Practical Teaching Method Based on MATLAB in AC Speed Regulating System Course

Because an AC speed regulating system is abstract and complex, the study on practical teaching method based on MATLAB software is quite necessary in the AC speed regulating system course at our university. The permanent magnet synchronous motor (PMSM) vector control system is taken as an example to explain in details the practical teaching method based on MATLAB. The course content is divided into several knowledge modules based on speed regulation methods. The concept of subsystem in Simulink is introduced. Each type of speed regulating systems can be divided into several subsystems according to its functions. The control principle, system constitution and design method of each AC speed regulation system is explained by different function subsystems. The instructor vividly demonstrates the control system simulation model of AC speed regulation systems in class, and the students establish and modify the existing simulation model by themselves on the computers under the guidance of the instructor. In this way, the students\u27 practical ability and teaching quality of specialized courses can be improved greatly

A Novel Web Based Support Tool for Learning Random Variables

Battle of Distributions: A Web Based Learning Support ToolIn probability and statistics, a random variable is a function that assigns a number to eachoutcome of a random experiment. Random variables have various applications in differentscientific and engineering fields. There is an inherent complexity in dealing with randomvariables and their distribution models. It takes a lot of careful thought and practice to fullyunderstand these concepts and their applications. We have identified two issues that contributethe most to the difficulty students experience learning the subject: 1) the complexity of themathematical logic behind the probability theory and 2) lack of motivation to attempt andexplore more problems due partly to the static nature of textbook problems. Therefore, ourobjectives were to enhance students\u27 understanding of random variables and to increasemotivation for learning by developing an interactive web-based tool.In creating our module, we have focused on 3 related random variables that rise in engineeringapplications frequently, namely, Poisson, Exponential and Erlang. The web module consists of 3main components: Gym, Shop, and Rink. Gym provides students with a framework to test theirknowledge on the mentioned random variables. They carry out the mathematical analysis hereand explore different problems to observe the link between the 3 random variables. Shop andRink provide entertaining elements where students have the opportunity to shop for items such asenergy drinks to empower them to face their opponent in Rink. The more problems they solvecorrectly in the Gym component, the more credit they will have to shop and increase their chanceof winning a fight. (Figures 1-3 on the next page depict some of the graphical user interfacewindows of the module). The Gym component addresses our first objective to increase studentsunderstanding of the concept of random variables. The Shop and Rink components address oursecond objective to increase students\u27 motivation by incorporating gaming elements into themodule.We assessed the effectiveness of the module by measuring the change in cognitive and affectivebehavior of students. We utilized independent diagnostic measures, a homework assignmentand a quiz that exclusively cover the three mentioned random variables to assess the changes inthe cognitive behavior. We performed the Mann-Whitney U-test to analyze the data and compareperformance in control and treatment groups. Our findings show that the treatment groupstudents did better than the control group in both measures. The p-value indicates that thesuperior performance of the treatment group is statistically significant.We also developed a survey to evaluate the students\u27 affective behavior by measuring theirmotivation for learning and their perceptions of effectiveness of the module. Majority of students(82%) enjoyed doing the web module problems more than the textbook problems. Students agree(91%) that they would explore the web module problems beyond what they are asked to and 86%feel that web module was more motivating than the textbook.Figure 1: Main WindowFigure 2: Gym WindowFigure 3: Rink Windo

Uncertain Knowledge Reasoning Based on the Fuzzy Multi-Entity Bayesian Network

With the rapid development of the semantic web and the ever-growing size of uncertain data, representing and reasoning uncertain information has become a great challenge for the semantic web application developers. In this paper, we present a novel reasoning framework based on the representation of fuzzy PR-OWL. Firstly, the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning, incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory, and introduces fuzzy PR-OWL, an Ontology language based on OWL2. Fuzzy PR-OWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation. Secondly, the paper explains the integration of the Fuzzy Probability theory and the Belief Propagation algorithm. The influencing factors of fuzzy rules are added to the belief that is propagated between the nodes to create a reasoning framework based on fuzzy PR-OWL. After that, the reasoning process, including the SSFBN structure algorithm, data fuzzification, reasoning of fuzzy rules, and fuzzy belief propagation, is scheduled. Finally, compared with the classical algorithm from the aspect of accuracy and time complexity, our uncertain data representation and reasoning method has higher accuracy without significantly increasing time complexity, which proves the feasibility and validity of our solution to represent and reason uncertain information

S3: Social-network Simulation System with Large Language Model-Empowered Agents

Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness the formidable human-like capabilities exhibited by large language models (LLMs) in sensing, reasoning, and behaving, and utilize these qualities to construct the S$^3$ system (short for $\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$ystem). Adhering to the widely employed agent-based simulation paradigm, we employ prompt engineering and prompt tuning techniques to ensure that the agent's behavior closely emulates that of a genuine human within the social network. Specifically, we simulate three pivotal aspects: emotion, attitude, and interaction behaviors. By endowing the agent in the system with the ability to perceive the informational environment and emulate human actions, we observe the emergence of population-level phenomena, including the propagation of information, attitudes, and emotions. We conduct an evaluation encompassing two levels of simulation, employing real-world social network data. Encouragingly, the results demonstrate promising accuracy. This work represents an initial step in the realm of social network simulation empowered by LLM-based agents. We anticipate that our endeavors will serve as a source of inspiration for the development of simulation systems within, but not limited to, social science