IET Science, Measurement and Technology

Top Research Topics at Iet Science Measurement & Technology?

The journal generally zeroes in on subjects such as Electronic engineering, Control theory, Voltage, Acoustics and Electrical engineering. The journal addresses concerns in Electronic engineering which are intertwined with other disciplines, such as Partial discharge, Algorithm, Fault (power engineering) and Signal. Topics in Partial discharge explored in the journal were investigated in conjunction with research in Artificial intelligence and Pattern recognition.

• Electronic engineering (20.65%)
• Control theory (11.70%)
• Voltage (11.18%)

What are the most cited papers published in the journal?

• Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks (180 citations)
• Photovoltaic power forecasting using statistical methods: impact of weather data (119 citations)
• Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network (111 citations)

Research areas of the most cited articles at Iet Science Measurement & Technology:

The journal articles investigate studies in Electronic engineering, Control theory, Artificial intelligence, Pattern recognition and Partial discharge. While Electronic engineering is the focus of the most cited publications, it also provides insights into the studies of Fault (power engineering), Fault detection and isolation, Dielectric, Voltage and Algorithm. While Partial discharge is the key highlight in the published papers, thet also covered some subjects on Transformer and Acoustics.

What topics the last edition of the journal is best known for?

• Electrical engineering
• Artificial intelligence
• Statistics

The previous edition focused in particular on these issues:

Iet Science Measurement & Technology focuses largely on the fields of Composite material, Acoustics, Artificial intelligence, Electrical engineering and Pattern recognition. Aside from investigating topics in Silicone rubber under Composite material, it also explores concepts in Charge (physics). Some problems in Acoustics that were presented in Iet Science Measurement & Technology overlapped with concepts under Electrical impedance, Signal and Ground.

Iet Science Measurement & Technology explores topics in Pattern recognition which can be helpful for research in disciplines like Transformer, Artificial neural network, Partial discharge, Fault (power engineering) and Bearing (mechanical). Ultrasonic sensor research discussed connects with the study of Flow measurement.

The most cited articles from the last journal are:

• Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system (4 citations)
• Investigation on soil resistivity of two‐layer soil structures using finite element analysis method (4 citations)
• Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method (2 citations)

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Iet Science Measurement & Technology (based on the number of publications) are:

• Jan K. Sykulski (14 papers) absent at the last edition,
• Mohammad Hassan Khooban (13 papers) absent at the last edition,
• Taher Niknam (11 papers) absent at the last edition,
• Teymoor Ghanbari (10 papers) absent at the last edition,
• Arijit Baral (10 papers) published 1 paper at the last edition the same number as at the previous edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Iet Science Measurement & Technology (based on the number of publications) are:

• Electric Power Research Institute (22 papers) published 9 papers at the last edition, 5 more than at the previous edition,
• University of Southampton (16 papers) absent at the last edition,
• Indian Institute of Technology Delhi (15 papers) absent at the last edition,
• North China Electric Power University (12 papers) published 3 papers at the last edition, 2 more than at the previous edition,
• Tianjin University (11 papers) published 5 papers at the last edition, 4 more than at the previous edition.

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.

Publication chance based on affiliation

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, 3.70% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 24.36% were posted by at least one author from the top 10 institutions publishing in the journal. Another 3.85% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.82% of all publications and 58.97% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.