As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not...As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not only the academic, integrity-based concerns of students using AI for their homework, but also as the forebearers of new learning and technology, how AI will change their students’ futures and careers. In this study, we will explore the different factors, such as Computer Science Score and location, that might affect how much a college discusses AI, ChatGPT specifically. To demonstrate the validity of our research, we used self-collected data with our methods detailed below.展开更多
Nowadays,power quality problems are affecting people’s daily life and production activities.With an aim to improve disturbance detection accuracy,a novel analysis approach,based on multiple impact factors,is proposed...Nowadays,power quality problems are affecting people’s daily life and production activities.With an aim to improve disturbance detection accuracy,a novel analysis approach,based on multiple impact factors,is proposed in this paper.First,a multiple impact factors analysis is implemented in which two perspectives,i.e.,the wavelet analysis and disturbance features are simultaneously considered.Five key factors,including wavelet function,wavelet decomposition level,redundant algorithm,event type and disturbance intensity,and start and end moment of disturbance,have been considered.Next,an impact factor based accuracy analysis algorithm is proposed,through which each factor’s potential impact on disturbance location accuracy is investigated.Three transforms,i.e.,the classic wavelet,lifting wavelet and redundant lifting wavelet are employed,and their superiority on disturbance location accuracy is investigated.Finally,simulations are conducted for verification.Through the proposed method,the wavelet based parameters can be validly selected in order to accurately detect power quality disturbance.展开更多
文摘As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not only the academic, integrity-based concerns of students using AI for their homework, but also as the forebearers of new learning and technology, how AI will change their students’ futures and careers. In this study, we will explore the different factors, such as Computer Science Score and location, that might affect how much a college discusses AI, ChatGPT specifically. To demonstrate the validity of our research, we used self-collected data with our methods detailed below.
基金This study is supported by the National Natural Science Foundation of China(Grant No.61501040)Beijing Key Laboratory of Digital Printing Equipment,Fundamental Research Funds for the Central Universities of China(Grant No.B200201071)+1 种基金National Key Research and Development Program of China(Grant No.2017YFE0132100)BNRist Program(Grant No.BNR2020TD01009).
文摘Nowadays,power quality problems are affecting people’s daily life and production activities.With an aim to improve disturbance detection accuracy,a novel analysis approach,based on multiple impact factors,is proposed in this paper.First,a multiple impact factors analysis is implemented in which two perspectives,i.e.,the wavelet analysis and disturbance features are simultaneously considered.Five key factors,including wavelet function,wavelet decomposition level,redundant algorithm,event type and disturbance intensity,and start and end moment of disturbance,have been considered.Next,an impact factor based accuracy analysis algorithm is proposed,through which each factor’s potential impact on disturbance location accuracy is investigated.Three transforms,i.e.,the classic wavelet,lifting wavelet and redundant lifting wavelet are employed,and their superiority on disturbance location accuracy is investigated.Finally,simulations are conducted for verification.Through the proposed method,the wavelet based parameters can be validly selected in order to accurately detect power quality disturbance.
