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:
Image classification plays a significant role in robotic vision. This paper proposes an image classification model: Xception-LightGBM, which combines with Xception and Light Gradient Boosting Machine for hybrid image classification. The proposed algorithm produces the image feature extraction via Xception and classifies these feature vectors using Light Gradient Boosting Machine (LightGBM). The Xception-LightGBM model is compared with five representative image prediction models, such as VGG16, VGG19, InceptionV3, DenseNet121, and Xception. The experiments on six data sets demonstrate this proposed model leads to successful runs and provides optimal performances. It shows this model achieves the best results for all six evaluation metrics: accuracy, precision, recall, F1-Score, loss, and Jaccard. Furthermore, this proposed model acquires the highest accuracy on six image data sets, which has at least 1.1% in accuracy improved to the Xception architecture. It suggests this model may be preferable for robotic vision.
作者机构:
[Ashraf, Muhammad Usman] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Ashraf, Muhammad Usman] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[STAR Collaboration; Ashraf, Muhammad Usman] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China.
通讯机构:
[Ashraf, Muhammad Usman] C;[Ashraf, Muhammad Usman] T;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.;Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China.
会议名称:
28th International Conference on Ultrarelativistic 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:
[Ashraf, Muhammad Usman] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.^[Ashraf, Muhammad Usman] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.^[Ashraf, Muhammad Usman;STAR Collaboration] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China.
摘要:
We report recent results of strangeness production in Au+Au collisions at root s(NN) = 54.4 GeV and Al+Au fixed-target collisions at root s(NN) = 4.9 GeV from the STAR experiment at RHIC. The collision-energy dependence of strange hadron yields is presented. The p(T) dependance of nuclear modification factor (R-CP) and baryon to meson ratio ((Lambda) over bar /K-S(0) are measured to study the recombination and parton energy loss mechanisms. The STAR fixed-target data are compared to the previous published data from different AGS experiments. The physics implications for collision dynamics are discussed.
作者机构:
[Hu Z.] School of Educational Information Technology, Central China Normal University, Wuhan, China;[Su J.] School of Computer Science, Hubei University of Technology, Wuhan, China;[Koroliuk Y.] Chernivtsi Institute of Trade and Economics, Kyiv National University of Trade and Economics, Chernivtsi, Ukraine
会议名称:
3rd International Conference on Computer Science, Engineering and Education Applications, ICCSEEA 2020
会议时间:
21 January 2020 through 22 January 2020
关键词:
Collaborative learning;Interactive learning environments;Predicting of academic performance;Simulations;Teaching strategies