2005-2010，北京大学，信号与信息处理，博士学位 

2001-2005，伟德bevictor中文版，信息工程，学士学位 

2010-2012，特任研究员（博士后研究员），东京大学，空间信息科学中心，日本

2012-2015，特任助理教授，东京大学，空间信息科学中心，日本

2015-2017，特任副教授，东京大学，空间信息科学中心，日本

2017-2019，主任研究员（终身职位），日本国家产业技术综合研究所，人工智能研究中心，日本

2018-2019，副教授，东京大学，空间信息科学中心，日本

2019-2024，执行主任，南方科技大学，南方科技大学-东京大学超智慧城市联合研究中心，中国

2020-2024，智慧城市研究中心主任，南方科技大学，斯发基斯可信自主系统研究院（深圳市诺奖研究院），中国

2024-至今， 经理，伟德bevictor中文版，BETVLCTOR伟德官方网站，中国

(1) 项目负责人, 国家重点研发计划“网络协同制造和智能工厂”重点专项，中国科学与技术部：制造业产品生命周期价值链多维数据空间及服务理论 （2021YFB1714400），2021‐2024，合同总额：750万元人民币。

(2) 项目负责人, ***项目，***企业（企业横向项目），2021‐2026，合同总额：1500万元人民币。

(3) 项目负责人, ***项目，***企业（企业横向项目），2022‐2027，合同总额：500万元人民币。

(4) 项目负责人,  ***项目，***：人工智能和大数据驱动的城市智能化管理，2019‐2022，合同总额：200万元人民币。

(5) 项目负责人, 面向城市轨道交通场景的客流动态分析与突发事件的应急客流分析预测，华为技术有限公司（企业横向项目），2020‐2022，合同总额：159万元人民币。

(6) 项目负责人, 深圳市高层次人才科研启动经费，深圳市人民政府，2021‐2026，合同总额：500万元人民币。

(7) 项目负责人, 日本国家卓越研究员辅助金，日本文部科学省：基于人工智能和大数据的城市大脑平台, 2017‐2021，合同总额：2400万元日元。

(8) 项目负责人, 日本自然科学基金基盘项目B (17H01784)，日本学术振兴会：新一代的应急管理系统设计：基于多模态大数据的深度知识挖掘, 2017‐2020，合同总额：1235万元日元。

(9) 项目负责人, 日美大数据和灾难项目, 日本国家科学技术振兴机构（JST），大数据驱动的灾难信息共享与推荐, 2015-2017，合同总额：3000万元日元。

(10) 项目负责人, 青年科学家基金项目 B (26730113)，日本学术振兴会：大数据时代下的灾难行为建模与预测, 2014‐2015，合同总额：377万元日元。

(11) 项目负责人, 微软CORE项目，微软研究院，城市信息学：大数据时代下的城市应急管理, 2014‐2015，合同总额：200万元日元。

(12) 项目负责人, 青年科学家基金项目 B (23700192)，日本学术振兴会：基于分布式传感器的针对高密度人群的智能监控, 2011-2013，合同总额：239万元日元。

(13) 项目负责人, 国土政策研究支援项目，日本国土交通省：基于160万人GPS轨迹数据的灾难行为分析与模拟（东日本大地震和福岛核事故），2012- 2013，合同总额：200万元日元。

(14) 项目负责人, 微软CORE项目，微软研究院：基于Kinect传感器的智能监控、运动捕捉和物体识别, 2012‐2013，合同总额：250万元日元。

（1）中华人民共和国驻日本国大使奖（自然科学领域成就奖），颁发机构：中国留日同学会、中国驻日大使馆，2022年。

（2）***，颁发机构：***，2019年。

（3）日本国家卓越研究员，颁发机构：日本文部科学省，2017年。

（4）广东省珠江人才 青年拔尖人才，颁发机构：广东省人民政府，2019年。

（5）深圳市孔雀人才 B类人才，颁发机构：深圳市人民政府，2019年。

（6）中国发明创业奖人物奖，颁发机构：中国发明协会，2021年。

（7）中国发明创新奖二等奖，颁发机构：中国发明协会，2021年。

（8）中国产学研合作创新奖（个人奖），颁发机构：中国产学研合作促进会，2021年。

（9）普适计算年会最佳论文提名奖，颁发机构：国际计算机学会（ACM），2015年。

（10）南方科技大学董事长青年科研奖，颁发机构：南方科技大学，2022年。

（11）南方科技大学优秀共产党员，颁发机构：南方科技大学，2023年。

（12）南方科技大学最受本科生欢迎的十位任课老师，颁发机构：南方科技大学，2023年。

（13）南方科技大学优秀书院导师奖，颁发机构：南方科技大学，2020年。

（14）北京大学信息学院学术十杰，颁发机构：北京大学，2010年。

在如下期刊或会议任职副主编或领域主席:

Associate Editor，The Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)

Guest Editor，IEEE Transactions on Multimedia

Guest Editor，World Wide Web Journal （WWW Journal）

Associate Editor，Big Data Journal

Area Chair，The conference of the IEEE Intelligent Transportation Systems

Area Chair，IEEE International Conference on Multimedia Information Processing and Retrieval

Senior Program Committee Member, International Joint Conference on Artificial Intelligence

Senior Program Committee Member, AAAI Conference on Artificial Intelligence

学术专著：

[1] 宋轩，张浩然, "制造业产品生命周期多维数据空间服务理论", 人民邮电出版社, 2024年。

[2] Haoran Zhang, Xuan Song, Ryousuke Shibasaki, "Big Data and Mobility as a Service", Elsevier，2021.

15篇代表性学术论文：

[1] X. Song, R. Shibasaki, N. J. Yuan, X. Xie, T. Li, and R. Adachi, “DeepMob: learning deep knowledge of human emergency behavior and mobility from big and heterogeneous data,” ACM Transactions on Information Systems (TOIS), vol. 35, no. 4, pp. 1–19, 2017.

[2] X. Song, Q. Zhang, Y. Sekimoto, and R. Shibasaki, “Prediction of human emergency behavior and their mobility following large-scale disaster,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), 2014, pp. 5–14.

[3] X. Song, H. Kanasugi, and R. Shibasaki, “Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level,” in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI), 2017, pp. 2618–2624.

[4] X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, N. J. Yuan, and X. Xie, “Prediction and simulation of human mobility following natural disasters,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 8, no. 2, pp. 1–23, 2017.

[5] X. Song, Q. Zhang, Y. Sekimoto, T. Horanont, S. Ueyama, and R. Shibasaki, “Modeling and probabilistic reasoning of population evacuation during large-scale disaster,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), 2013, pp. 1231–1239.

[6] X. Song, H. Zhang, R. A. Akerkar, H. Huang, S. Guo, L. Zhong, Y. Ji, A. L. Opdahl, H. Purohit, A. Skupin, and others, “Big data and emergency management: concepts, methodologies, and applications,” IEEE Transactions on Big Data, pp. 397-419，2022.

[7] 宋轩, 孟小峰, 刘克, “空间数据智能技术发展与应用分析,”中国科学基金, vol. 37, no. 6, pp. 1039-1046，2023.

[8] Q. Zhang, X. Song, X. Shao, H. Zhao, and R. Shibasaki, “Object discovery: Soft attributed graph mining,” IEEE transactions on pattern analysis and machine intelligence (TPAMI), vol. 38, no. 3, pp. 532–545, 2015.

[9] H. Wang, Z. Zhang, Z. Fan, J. Chen, L. Zhang, R. Shibasaki, and X. Song, “Multi-Task Weakly Supervised Learning for Origin–Destination Travel Time Estimation,” IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 35, no. 11, pp. 11628–11641, 2023.

[10] H. Wang, J. Chen, Z. Fan, Z. Zhang, Z. Cai, and X. Song, “ST-ExpertNet: A Deep Expert Framework for Traffic Prediction,” IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 35, no. 7, pp. 7512–7525, 2023.

[11] R. Jiang, Z. Cai, Z. Wang, C. Yang, Z. Fan, Q. Chen, K. Tsubouchi, X. Song, and R. Shibasaki, “DeepCrowd: A deep model for large-scale citywide crowd density and flow prediction,” IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021.

[12] J. Deng, X. Chen, R. Jiang, X. Song, and I. W. Tsang, “A Multi-View Multi-Task Learning Framework for Multi-Variate Time Series Forecasting,” IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 35, no. 8, pp. 7665–7680, 2023.

[13] Y. Yao, H. Zhang, F. Defan, J. Chen, W. Li, R. Shibasaki, and X. Song, “Modifiable Areal Unit Problem on Grided Mobile Crowd Sensing: Analysis and Restoration,” IEEE Transactions on Mobile Computing (TMC), 2022.

[14] C. Yang, Z. Zhang, Z. Fan, R. Jiang, Q. Chen, X. Song, and R. Shibasaki, “EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control,” IEEE Transactions on Visualization and Computer Graphics (TVCG), 2022.

[15] Z. Cai, R. Jiang, X. Lian, C. Yang, Z. Wang, Z. Fan, K. Tsubouchi, H. H. Kobayashi, X. Song, and R. Shibasaki, “Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural Networks,” IEEE Transactions on Mobile Computing (TMC), 2023.

完整学术论文列表：

[1] Z. Zhiwen, H. Wang, Z. Fan, R. Shibasaki, and X. Song, “Assessing the Continuous Causal Responses of Typhoon-related Weather on Human Mobility: An Empirical Study in Japan,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023, pp. 3524–3533.

