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戴国骏

博士 教授 | 博导 学科：计算机科学与技术 职务：

毕业院校：浙江大学

研究方向：脑机接口、人工智能

电话：13906524548 邮箱：daigj@hdu.edu.cn

地址：
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  • 个人简介
  • 教育经历
  • 工作经历
  • 社会职务
  • 研究领域
  • 教学与课程
  • 科研项目
  • 论文与著作
  • 专利成果
  • 软件成果
  • 荣誉及奖励



1，计算机科学与技术、生物医学工程及认知实验心理学交叉研究的著名学者，开创了基于非侵入式脑机接口技术的认知心理实验方法学。在中国、意大利科技合作计划项目资助下，结合多年AI Agent、BME和BCI技术的研究，提出了认知心理学实验的革命性的方法，促进了实验效率提升和评价的客观准确，推动认知心理学的发展。成果受到欧洲科学院院士Fabio教授好评，发表相关论文10余篇，指导博士研究生2名。

 

2，针对AD等潜伏期较长的认知障碍疾病，在中国、西班牙科技合作计划（CHINEKA）项目资助下，开展了认知障碍疾病早期的基于行为和多电生理参数融合的研究，提出了科学定量指标MCI。研发了可穿戴设备和系统，并在中国的杭州、广州等大城市的社区进行试验验证，取得显著效果，成果受到西班牙皇家医学院院士Herrero女士的好评。发表相关论文20余篇，指导博士研究生2名。目前与西班牙Neuro UP公司合作在欧洲推广。


3，创建了面向欧洲国家的“国家脑机协同智能技术国际联合研究中心”，并在罗马大学设立机构，推动中国和欧洲学者在神经科学等诸多方面开展广泛的合作。在中国、欧盟旗舰项目资助下，推动中国和欧洲科学家围绕神经科学等诸多方面合作。参与了由西班牙ITCL研究机构牵头的“SimuSafe”H2020项目，受到中国驻意大利大使馆科技参赞赵俊杰博士的好评。联合培养博士研究生5名，发表论文20余篇，并资助5名欧洲博士研究生到中国作半年的访问，同时资助3名中国博士研究生到欧洲联合培养。


1983.9~1988.6     浙江大学          生物医学工程及仪器专业（学士）

1988.9~1991.6     浙江大学          生物医学工程及仪器专业（硕士）

1994.9~1998.6     浙江大学          电力电子技术专业（博士）

1998.4  ~2000.10    杭州电子工业学院计算机学院      讲师

2000.10~2004.4      杭州电子工业学院计算机学院      副教授

2004.5  ~2005.9      杭州电子科技大学计算机学院      副教授

2005.10 ~                杭州电子科技大学计算机学院      教授

                                                                                    计算机应用研究所所长

科技部脑际协同智能技术国际联合研究中心   主任；

浙江省脑健康产业工程技术研究中心             主任；

贵州省第三医学研究院                              副主任；


1，感知计算：

                      研究如何让机器具有感觉器官的能力，促进人与机器的自然交互；

2，认知计算：

                     研究如何让机器具有认知器官的能力，促进人与机器的意识交互；

3，社会计算：

                     研究如何让机器具有为人类服务的能力，促进社会的和谐发展；


纵向科研

(1) 基于脑电的安全驾驶预警仿真平台关键技术究, 2018-12


(2) 面向浙江省制造业的大数据分析理论与关键技术研究,2015-12


(3) 低剂量X射线三维成像关键技术及装置研究,2014-04


(4) 3D产业关键技术科技创新团队,2012-08


(5) 规模型无线传感器网络可定位性理论与非测距定位方法,2011-12


(6) 加快智慧浙江建设，促进战略性新兴产业发展的对策研究,2011-09


(7) 支持可信服务的物联网基础理论与关键技术研究,2010-04


(9) 多目标跟踪的智能监控多媒体传感器网络关键理论和技术,2008-11


(10) 可重构硬实时嵌入式系统中能量有效的任务放置方法研究,2007-09-20



横向科研
论文

Citations: 3063;  H-Index: 27;  i10-Index:62  

Sources:   https://scholar.google.com/citations?hl=zh-CN&user=EwVSbacAAAAJ

 

[1] Zhou, W., Qu, N., Yang, C., Li, Y., Mo, L., Lin, L., & Dai, G. (2025). Joint Temporal-Frequency-Channel Attention Learning for EEG-based Visual Object Classification. Journal of the Franklin Institute, 108128.

