Xinglong Ju

419 total citations
11 papers, 319 citations indexed

About

Xinglong Ju is a scholar working on Aerospace Engineering, Computational Theory and Mathematics and Ocean Engineering. According to data from OpenAlex, Xinglong Ju has authored 11 papers receiving a total of 319 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Aerospace Engineering, 3 papers in Computational Theory and Mathematics and 2 papers in Ocean Engineering. Recurrent topics in Xinglong Ju's work include Wind Energy Research and Development (4 papers), Advanced Multi-Objective Optimization Algorithms (3 papers) and Solar Radiation and Photovoltaics (2 papers). Xinglong Ju is often cited by papers focused on Wind Energy Research and Development (4 papers), Advanced Multi-Objective Optimization Algorithms (3 papers) and Solar Radiation and Photovoltaics (2 papers). Xinglong Ju collaborates with scholars based in United States and China. Xinglong Ju's co-authors include Feng Liu, Wei-Jen Lee, Li Wang, Jay Michael Rosenberger, Victoria C. P. Chen, Shouyi Wang, Wenyong Zhou, Ning Wang, Timothy R. Church and Onur Asan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Energy and Expert Systems with Applications.

In The Last Decade

Xinglong Ju

10 papers receiving 300 citations

Peers

Xinglong Ju

EEE

CTM

Yunus Eroğlu Türkiye

Martina Fischetti Denmark

Muhammad Haris China

Daeyoung Choi South Korea

Muhammad Mujtaba Shaikh Pakistan

Jun-young Kwak United States

Adnane Addaim Morocco

Stefanie Kuenzel United Kingdom

Andreas Eichhorn Germany

Muhammad Ahmad Pakistan

Yunus Eroğlu Türkiye

Xinglong Ju

213 ×1.4

138 ×1.1

EEE

81 ×1.4

56 ×1.6

54 ×1.6

CTM

Citations per year, relative to Xinglong Ju Xinglong Ju (= 1×) peers Yunus Eroğlu

Countries citing papers authored by Xinglong Ju

Since Specialization

Citations

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

Fields of papers citing papers by Xinglong Ju

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xinglong Ju

This figure shows the co-authorship network connecting the top 25 collaborators of Xinglong Ju. A scholar is included among the top collaborators of Xinglong Ju 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 Xinglong Ju. Xinglong Ju is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

Wan, Guihong, Meng Jiao, Xinglong Ju, et al.. (2023). Electrophysiological Brain Source Imaging via Combinatorial Search with Provable Optimality. Proceedings of the AAAI Conference on Artificial Intelligence. 37(10). 12491–12499. 2 indexed citations

Ju, Xinglong, et al.. (2023). A machine learning approach for predicting hurricane evacuee destination location using smartphone location data. SHILAP Revista de lepidopterología. 3(1). 4 indexed citations

Ju, Xinglong, et al.. (2023). A Machine Learning Approach for Predicting Hurricane Evacuee Destination Location Using Smartphone Location Data. SSRN Electronic Journal.

Ju, Xinglong, Jay Michael Rosenberger, Victoria C. P. Chen, & Feng Liu. (2022). Global optimization on non-convex two-way interaction truncated linear multivariate adaptive regression splines using mixed integer quadratic programming. Information Sciences. 597. 38–52. 17 indexed citations

Ju, Xinglong, et al.. (2021). Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models. IEEE Open Journal of Engineering in Medicine and Biology. 2. 291–298. 13 indexed citations

Ju, Xinglong, Victoria C. P. Chen, Jay Michael Rosenberger, & Feng Liu. (2021). Fast knot optimization for multivariate adaptive regression splines using hill climbing methods. Expert Systems with Applications. 171. 114565–114565. 32 indexed citations

Ju, Xinglong, et al.. (2021). Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo Tree Search reinforcement learning. Energy Conversion and Management. 252. 115047–115047. 57 indexed citations

Liu, Feng, Xinglong Ju, Ning Wang, Li Wang, & Wei-Jen Lee. (2020). Wind farm macro-siting optimization with insightful bi-criteria identification and relocation mechanism in genetic algorithm. Energy Conversion and Management. 217. 112964–112964. 44 indexed citations

Ju, Xinglong, Feng Liu, Li Wang, & Wei-Jen Lee. (2019). Wind farm layout optimization based on support vector regression guided genetic algorithm with consideration of participation among landowners. Energy Conversion and Management. 196. 1267–1281. 65 indexed citations

10.

Ju, Xinglong & Feng Liu. (2019). Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation. Applied Energy. 248. 429–445. 84 indexed citations

11.

Ju, Xinglong, et al.. (2013). Predicting Service Execution Time towards a Runtime Monitoring Approach. 221–224. 1 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.

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