[2] Z. Zhang, H. Wang, Z. Fan, X. Song, and R. Shibasaki, “Missing Road Condition Imputation Using a Multi-View Heterogeneous Graph Network From GPS Trajectory,” IEEE Transactions on Intelligent Transportation Systems, 2023.

[3] Z. Zhang, Z. Fan, and X. Song, “Returning Home Strategy Analysis using Mobile Sensing Data in Tohoku Earthquake,” in Symposium on AI, Data and Digitalization (SAIDD 2023), 2023, p. 86.

[4] M. Zhang, Z. Fan, R. Shibasaki, and X. Song, “Domain adversarial graph convolutional network based on rssi and crowdsensing for indoor localization,” IEEE Internet of Things Journal, 2023.

[5] D. Yin, R. Jiang, J. Deng, Y. Li, Y. Xie, Z. Wang, Y. Zhou, X. Song, and J. S. Shang, “MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction,” GeoInformatica, vol. 27, no. 1, pp. 77–105, 2023.

[6] Y. Yao, H. Zhang, X. Shi, J. Chen, W. Li, X. Song, and R. Shibasaki, “LTP-Net: Life-Travel Pattern Based Human Mobility Signature Identification,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 12, pp. 14306–14319, 2023.

[7] Y. Yao, H. Zhang, J. Chen, W. Li, R. Shibasaki, and X. Song, “Mobility Tableau: Human Mobility Similarity Measurement for City Dynamics,” IEEE Transactions on Intelligent Transportation Systems, 2023.

[8] Z. Wang, R. Jiang, Z. Fan, X. Song, and R. Shibasaki, “Towards an Event-Aware Urban Mobility Prediction System,” in Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023, pp. 1303–1304.

[9] Y. Wang, R. Jiang, H. Liu, D. Yin, and X. Song, “Sequence-Graph Fusion Neural Network for User Mobile App Behavior Prediction,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2023, pp. 105–121.

[10] H. Wang, Z. Zhang, Z. Fan, J. Chen, L. Zhang, R. Shibasaki, and X. Song, “Multi-Task Weakly Supervised Learning for Origin–Destination Travel Time Estimation,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11628–11641, 2023, doi: 10.1109/TKDE.2023.3236060.

[11] H. Wang, J. Chen, T. Pan, Z. Fan, X. Song, R. Jiang, L. Zhang, Y. Xie, Z. Wang, and B. Zhang, “Easy begun is half done: spatial-temporal graph modeling with st-curriculum dropout,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2023, vol. 37, no. 4, pp. 4668–4675.

[12] H. Wang, J. Chen, Z. Fan, Z. Zhang, Z. Cai, and X. Song, “ST-ExpertNet: A Deep Expert Framework for Traffic Prediction,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 7, pp. 7512–7525, 2023, doi: 10.1109/TKDE.2022.3196936.

[13] X. Meng, Y. Li, K. Liu, Y. Liu, B. Yang, X. Song, G. Liao, S. Wang, Z. Yu, L. Chen, and others, “Spatial Data Intelligence and City Metaverse: a Review,” Fundamental Research, 2023.

[14] H. Liu, Z. Dong, R. Jiang, J. Deng, J. Deng, Q. Chen, and X. Song, “Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023, pp. 4125–4129.

[15] G. Lin, H. Zhang, X. Song, and R. Shibasaki, “Blockchain for location-based big data-driven services,” in Handbook of Mobility Data Mining, Elsevier, 2023, pp. 153–171.

[16] Y. Li, Z. Fan, D. Yin, R. Jiang, J. Deng, and X. Song, “HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation,” World Wide Web, vol. 26, no. 4, pp. 1625–1648, 2023.

[17] W. Li, X. Shi, D. Huang, X. Shen, J. Chen, H. H. Kobayashi, H. Zhang, X. Song, and R. Shibasaki, “PredLife: Predicting Fine-grained Future Activity Patterns,” IEEE Transactions on Big Data, 2023.

[18] P. Li, H. Zhang, W. Li, J. Chen, J. Zhang, X. Song, and R. Shibasaki, “User demographic characteristics inference based on big GPS trajectory data,” in Handbook of Mobility Data Mining, Elsevier, 2023, pp. 75–93.

[19] Y. Jin, P. Li, Z. Chen, S. Bharule, N. Jia, J. Chen, X. Song, R. Shibasaki, and H. Zhang, “Understanding railway usage behavior with ten million GPS records,” Cities, vol. 133, p. 104117, 2023.

[20] R. Jiang, Z. Wang, J. Yong, P. Jeph, Q. Chen, Y. Kobayashi, X. Song, S. Fukushima, and T. Suzumura, “Spatio-temporal meta-graph learning for traffic forecasting,” in Proceedings of the AAAI conference on artificial intelligence, 2023, vol. 37, no. 7, pp. 8078–8086.

[21] R. Jiang, Z. Wang, Y. Tao, C. Yang, X. Song, R. Shibasaki, S.-C. Chen, and M.-L. Shyu, “Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster,” in Proceedings of the ACM Web Conference 2023, 2023, pp. 2655–2665.

[22] D. Feng, H. Zhang, and X. Song, “Noise filter method for mobile trajectory data,” in Handbook of Mobility Data Mining, Elsevier, 2023, pp. 35–50.

[23] J. Deng, X. Chen, R. Jiang, X. Song, and I. W. Tsang, “A Multi-View Multi-Task Learning Framework for Multi-Variate Time Series Forecasting,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 8, pp. 7665–7680, 2023, doi: 10.1109/TKDE.2022.3218803.

[24] J. Deng, J. Deng, D. Yin, R. Jiang, and X. Song, “TTS-Norm: Forecasting Tensor Time Series via Multi-way Normalization,” ACM Transactions on Knowledge Discovery from Data, 2023.

[25] Z. Chen, P. Li, Y. Jin, S. Bharule, N. Jia, W. Li, X. Song, R. Shibasaki, and H. Zhang, “Using mobile phone big data to identify inequity of aging groups in transit-oriented development station usage: A case of Tokyo,” Transport policy, vol. 132, pp. 65–75, 2023.

[26] J. Chen, J. Zheng, Z. Fan, and X. Song, “Causally guided Intelligent Transportation System,” in Symposium on AI, Data and Digitalization (SAIDD 2023), 2023, p. 80.

[27] J. Chen, X. Shi, H. Zhang, W. Li, P. Li, Y. Yao, X. Song, and R. Shibasaki, “Mutual adaptation: learning from prototype for time series prediction,” IEEE Transactions on Artificial Intelligence, 2023.

[28] J. Chen, X. Shi, H. Zhang, W. Li, P. Li, Y. Yao, S. Miyazawa, X. Song, and R. Shibasaki, “MobCovid: Confirmed Cases Dynamics Driven Time Series Prediction of Crowd in Urban Hotspot,” IEEE Transactions on Neural Networks and Learning Systems, 2023.

[29] D. Chen, X. Shi, X. Song, Z. Chen, and H. Zhang, “Phone-based Ambient Temperature Measurement with a New Confidence-based Truth Inference Model,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 6, no. 4, pp. 1–18, 2023.

[30] Z. Cai, R. Jiang, X. Lian, C. Yang, Z. Wang, Z. Fan, K. Tsubouchi, H. H. Kobayashi, X. Song, and R. Shibasaki, “Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural Networks,” IEEE Transactions on Mobile Computing, 2023.

[31] Z. Zhang, H. Wang, Z. Fan, J. Chen, X. Song, and R. Shibasaki, “GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation,” IEEE Internet of Things Journal, 2022.

[32] L. Zhang, X. Geng, Z. Qin, H. Wang, X. Wang, Y. Zhang, J. Liang, G. Wu, X. Song, and Y. Wang, “Multi-modal graph interaction for multi-graph convolution network in urban spatiotemporal forecasting,” Sustainability, vol. 14, no. 19, p. 12397, 2022.

[33] K. Zhang, X. Song, C. Zhang, and S. Yu, “Challenges and future directions of secure federated learning: a survey,” Frontiers of computer science, vol. 16, no. 5, pp. 1–8, 2022.

[34] K. Zhang, Z. Fan, X. Song, and S. Yu, “Enhancing Trajectory Recovery from Gradients via Mobility Prior Knowledge,” IEEE Internet of Things Journal, 2022.

[35] H. Zhang, J. Chen, Q. Chen, T. Xia, X. Wang, W. Li, X. Song, and R. Shibasaki, “A universal mobility-based indicator for regional health level,” Cities, vol. 120, p. 103452, 2022.

[36] D. Yin, R. Jiang, J. Deng, Y. Li, Y. Xie, Z. Wang, Y. Zhou, X. Song, and J. S. Shang, “MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction,” GeoInformatica, pp. 1–29, 2022.

[37] Y. Yao, H. Zhang, L. Lin, G. Lin, R. Shibasaki, X. Song, and K. Yu, “Internet of Things Positioning Technology Based Intelligent Delivery System,” IEEE Transactions on Intelligent Transportation Systems, 2022.

[38] Y. Yao, H. Zhang, F. Defan, J. Chen, W. Li, R. Shibasaki, and X. Song, “Modifiable Areal Unit Problem on Grided Mobile Crowd Sensing: Analysis and Restoration,” IEEE Transactions on Mobile Computing, 2022.

[39] C. Yang, Z. Zhang, Z. Fan, R. Jiang, Q. Chen, X. Song, and R. Shibasaki, “EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control,” IEEE Transactions on Visualization and Computer Graphics, 2022.

[40] H. Xie, X. Song, and H. Zhang, “MaaS and IoT: Concepts, methodologies, and applications,” Big Data and Mobility as a Service, pp. 229–243, 2022.

