This study contributes to the growing body of knowledge in process mining by highlighting the importance of adapting existing algorithms and methodologies to fit the specific needs and conditions of the banking industry, particularly in developing regions. In this research, in the first step, manual and system data related to the studied process were combined to ensure the comprehensiveness of the model, and the level of model details was adjusted based on the opinions of process owners before performing the mining process. After converting the integrated data file to the event log, the process model was implemented using ProM 5.2 and Genetics, Heuristics, Alpha ++, and Alpha algorithms. The results showed that the genetic algorithm has the best performance in issuing credit cards.

Client churn is a significant issue affecting companies across various industries. In the gaming sector, customer loss is particularly critical as it directly impacts revenue, profit margins, and customer retention. Inaccurate predictions of client churn can lead to substantial revenue losses. Churn prediction involves identifying customers who are most likely to cancel their subscriptions. This practice has become essential for many modern organizations due to its performance benefits, aiding businesses in calculating revenue growth and client retention metrics. This paper classifies player churn prediction models into seven main categories to comprehensively review the existing literature. This classification enhances the understanding of various methodologies used in the field and highlights potential areas for future research. Notably, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, have demonstrated significant potential among deep learning models. This paper examines the contribution of LSTM networks in predicting churn in computer games.

In this digital age, artificial intelligence (AI) technologies play a significant role in English as a foreign language (EFL) teaching and learning. One area that has received increasing attention is AI-powered writing assistants, in which AI-based tools are developed to assist EFL learners in improving their English writing output. This paper provides an overview of the advantages, and limitations of AI technologies applied to some popular writing assistants for English writing purposes. The overview indicates that there is still a critical gap between what AI educational technologies could do and how they are implemented in the EFL context. Therefore, interdisciplinary and transdisciplinary collaborative research is essential to overcome the limitations of current AI-powered writing assistants. This overview would serve as a good reference for guiding technology experts to develop more sophisticated intelligent writing assistants that adapt to EFL learners’ diverse needs and preferences. The paper also provides rich discussions on future research directions from multiple perspectives.