Journal of Shandong University(Engineering Science) ›› 2024, Vol. 54 ›› Issue (3): 55-63.doi: 10.6040/j.issn.1672-3961.0.2023.217
• Machine Learning & Data Mining • Previous Articles
LIU Xin1,2, LIU Donglan1,2*, FU Ting3, WANG Yong4, CHANG Yingxian4, YAO Honglei1,2, LUO Xin3, WANG Rui1,2, ZHANG Hao1,2
CLC Number:
[1] 张美英, 何杰. 时间序列预测模型研究综述[J]. 数学的实践与认识, 2011, 41(18): 189-195. ZHANG Meiying, HE Jie. A comprehensive review of time series forecasting models[J]. Mathematical Practice and Understanding, 2011, 41(18): 189-195. [2] 李英惠,胥超. 基于时间序列模型的售电量预测方法[J].山东电力技术,2014,41(6):56-59. LI Yinghui, XU Chao. Sales Electricity forecasting method based on time series model[J]. Shandong Electric Power Technology, 2014, 41(6): 56-59. [3] 杨海民, 潘志松, 白玮. 时间序列预测方法综述[J]. 计算机科学, 2019, 46(1): 21-28. YANG Haimin, PAN Zhisong, BAI Wei. A comprehensive review of time series forecasting methods[J]. Computer Science, 2019, 46(1): 21-28. [4] 王连成,代桃桃. 数据驱动创新场景引领未来[J].山东电力技术,2018,45(10):22-26. WANG Liancheng, DAI Taotao. Data-driven innovation leading the future of scenarios[J]. Shandong Electric Power Technology, 2018, 45(10): 22-26. [5] 吕秋霞,孙亮,车延华,等. 基于深度置信网络的配电网负荷预测[J]. 山东电力技术,2023,50(8):20-26. LÜ Qiuxia, SUN Liang, CHE Yanhua, et al. Distribution network load forecasting based on deep belief networks[J]. Shandong Electric Power Technology, 2023, 50(8): 20-26. [6] TORRES J F, HADJOUT D, SEBAA A, et al. Deep learning for time series forecasting: a survey[J]. Big Data, 2021, 9(1): 3-21. [7] LI T, SAHU A K, TALWALKAR A, et al. Federated learning: challenges, methods, and future directions[J]. IEEE Signal Processing Magazine, 2020, 37(3): 50-60. [8] BONAWITZ K, EICHNER H, GRIESKAMP W, et al. Towards federated learning at scale: system design[J]. Machine Learning and Systems, 2019, 1: 374-388. [9] 王健宗, 孔令炜, 黄章成,等. 联邦学习算法综述[J]. 大数据, 2020, 6(6): 64-82. WANG Jianzong, KONG Lingwei, HUANG Zhangcheng, et al. A comprehensive review of federated learning algorithms[J]. Big Data, 2020, 6(6): 64-82. [10] 周传鑫, 孙奕, 汪德刚,等. 联邦学习研究综述[J]. 网络与信息安全学报, 2021, 7(5): 77-92. ZHOU Chuanxin, SUN Yi, WANG Degang, et al. A comprehensive review of federated learning[J]. Journal of Network and Information Security, 2021, 7(5): 77-92. [11] 陈兵, 成翔, 张佳乐,等. 联邦学习安全与隐私保护综述[J]. 南京航空航天大学学报, 2020, 52(5): 675-684. CHEN Bing, CHENG Xiang, ZHANG Jiale, et al. A comprehensive review of security and privacy protection in federated learning[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2020, 52(5): 675-684. [12] ZHANG G P. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003, 50: 159-175. [13] NING Y, KAZEMI H, TAHMASEBI P. A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet[J]. Computers & Geosciences, 2022, 164: 105126. [14] 林颖,刘萌,白德盟,等. 基于深度学习的电力设备红外可见光图像智能配准方法研究[J]. 山东电力技术,2022,49(8):22-27. LIN Ying, LIU Meng, BAI Demeng, et al. Research on intelligent registration of infrared and visible light images of power equipment based on deep learning[J]. Shandong Electric Power Technology, 2022, 49(8): 22-27. [15] 夏瑜潞. 循环神经网络的发展综述[J]. 电脑知识与技术, 2019, 15(21): 182-184. XIA Yulu. Development overview of recurrent neural networks[J]. Computer Knowledge and Technology, 2019, 15(21): 182-184. [16] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251. ZHOU Feiyan, JIN Linpeng, DONG Jun. A com-prehensive review of convolutional neural networks[J]. Chinese Journal of Computers, 2017, 40(6): 1229-1251. [17] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Neural Information Processing Systems, 2017, 30: 5998-6008. [18] SALINAS D, FLUNKERT V, GASTHAUS J, et al. DeepAR: probabilistic forecasting with autoregressive recurrent networks[J]. International Journal of Forecasting, 2020, 36(3): 1181-1191. [19] 张宸嘉, 朱磊, 俞璐. 卷积神经网络中的注意力机制综述[J]. 计算机工程与应用, 2021, 57(20): 64-72. ZHANG Chenjia, ZHU Lei, YU Lu. A survey of attention mechanisms in convolutional neural networks[J]. Computer Engineering and Applications, 2021, 57(20): 64-72. [20] 田永林, 王雨桐,王建功, 等. 视觉 Transformer 研究的关键问题: 现状及展望[J]. 自动化学报, 2022, 48(4): 957-979. TIAN Yonglin, WANG Yutong, WANG Jiangong, et al. Key issues in visual transformer research: current status and prospects[J]. Acta Automatica Sinica, 2022, 48(4): 957-979. [21] ZHOU H, ZHANG S, PENG J, et al. Informer: Beyond efficient transformer for long sequence time-series forecasting[C] //Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2021: 11106-11115. [22] MCMAHAN B, MOORE E, RAMAGE D, et al. Communication efficient learning of deep networks from decentralized data[C] // Proceedings of the Artificial Intelligence and Statistics. Florence, Italy: PMLR, 2017: 1273-1282. [23] LI T, SAHU A K, ZAHEER M, et al. Federated optimization in heterogeneous networks[C] //Procee-dings of Machine Learning and Systems. Texas, USA: PMLR, 2020: 429-450. [24] LI Q, HE B, SONG D. Model-contrastive federated learning[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos, USA: IEEE, 2021: 10713-10722. [25] MENDIETA M, YANG T, WANG P, et al. Local learning matters: rethinking data heterogeneity in federated learning[C] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco-gnition. New Orleans, USA: IEEE, 2022: 8397-8406. [26] 王文鑫, 张健毅. 