[41] Y. Wu, T. Xia, Y. Wang, H. Zhang, X. Feng, X. Song, and R. Shibasaki, “A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network,” Renewable Energy, vol. 185, pp. 302–320, 2022.

[42] Z. Wang, R. Jiang, H. Xue, F. D. Salim, X. Song, and R. Shibasaki, “Event-Aware Multimodal Mobility Nowcasting,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2022, vol. 36, no. 4, pp. 4228–4236.

[43] H. Wang, Q. Chen, Z. Dong, X. Song, H. Tian, D. Yang, and M. Liu, “A Geomagnetic Sensor Dataset for Traffic Flow Prediction,” in 2022 IEEE International Conference on Big Data (Big Data), 2022, pp. 2419–2422.

[44] H. Wang, Z. Fan, J. Chen, L. Zhang, and X. Song, “Discovering Key Sub-Trajectories to Explain Traffic Prediction,” Sensors, vol. 23, no. 1, p. 130, 2022.

[45] Y. Tao, C. Yang, T. Wang, E. Coltey, Y. Jin, Y. Liu, R. Jiang, Z. Fan, X. Song, R. Shibasaki, and others, “A Survey on Data-Driven COVID-19 and Future Pandemic Management,” ACM Computing Surveys (CSUR), 2022.

[46] X. Song, H. Zhang, R. A. Akerkar, H. Huang, S. Guo, L. Zhong, Y. Ji, A. L. Opdahl, H. Purohit, A. Skupin, and others, “Big data and emergency management: concepts, methodologies, and applications,” IEEE Transactions on Big Data, 2022.

[47] H. Ma, M. Zhou, X. Ouyang, D. Yin, R. Jiang, and X. Song, “Forecasting Regional Multimodal Transportation Demand with Graph Neural Networks: An Open Dataset,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022, pp. 3263–3268.

[48] Y. Li, Z. Fan, J. Zhang, D. Shi, T. Xu, D. Yin, J. Deng, and X. Song, “Heterogeneous Hypergraph Neural Network for Friend Recommendation with Human Mobility,” in Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, pp. 4209–4213.

[49] Y. Li, Z. Fan, D. Yin, R. Jiang, J. Deng, and X. Song, “HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation,” World Wide Web, pp. 1–24, 2022.

[50] W. Li, H. Zhang, J. Chen, P. Li, Y. Yao, X. Shi, M. Shibasaki, H. H. Kobayashi, X. Song, and R. Shibasaki, “Metagraph-based Life Pattern Clustering with Big Human Mobility Data,” IEEE Transactions on Big Data, 2022.

[51] P. Li, H. Zhang, W. Li, K. Yu, A. K. Bashir, A. A. Al Zubi, J. Chen, X. Song, and R. Shibasaki, “IIoT based Trustworthy Demographic Dynamics Tracking with Advanced Bayesian Learning,” IEEE Transactions on Network Science and Engineering, 2022.

[52] R. Jiang, Z. Cai, Z. Wang, C. Yang, Z. Fan, Q. Chen, K. Tsubouchi, X. Song, and R. Shibasaki, “Yahoo! Bousai Crowd Data: A Large-Scale Crowd Density and Flow Dataset in Tokyo and Osaka,” in 2022 IEEE International Conference on Big Data (Big Data), 2022, pp. 6676–6677.

[53] R. Jiang, Z. Cai, Z. Wang, C. Yang, Z. Fan, Q. Chen, X. Song, and R. Shibasaki, “Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 13, no. 2, pp. 1–24, 2022.

[54] X. Hao, R. Jiang, J. Deng, and X. Song, “The Impact of COVID-19 on Human Mobility: A Case Study on New York,” in 2022 IEEE International Conference on Big Data (Big Data), 2022, pp. 4365–4374.

[55] Z. Fan, X. Yang, W. Yuan, R. Jiang, Q. Chen, X. Song, and R. Shibasaki, “Online trajectory prediction for metropolitan scale mobility digital twin,” in Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022, pp. 1–12.

[56] Z. Fan, C. Yang, Z. Zhang, X. Song, Y. Liu, R. Jiang, Q. Chen, and R. Shibasaki, “Human Mobility-based Individual-level Epidemic Simulation Platform,” ACM Transactions on Spatial Algorithms and Systems (TSAS), vol. 8, no. 3, pp. 1–16, 2022.

[57] Z. Fan, G. Lin, W. Yuan, R. Shibasaki, P. E, and X. Song, “Exploring intercity regional similarity using worldwide location-based social network data (demo paper),” in Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022, pp. 1–4.

[58] Z. Dong, Q. Chen, R. Jiang, H. Wang, X. Song, and H. Tian, “Learning Latent Road Correlations from Trajectories,” in 2022 IEEE International Conference on Big Data (Big Data), 2022, pp. 5458–5467.

[59] Z. Chen, P. Li, Y. Jin, S. Bharule, N. Jia, W. Li, X. Song, R. Shibasaki, and H. Zhang, “Using mobile phone big data to identify inequity of aging groups in transit-oriented development station usage: A case of Tokyo,” Transport Policy, 2022.

[60] J. Chen, Q. Zhang, N. Xu, W. Li, Y. Yao, P. Li, Q. Yu, C. Wen, X. Song, R. Shibasaki, and others, “Roadmap to hydrogen society of Tokyo: Locating priority of hydrogen facilities based on multiple big data fusion,” Applied Energy, vol. 313, p. 118688, 2022.

[61] S. Ao, T. Zhou, J. Jiang, G. Long, X. Song, and C. Zhang, “EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning,” in International Conference on Machine Learning, 2022, pp. 822–843.

[62] H. Zhang, X. Song, and R. Shibasaki, “Big Data and Mobility as a Service.” Elsevier, 2021.

[63] H. Zhang, J. Chen, J. Yan, X. Song, R. Shibasaki, and J. Yan, “Urban power load profiles under ageing transition integrated with future EVs charging,” Advances in Applied Energy, vol. 1, p. 100007, 2021.

[64] Z. Wang, T. Xia, R. Jiang, X. Liu, K.-S. Kim, X. Song, and R. Shibasaki, “Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks,” in 2021 IEEE 37th International Conference on Data Engineering (ICDE), 2021, pp. 1751–1762.

[65] Z. Wang, R. Jiang, Z. Cai, Z. Fan, X. Liu, K.-S. Kim, X. Song, and R. Shibasaki, “Spatio-temporal-categorical graph neural networks for fine-grained multi-incident co-prediction,” in Proceedings of the 30th ACM international conference on information & knowledge management, 2021, pp. 2060–2069.

[66] Y. Tao, R. Jiang, E. Coltey, C. Yang, X. Song, R. Shibasaki, M.-L. Shyu, and S.-C. Chen, “Data-driven in-crisis community identification for disaster response and management,” in 2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC), 2021, pp. 96–104.

[67] Y. Liu, Z. Fan, X. Song, and R. Shibasaki, “FedVoting: A Cross-Silo Boosting Tree Construction Method for Privacy-Preserving Long-Term Human Mobility Prediction,” Sensors, vol. 21, no. 24, p. 8282, 2021.

[68] P. Li, H. Zhang, Z. Guo, S. Lyu, J. Chen, W. Li, X. Song, R. Shibasaki, and J. Yan, “Understanding rooftop PV panel semantic segmentation of satellite and aerial images for better using machine learning,” Advances in applied energy, vol. 4, p. 100057, 2021.

[69] W. Jiang, H. Zhang, Y. Long, J. Chen, Y. Sui, X. Song, R. Shibasaki, and Q. Yu, “GPS data in urban online ride-hailing: the technical potential analysis of demand prediction model,” Journal of Cleaner Production, vol. 279, p. 123706, 2021.

[70] R. Jiang, D. Yin, Z. Wang, Y. Wang, J. Deng, H. Liu, Z. Cai, J. Deng, X. Song, and R. Shibasaki, “Dl-traff: Survey and benchmark of deep learning models for urban traffic prediction,” in Proceedings of the 30th ACM international conference on information & knowledge management, 2021, pp. 4515–4525.

[71] R. Jiang, Z. Wang, Z. Cai, C. Yang, Z. Fan, T. Xia, G. Matsubara, H. Mizuseki, X. Song, and R. Shibasaki, “Countrywide origin-destination matrix prediction and its application for covid-19,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2021, pp. 319–334.

[72] R. Jiang, X. Song, Z. Fan, T. Xia, Z. Wang, Q. Chen, Z. Cai, and R. Shibasaki, “Transfer urban human mobility via POI embedding over multiple cities,” ACM Transactions on Data Science, vol. 2, no. 1, pp. 1–26, 2021.

[73] R. Jiang, Z. Cai, Z. Wang, C. Yang, Z. Fan, Q. Chen, K. Tsubouchi, X. Song, and R. Shibasaki, “DeepCrowd: A deep model for large-scale citywide crowd density and flow prediction,” IEEE Transactions on Knowledge and Data Engineering, 2021.

[74] D. Feng, Y. Mo, Z. Tang, Q. Chen, H. Zhang, R. Akerkar, and X. Song, “Data-driven hospital personnel scheduling optimization through patients prediction,” CCF Transactions on Pervasive Computing and Interaction, vol. 3, no. 1, pp. 40–56, 2021.

[75] J. Deng, X. Chen, R. Jiang, X. Song, and I. W. Tsang, “St-norm: Spatial and temporal normalization for multi-variate time series forecasting,” in Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 2021, pp. 269–278.