[2] Xiang, X., Zhou, W., Zhu, H., Li, Y., Dai, G., & Lin, L. (2025). EEG-Driven Natural Image Reconstruction with Regional Semantic Awareness. Pattern Recognition, 112589.

[3] Xiang, X., Zhou, W., & Dai, G. (2025). Electroencephalography-driven three-dimensional object decoding with multi-view perception diffusion. Engineering Applications of Artificial Intelligence, 156, 111180.

[4] Shi, J., Zhao, Y., Wang, C., Zeng, H., & Dai, G. (2025). EEGSNet: A novel EEG cognitive recognition model using spiking neural network. Biomedical Signal Processing and Control, 105, 107610.

[5] Ye, C., Duan, H., Zhang, H., Wu, Y., & Dai, G. (2024). Learned Query Optimization by Constraint-Based Query Plan Augmentation. Mathematics, 12(19), 3102.

[6] Zeng, H., Zhao, Y., Babiloni, F., Tao, M., Kong, W., & Dai, G. (2024). A General DNA-Like Hybrid Symbiosis Framework: An EEG Cognitive Recognition Method. IEEE Journal of Biomedical and Health Informatics,volume: 28 Issue: 11.

[7] Zhou, W., Ren, L., Yu, J., Qu, N., & Dai, G. (2024). Boosting rgb-d point cloud registration via explicit position-aware geometric embedding. IEEE Robotics and Automation Letters, 9(6), 5839-5846.

[8] Wu, J., Dai, G., Zhou, W., Zhu, X., & Wang, Z. (2024). Multi-scale feature fusion with attention mechanism for crowded road object detection. Journal of Real-Time Image Processing, 21(2), 29.

[9] Zhou, W., Wang, Y., Mo, L., Li, C., Xu, M., Kong, W., & Dai, G. (2024). Temporal-channel cascaded transformer for imagined handwriting character recognition. Neurocomputing, 573, 127243.

[10] Zhao, Y., Zeng, H., Zheng, H., Wu, J., Kong, W., & Dai, G. (2023). A bidirectional interaction-based hybrid network architecture for eeg cognitive recognition. Computer Methods and Programs in Biomedicine, 238, 107593.

[11] Ye, C., Xu, H., Zhang, H., Wu, Y., & Dai, G. (2023). Grier: graph repairing based on iterative embedding and rules. Knowledge and Information Systems, 65(8), 3273-3294.

[12] Ye, C., Duan, H., Zhang, H., Zhang, H., Wang, H., & Dai, G. (2023). Multi-Source Data Repairing: A Comprehensive Survey. Mathematics, 11(10), 2314.

[13] Ye, C., Zhi, H., Jiang, S., Zhang, H., Wu, Y., & Dai, G. (2023, April). TETA: text-enhanced tabular data annotation with multi-task graph convolutional network. In International Conference on Database Systems for Advanced Applications (pp. 523-533). Cham: Springer Nature Switzerland.

[14] Zeng, H., Xia, N., Tao, M., Pan, D., Zheng, H., Wang, C., ... & Dai, G. (2023). DCAE: A dual conditional autoencoder framework for the reconstruction from EEG into image. Biomedical Signal Processing and Control, 81, 104440.

[15] Yu, J., Ren, L., Zhang, Y., Zhou, W., Lin, L., & Dai, G. (2023). PEAL: Prior-embedded explicit attention learning for low-overlap point cloud registration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 17702-17711).

[16] Ye, C., Jiang, S., Zhang, H., Wu, Y., Shi, J., Wang, H., & Dai, G. (2022). JointMatcher: Numerically-aware entity matching using pre-trained language models with attention concentration. Knowledge-Based Systems, 251, 109033.

