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学者姓名：韩红桂
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Abstract ：
Distributed learning-based high-dimensional temporal modeling for the industrial Internet of Things (IIoT) has become a prevailing trend. However, traditional distributed learning inefficiently extracts information by straightforward architects, resulting in low modeling accuracy and high communication costs. We propose a distributed hierarchical temporal graph learning (DHTGL) approach. In terminal equipment, we construct an adaptive hierarchical dilation convolutional network to dynamically capture spatiotemporal features by adjusting the dilation factor at each layer. Next, we construct the adaptive graphs according to the connection similarity between dimensions to capture implicit connections. In the edge device, we design a node-edge graph distance calculation based on the Gromov-Wasserstein distance to group feature graphs and construct representative cluster feature graphs. Edge devices upload cluster feature graphs to reduce communication costs while minimizing information loss. In the central server, we incorporate graph attention networks into the graph neural networks for edge updating in training models on clustered feature graphs. Experiments using the public IIoT data sets and the self-built IIoT platform demonstrate the effectiveness of DHTGL in comparison with common distributed learning approaches. The results confirm that DHTGL consumes fewer communications while achieving higher accuracies.
Keyword ：
graph convolutional network (GCN) graph convolutional network (GCN) Feature extraction Feature extraction Industrial Internet of Things Industrial Internet of Things Computational modeling Computational modeling Data models Data models Distance learning Distance learning Costs Costs industrial Internet of Things (IIoT) industrial Internet of Things (IIoT) Distributed learning Distributed learning Computer aided instruction Computer aided instruction
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| GB/T 7714 | Li, Fangyu , Lin, Junnuo , Wang, Yu et al. Distributed Hierarchical Temporal Graph Learning for Communication-Efficient High-Dimensional Industrial IoT Modeling [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (17) : 28578-28590 . |
| MLA | Li, Fangyu et al. "Distributed Hierarchical Temporal Graph Learning for Communication-Efficient High-Dimensional Industrial IoT Modeling" . | IEEE INTERNET OF THINGS JOURNAL 11 . 17 (2024) : 28578-28590 . |
| APA | Li, Fangyu , Lin, Junnuo , Wang, Yu , Du, Yongping , Han, Honggui . Distributed Hierarchical Temporal Graph Learning for Communication-Efficient High-Dimensional Industrial IoT Modeling . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (17) , 28578-28590 . |
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Abstract ：
Transfer learning algorithms are capable to apply previously learned knowledge in source domain, which alleviates much expensive efforts of knowledge recollection in target domain. But the knowledge in source domain is always imperfect due to redundant or contaminated information. To solve this problem, an ensemble filtertransfer learning (EFTL) algorithm based on the source knowledge reconstruction is proposed in this paper. First, a knowledge partition strategy based on model is developed to classify the source knowledge into different types. Then, the positive knowledge can be identified, which contributes to target domain with a rejection of the negative transfer. Second, a knowledge filter algorithm is introduced to filter out the redundant information in non-positive knowledge. Then, the non-positive knowledge can be reconstructed by this algorithm to prevent the loss of available information. Third, an ensemble transfer mechanism is established to realize the synchronous transfer of omnidirectional knowledge for the target domain. Finally, comparative experiments on model prediction in practical applications are provided to illustrate the dependability of EFTL.
Keyword ：
Knowledge filter algorithm Knowledge filter algorithm Transfer learning Transfer learning Model prediction Model prediction Ensemble transfer mechanism Ensemble transfer mechanism
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| GB/T 7714 | Han, Honggui , Li, Mengmeng , Yang, Hongyan et al. Ensemble filter-transfer learning algorithm [J]. | PATTERN RECOGNITION , 2024 , 154 . |
| MLA | Han, Honggui et al. "Ensemble filter-transfer learning algorithm" . | PATTERN RECOGNITION 154 (2024) . |
| APA | Han, Honggui , Li, Mengmeng , Yang, Hongyan , Wu, Xiaolong , Han, Huayun . Ensemble filter-transfer learning algorithm . | PATTERN RECOGNITION , 2024 , 154 . |
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Abstract ：
Feature pyramids are widely adopted in visual detection models for capturing multiscale features of objects. However, the utilization of feature pyramids in practical object detection tasks is prone to complex background interference, resulting in suboptimal capture of discriminative multiscale foreground semantic features. In this article, a foreground capture feature pyramid network (FCFPN) for multiscale object detection is proposed, to address the problem of inadequate feature learning in complex backgrounds. FCFPN consists of a foreground dual attention (FDA) module and a pathway aggregation (PA) structure. Specifically, the FDA mechanism activates top-down foreground channel responses and lateral spatial foreground location features, so that channel and spatial foreground features are adequately captured. Then, the PA module adaptively learns the fusion weights of multiscale features at different levels of the feature pyramid, which enhances the complementarity of semantic information between different levels of the foreground feature maps. Since the fusion weights are learned adaptively based on different pyramid levels, the detection model accordingly retains the gained information of feature sizes and suppresses the conflicting information. The evaluations on public datasets and the self-built complex background dataset demonstrate that the detection average precision (AP) and the feature learning performance of the proposed method are superior compared with other FPNs, which proves the effectiveness of the proposed FCFPN.