联邦学习公平性研究综述[J]. 北京电子科技学院学报, 2022, 30(2): 122-134. WANG Wenxin, ZHANG Jianyi. A comprehensive review of fairness in federated learning[J]. Journal of Beijing Electronic Science and Technology Institute, 2022, 30(2): 122-134. [27] 田家会, 吕锡香, 邹仁朋,等. 一种联邦学习中的公平资源分配方案[J]. 计算机研究与发展, 2022, 59(6): 1240-1254. TIAN Jiahui, LÜ Xixiang, ZOU Renpeng, et al. A fair resource allocation scheme in federated learning[J]. Journal of Computer Research and Development, 2022, 59(6): 1240-1254. [28] 张军阳, 王慧丽, 郭阳, 等. 深度学习相关研究综述[J]. 计算机应用研究, 2018, 35(7): 1921-1936. ZHANG Junyang, WANG Huili, GUO Yang, et al. A comprehensive review of deep learning research[J]. Journal of Computer Applications Research, 2018, 35(7): 1921-1936. [29] LUO K, LI X, LAN Y, et al. GradMA: a gradient-memory-based accelerated federated learning with alleviated catastrophic forgetting[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver, Canada: IEEE, 2023: 3708-3717. [30] WU H, XU J, WANG J, et al. Autoformer: decomposition transformers with auto-correlation for long-term series forecasting[J]. Neural Information Processing Systems, 2021, 34: 22419-22430. |
[1] | Yan PENG,Tingting FENG,Jie WANG. An integrated learning approach for O3 mass concentration prediction model [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 1-7. |
[2] | Yibin WANG,Tianli LI,Yusheng CHENG,Kun QIAN. Label distribution learning based on kernel extreme learning machine auto-encoder [J]. Journal of Shandong University(Engineering Science), 2020, 50(3): 58-65. |
[3] | Chunyang LI,Nan LI,Tao FENG,Zhuhe WANG,Jingkai MA. Abnormal sound detection of washing machines based on deep learning [J]. Journal of Shandong University(Engineering Science), 2020, 50(2): 108-117. |
[4] | Yingda LI,Zongxia XIE. Support vector regression algorithm based on kernel similarity reduced strategy [J]. Journal of Shandong University(Engineering Science), 2019, 49(3): 8-14. |
[5] | Kuo PANG,Siqi CHEN,Xiaoying SONG,Li ZOU. Linguistic concept formal decision context analysis based on granular computing [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 74-81. |
[6] | HE Zhengyi, ZENG Xianhua, GUO Jiang. An ensemble method with convolutional neural network and deep belief network for gait recognition and simulation [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 88-95. |
[7] | WANG Tingting, ZHAI Junhai, ZHANG Mingyang, HAO Pu. K-NN algorithm for big data based on HBase and SimHash [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 54-59. |
[8] | CUI Xiaosong, WANG Ying, MENG Jia, ZOU Li. Online business self-evaluation system based on linguistic-valued similarity reasoning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(1): 1-7. |
[9] | YAO Yu, FENG Jian, ZHANG Huaguang, HAN Kezhen. Weighted hyper-ellipsoidal support vector data description with negative samples for outlier detection [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 195-202. |
[10] | LI Sushu, WANG Shitong, LI Tao. A feature selection method based on LS-SVM and fuzzy supplementary criterion [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 34-42. |
[11] | LIU Yingxia, WANG Xichang, TANG Xiaoli, CHANG Faliang. Object detection algorithm based on Bayesian probability estimation in wavelet domain [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(2): 63-70. |
[12] | HE Zhengyi, ZENG Xianhua, QU Shengwei, WU Zhilong. The time series prediction model based on integrated deep learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 40-47. |
[13] | CHEN Zehua, SHANG Xiaohui, CHAI Jing. Neighborhood related multiple-instance classifiers based on integrated Hausdorff distance [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 15-22. |
[14] | WANG Zhiqiang, WEN Yimin, LI Fang. Collaborative recommendation for scenic spots based on multi-aspect ratings [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 54-61. |
[15] | WANG Mei, ZENG Zhaohu, SUN Yingqi, YANG Erlong, SONG Kaoping. Bayesian combination of SVR on regularization path based on KNN of input [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 8-14. |
|