[17] Zeng, H., Fang, X., Zhao, Y., Wu, J., Li, M., Zheng, H., ... & Dai, G. (2022). EMCI: A novel EEG-based mental workload assessment index of mild cognitive impairment. IEEE Transactions on Biomedical Circuits and Systems, 16(5), 902-914.

[18] Ye, C., Wang, H., & Dai, G. (2022). Pattern Discovery for Heterogeneous Data. In Knowledge Discovery from Multi-Sourced Data (pp. 53-67). Singapore: Springer Nature Singapore.

[19] Ye, C., Wang, H., & Dai, G. (2022). Functional-Dependency-Based Truth Discovery for Isomorphic Data. In Knowledge Discovery from Multi-Sourced Data (pp. 13-31). Singapore: Springer Nature Singapore.

[20] Ye, C., Wang, H., & Dai, G. (2022). Denial-constraint-based truth discovery for Isomorphic data. In Knowledge Discovery from Multi-Sourced Data (pp. 33-51). Singapore: Springer Nature Singapore.

[21] Ye, C., Wang, H., & Dai, G. (2022). Fact Discovery for Text Data. In Knowledge Discovery from Multi-Sourced Data (pp. 69-83). Singapore: Springer Nature Singapore.

[22] Ye, C., Wang, H., & Dai, G. (2022). Knowledge Discovery from Multi-Sourced Data. Springer.

[23] Di Flumeri, G., Ronca, V., Giorgi, A., Vozzi, A., Aricò, P., Sciaraffa, N., ... & Borghini, G. (2022). EEG-based index for timely detecting user’s drowsiness occurrence in automotive applications. Frontiers in Human Neuroscience, 16, 866118.

[24] Shen, F., Peng, Y., Dai, G., Lu, B., & Kong, W. (2022). Coupled projection transfer metric learning for cross-session emotion recognition from EEG. Systems, 10(2), 47.

[25] Zhao, Y., Dai, G., Fang, X., Wu, Z., Xia, N., Jin, Y., & Zeng, H. (2022). E3GCAPS: Efficient EEG-based multi-capsule framework with dynamic attention for cross-subject cognitive state detection. China Communications, 19(2), 73-89.

[26] Jin, R., Huang, H., Jiang, S., Zhang, H., Wu, Y., & Dai, G. (2021, November). Garbage classification method based on YOLOv3. In 2021 17th International Conference on Computational Intelligence and Security (CIS) (pp. 108-112). IEEE.

[27] Ye, C., Wang, H., Lu, W., Gao, J., & Dai, G. (2021). Deep truth discovery for pattern-based fact extraction. Information Sciences, 580, 478-494.

[28] Zhao, Y., Dai, G., Borghini, G., Zhang, J., Li, X., Zhang, Z., ... & Zeng, H. (2021). Label-based alignment multi-source domain adaptation for cross-subject EEG fatigue mental state evaluation. Frontiers in Human Neuroscience, 15, 706270.

[29] Zhang, H., Yao, W., Huang, H., Wu, Y., & Dai, G. (2021). Adaptive coding unit size convolutional neural network for fast 3D-HEVC depth map intracoding. Journal of Electronic Imaging, 30(4), 041405-041405.

[30] Zhang, H., Gou, R., Shang, J., Shen, F., Wu, Y., & Dai, G. (2021). Pre-trained deep convolution neural network model with attention for speech emotion recognition. Frontiers in Physiology, 12, 643202.

[31] Shen, F., Peng, Y., Kong, W., & Dai, G. (2021). Multi-scale frequency bands ensemble learning for EEG-based emotion recognition. Sensors, 21(4), 1262.

[32] Sun, L., Feng, S., Lyu, G., Zhang, H., & Dai, G. (2021). Partial multi-label learning with noisy side information. Knowledge and Information Systems, 63(2), 541-564.

[33] Shen, F., Dai, G., Lin, G., Zhang, J., Kong, W., & Zeng, H. (2020). EEG-based emotion recognition using 4D convolutional recurrent neural network. Cognitive Neurodynamics, 14(6), 815-828.