Keyword ：
Interference Interference Complex backgrounds Complex backgrounds feature pyramid feature pyramid object detection object detection Adaptation models Adaptation models Object detection Object detection Neck Neck Semantics Semantics Feature extraction Feature extraction Detectors Detectors foreground capture foreground capture
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| GB/T 7714 | Han, Honggui , Zhang, Qiyu , Li, Fangyu et al. Foreground Capture Feature Pyramid Network-Oriented Object Detection in Complex Backgrounds [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 . |
| MLA | Han, Honggui et al. "Foreground Capture Feature Pyramid Network-Oriented Object Detection in Complex Backgrounds" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2024) . |
| APA | Han, Honggui , Zhang, Qiyu , Li, Fangyu , Du, Yongping . Foreground Capture Feature Pyramid Network-Oriented Object Detection in Complex Backgrounds . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 . |
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Abstract ：
本发明提出了一种基于部分标签特征学习的生活垃圾识别方法，针对数据缺少标签使模型难以挖掘数据特征，导致分类精度低的问题。本发明在真实垃圾图像数据的基础上，建立基于部分标签特征学习的生活垃圾识别方法，利用信息不确定性选择具有丰富未知信息的样本进行标注，实现特征的自适应学习，并降低学习成本，最终完成垃圾的准确分类。这种基于部分标签特征学习的生活垃圾识别方法在实际垃圾回收过程中，可以解决垃圾由于数据量大，标签难以获取，从而导致垃圾特征挖掘不充分的问题，实现了高精度垃圾分类，为垃圾回收行业提供技术支持。
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| GB/T 7714 | 韩红桂 , 范晓晔 , 李方昱 et al. 一种基于部分标签特征学习的生活垃圾识别方法 : CN202310557795.5[P]. | 2023-05-18 . |
| MLA | 韩红桂 et al. "一种基于部分标签特征学习的生活垃圾识别方法" : CN202310557795.5. | 2023-05-18 . |
| APA | 韩红桂 , 范晓晔 , 李方昱 , 杜永萍 . 一种基于部分标签特征学习的生活垃圾识别方法 : CN202310557795.5. | 2023-05-18 . |
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Abstract ：
一种基于任务聚类的污水处理过程多工况双层优化控制方法，属于污水处理领域。为了实现污水处理过程中多工况双层优化控制，本发明建立基于数据的污水处理过程多工况双层优化模型，分别描述优化设定值与每个工况双层优化目标间的关系，包括每个工况中的领导层优化目标温室气体排放量模型和跟随层优化目标运行能耗模型，研究基于任务聚类的优化设定方法，求解污水处理过程溶解氧和硝态氮优化设定值，并设计模糊神经网络控制器完成优化设定值跟踪控制，从而促进污水处理过程多工况双层运行优化控制。
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| GB/T 7714 | 韩红桂 , 白星 , 侯莹 . 一种基于任务聚类的污水处理过程多工况双层优化控制方法 : CN202310884510.9[P]. | 2023-07-18 . |
| MLA | 韩红桂 et al. "一种基于任务聚类的污水处理过程多工况双层优化控制方法" : CN202310884510.9. | 2023-07-18 . |
| APA | 韩红桂 , 白星 , 侯莹 . 一种基于任务聚类的污水处理过程多工况双层优化控制方法 : CN202310884510.9. | 2023-07-18 . |
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Abstract ：
本发明涉及一种生活垃圾收集过程臭气智能监测预警方法，实现了臭气浓度的预测，并确定预警级别，实现臭气智能监测预警，包括以下步骤：首先，采集数据并对数据进行预处理；确定模型的输入变量和输出变量；然后，采用模块化神经网络建立臭气浓度预测模型；最后，参考《恶臭污染物排放标准(GB 14554‑93)》及《室内空气质量标准(GB/T 18883‑2002)》，设置硫化氢H2S、氨气NH3浓度预警阈值，拟定臭气预警级别，实现臭气监测预警。本发明有效地实现了臭气浓度的准确预测以及监测预警，具有重要的理论意义和应用价值。
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| GB/T 7714 | 蒙西 , 王岩 , 孙子健 et al. 一种生活垃圾收集过程臭气智能监测预警方法 : CN202310657368.4[P]. | 2023-06-05 . |