[76] J. Deng, X. Chen, Z. Fan, R. Jiang, X. Song, and I. W. Tsang, “The pulse of urban transport: Exploring the co-evolving pattern for spatio-temporal forecasting,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 15, no. 6, pp. 1–25, 2021.

[77] J. Chen, W. Li, H. Zhang, Z. Cai, Y. Sui, Y. Long, X. Song, and R. Shibasaki, “GPS data in urban online ride-hailing: A simulation method to evaluate impact of user scale on emission performance of system,” Journal of Cleaner Production, vol. 287, p. 125567, 2021.

[78] H. Zhang, J. Chen, W. Li, X. Song, and R. Shibasaki, “Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential,” Applied energy, vol. 269, p. 115038, 2020.

[79] Q. Yu, H. Zhang, W. Li, X. Song, D. Yang, and R. Shibasaki, “Mobile phone GPS data in urban customized bus: Dynamic line design and emission reduction potentials analysis,” Journal of Cleaner Production, vol. 272, p. 122471, 2020.

[80] Q. Yu, H. Zhang, W. Li, Y. Sui, X. Song, D. Yang, R. Shibasaki, and W. Jiang, “Mobile phone data in urban bicycle-sharing: Market-oriented sub-area division and spatial analysis on emission reduction potentials,” Journal of Cleaner Production, vol. 254, p. 119974, 2020.

[81] T. Xia, A. Jatowt, Z. Wang, R. Si, H. Zhang, X. Liu, R. Shibasaki, X. Song, and K. Kim, “CoolPath: an application for recommending pedestrian routes with reduced heatstroke risk,” in International Symposium on Web and Wireless Geographical Information Systems, 2020, pp. 14–23.

[82] Y. Wang, J. Chen, N. Xu, W. Li, Q. Yu, and X. Song, “GPS data in urban online Car-hailing: simulation on optimization and prediction in reducing void cruising distance,” Mathematical Problems in Engineering, vol. 2020, 2020.

[83] Y. Sui, H. Zhang, W. Shang, R. Sun, C. Wang, J. Ji, X. Song, and F. Shao, “Mining urban sustainable performance: Spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future,” Applied Energy, vol. 280, p. 115966, 2020.

[84] X. Song, R. Guo, T. Xia, Z. Guo, Y. Long, H. Zhang, X. Song, and S. Ryosuke, “Mining urban sustainable performance: Millions of GPS data reveal high-emission travel attraction in Tokyo,” Journal of Cleaner Production, vol. 242, p. 118396, 2020.

[85] V.-H. Nhu, P.-T. Thi Ngo, T. D. Pham, J. Dou, X. Song, N.-D. Hoang, D. A. Tran, D. P. Cao, I. B. Aydilek, M. Amiri, and others, “A new hybrid firefly–PSO optimized random subspace tree intelligence for torrential rainfall-induced flash flood susceptible mapping,” Remote Sensing, vol. 12, no. 17, p. 2688, 2020.

[86] X. Lian, W. Yuan, Z. Guo, Z. Cai, X. Song, and R. Shibasaki, “End-to-end building change detection model in aerial imagery and digital surface model based on neural networks,” The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 43, pp. 1239–1246, 2020.

[87] R. Jiang, Q. Chen, Z. Cai, Z. Fan, X. Song, K. Tsubouchi, and R. Shibasaki, “Will you go where you search? a deep learning framework for estimating user search-and-go behavior,” Neurocomputing, 2020.

[88] Z. Fan, X. Song, and R. Shibasaki, “Big Data-Driven Citywide Human Mobility Modeling for Emergency Management,” in Big Data in Emergency Management: Exploitation Techniques for Social and Mobile Data, Springer, Cham, 2020, pp. 109–130.

[89] Z. Fan, X. Song, Q. Chen, R. Jiang, R. Shibasaki, and K. Tsubouchi, “Trajectory fingerprint: one-shot human trajectory identification using Siamese network,” CCF Transactions on Pervasive Computing and Interaction, vol. 2, no. 2, pp. 113–125, 2020.

[90] S. Dong, H. Wang, A. Mostafizi, and X. Song, “A network-of-networks percolation analysis of cascading failures in spatially co-located road-sewer infrastructure networks,” Physica A: Statistical Mechanics and its Applications, vol. 538, p. 122971, 2020.

[91] Q. Chen, R. Jiang, C. Yang, Z. Cai, Z. Fan, K. Tsubouchi, R. Shibasaki, and X. Song, “Dualsin: Dual sequential interaction network for human intentional mobility prediction,” in Proceedings of the 28th International Conference on Advances in Geographic Information Systems, 2020, pp. 283–292.

[92] J. Chen, W. Li, H. Zhang, W. Jiang, W. Li, Y. Sui, X. Song, and R. Shibasaki, “Mining urban sustainable performance: GPS data-based spatio-temporal analysis on on-road braking emission,” Journal of Cleaner Production, vol. 270, p. 122489, 2020.

[93] J. Zheng, H. Zhang, L. Yin, Y. Liang, B. Wang, Z. Li, X. Song, and Y. Zhang, “A voyage with minimal fuel consumption for cruise ships,” Journal of Cleaner Production, vol. 215, pp. 144–153, 2019.

[94] Q. Zhang, X. Song, Y. Yang, H. Ma, and R. Shibasaki, “Visual graph mining for graph matching,” Computer Vision and Image Understanding, vol. 178, pp. 16–29, 2019.

[95] H. Zhang, X. Song, X. Song, D. Huang, N. Xu, R. Shibasaki, and Y. Liang, “Ex-ante online risk assessment for building emergency evacuation through multimedia data,” Plos one, vol. 14, no. 4, p. e0215149, 2019.

[96] H. Zhang, X. Song, Y. Long, T. Xia, K. Fang, J. Zheng, D. Huang, R. Shibasaki, and Y. Liang, “Mobile phone GPS data in urban bicycle-sharing: Layout optimization and emissions reduction analysis,” Applied Energy, vol. 242, pp. 138–147, 2019.

[97] Y. Yan, H. Zhang, Y. Long, Y. Wang, Y. Liang, X. Song, and J. James, “Multi-objective design optimization of combined cooling, heating and power system for cruise ship application,” Journal of cleaner production, vol. 233, pp. 264–279, 2019.

[98] S. Xin, Y. Liang, X. Zhou, W. Li, J. Zhang, X. Song, C. Yu, and H. Zhang, “A two-stage strategy for the pump optimal scheduling of refined products pipelines,” Chemical Engineering Research and Design, vol. 152, pp. 1–19, 2019.

[99] T. Xia, X. Song, H. Zhang, X. Song, H. Kanasugi, and R. Shibasaki, “Measuring spatio-temporal accessibility to emergency medical services through big GPS data,” Health & place, vol. 56, pp. 53–62, 2019.

[100] T. Xia, X. Song, X. Song, M. Lu, S. Huang, R. Shibasaki, and K.-S. Kim, “From walkability to bikeability: A GIS based analysis of integrating bike sharing service in Tokyo TOD system.,” Abstracts of the ICA, vol. 1, p. NA-NA, 2019.

[101] T. Xia, S. Huang, X. Song, R. Si, X. Song, R. Shibasaki, and K.-S. Kim, “Evaluating transport time in emergency medical service via GIS: an observational study of Tokyo.,” Abstracts of the ICA, vol. 1, p. NA-NA, 2019.

[102] Y. Sui, H. Zhang, X. Song, F. Shao, X. Yu, R. Shibasaki, R. Sun, M. Yuan, C. Wang, S. Li, and others, “GPS data in urban online ride-hailing: A comparative analysis on fuel consumption and emissions,” Journal of Cleaner Production, vol. 227, pp. 495–505, 2019.

[103] A. Sudo, Y. Sekimoto, L. H. Chuin, X. Song, and T. Yabe, “Predictgis 2018 workshop report held in conjunction with ACM SIGSPATIAL 2018,” SIGSPATIAL Special, vol. 10, no. 3, pp. 26–27, 2019.

[104] X. Song, S. Guo, and H. Wang, “Guest editorial: special issue on big data for effective disaster management (In Memorial of Tao Li),” World Wide Web, vol. 22, no. 5, pp. 1889–1891, 2019.

[105] M. Shariati, M. S. Mafipour, P. Mehrabi, A. Bahadori, Y. Zandi, M. N. Salih, H. Nguyen, J. Dou, X. Song, and S. Poi-Ngian, “Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete,” Applied Sciences, vol. 9, no. 24, p. 5534, 2019.

[106] S. Miyazawa, X. Song, T. Xia, R. Shibasaki, and H. Kaneda, “Integrating GPS trajectory and topics from Twitter stream for human mobility estimation,” Frontiers of Computer Science, vol. 13, no. 3, pp. 460–470, 2019.

[107] R. Jiang, X. Song, D. Huang, X. Song, T. Xia, Z. Cai, Z. Wang, K.-S. Kim, and R. Shibasaki, “Deepurbanevent: A system for predicting citywide crowd dynamics at big events,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019, pp. 2114–2122.

[108] D. Huang, X. Song, Z. Fan, R. Jiang, R. Shibasaki, Y. Zhang, H. Wang, and Y. Kato, “A variational autoencoder based generative model of urban human mobility,” in 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2019, pp. 425–430.

[109] Z. Fan, X. Song, R. Jiang, Q. Chen, and R. Shibasaki, “Decentralized attention-based personalized human mobility prediction,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, no. 4, pp. 1–26, 2019.

[110] Z. Fan, Q. Chen, R. Jiang, R. Shibasaki, X. Song, and K. Tsubouchi, “Deep multiple instance learning for human trajectory identification,” in Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019, pp. 512–515.

