Tak Hur

43 papers receiving 1.1k citations

Tak Hur's Hit Papers

Quantum convolutional neural network for classical data classification 2022 · 216 citations
2160+1+2Years since publication50100150200

Peers

Tak Hur
Comparison fields: 5 of 96
  • Environmental Engineering 419
  • Energy Engineering and Power Technology 91
  • Industrial and Manufacturing Engineering 219
  • Pollution 151
  • Strategy and Management 193
Replace Abhijeet K. Digalwar with:
Abhijeet K. Digalwar India
Scott Kennedy United States
Meihui Jiang China
Di Xu China
Jonathan Busch United Kingdom
Buyuan Zhang China
Andrés Honrubia‐Escribano Spain
J.D. Nixon United Kingdom
Alexander Sauer Germany
Şebnem Yılmaz Balaman Türkiye
Tak Hur relative to Abhijeet K. Digalwar India Abhijeet K. Digalwar's profile →
Citations per field
00.5×4.8×
Abhijeet K. Digalwar · 1×
Citations per year

Countries citing papers authored by Tak Hur

Since Specialization
Citations

This map shows the geographic impact of Tak Hur'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 Tak Hur with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tak Hur more than expected).

Fields of papers citing papers by Tak Hur

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tak Hur. 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 Tak Hur. The network helps show where Tak Hur may publish in the future.

Co-authors

The 25 scholars most cited alongside Tak Hur, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tak Hur Line = papers co-authored together Tak Hur links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Quantum convolutional neural network for classical data classification
Hit paper breakdown →
2022216
2 200499
3 200598
4 200389
5 201479
6 201171
7 200969
8 201060
9 201360
10 200339
11 200735
12 201032
13 201225
14 201219
15 200916
16
Life Cycle Assessment on End-of-Life Vehicle Treatment System in Korea
200715
17 200915
18 202111
19 200310
20 20039

About Tak Hur

Tak Hur is a scholar working on Industrial and Manufacturing Engineering, Environmental Engineering, Electrical and Electronic Engineering, Strategy and Management and Mechanical Engineering, having authored 47 papers that have together received 1.1k indexed citations. Recurring topics across this work include Recycling and Waste Management Techniques (15 papers), Environmental Impact and Sustainability (11 papers), Sustainable Supply Chain Management (8 papers), Sustainable Industrial Ecology (6 papers), Extraction and Separation Processes (5 papers), Metallurgy and Material Science (4 papers), Quantum Computing Algorithms and Architecture (4 papers) and Quantum Information and Cryptography (4 papers). The work is most often cited by research in Environmental Engineering (419 citations), Energy Engineering and Power Technology (91 citations), Industrial and Manufacturing Engineering (219 citations), Pollution (151 citations) and Strategy and Management (193 citations). Tak Hur has collaborated with scholars based in South Korea, United States and Japan. Frequent co-authors include Daniel K. Park, Kyounghoon Cha, Ryōichi Yamamoto, Eunsun Kwon, Vasilis Fthenakis, Jiyong Lee, Su‐Yol Lee, Hang‐Sik Shin, Seok Jin Hong and Parikhit Sinha. Their work appears in journals such as International Journal of Hydrogen Energy, The International Journal of Life Cycle Assessment, Resources Conservation and Recycling, Journal of Cleaner Production and SAE technical papers on CD-ROM/SAE technical paper series.

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.

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