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ICMIC' 2014: International Conference on Modelling, Identification and Control

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ICMIC'
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截稿日期:
2014-09-01 Extended
通知日期:
2014-09-15
会议日期:
2014-12-03
会议地点:
Melbourne, Australia
届数:
6
QUALIS: B5   浏览: 18058   关注: 0   参加: 0

会伴指数 (CP-I)

41.8 / 100
全站第 4,607 名 / 共 5,693 个会议 · 前 81%

机器人与控制 第 347 / 470

学术认可 (35%)
48
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
52
社区关注 (10%)
8
资料公开度 (15%)
25

用到的输入: 收录等级：QUALIS B5 · 有据可查的届次：6 · 过去 24 个月打开过本页的研究者：2 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 80% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-03

征稿

ICMIC' 2014 (International Conference on Modelling, Identification and Control) is a QUALIS B5 conference held in Melbourne, Australia on 2014-12-03. The paper submission deadline is 2014-09-01 (extended). Acceptance notifications are sent on 2014-09-15.

The 6th International Conference on Modelling, Identification and Control (ICMIC 2014) is sponsored by both International Journal of Modelling, Identification and Control (IJMIC) and Swinburne University of Technology (Australia). It provides an international forum for professionals, academics, and researchers to present latest developments in modeling identification and control. It particularly welcomes those emerging methodologies and techniques that bridge theoretical studies and applications in all engineering and science branches. The conference will be held at Swinburne University of Technology, Melbourne Australia. Papers are solicited on all aspects of research, development, and application of modelling, identification and control, including but not limited to: • Model Calibration and Validation • Data-driven Modelling and Control • Modelling of Physiological and Biomedical Systems • System Identification • Nonlinear Control Systems • Optimization Techniques • Intelligent Systems & Soft Computing Techniques • Robotics • Mechatronics Systems • Fault Diagnosis and Fault Tolerance • Networked Control Systems • Cooperative Control Systems • Iterative Learning Control • Predictive Control • Process Modeling and Process Control
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