[111] H. Zhang, X. Song, T. Xia, J. Zheng, D. Haung, R. Shibasaki, Y. Yan, and Y. Liang, “MaaS in bike-sharing: smart phone GPS data based layout optimization and emission reduction potential analysis,” Energy Procedia, vol. 152, pp. 649–654, 2018.

[112] H. Zhang, X. Song, T. Xia, M. Yuan, Z. Fan, R. Shibasaki, and Y. Liang, “Battery electric vehicles in Japan: Human mobile behavior based adoption potential analysis and policy target response,” Applied Energy, vol. 220, pp. 527–535, 2018.

[113] T. Xia, X. Song, Z. Fan, H. Kanasugi, Q. Chen, R. Jiang, and R. Shibasaki, “DeepRailway: a deep learning system for forecasting railway traffic,” in 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2018, pp. 51–56.

[114] T. Li, X. Song, S.-C. Chen, R. Shibasaki, and R. Akerkar, “Editorial Introduction to the Special Issue on Multimedia Big Data for Extreme Events,” IEEE Transactions on Multimedia, vol. 20, no. 10, pp. 2547–2550, 2018.

[115] R. Jiang, X. Song, Z. Fan, T. Xia, Q. Chen, S. Miyazawa, and R. Shibasaki, “Deepurbanmomentum: An online deep-learning system for short-term urban mobility prediction,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2018, vol. 32, no. 1.

[116] R. Jiang, X. Song, Z. Fan, T. Xia, Q. Chen, Q. Chen, and R. Shibasaki, “Deep ROI-based modeling for urban human mobility prediction,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 1, pp. 1–29, 2018.

[117] Z. Fan, X. Song, T. Xia, R. Jiang, R. Shibasaki, and R. Sakuramachi, “Online deep ensemble learning for predicting citywide human mobility,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 3, pp. 1–21, 2018.

[118] Q. Chen, X. Song, Z. Fan, T. Xia, H. Yamada, and R. Shibasaki, “A context-aware nonnegative matrix factorization framework for traffic accident risk estimation via heterogeneous data,” in 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2018, pp. 346–351.

[119] T. Xia, X. Song, D. Huang, S. Miyazawa, Z. Fan, R. Jiang, and R. Shibasaki, “Outbound behavior analysis through social network data: A case study of Chinese people in Japan,” in 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 2778–2786.

[120] X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, N. J. Yuan, and X. Xie, “Prediction and simulation of human mobility following natural disasters,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 8, no. 2, pp. 1–23, 2017.

[121] X. Song, R. Shibasaki, N. J. Yuan, X. Xie, T. Li, and R. Adachi, “DeepMob: learning deep knowledge of human emergency behavior and mobility from big and heterogeneous data,” ACM Transactions on Information Systems (TOIS), vol. 35, no. 4, pp. 1–19, 2017.

[122] X. Song, H. Kanasugi, and R. Shibasaki, “Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level,” in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2017, pp. 2618–2624.

[123] A. Sudo, T. Kashiyama, T. Yabe, H. Kanasugi, X. Song, T. Higuchi, S. Nakano, M. Saito, and Y. Sekimoto, “Particle filter for real-time human mobility prediction following unprecedented disaster,” in Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2016, pp. 1–10.

[124] Z. Fan, X. Song, R. Shibasaki, T. Li, and H. Kaneda, “CityCoupling: bridging intercity human mobility,” in Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing, 2016, pp. 718–728.

[125] Z. Fan, A. Arai, X. Song, A. Witayangkurn, H. Kanasugi, and R. Shibasaki, “A Collaborative Filtering Approach to Citywide Human Mobility Completion from Sparse Call Records.,” in IJCAI, 2016, pp. 2500–2506.

[126] Q. Chen, X. Song, H. Yamada, and R. Shibasaki, “Learning deep representation from big and heterogeneous data for traffic accident inference,” 2016.

[127] Q. Zhang, X. Song, X. Shao, H. Zhao, and R. Shibasaki, “Object discovery: Soft attributed graph mining,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 3, pp. 532–545, 2015.

[128] Q. Zhang, X. Song, X. Shao, H. Zhao, and R. Shibasaki, “From rgb-d images to rgb images: Single labeling for mining visual models,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 6, no. 2, pp. 1–29, 2015.

[129] X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, N. J. Yuan, and X. Xie, “A simulator of human emergency mobility following disasters: Knowledge transfer from big disaster data,” 2015.

[130] Z. Fan, X. Song, R. Shibasaki, and R. Adachi, “Citymomentum: an online approach for crowd behavior prediction at a citywide level,” in Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015, pp. 559–569.

[131] J. Dou, H. Yamagishi, H. R. Pourghasemi, A. P. Yunus, X. Song, Y. Xu, and Z. Zhu, “An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan,” Natural Hazards, vol. 78, no. 3, pp. 1749–1776, 2015.

[132] Q. Zhang, X. Song, X. Shao, H. Zhao, and R. Shibasaki, “When 3D reconstruction meets ubiquitous RGB-D images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 700–707.

[133] Q. Zhang, X. Song, X. Shao, H. Zhao, and R. Shibasaki, “Start from minimum labeling: Learning of 3d object models and point labeling from a large and complex environment,” in 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014, pp. 3082–3089.

[134] Q. Zhang, X. Song, X. Shao, H. Zhao, and R. Shibasaki, “Attributed graph mining and matching: An attempt to define and extract soft attributed patterns,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1394–1401.

[135] X. Song, Q. Zhang, Y. Sekimoto, and R. Shibasaki, “Prediction of human emergency behavior and their mobility following large-scale disaster,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp. 5–14.

[136] X. Song, Q. Zhang, Y. Sekimoto, and R. Shibasaki, “Intelligent system for urban emergency management during large-scale disaster,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2014, vol. 28, no. 1.

[137] Z. Fan, X. Song, and R. Shibasaki, “CitySpectrum: A non-negative tensor factorization approach,” in Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing, 2014, pp. 213–223.

[138] Q. Zhang, X. Song, X. Shao, R. Shibasaki, and H. Zhao, “Unsupervised skeleton extraction and motion capture from 3D deformable matching,” Neurocomputing, vol. 100, pp. 170–182, 2013.

[139] Q. Zhang, X. Song, X. Shao, H. Zhao, and R. Shibasaki, “Unsupervised 3D category discovery and point labeling from a large urban environment,” in 2013 IEEE International Conference on Robotics and Automation, 2013, pp. 2685–2692.

[140] Q. Zhang, X. Song, X. Shao, H. Zhao, and R. Shibasaki, “Learning graph matching: Oriented to category modeling from cluttered scenes,” in Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 1329–1336.

[141] Q. Zhang, X. Song, X. Shao, R. Shibasaki, and H. Zhao, “Category modeling from just a single labeling: Use depth information to guide the learning of 2d models,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 193–200.

[142] X. Song, H. Zhao, J. Cui, X. Shao, R. Shibasaki, and H. Zha, “An online system for multiple interacting targets tracking: Fusion of laser and vision, tracking and learning,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 4, no. 1, pp. 1–21, 2013.

[143] X. Song, Q. Zhang, Y. Sekimoto, T. Horanont, S. Ueyama, and R. Shibasaki, “Modeling and probabilistic reasoning of population evacuation during large-scale disaster,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013, pp. 1231–1239.

[144] X. Song, Q. Zhang, Y. Sekimoto, T. Horanont, S. Ueyama, and R. Shibasaki, “Intelligent system for human behavior analysis and reasoning following large-scale disasters,” IEEE Intelligent Systems, vol. 28, no. 4, pp. 35–42, 2013.

[145] X. Song, X. Shao, Q. Zhang, R. Shibasaki, H. Zhao, and H. Zha, “A novel dynamic model for multiple pedestrians tracking in extremely crowded scenarios,” Information Fusion, vol. 14, no. 3, pp. 301–310, 2013.

[146] X. Song, X. Shao, Q. Zhang, R. Shibasaki, H. Zhao, J. Cui, and H. Zha, “A fully online and unsupervised system for large and high-density area surveillance: Tracking, semantic scene learning and abnormality detection,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 4, no. 2, pp. 1–21, 2013.

[147] X. Song, T. Horanont, S. Ueyama, Q. Zhang, Y. Sekimoto, and R. Shibasaki, “An intelligent system for large-scale disaster behavior analysis and reasoning,” IEEE Intelligent Systems, vol. 99, no. 1, p. 1, 2013.

[148] X. Song, J. Cui, H. Zhao, H. Zha, and R. Shibasaki, “Laser-based tracking of multiple interacting pedestrians via on-line learning,” Neurocomputing, vol. 115, pp. 92–105, 2013.

[149] X. Song, X. Shao, Q. Zhang, R. Shibasaki, H. Zhao, and H. Zha, “Laser-based intelligent surveillance and abnormality detection in extremely crowded scenarios,” in 2012 IEEE International Conference on Robotics and Automation, 2012, pp. 2170–2176.

[150] H. Zha, H. Zhao, J. Cui, X. Song, and X. Ying, “Combining laser-scanning data and images for target tracking and scene modeling,” in Robotics Research, Springer, Berlin, Heidelberg, 2011, pp. 573–587.

[151] X. Song, X. Shao, R. Shibasaki, H. Zhao, J. Cui, and H. Zha, “A novel laser-based system: Fully online detection of abnormal activity via an unsupervised method,” in 2011 IEEE International Conference on Robotics and Automation, 2011, pp. 1317–1322.

[152] X. Song, H. Zhao, J. Cui, X. Shao, R. Shibasaki, and H. Zha, “Fusion of laser and vision for multiple targets tracking via on-line learning,” in 2010 IEEE International Conference on Robotics and Automation, 2010, pp. 406–411.