[34] Sun, L., Ye, P., Lyu, G., Feng, S., Dai, G., & Zhang, H. (2020). Weakly-supervised multi-label learning with noisy features and incomplete labels. Neurocomputing, 413, 61-71.

[35] Xiang, L., Zhao, Y., Dai, G., Gou, R., Zhang, H., & Shi, J. (2020, October). The study of Chinese calligraphy font style based on edge-guided filter and convolutional neural network. In 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP) (pp. 883-887). IEEE.

[36] Lyu, G., Feng, S., Li, Y., Jin, Y., Dai, G., & Lang, C. (2020). HERA: partial label learning by combining heterogeneous loss with sparse and low-rank regularization. ACM Transactions on Intelligent Systems and Technology (TIST), 11(3), 1-19.

[37] Lyu, G., Feng, S., Huang, W., Dai, G., Zhang, H., & Chen, B. (2020). Partial label learning via low-rank representation and label propagation: G. Lyu et al. Soft Computing, 24(7), 5165-5176.

[38] Ye, P., Feng, S., Feng, H., & Dai, G. (2019, November). Robust Multi-Label Learning with Corrupted Features and Incomplete Labels. In 2019 Chinese Automation Congress (CAC) (pp. 4411-4416). IEEE.

[39] Zeng, H., Wu, Z., Zhang, J., Yang, C., Zhang, H., Dai, G., & Kong, W. (2019). EEG emotion classification using an improved SincNet-based deep learning model. Brain sciences, 9(11), 326.

[40] Yu, S., Dai, G., Zhang, H., & Huang, H. (2019, October). Complexity Reduction for Depth Map Coding in 3D-HEVC. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) (pp. 783-793). Cham: Springer International Publishing.

[41] Dai, M., Dai, G., Wu, Y., Xia, Y., Shen, F., & Zhang, H. (2019, June). An improved feature fusion for speaker recognition. In 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC) (pp. 183-187). IEEE.

[42] Huang, J., Dai, G., Wu, Y., Zhang, H., & Shi, J. (2019, May). An IoT-supported small-scale liquefied natural gas distribution system using tank trucks in local areas. In 2019 IEEE 2nd International Conference on Electronics Technology (ICET) (pp. 20-27). IEEE.

[43] Zeng, H., Yang, C., Zhang, H., Wu, Z., Zhang, J., Dai, G., ... & Kong, W. (2019). A LightGBM‐based EEG analysis method for driver mental states classification. Computational intelligence and neuroscience, 2019(1), 3761203.

[44] Zeng, H., Yang, C., Dai, G., Qin, F., Zhang, J., & Kong, W. (2018). EEG classification of driver mental states by deep learning. Cognitive neurodynamics, 12(6), 597-606.

[45] Yang, R., Dai, G., Zhang, H., Zhou, W., Yu, S., & Feng, J. (2018, November). Fast Depth Intra Mode Decision Based on DCT in 3D-HEVC. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) (pp. 226-236). Cham: Springer International Publishing.

[46] Liu, X., Zheng, K., Liu, X. Y., Wang, X., & Dai, G. (2018). Towards secure and energy-efficient CRNs via embracing interference: A stochastic geometry approach. IEEE Access, 6, 36757-36770.

[47] Liu, X., Zheng, K., Fu, L., Liu, X. Y., Wang, X., & Dai, G. (2018). Energy efficiency of secure cognitive radio networks with cooperative spectrum sharing. IEEE Transactions on Mobile Computing, 18(2), 305-318.

[48] Luo, Y., Zhou, W., Fang, J., Liang, L., Zhang, H., & Dai, G. (2017, October). Epi-patch based convolutional neural network for depth estimation on 4d light field. In International Conference on Neural Information Processing (pp. 642-652). Cham: Springer International Publishing.

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[50] Zeng, H., Dai, G., Kong, W., Chen, F., & Wang, L. (2017). A novel nonlinear dynamic method for stroke rehabilitation effect evaluation using eeg. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(12), 2488-2497.

