| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Electronics and Electrical Engineering | 63 | 253 | 620 | 43 |
| Mechanical and Aerospace Engineering | 88 | 60 | 104 | 24 |
| Engineering and Technology | 190 | 123 | 244 | 32 |
The journal is mainly concerned with subjects like Artificial intelligence, Mathematical optimization, Control theory, Robot and Control engineering. While work presented in it provided substantial information on Artificial intelligence, it also covered topics in Machine learning, Computer vision and Pattern recognition. Mathematical optimization research discussed connects with the study of Job shop scheduling.
The journal facilitates discussions on Job shop scheduling that incorporate concepts from other fields like Schedule and Scheduling (production processes). The journal concentrates on Control theory topics that focus on Control theory, Control system, Trajectory and Nonlinear system. In the Robot research discussed, Mobile robot, Robot kinematics and Motion planning are all tackled.
The published articles facilitate discussions on Control theory, Mathematical optimization, Control engineering, Artificial intelligence and Petri net. While Mathematical optimization is the focus of the journal papers, it also provides insights into the studies of Artificial neural network and Job shop scheduling. Computer vision and Pattern recognition are some topics wherein Artificial intelligence research discussed in the published papers has an impact.
The aim of IEEE Transactions on Automation Science and Engineering is to expand the discussion of research in Artificial intelligence, Mathematical optimization, Robot, Control theory and Control theory. The close relationship between Computer vision and Robustness (computer science) is one of the points of interest dissected in Artificial intelligence research. The concepts on Mathematical optimization presented in IEEE Transactions on Automation Science and Engineering can also apply to other research fields, including Energy consumption and Job shop scheduling.
Job shop scheduling research featured in IEEE Transactions on Automation Science and Engineering incorporates concerns from various other topics such as Schedule and Scheduling (production processes). While IEEE Transactions on Automation Science and Engineering focused on Robot, it was also able to explore topics like Real-time computing, Task (project management), Task analysis and Human–computer interaction. The presentations discussing Control theory offer insights in topics such as Trajectory and Actuator.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in IEEE Transactions on Automation Science and Engineering (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in IEEE Transactions on Automation Science and Engineering (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 14.03% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 33.68% were posted by at least one author from the top 10 institutions publishing in the journal. Another 11.11% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.71% of all publications and 37.50% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Jiankun Wang;Wenzheng Chi;Chenming Li;Chaoqun Wang
(2020)Wei He;Chengqian Xue;Xinbo Yu;Zhijun Li
(2020)Unknown
(2023)Shahab Heshmati-Alamdari;Alexandros Nikou;Dimos V. Dimarogonas
(2021)Sebastian Hofer;Kostas Bekris;Ankur Handa;Juan Camilo Gamboa
(2021)Yuxiang Sun;Weixun Zuo;Peng Yun;Hengli Wang
(2021)Hung V. Dang;Hoa Tran-Ngoc;Tung V. Nguyen;T. Bui-Tien
(2021)Pursuing an online degree in Mechanical or Aerospace Engineering opens doors to some of the highest-paying college majors in today's job market. Engineering careers often lead to well-compensated roles, making them a valuable investment for students seeking financial stability and long-term growth.
For many, affordability is a key concern when choosing how to study. Many institutions offer options identified among the affordable online bachelor's degree programs, helping students achieve quality education without the burden of excessive debt.
Flexibility is another important factor. Busy adults—especially parents—can find programs designed with their needs in mind, such as those highlighted in college programs for moms. These tailored courses support balancing family, work, and education.
Additionally, accelerated courses like 6 week college courses offer a streamlined path to completing degrees faster, which is ideal for students seeking to enter the workforce quickly or upgrade their skills efficiently.