[153] X. Song, X. Shao, H. Zhao, J. Cui, R. Shibasaki, and H. Zha, “An online approach: Learning-semantic-scene-by-tracking and tracking-by-learning-semantic-scene,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 739–746.

[154] X. Song, B. Wen, J. Cui, H. Zhao, X. Shao, R. Shibasaki, and H. Zha, “A boosted JPDA-particle filter for multi-target tracking,” in Proc. Asian Workshop Sens. Vis. City-Human Interact.(AWSVCI), 2009, pp. 1–4.

[155] J. Cui, X. Song, H. Zhao, H. Zha, and R. Shibasaki, “Real-Time Detection and Tracking of Multiple People in Laser Scan Frames,” in Augmented Vision Perception in Infrared, Springer, London, 2009, pp. 405–439.

[156] X. Song, J. Cui, H. Zhao, and H. Zha, “Bayesian fusion of laser and vision for multiple people detection and tracking,” in 2008 SICE Annual Conference, 2008, pp. 3014–3019.

[157] X. Song, J. Cui, H. Zha, and H. Zhao, “Vision-based multiple interacting targets tracking via on-line supervised learning,” in European Conference on Computer Vision, 2008, pp. 642–655.

[158] X. Song, J. Cui, H. Zha, and H. Zhao, “Probabilistic Detection-based Particle Filter for Multi-target Tracking.,” in BMVC, 2008, vol. 8, pp. 223–232.

[159] X. Song, J. Cui, X. Wang, H. Zhao, and H. Zha, “Tracking interacting targets with laser scanner via on-line supervised learning,” in 2008 IEEE International Conference on Robotics and Automation, 2008, pp. 2271–2276.

[160] X. Song, J. Chi, H. Zhao, and H. Zha, “A Bayesian approach: Fusion of laser and vision for multiple pedestrians tracking,” Int. J. Adv. Comput. Eng, vol. 3, no. 1, pp. 1–9, 2008.

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[3] 宋轩（标准牵头人）等人，基于新一代物联网技术的智慧电池技术规范，团体标准

[4] 宋轩（标准牵头人）等人，深度学习驱动的智慧交通数据挖掘预测技术规范，团体标准

[5] 宋轩（标准牵头人）等人，深度学习驱动的智慧交通数据挖掘预测技术规范，团体标准

中国专利：

1 宋轩,张浩然,黄立乔,柴崎亮介. 预测传染病的确诊人数的方法、装置、设备和存储介质. 发明.中国. ZL 202010330942.1. 2020/4/24（受理）2022/7/6(授权). 南方科技大学

2 宋轩,张浩然,黄立乔,徐宁,范子沛,柴崎亮介.救援计划的确定方法、装置、服务器和存储介质.发明.中国. ZL 202010235312.6. 2020/3/30（受理）2022/8/22(授权). 南方科技大学

3 宋轩,范子沛,陈全俊,姜仁河,蔡泽坤,柴崎亮介.出行预测方法、装置、设备和存储介质. 发明.中国. ZL202010255483.5. 2020/4/2（受理）2022/06/28(授权) .南方科技大学

4 宋轩, 范子沛, 姜仁河 ,陈全俊, 杨闯, 张志文, 柴崎亮介. 传染病传播的评估方法、装置、计算机设备和存储介质. 发明. 中国. ZL 202010567188.3 . 2020/6/19（受理）2022/11/5（授权）. 南方科技大学

5 宋轩,唐之遥,莫宇,冯德帆,陈全俊,张浩然. 医院门诊规划方法、装置、设备及存储介质. 发明.中国. ZL202011552305.5. 2020/12/24（受理）2022/4/29(授权) .南方科技大学

6 宋轩,张浩然,徐宁,黄立乔,范子沛,柴崎亮介. 应急救助站的布局方法、装置、服务器及存储介质.发明.中国. ZL 202010263747.1 .2020/4/7（受理）2022/9/2(授权). 南方科技大学

7 宋轩,蔡泽坤,姜仁河,连欣蕾,杨闯,王肇南,范子沛,陈全俊,柴崎亮介.人流转移预测方法、装置、设备及存储介质.发明.中国. ZL202110011553.7. 2021/1/6（受理）2022/4/12(授权) . 南方科技大学8 宋轩,颜秋阳,张浩然,陈达寅,赵奕丞,江亦凡 .好友添加方法、装置、设备及存储介质. 发明.中国. ZL202011614880.3. 2020/12/30（受理）2022/05/31 (授权) . 南方科技大学

9 宋轩,李永康,范子沛,尹渡,邓锦亮. 好友推荐方法、装置、设备和存储介质.发明.中国. ZL202210490518.2. 2022/5/8（受理）2022/06/29(授权) . 南方科技大学

10 宋轩,陈达寅,史小丹,张浩然. 基于众包的温度预测方法、装置、设备和存储介质.发明.中国. CN202110966275.0. 2021/8/23（受理）2021/11/16(授权) .南方科技大学

11 宋轩,李永康,范子沛,尹渡,冯德帆,邓锦亮,王宏骏.本地事件检测方法、装置、设备和存储介质.发明.中国. ZL202111381988.7. 2021/11/22（受理）2022/03/08(授权) .南方科技大学

12 宋轩,陈全俊,董正,崔俞崧,王宇辰.交通状态检测方法、装置、设备和存储介质. 发明.中国. ZL202111487737.7. 2021/12/8（受理）2022/03/08(授权) . 南方科技大学

13 宋轩,马浩原,舒家阳,姜仁河,欧阳晓东.景区疫情风险预测与限流方法、装置、设备和存储介质.发明.中国. ZL202111258913.X. 2021/10/28(受理)2022/07/08(授权) .南方科技大学

14 宋轩,蔡泽坤,姜仁河,杨闯,柴崎亮介. 城市人流监控方法、装置、电子设备及存储介质. 发明. 中国. ZL 202011553972.5. 2020/12/24（受理）2024/1/2（授权）. 南方科技大学

15 宋轩,陈达寅,张浩然,赵奕丞,颜秋阳,江亦凡 . 密切接触处理方法、装置、电子设备及介质. 发明.中国. ZL 202110024004.3 . 2021/1/8（受理）2023/2/21（授权）. 南方科技大学

16 宋轩,江亦凡,张浩然,陈达寅,赵奕丞,颜秋阳. 密切接触者的确定方法、装置、设备和存储介质.发明.中国. ZL 202011626835.X. 2020/12/31（受理）2023/2/21（授权).南方科技大学

17 宋轩,谢洪彬,张浩然,云沐晟,陈宇. 用户匹配方法、装置、电子设备及介质. 发明. 中国. ZL 202110024003.9. 2021/1/8（受理）2023/3/24 (授权). 南方科技大学

18 宋轩,聂雨荷,张浩然,庄湛. 获取密接人员信息方法、装置、服务器和存储介质. 发明. 中国. ZL 202110077477.X .2021/1/20（受理）2023/7/11 (授权). 南方科技大学

19 宋轩,夏楚洋,张浩然,全伊伦,杨智宇,云沐晟,谢洪彬. 一种密接数据验证方法、客户端、服务器及存储介质.发明.中国. ZL 202110171155.1. 2021/2/8（受理）2023/7/14 (授权).南方科技大学

20 宋轩,莫宇,张浩然,冯德帆,唐之遥. 密接人群识别方法、装置、电子设备及存储介质. 发明. 中国. ZL 202110315764.X. 2021/3/24（受理）2023/1/13 (授权). 南方科技大学

21 宋轩,莫宇,张浩然,冯德帆,唐之遥. 密接人员感染风险评估方法、装置、电子设备及存储介质. 发明.中国. ZL 202110315755.0 . 2021/3/24（受理）2023/1/13(授权).南方科技大学

22 宋轩,谢洪彬,张浩然,云沐晟,全伊伦,杨智宇,夏楚洋. 一种密接组队方法、装置、终端及存储介质.发明.中国. ZL 202110077474.6 .2021/1/20（受理）2023/3/3(授权).南方科技大学

23 宋轩,李永康,许天淇,张骥霄,时邓珩,范子沛.一种好友和兴趣点推荐方法及终端. 发明. 中国.ZL202211068518.X. 2022/9/2(受理)2022/11/29 (授权).南方科技大学

24 宋轩,朱世博,冯德帆,陈星宇,朱佳文.发车调度方法、装置、设备和存储介质. 发明. 中国. ZL202211095230.1. 2022/9/9(受理) 2022/11/29(授权).南方科技大学

25 宋轩,郑少铭,陈永豪,舒家阳,胡威,庄卓航,陈睿瑶. 一种三维图像风格化迁移方法及终.发明. 中国. ZL202211587800.9. 2022/12/12 (受理) 2023/3/14 (授权).南方科技大学

26 宋轩,彭金全,林贵旭. 一种基于区块链和零知识证明的验证方法、系统及设备.发明. 中国. ZL202310033045.8. 2023/1/10 (受理) 2023/4/18 (授权).南方科技大学

27 宋轩,余庆,李佳奇,沈徐檑,黄梓通,舒家阳,赵奕丞,谢嘉楠. 一种基于弧形屏幕的裸眼3D显示方法及终端.发明. 中国. ZL202310033045.8. 2023/1/10 (受理) 2023/4/18 (授权).南方科技大学

28 宋轩,郑少铭,陈睿瑶,舒家阳,庄卓航,胡威,陈永豪. 一种选择性风格迁移方法及终端.发明. 中国. ZL202211661026.1 . 2022/12/23(受理) 2023/3/21(授权).南方科技大学