[51] Lei, X., Wang, L., Kong, W., Peng, Y., Hu, S., Zeng, H., ... & Tong, S. (2017, May). Identification of eeg features in stroke patients based on common spatial pattern and sparse representation classification. In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 114-117). IEEE.

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[53] Hu, Y., Dai, G., Fan, J., Wu, Y., & Zhang, H. (2016, April). BlueAer: A fine-grained urban PM2. 5 3D monitoring system using mobile sensing. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications (pp. 1-9). IEEE.

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[55] Hu, Y., Fan, J., Zhang, H., Chen, X., & Dai, G. (2016). An estimated method of urban PM2. 5 concentration distribution for a mobile sensing system. Pervasive and Mobile Computing, 25, 88-103.

[56] Shen, X., Chen, Y., Zhang, J., Wang, L., Dai, G., & He, T. (2015, October). BarFi: Barometer-aided Wi-Fi floor localization using crowdsourcing. In 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems (pp. 416-424). IEEE.

[57] Zhang, B., Fan, J., Dai, G., & Luan, T. H. (2015). A hybrid localization approach in 3D wireless sensor network. International Journal of Distributed Sensor Networks, 11(10), 692345.

[58] Hua, H., Qiu, J., Song, S., Wang, X., & Dai, G. (2015, August). A cluster head rotation cooperative MIMO scheme for wireless sensor networks. In International Conference on Wireless Algorithms, Systems, and Applications (pp. 212-221). Cham: Springer International Publishing.

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[61] Fan, J., Zhang, B., & Dai, G. (2015). D3D-MDS: a distributed 3D localization scheme for an irregular wireless sensor network using multidimensional scaling. International Journal of Distributed Sensor Networks, 11(2), 103564.

[62] Qiu, J., Mitchell, P., Grace, D., Lin, B., & Dai, G. (2015). An adaptive neighbour detection scheme for rapid configuration of wireless sensor networks. International Journal of Sensor Networks, 18(3-4), 130-139.

[63] Wu, Y., Liu, P., Qiu, J., & Dai, G. (2015). Enable sustainable sensor networks with non-contact charging: efficient deployment of energy hubs. International Journal of Sensor Networks, 18(3-4), 172-181.

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[70] Wu, Y., Gao, Z., & Dai, G. (2014). Deadline and activation time assignment for partitioned real-time application on multiprocessor reservations. Journal of Systems Architecture, 60(3), 247-257.

[71] Zeng, H., Zhang, J., Dai, G., Gao, Z., & Hu, H. (2014). Security visiting: RFID-based smartphone indoor guiding system. International Journal of Distributed Sensor Networks, 10(1), 212741.

[72] Liu, P., Wu, Y., Qiu, J., Dai, G., & Fu, T. (2013). elighthouse: Enhance solar power coverage in renewable sensor networks. International Journal of Distributed Sensor Networks, 9(11), 256569.

[73] Kong, W., Hu, S., Zhang, J., & Dai, G. (2013). Robust and smart spectral clustering from normalized cut. Neural Computing and Applications, 23(5), 1503-1512.

[74] Kong, W., Zhou, Z., Hu, S., Zhang, J., Babiloni, F., & Dai, G. (2013). Automatic and direct identification of blink components from scalp EEG. Sensors, 13(8), 10783-10801.

[75] Zhang, J., Shen, X., Zeng, H., Dai, G., Bo, C., Chen, F., & Lv, C. (2013). Energy‐efficient and localized lossy data aggregation in asynchronous sensor networks. International Journal of Communication Systems, 26(8), 989-1010.

[76] Shen, X., Bo, C., Zhang, J., Tang, S., Mao, X., & Dai, G. (2013). EFCon: Energy flow control for sustainable wireless sensor networks. Ad Hoc Networks, 11(4), 1421-1431.

[77] Wan, P. J., Jia, X., Dai, G., Du, H., Wan, Z., & Frieder, O. (2013, April). Scalable algorithms for wireless link schedulings in multi-channel multi-radio wireless networks. In 2013 Proceedings IEEE INFOCOM (pp. 2121-2129). IEEE.

