Modeling Fault Propagation Paths in Power Systems: A New Framework Based on Event SNP Systems With Neurotransmitter Concentration

To reveal fault propagation paths is one of the most critical studies for the analysis of power system security; however, it is rather dif cult. This paper proposes a new framework for the fault propagation path modeling method of power systems based on membrane computing.We rst model the fault propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP system and make them suitable for large-scale power systems, we propose a model reduction method for the Ev-SNP system and devise its simpli ed model by constructing single-input and single-output neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE 14- and 118-bus systems to study their fault propagation paths. The proposed approach rst extends the SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction, and the simulation results show a new success and promising approach to the engineering domain

Review on learning orientations

The need has arises towards the consideration of individual difference to let learners engage in and responsible for their own learning, retain information longer, apply the knowledge more effectively, have positive attitudes towards the subject, have more interest in learning materials, score higher and have high intrinsic motivation level. As regard to the importance of individual differences, Martinez (2000) has grounded a new theory, which is Intentional Learning Theory that covered individual aspects of cognitive, intention, social and emotion. This theory hypothesizes that the fundamental of understanding how individual learns, interact with an environment, performs, engages in learning, experiences learning, and assimilate and accommodate the new knowledge is by understanding individual’s fundamental emotions and intentions about how to use learning, why it is important, when the suitable time, and how it can accomplish personal goals and change. The intent of this theory is to focus on emotions and intentions of an individual regarding why, when and how learning goals are organized, processed, and achieved. In conclusion, Learning Orientations introduced by this theory describes the disposition of an individual in approaching, managing and achieving their learning intentionally and differently from others

Classification of Student Majors with C4.5 and Naive Bayes Algorithms (Case Study: SMAN 2 Bekasi City)

School majors conducted in high school are based on interests and these have a goal to provide opportunities for learners to develop the competence of attitudes, skills competence of learners in accordance with interests, talents, and academic ability in a group of scientific subjects.In this research, the researcher uses two algorithm models that is a comparison between the C4.5 algorithm and also the Naive Bayes algorithm. In this study, the data used is the results of school entrance test data and also the data from psychological results for students who have been declared passed the entrance test school SMAN 2 Bekasi City academic year 2018/2019. By comparison of two data mining classification algorithm, can be proved with accuracy result and AUC value from each algorithm that is for Naive Bayes accuracy = 76,43% and AUC value = 0,846, while for algorithm C4.5 accuracy = 70,29% and AUC value = 0.738

A brief network analysis of Artificial Intelligence publication

In this paper, we present an illustration to the history of Artificial Intelligence(AI) with a statistical analysis of publish since 1940. We collected and mined through the IEEE publish data base to analysis the geological and chronological variance of the activeness of research in AI. The connections between different institutes are showed. The result shows that the leading community of AI research are mainly in the USA, China, the Europe and Japan. The key institutes, authors and the research hotspots are revealed. It is found that the research institutes in the fields like Data Mining, Computer Vision, Pattern Recognition and some other fields of Machine Learning are quite consistent, implying a strong interaction between the community of each field. It is also showed that the research of Electronic Engineering and Industrial or Commercial applications are very active in California. Japan is also publishing a lot of papers in robotics. Due to the limitation of data source, the result might be overly influenced by the number of published articles, which is to our best improved by applying network keynode analysis on the research community instead of merely count the number of publish.Comment: 18 pages, 7 figure

Improving the Major Recommendation Systems: Analysis of Hybrid Naïve Bayes-based Collaborative Filtering and Fuzzy Logic

Major recommendation systems have been widely used to assist prospective students in choosing major that matches their interests and potential. In an effort to improve the performance of the recommendation system, this study proposed to use collaborative filtering techniques with naïve Bayes approach. In addition, this study improved the input parameters using fuzzy logic in determining the recommended majors. The methodology used started from collecting user data, including gender, academic history, interests, and other relevant attributes. The data were used to train the naïve Bayes technique by estimating the probability of feature conformity between users and students in the recommended majors. However, there were problems such as uncertainty and ambiguity in user preferences for input data. The fuzzy logic method aimed to improve the input parameters to more accurately reflect the user preferences. The results of improving the input parameters by using fuzzy logic were then used in the naïve Bayes technique to obtain recommendations for the direction that best suits the user’s preferences. The final stage of this study used evaluation metrics such as precision, recall, and f1-score to measure the performance of the recommendation system in providing accurate recommendations. The use of a hybrid of naïve Bayes and fuzzy logic algorithms obtains an accuracy value of 87.27%, a precision value of 87.33%, a recall value of 87.24%, and an f1-score value of 87.26%. These results are higher than the usual naïve Bayes model applied in major recommendation systems

Relational Research between China’s Marine S&T and Economy Based on RPGRA Model

To make up the defect of the existing model, an improved grey relational model based on radian perspective (RPGRA) is put forward. According to the similarity of the relative change trend of time series translating traditional grey relational degree into radian algorithm within different piecewise functions, it greatly improves the accuracy and validity of the research results by making full use of the poor information in time series. Meanwhile, the properties of the RPGRA were discussed. The relationship between China’s marine S&T and marine economy is researched using the new model, so the validity and creditability of RPGRA are illustrated. The empirical results show that marine scientific and technological research projects, marine scientific and technological patents granted, and research funds receipts of the marine scientific research institutions have greater relationship with GOP, which indicates that they have more impact on China’s marine economy