29 宋轩;高昊天;范子沛;洪学海;魏田纭溪. 一种交通异常流量因果检测方法及设备.发明. 中国. ZL202310160350.3 . 2023/2/24 (受理) 2023/6/13 (授权).南方科技大学

30 宋轩,余庆,朱世博,宋歌,谢洪彬,舒家阳. 救援计划制定方法、装置、设备和存储介质 .发明. 中国. ZL202211619099.4. 2022/12/16 (受理) 2023/6/19 (授权).南方科技大学

31 宋轩;宋歌;张浩然;谢洪彬;舒家阳;赵奕丞. 一种多智能体路径规划方法及终端.发明. 中国. ZL202310452118.7. 2023/4/25 (受理) 2023/7/25 (授权).南方科技大学

32 宋轩、谢洪彬 . 一种芯片设计方法及终端.发明. 中国. ZL202310475742.9 . 2023/4/28 (受理) 2023/7/25 (授权).南方科技大学

33 宋轩、马畅翼. 一种非对称的哈希检索方法及终端.发明. 中国. ZL202310823283.9 . 2023/7/6 (受理) 2023/9/5 (授权).南方科技大学

34 宋轩、张家祺、范子沛、赵奕丞、舒家阳. 一种出行目的推断方法及终端. 发明. 中国. ZL202310738026.5. 2023/6/21 (受理) 2023/9/12 (授权).南方科技大学

35 宋轩、宋歌、张浩然、谢洪彬. 一种多无人机通信资源分配方法及终端.发明. 中国. ZL202310729799.7. 2023/6/20 (受理) 2023/9/19 (授权).南方科技大学

36 宋轩、马畅翼. 一种哈希检索模型构建方法、系统、电子设备及存储介质.发明. 中国. ZL202310842571.9 . 2023/7/11 (受理) 2023/12/28(授权).南方科技大学

37 宋轩,莫宇,冯德帆,张浩然,唐之遥,云沐晟. 疫情防控方法、装置、设备和介质. 发明. 实审. 中国. ZL 202011615837.9. 2020/12/30（受理）. 南方科技大学

38 宋轩,庄湛,张浩然,云沐晟,林贵旭. 基于区块链的数据处理方法、装置、设备及存储介质. 发明. 实审. 中国. ZL 202011613850.0. 2020/12/30（受理）. 南方科技大学

39 宋轩,云沐晟,张浩然,林贵旭,庄湛. 基于区块链的数据处理方法、装置、电子设备及存储介质. 发明. 实审. 中国. ZL 202011613853.4 . 2020/12/30（受理）. 南方科技大学

40 宋轩，冯德帆,林贵旭,谢洪彬,李永康. 一种基于汽车制造业的故障溯源方法及终端.发明. 实审. 中国. ZL 202310301022.0. 2023/03/20（受理）. 南方科技大学

41 宋轩、谢洪彬、郑少铭、张嘉晖. 一种芯片质量检测模型的构建方法及终端.发明. 实审. 中国. ZL 202310663121.3 . 2023/06/06（受理）. 南方科技大学

42 宋轩,彭金全,林贵旭，徐剑，刘思远. 一种数据驱动的区块链异常检测方法及终端.发明. 中国. ZL202310237526.0. 2023/03/02（受理）. 南方科技大学

43 宋轩，张凌宇，邓淞航. 一种云计算的资源调度的方法与终端.发明. 中国. ZL202311563248.4. 2023/11/22（受理）. 南方科技大学

44 宋轩,庄卓航,胡威,陈永豪,赵奕丞,舒家阳. 车辆远程检修方法、终端及可读存储介质.发明. 复审受理.中国. ZL202211434602.9 2022/11/16（受理）. 南方科技大学

45 宋轩,范子沛,张志文,杨闯,刘英豪,姜仁河,陈全俊,柴崎亮介.预测传染病传播的方法、装置、计算机设备和存储介质. 发明. 复审受理. 中国. ZL 202010242822.6. 2020/3/31（受理）. 南方科技大学

46 宋轩,张浩然. 接触数据存储方法、装置、设备及存储介质. 发明. 复审受理. 中国. ZL 202110050250.6 . 2021/1/14（受理）. 南方科技大学

47 宋轩,颜秋阳,张浩然,陈达寅,赵奕丞,江亦凡 . 移动支付方法、装置、设备及存储介质. 发明. 复审受理. 中国. ZL 202011613572.9 . 2020/12/30（受理）. 南方科技大学

48 宋轩,林贵旭,张浩然,云沐晟,庄湛. 区块链风险值管理方法、装置、电子设备及存储介质. 发明. 复审受理. 中国. ZL 202110018364.2 . 2021/1/7（受理）. 南方科技大学

49 宋轩,陈宇,张浩然,谢洪彬,云沐晟. 一种游戏玩家的匹配方法、装置、设备及存储介质 . 发明. 复审受理. 中国. ZL 202011553247.8 . 2020/12/24（受理）. 南方科技大学

50 宋轩,陈达寅,张浩然. 一种传染病防控方法、装置、计算机设备及存储介质.发明.复审受理. 中国. ZL 202110410104.X. 2021/4/16（受理）. 南方科技大学

51 宋轩,陈达寅,张浩然. 移动终端获取环境温度的方法、装置、移动终端及介质.发明. 复审受理. 中国. ZL 202011633454.4 . 2020/12/31（受理）. 南方科技大学

52 宋轩,杨佳雨,张浩然,陈宇,曾焓,谢洪彬. 一种智能家居控制系统. 发明. 复审受理. 中国. ZL 202110181641.1. 2021/2/8（受理）. 南方科技大学

53 宋轩,谢洪彬,张浩然,江宇辰,陈纪元,黄文杰 . 一种防走失监护系统. 发明.复审受理. 中国. ZL 202110077472.7 .2021/1/20（受理）. 南方科技大学

54 宋轩,曾焓,张浩然,陈宇,杨佳雨,谢洪彬. 基于密接的游戏好友推荐方法、系统、服务器及存储介质. 发明. 复审受理. 中国. ZL 202110351722.1 .2021/3/31（受理）.南方科技大学

55 宋轩,陈纪元,张浩然,江宇辰,黄文杰,谢洪彬 . 游戏互动方法、系统、服务器及存储介质. 发明. 复审受理. 中国. ZL202110402360.4 . 2021/4/14（受理）. 南方科技大学

56 宋轩,张浩然,谢洪彬,赵奕丞,林贵旭,舒家阳,邓锦亮,冯德帆,云沐晟. 防疫链IOS端软件. 软著.中国. ZL 2021SR0201005. 2020/12/18(授权). 南方科技大学

57 宋轩,张浩然,谢洪彬,赵奕丞,林贵旭,舒家阳,邓锦亮,冯德帆,云沐晟. 防疫链安卓端软件. 软著.中国. ZL 2021SR0201006. 2020/12/18(授权). 南方科技大学

58 南方科技大学.基于大数据和模型可解释性的交通流量预测交互式可视化系统. 软著.中国. ZL 2021SR02053758. 2021/11/15(受理）2021/05/30 (授权).

59 南方科技大学.基于多维数据空间的智能制造大脑管控平台. 软著.中国. ZL 2022R11S1454567. 2022/10/24(受理）2022/11/8 (授权).

60 南方科技大学.基于时空数据分析的紧急救援大数据平台. 软著.中国. ZL 2022R11S1454573. 2022/10/24(受理）2022/11/8(授权).

61 南方科技大学.ODview交通出行可视化分析系统. 软著.中国. ZL2022R11S1454559. 2022/10/24(受理）2022/11/8 (授权).

62 南方科技大学.芯片制造价值链因果推断平台[简称：芯片制造大脑]V1.0. 软著.中国. ZL 2023R11S0263141. 2023/2/28(受理）2023/3/27 (授权).

63 南方科技大学.面向智能制造的区块链数据分析平台. 软著.中国. ZL 2023R11S0286366. 2023/3/3(受理）2023/4/3(授权).

64 南方科技大学.高端制造设备管理系统微信小程序. 软著.中国. ZL2023R11L1089479. 2023/6/19(受理）2023/8/16 (授权).

65 南方科技大学.智能制造管理系统移动应用系统.软著.中国. ZL2023R11L1089517. 2023/6/19(受理）2023/8/16(授权).

