作者:
Chunyan Zeng;Dongliang Zhu;Zhifeng Wang;Yao Yang
期刊:
Advances in Intelligent Systems and Computing,2021年 1263: 372-381 ISSN:2194-5357
通讯作者:
Wang, Z.
作者机构:
[Zhu D.; Zeng C.; Yang Y.] Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, Hubei 430068, China;[Wang Z.] Department of Digital Media Technology, Central China Normal University, Wuhan, Hubei 430079, China
通讯机构:
[Wang, Z.] D;Department of Digital Media Technology, China
会议名称:
12th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2020
会议时间:
31 August 2020 through 2 September 2020
会议论文集名称:
Advances in Intelligent Networking and Collaborative Systems
摘要:
In order to further improve the effect of cooperative learning and promote the discussion and interaction among group members, this paper designs and verifies a grouping strategy. This strategy elicits empathy ability on the basis of homogeneity among groups and heterogeneity within groups. The influence of empathy on cooperative learning is studied. Forty-six fourth grade students who participated in science courses are selected as the research objects. The learner with high empathy ability is chosen as the group leader in the experimental group, while the learner with low empathy ability is chosen as the group leader in the control group. At the same time, statistical analysis and social network analysis method are used to explore the influence of empathy on learning effects and group interaction. It is found that the group of high empathy ability is significantly higher than the group of low empathy ability in group discussion interaction density and learning effect. This also provides a reference to the later development of learners and the future development of cooperative learning.
作者机构:
[Pang, Long-Gang] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Pang, Long-Gang] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
通讯机构:
[Pang, Long-Gang] C;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
会议名称:
28th International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions (Quark Matter)
会议时间:
NOV 04-09, 2019
会议地点:
Wuhan, PEOPLES R CHINA
会议主办单位:
[Pang, Long-Gang] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.^[Pang, Long-Gang] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
关键词:
Heavy ion collisions;deep learning;machine learning for physics
摘要:
The high energy heavy ion collision is a multi-stage process that is described by complex hybrid models. The initial state fluctuations in event-by-event simulations of heavy ion collisions convert to final state correlations by collective flow and hadronic cascade. It is not easy to design final state correlations (observables) from particles in momentum space, that can help to extract useful information, such as the initial state nuclear structure, the properties of quark gluon plasma and the nuclear equation of state. Machine learning is helpful in automatic feature extraction in heavy ion collisions. This article reviews the applications, challenges and possible future developments of machine learning in heavy ion physics.
期刊:
Advances in Intelligent Systems and Computing,2021年1264:390-399 ISSN:2194-5357
通讯作者:
Zeng, C.
作者机构:
[Duan S.; Wang Z.; Ouyang H.; Xu H.] Department of Digital Media Technology, Central China Normal University, Wuhan, Hubei 430079, China;[Zeng C.] Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, Hubei 430068, China
通讯机构:
[Zeng, C.] H;Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, China
会议名称:
23rd International Conference on Network-Based Information Systems, NBiS 2020
会议时间:
31 August 2020 through 2 September 2020
会议论文集名称:
Advances in Networked-Based Information Systems
摘要:
A common challenge for adopting virtual reality (VR) in education is that limited VR devices are often shared among a large group of students. Consequently, there are two types of VR learners: Performers who acquire virtual learning experience through direct engagement in VR and observers who acquire such experience vicariously through observation. To explore the influence of learner type on VR learning, this study conducted a quasi-experiment with 53 elementary school students to examine the difference in VR learning experiences between the performers and the observers. The study results supported the observed VR learning experience as an adequate alternative to direct VR engagement as the observers demonstrated overall comparable learning patterns in reflection, emotion, engagement, and social interaction during the post-VR debriefing, except for the behaviors of recall and interpretation. The research findings can shed light on the issues of accessibility and equity in VR-based instruction and inform the design and implementation of large-scale VR educational programs.
期刊:
PROCEEDINGS OF 2021 IEEE 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2021),2021年:151-155
通讯作者:
Xiao, Kejiang
作者机构:
[Yang, Maotao; Zou, Wei] State Grid Hunan Elect Power Ltd Co, China Hunan Prov Key Lab Intelligent Elect Measur, Power Supply Serv Ctr, Metrol Ctr, Changsha, Hunan, Peoples R China.;[Xiao, Kejiang] Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.
通讯机构:
[Xiao, Kejiang] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.
会议名称:
11th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)
会议时间:
JUN 18-20, 2021
会议地点:
Beijing, PEOPLES R CHINA
会议主办单位:
[Zou, Wei;Yang, Maotao] State Grid Hunan Elect Power Ltd Co, China Hunan Prov Key Lab Intelligent Elect Measur, Power Supply Serv Ctr, Metrol Ctr, Changsha, Hunan, Peoples R China.^[Xiao, Kejiang] Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.
关键词:
Data processing;Abnormal data;Load Forecasting
摘要:
Electric load forecasting is a very important task, but there are often many abnormal data in the load data (Burrs). This paper proposes a load forecasting method in view of the large number of burrs existing in load forecasting. We first used the preprocessed load data to cluster the courts and got the 7050 and the 3033 these two categories (7050 and 3033 are the numbers of the two categories respectively, here we use the numbers as their indexes). Next, we use two methods the sliding box filter method and the comparison method to remove burrs. After extracting the features, we use XGBoost and LightGBM for load prediction. Finally, we analyzed the courts with large prediction errors.
关键词:
Heavy-ion physics;QCD equation of state;Hybrid model;Deep learning
摘要:
In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.