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[79] Zeng, H., Zhang, J., & Dai, G. (2013). Construction of low weighted and fault–tolerant topology for wireless ad hoc and sensor network. International Journal of Sensor Networks, 14(4), 197-210.

[80] Wang, T., Dai, G., Ni, B., Xu, D., & Siewe, F. (2012). A distance measure between labeled combinatorial maps. Computer Vision and Image Understanding, 116(12), 1168-1177.0.

[81] Zhao, X., Hu, S., Zhang, J., Dai, G., Vecchiato, G., & Babiloni, F. (2012, May). The study of memorization index based on W-GFP during the observation of TV commercials. In 2012 IEEE International Conference on Systems and Informatics (ICSAI2012) (pp. 2198-2202).

[82] Gao, Z., Wu, Y., Dai, G., & Xia, H. (2012). Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of Things. Sensors, 12(8), 11334-11359.

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著作


系统可配置单片机原理与应用 2009-04-15


(1) 基于标签对齐的多源域自适应跨被试EEG 认知状态评估方法,发明专利,专利授权,202110601409.9,CN 113392733 B,2022-06-21,计算机学院（软件学院）,戴国骏

(2) 基于注意力机制和卷积神经网络的语音抑郁症识别方法,发明专利,专利授权,201811343483.X,CN 109599129 B,2021-09-14,计算机学院（软件学院）,戴国骏

(3) 智能家居系统中基于哈希算法的终端网关负载分配方法,发明专利,专利授权,201811388232.3,CN 109462652 B,2021-06-01,计算机学院（软件学院）,戴国骏

(4) 基于卷积神经网络的单目光场图像无监督深度估计方法,发明专利,专利授权,201910276356.0,CN 110163246 B,2021-03-30,计算机学院（软件学院）,戴国骏

(5) 一种易拓展数据传输装置,发明专利,专利授权,201610627507.9,3468079,2019-07-26,计算机学院（软件学院）,戴国骏

(6) 一种基于城市区域网格自适应的PM2.5浓度推测方法,发明专利,专利授权,201610146147.0,3023637,2018-08-07,计算机学院（软件学院）,戴国骏

(7) 火灾逃生导航系统,发明专利,专利授权,201310175230.7,2016-03-30,计算机学院（软件学院）,戴国骏

(8) 红酒电动开瓶器的智能控制方法,发明专利,专利授权,201210449755.0,2014-09-17,计算机学院（软件学院）,戴国骏

(9) 一种火灾逃生导航系统,实用新型,专利授权,201320258250.6,2013-09-25,计算机学院（软件学院）,戴国骏

(10) 一种低功耗RFID可定位无源标签,实用新型,专利授权,201220643230.6,2013-05-22,计算机学院（软件学院）,戴国骏

(11) 一种硬件实时容错的动态部分可重构系统,发明专利,专利授权,201010105225.5,2013-03-27,计算机学院（软件学院）,戴国骏

(12) 一种高压气体放电灯电子镇流器的智能触发方法,发明专利,专利授权,200610154583.9,2010-08-25,计算机学院（软件学院）,戴国骏

(13) 无线感知鼠标控制方法,发明专利,专利授权,200710199426.4,2010-06-09,计算机学院（软件学院）,戴国骏

(14) 一种基于CAN总线的信号实时性处理方法,发明专利,专利授权,200710070565.7,2009-08-05,计算机学院（软件学院）,戴国骏

(15) 一种基于USB总线的嵌入式虚拟仪器的信号处理方法,发明专利,专利授权,200610053444.7,2008-07-16,计算机学院（软件学院）,戴国骏