66 宋轩,范子沛,张志文,杨闯,陈全俊,姜仁河,柴崎亮介. 用于电脑的疫情防控模拟可视化图形用户界面.外观设计.中国. ZL202030128454.3. 2020/3/31(受理)2020/8/7(授权). 南方科技大学

67 宋轩,范子沛,姜仁河,陈全俊,杨闯,张志文,柴崎亮介.电脑的传染病防控政策可视化图形用户界面.外观设计.中国. ZL202030263392.7. 2020/5/9(受理)2021/3/12(授权).南方科技大学

68 宋轩,张浩然,谢洪彬,舒家阳,赵奕丞,林贵旭,冯德帆,云沐晟. 显示屏幕面板的传染病防控的图形用户界面. 外观设计.中国. ZL202030762732.0. 2020/12/10(受理).2021/7/30(授权). 南方科技大学

69  宋轩,谢洪彬,江亦凡,王宏俊,范子沛,陈林尧 .显示屏幕面板的交通流量预测图形用户界面.外观设计.中国. ZL202130581214.3. 2021/9/3（受理）2022/04/26(授权) .南方科技大学

70 宋轩,范子沛,江亦凡,王宏俊,陈林尧.显示屏幕面板的交通流预测的图形用户界面.外观设计.中国. ZL 202130098514.6. 2021/02/19（受理）2021/09/24(授权).南方科技大学

71 宋轩、谢洪彬、张浩然、赵奕丞、林贵旭、舒家阳、冯德帆、张家祺、陈林尧、彭金全、宋歌. 用于电脑的制造管理图形用户界面.外观设计. 受理. 中国.ZL 202230241986.7. 2022/04/28（受理）. 南方科技大学

72  宋轩、谢洪彬、张浩然、赵奕丞、林贵旭、舒家阳、徐宁、冯德帆.显示屏幕面板的紧急救援大数据平台图形用户界面.外观设计.受理.中国.ZL202230543797.5. 2022/8/19（理）. 南方科技大学  

73 宋轩、谢洪彬、陈孙兵、陈天乐、罗启航. 显示屏幕面板的芯片制造管理图形用户界面.外观设计.中国. ZL202330034589.7. 2023/1/16 (受理) 2023/11/14(授权).南方科技大学

74 宋轩，何辰睿，谢洪彬. 用于手机的智能制造管理系统图形用户界面(.外观设计.中国. ZL202330373247.8. 2023/6/17 (受理).南方科技大学

75 宋轩，魏一磊，谢洪彬. 用于手机的高端制造设备管理图形用户界面.外观设计.中国. ZL202330373234.0. 2023/6/16 (受理). 南方科技大学

76 宋轩，刘仝，谢洪彬 .用于显示屏幕的芯片制造价值链图形用户界面.外观设计.中国. ZL202330801496.2. 2023/12/6 (受理). 南方科技大学

PCT 国际专利:     

77 宋轩,陈达寅,张浩然,赵奕丞,颜秋阳,江亦凡 . 密切接触处理方法、装置、电子设备及存储介质. 发明. 受理. 世界知识产权组织（WO）.PCT/CN2021/116618. 2021/9/6. 南方科技大学

78 宋轩,赵奕丞,张浩然,陈达寅,颜秋阳,江亦凡 . 一种密切接触判断方法、装置、电子设备和介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/116604. 2021/9/6. 南方科技大学

79 宋轩,云沐晟,张浩然,林贵旭,庄湛. 基于区块链的数据处理方法、装置、电子设备及存储介质. 发明. 受理. 世界知识产权组织（WO）.PCT/CN2021/116605. 2021/9/6. 南方科技大学

80 宋轩,颜秋阳,张浩然,陈达寅,赵奕丞,江亦凡 . 移动支付方法、装置、设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/116603. 2021/9/6. 南方科技大学

81 宋轩,颜秋阳,张浩然,陈达寅,赵奕丞,江亦凡 . 好友添加方法、装置、设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/115550. 2021/8/31. 南方科技大学

82 宋轩,庄湛,张浩然,云沐晟,林贵旭. 基于区块链的数据处理方法、装置、设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/116248. 2021/9/2. 南方科技大学

83 宋轩,张浩然. 接触数据存储方法、装置、设备及存储介质. 发明. 受理.  世界知识产权组织（WO）. PCT/CN2021/115369 . 2021/8/30. 南方科技大学

84 宋轩,蔡泽坤,姜仁河,杨闯,柴崎亮介. 城市人流监控方法、装置、电子设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/115551. 2021/8/31. 南方科技大学

85 宋轩,蔡泽坤,姜仁河,连欣蕾,杨闯,王肇南,范子沛,陈全俊,柴崎亮介.人流转移预测方法、装置、设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/115377. 2021/8/30. 南方科技大学

86 宋轩,唐之遥,莫宇,冯德帆,陈全俊,张浩然. 医院门诊规划方法、装置、设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/115570. 2021/8/31. 南方科技大学

87 宋轩,陈达寅,史小丹,张浩然. 基于众包的温度预测方法、装置、设备和存储介质发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/121177. 2021/9/28 南方科技大学

88 宋轩,江宇辰,张浩然,陈纪元,黄文杰,谢洪彬 . 一种基于密接的游戏交互方法、系统、服务器及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/117892 . 2021/9/13. 南方科技大学

89 宋轩,陈纪元,张浩然,江宇辰,黄文),谢洪彬. 游戏互动方法、系统、服务器及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/120554. 2021/9/26. 南方科技大学

90 宋轩,曾焓,张浩然,陈宇,杨佳雨,谢洪彬. 基于密接的游戏好友推荐方法、系统、服务器及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/119643. 2021/9/22 . 南方科技大学

91 宋轩,谢洪彬,张浩然,江宇辰,陈纪元,黄文杰 . 一种防走失监护系统. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/119341. 2021/9/18. 南方科技大学

92 宋轩,邹若彤,张浩然,庄湛,云沐晟,潘泰仰. 一种基于区块链的挖矿方法、装置、移动终端及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/119096.  2021/9/17. 南方科技大学

93 宋轩,庄湛,张浩然,邹若彤,聂雨荷,云沐晟,潘泰仰. 基于区块链的挖矿方法、装置、计算机设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/117928 2021/9/13 南方科技大学

94 宋轩,杨佳雨,张浩然,陈宇,曾焓,谢洪彬. 一种智能家居控制系统. 发明. 受理世界知识产权组织（WO）. PCT/CN2021/117891 2021/9/13. 南方科技大学

95 宋轩,谢洪彬,张浩然,陈宇,杨佳雨,曾焓. 智能开锁方法、系统、服务器和存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/119619. 2021/9/22. 南方科技大学

96 宋轩,谢洪彬,张浩然,云沐晟,全伊伦,杨智宇,夏楚洋. 一种密接组队方法、装置、终端及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/119038. 2021/9/17. 南方科技大学

97 宋轩,陈达寅,张浩然. 移动终端获取环境温度的方法、装置、移动终端及介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/118088. 2021/9/14. 南方科技大学

98 宋轩,陈达寅,张浩然. 一种传染病防控方法、装置、计算机设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/118521. 2021/9/15. 南方科技大学

99 宋轩,莫宇,张浩然,冯德帆,唐之遥. 密接人员感染风险评估方法、装置、电子设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/119077 2021/9/17. 南方科技大学

100 宋轩,莫宇,张浩然,冯德帆,唐之遥. 密接人群识别方法、装置、电子设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/119616. 2021/9/22. 南方科技大学

101 宋轩,夏楚洋,张浩然,全伊伦,杨智宇,云沐晟,谢洪彬. 一种密接数据验证方法、客户端、服务器及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/119618. 2021/9/22. 南方科技大学

102 宋轩,聂雨荷,张浩然,庄湛. 获取密接人员信息方法、装置、服务器和存储介质.发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/118087. 2021/9/14. 南方科技大学

103 宋轩,陈宇,张浩然,谢洪彬,云沐晟. 一种游戏玩家的匹配方法、装置、设备及存储介质 . 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/119078. 2021/9/17. 南方科技大学

104 宋轩,谢洪彬,张浩然,云沐晟,陈宇. 用户匹配方法、装置、电子设备及介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/119366 . 2021/9/18. 南方科技大学

105 宋轩,江亦凡,张浩然,陈达寅,赵奕丞,颜秋阳 . 密切接触者的确定方法、装置、设备和存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/118476. 2021/9/15. 南方科技大学

106 宋轩,林贵旭,张浩然,云沐晟,庄湛. 区块链风险值管理方法、装置、电子设备及存储介质. 发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/118089. 2021/9/14. 南方科技大学

107 宋轩,莫宇,张浩然,冯德帆,唐之遥,云沐晟. 疫情防控方法、装置、设备和介质.  发明. 受理. 世界知识产权组织（WO）. PCT/CN2021/117893.  2021/9/13. 南方科技大学

美国专利:

108 Xuan Song, Zipei Fan, Zhiwen Zhang, Chuang Yang, Quanjun Chen, Renhe Jiang, and Ryosuke Shibas. Visual graphical user interface for computer-based epidemics prevention and control simulation.（US）. 29/747,086. 2020/4/09(Filing Date). 2022/2/6(Date Mailed). Southern University of Science and Technology

109 Xuan Song, Zipei Fan, Quanjun Chen, Renhe Jiang, Zekun Cai, and Ryosuke Shibas. Travel prediction method and apparatus, device, and storage medium.（US）. 16/931,757 . 2020/7/17. Southern University of Science and Technology

110 Xuan Song, Haoran Zhang. Liqiao Huang, and Ryosuke Shibas. Method and device for predicting a number of confirmed cases of an infectious disease, apparatus, and storage medium.（US）. 16/928,762. 2020/7/14. Southern University of Science and Technology

111 Xuan Song, Haoran Zhang. Ning Xu ,Liqiao Huang, Zipei Fan,and Ryosuke Shibas. Method and apparatus for arranging an emergency rescue station, server, and storage medium.（US）. 17/224,631. 2020/9/30. Southern University of Science and Technology

112 Xuan Song, Haoran Zhang. Blockchain-based mining method,mining device,computer equipment and storage medium.（US）. 17/463,891. 2021/9/1. Southern University of Science and Technology

113 Xuan Song, Zipei Fan, Renhe Jiang, Chuang Yang, Zhiwen Zhang, Quanjun Chen, and Ryosuke Shibas. Assessment method and device for infections disease transission computer equipment and storage medium.（US）. 17/488,792. 2021/9/29. Southern University of Science and Technology

114 Xuan Song, Chuang Yang, Zipei Fan, Renhe Jiang, Zhiwen Zhang, Quanjun Chen and Ryosuke Shibasaki. Display screen with graphical user Interface.（US）. 29/812,355. 2021/10/20. Southern University of Science and Technology