Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Brain Intelligence: Go beyond Artificial Intelligence
2017694 citationsHuimin Lu, Yujie Li et al.profile →
Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning
2017336 citationsHuimin Lu, Yujie Li et al.profile →
Underwater image dehazing using joint trilateral filter
Countries citing papers authored by Seiichi Serikawa
Since
Specialization
Citations
This map shows the geographic impact of Seiichi Serikawa's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Seiichi Serikawa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seiichi Serikawa more than expected).
Fields of papers citing papers by Seiichi Serikawa
This network shows the impact of papers produced by Seiichi Serikawa. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Seiichi Serikawa. The network helps show where Seiichi Serikawa may publish in the future.
Co-authorship network of co-authors of Seiichi Serikawa
This figure shows the co-authorship network connecting the top 25 collaborators of Seiichi Serikawa.
A scholar is included among the top collaborators of Seiichi Serikawa based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Seiichi Serikawa. Seiichi Serikawa is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Serikawa, Seiichi, et al.. (2016). Applying Sensor Node with Zero Standby Power to Door Monitor. Lecture notes in computer science. 2222(1). 576–580.3 indexed citations
Serikawa, Seiichi, et al.. (2007). Proposal of Optimization Method of Image Processing Parameter by Polytope Method. 한국지능시스템학회 국제학술대회 발표논문집. 105–108.2 indexed citations
17.
Serikawa, Seiichi, et al.. (2005). A Method for Position Detection and Shape Recognition with Ultrasonic Sensor. 한국지능시스템학회 국제학술대회 발표논문집. 75–78.1 indexed citations
18.
Serikawa, Seiichi, et al.. (1994). Extraction of Glossiness of Curved Surfaces by the Use of Spatial Filter Simulating Retina Function. IEICE Transactions on Information and Systems. 77(3). 335–342.1 indexed citations
19.
Serikawa, Seiichi, et al.. (1993). Method for Measuring Glossiness of Plane Surfaces Based on Psychological Sensory Scale. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 76(3). 439–446.3 indexed citations
20.
Serikawa, Seiichi, et al.. (1986). Improvement of The Instrument for Measuring Glossiness of Curved Surfaces. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 69(5). 589–592.4 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.