(16) 一种新型无线感知鼠标,实用新型,专利授权,200620140837.7,2008-03-05,计算机学院（软件学院）,戴国骏

(17) 一种基于MESH的空中无人机通信系统,发明专利,专利申请,201711294479.4,计算机学院（软件学院）,戴国骏

(18) 一种基于双流卷积神经网络的立体匹配方法,发明专利,专利申请,201710289393.6,计算机学院（软件学院）,戴国骏

(19) 一种基于三维全卷积神经网络的前列腺MRI分割方法,发明专利,专利申请,201711204994.9,计算机学院（软件学院）,戴国骏

(20) 一种针对嵌入式移动端的深度学习语义分割模型压缩方法,发明专利,专利申请,201910294185.4,计算机学院（软件学院）,戴国骏

(21) 基于原型聚类域适应算法的跨被试EEG认知状态识别方法,发明专利,专利申请,202011526572.5,计算机学院（软件学院）,戴国骏

(22) 一种基于高效多源胶囊网络的跨被试EEG认知状态检测方法,发明专利,专利申请,202111160386.9,计算机学院（软件学院）,戴国骏

(23) 一种基于深度学习的遥感目标四边形框快速检测方法,发明专利,专利申请,202111617324.6,计算机学院（软件学院）,戴国骏

(24) 一种基于环绕视点的位姿估计优化方法,发明专利,专利申请,202111528516.X,计算机学院（软件学院）,戴国骏

(25) 一种面向轻度认知功能障碍患者诊断的工作负荷评估方法,发明专利,专利申请,202111670306.4,计算机学院（软件学院）,戴国骏

(26) 一种基与掩膜传播网络的交互式视频抠图系统,发明专利,专利申请,202210193688.4,计算机学院（软件学院）,戴国骏

(27) 一种基于预训练语言模型的实体识别方法,发明专利,专利申请,202210361634.4,计算机学院（软件学院）,戴国骏

(28) 一种基于局部空间转换网络的实时目标编辑方法,发明专利,专利申请,202111654265.X,计算机学院（软件学院）,戴国骏

(29) 基于4D脉冲神经网络的脑电认知识别方法,发明专利,专利申请,202310045690.1,计算机学院（软件学院）,戴国骏

(30) 一种具有电极自贴合功能的脑电采集眼镜装置,发明专利,专利申请,202310337622.2,计算机学院（软件学院）,戴国骏

(31) 基于混合网络共生的EEG认知识别方法,发明专利,专利申请,202310045688.4,计算机学院（软件学院）,戴国骏

(32) 一种基于多模块神经网络的脑电信号伪迹去除方法,发明专利,专利申请,202310198897.2,计算机学院（软件学院）,戴国骏

(33) 基于ViT 的类内、类间相似度的细粒度图像分类方法及系统,发明专利,专利申请,2023114095608,计算机学院（软件学院）,戴国骏


(1) 多媒体传感节点图像编码软件V1.0,0376194,2012SR008158,2012-02-09

(2) 多媒体传感器节点任务调度软件,0376185,2012SR008149,2012-02-09

(3) 多目智能相机高精度图像融合软件V1.0,0376197,2012SR007851,2012-02-09

(4) 多媒体传感网定位软件V1.0,0376267,2012SR008231,2012-02-09

(5) 多目智能相机图像匹配识别软件,0376228,2012SR008192,2012-02-09

(6) 基于工业视觉的饮料瓶盖质量检测软件V1.0,0212631,2010SR024358,2010-05-22

(7) 组合仪表数据采集控制软件V1.0,0209058,2010SR020785,2010-05-07

(8) 组合仪表测试软件V1.0,0207983,2010SR019710,2010-05-04

(9) Gide管理系统项目管理软件V1.0,0179340,2009SR052341,2009-11-10

(10) 组合仪表控制软件V1.0,0170026,2009SR043027,2009-09-27

(11) 按摩椅无线手持终端—自动按摩参数配置模块,118447,2008SR31268,2008-12-03

(12) 按摩椅无线手持终端—手动按摩参数配置模块,118448,2008SR31269,2008-12-03

(13) 片上可编程系统数字键盘控制软件V1.0,104779,2008SR17600,2008-08-28

(14) 实时运动目标跟踪系统软件V1.0,095585,2008SR08406,2008-05-05

(15) 实时运动目标监测系统软件V1.0,095587,2008SR08408,2008-05-05


1， 国奖办社会奖,三等奖（铜奖）

2， 省部级,二等奖（银奖）

3， 省部级，二等奖（银奖）

4， 省部级,三等奖（铜奖）