| MLA | 蒙西 et al. "一种生活垃圾收集过程臭气智能监测预警方法" : CN202310657368.4. | 2023-06-05 . |
| APA | 蒙西 , 王岩 , 孙子健 , 韩红桂 . 一种生活垃圾收集过程臭气智能监测预警方法 : CN202310657368.4. | 2023-06-05 . |
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Abstract ：
基于工况自适应划分的膜渗透性鲁棒预测方法属于污水处理与资源化利用领域。由于MBR污水处理过程受到不同工况的影响，导致膜污染相关数据中普遍存在不平衡样本，可能会降低预测性能。为了解决这一问题，本发明提出一种基于工况自适应划分的膜渗透性鲁棒预测方法来预测膜渗透性。首先，提出基于空间隶属度计算的交叉熵指标，该指标通过评估输入信息的数据波动特征对膜污染相关数据集进行自适应划分；其次，利用一种非线性回归模型来预测膜渗透性。结果表明该方法能够有效的预测膜渗透性的值。
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| GB/T 7714 | 伍小龙 , 赵嘉欣 , 王威 et al. 基于工况自适应划分的膜渗透性鲁棒预测方法 : CN202310300004.0[P]. | 2023-03-26 . |
| MLA | 伍小龙 et al. "基于工况自适应划分的膜渗透性鲁棒预测方法" : CN202310300004.0. | 2023-03-26 . |
| APA | 伍小龙 , 赵嘉欣 , 王威 , 杨宏燕 , 韩红桂 . 基于工况自适应划分的膜渗透性鲁棒预测方法 : CN202310300004.0. | 2023-03-26 . |
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Abstract ：
本发明公开了一种基于数据挖掘技术的以太坊庞氏骗局合约检测方法，首先在数据获取，根据数据集中的合约地址，获取每一个合约得字节码和交易记录；在特征提取阶段，将合约字节码反汇编为操作码序列，通过n‑gram算法提取合约操作码的上下文特征，同时根据合约交易记录提取合约的账户特征，将操作码特征和账户特征进行结合，作为模型的输入；接着在模型训练阶段，针对合约特征数据集存在的类不平衡问题，采用ADASYN算法对训练集进行过采样，然后使用性能较好的AdaBoost对数据集进行训练，实现对庞氏骗局智能合约的检测。实验证明，该模型的相关评测指标取得了显著的提升，可以有效的检测出以太坊上的庞氏骗局智能合约。
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| GB/T 7714 | 黄静 , 王梦晓 , 韩红桂 et al. 一种基于数据挖掘技术的以太坊庞氏骗局合约检测方法 : CN202310010369.X[P]. | 2023-01-04 . |
| MLA | 黄静 et al. "一种基于数据挖掘技术的以太坊庞氏骗局合约检测方法" : CN202310010369.X. | 2023-01-04 . |
| APA | 黄静 , 王梦晓 , 韩红桂 , 吴启辉 , 公备 , 郭少勇 et al. 一种基于数据挖掘技术的以太坊庞氏骗局合约检测方法 : CN202310010369.X. | 2023-01-04 . |
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Abstract ：
本发明提出基于卷积神经网络特征可视化的典型垃圾识别方法。其中，方法包括：建立卷积神经网络的典型垃圾类别决策模型，设计基于类别决策的典型垃圾特征激活映射策略，突破网络学习过程中的典型垃圾识别可视化技术，实现可解释的特征可视化卷积神经网络典型垃圾识别，为垃圾回收行业提供强有力的技术支持，对典型生活垃圾分类具有显著的应用和经济效益。因此，本发明的研究成果在典型生活垃圾回收领域具有广阔的应用前景。
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| GB/T 7714 | 韩红桂 , 张奇宇 , 李方昱 et al. 基于卷积神经网络特征可视化的典型垃圾识别方法 : CN202310185219.2[P]. | 2023-02-24 . |
| MLA | 韩红桂 et al. "基于卷积神经网络特征可视化的典型垃圾识别方法" : CN202310185219.2. | 2023-02-24 . |
| APA | 韩红桂 , 张奇宇 , 李方昱 , 杜永萍 , 吴玉锋 . 基于卷积神经网络特征可视化的典型垃圾识别方法 : CN202310185219.2. | 2023-02-24 . |
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Abstract ：
本发明提出一种基于多属性二型模糊神经网络的总氮去除量检测方法，实现污水处理过程总氮去除量的在线智能检测。由于进水流量、进水成分、天气和运行工艺的变化，污水处理过程存在多种运行工况。然而，针对难以全面建模具有多种运行工况的污水处理过程，本发明设计一种基于多属性二型模糊神经网络总氮去除量智能检测方法，通过建立区间二型模糊神经网络总氮去除量检测模型，利用多属性模糊规则表达污水处理过程变量与总氮去除量的关联关系，基于梯度下降算法校正总氮去除量检测模型参数，实现多工况下总氮去除量的准确检测。
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| GB/T 7714 | 韩红桂 , 孙晨暄 , 伍小龙 et al. 一种基于多属性二型模糊神经网络的总氮去除量检测方法 : CN202310714351.8[P]. | 2023-06-15 . |
| MLA | 韩红桂 et al. "一种基于多属性二型模糊神经网络的总氮去除量检测方法" : CN202310714351.8. | 2023-06-15 . |
| APA | 韩红桂 , 孙晨暄 , 伍小龙 , 杨宏燕 , 乔俊飞 . 一种基于多属性二型模糊神经网络的总氮去除量检测方法 : CN202310714351.8. | 2023-06-15 . |
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