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学者姓名：王丹
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摘要 ：
一种基于强化学习的多冷源环状供冷系统压差控制方法，涉及多冷源环状区域供冷系统优化控制领域。首先，基于设计及运行数据建立多冷源环状供冷系统的Modelica模型；其次，构造多冷源环状供冷系统的状态空间、动作空间和奖励函数，并基于Python语言实现强化学习算法；再次，通过FMI(Functional Mockup Interface)接口协议，实现Python和Modelica模型的实时联合仿真功能；最后，利用联合仿真，优化各个冷源供回水压差值设定值。
引用：
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| GB/T 7714 | 王伟 , 王丹 , 高成 et al. 一种基于强化学习的多冷源环状供冷系统压差控制方法 : CN202310637307.1[P]. | 2023-05-31 . |
| MLA | 王伟 et al. "一种基于强化学习的多冷源环状供冷系统压差控制方法" : CN202310637307.1. | 2023-05-31 . |
| APA | 王伟 , 王丹 , 高成 , 朱世豪 , 马凯 , 孙育英 . 一种基于强化学习的多冷源环状供冷系统压差控制方法 : CN202310637307.1. | 2023-05-31 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Image super-resolution reconstruction is a research hotspot in the field of computer vision. Traditional image super-resolution reconstruction methods based on deep learning mostly up-sample low-resolution images ignoring categories and instances, which will cause some problems such as unrealistic texture in the reconstructed images or sawtooth phenomenon on the edge of instance. In this manuscript, we propose an image super-resolution reconstruction method based on instance spatial feature modulation and feedback mechanism. First, the prior knowledge of instance spatial features is introduced in the reconstruction process. Instance spatial features of low-resolution images are extracted to modulate super-resolution reconstruction features. Then, based on the feedback mechanism, the modulated low-resolution image features are iteratively optimized for the reconstruction results, so that the model can finally learn instance-level reconstruction ability. Experiments on COCO-2017 show that, compared with traditional deep learning-based image super-resolution reconstruction methods, the proposed method can obtain better image reconstruction results, and the reconstructed images have more realistic instance textures.
关键词 ：
Feedback network Feedback network Back projection Back projection Instance spatial feature Instance spatial feature Modulator Modulator Super-resolution Super-resolution
引用：
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| GB/T 7714 | Fu, Lihua , Jiang, Hanxu , Wu, Huixian et al. Image super-resolution reconstruction based on instance spatial feature modulation and feedback mechanism [J]. | APPLIED INTELLIGENCE , 2022 , 53 (1) : 601-615 . |
| MLA | Fu, Lihua et al. "Image super-resolution reconstruction based on instance spatial feature modulation and feedback mechanism" . | APPLIED INTELLIGENCE 53 . 1 (2022) : 601-615 . |
| APA | Fu, Lihua , Jiang, Hanxu , Wu, Huixian , Yan, Shaoxing , Wang, Junxiang , Wang, Dan . Image super-resolution reconstruction based on instance spatial feature modulation and feedback mechanism . | APPLIED INTELLIGENCE , 2022 , 53 (1) , 601-615 . |
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摘要 ：
Network security has emerged as a crucial universal issue that affects enterprises, governments, and individuals. The strategies utilized by the attackers are continuing to evolve, and therefore the rate of attacks targeting the network system has expanded dramatically. An Intrusion Detection System (IDS) is one of the significant defense solutions against sophisticated cyberattacks. However, the challenge of improving the accuracy, detection rate, and minimal false alarms of the IDS continues. This paper proposes a robust and effective intrusion detection framework based on the ensemble learning technique using eXtreme Gradient Boosting (XGBoost) and an embedded feature selection method. Further, the best uniform feature subset is extracted using the up-to-date real-world intrusion dataset Canadian Institute for Cybersecurity Intrusion Detection (CICIDS2017) for all attacks. The proposed IDS framework has successfully exceeded several evaluations on a big test dataset over both multi and binary classification. The achieved results are promising on various measurements with an accuracy overall, precision, detection rate, specificity, F-score, false-negative rate, false-positive rate, error rate, and The Area Under the Curve (AUC) scores of 99.86%, 99.69%, 99.75%, 99.69%, 99.72%, 0.17%, 0.2%, 0.14%, and 99.72 respectively for abnormal class. Moreover, the achieved results of multi-classification are also remarkable and impressively great on all performance metrics.
关键词 ：
xgboost algorithm xgboost algorithm ensemble learning ensemble learning intrusion detection intrusion detection Network security Network security features selection features selection
引用：
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| GB/T 7714 | Mokbal, Fawaz , Dan, Wang , Osman, Musa et al. An Efficient Intrusion Detection Framework Based on Embedding Feature Selection and Ensemble Learning Technique [J]. | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY , 2022 , 19 (2) : 237-248 . |
| MLA | Mokbal, Fawaz et al. "An Efficient Intrusion Detection Framework Based on Embedding Feature Selection and Ensemble Learning Technique" . | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY 19 . 2 (2022) : 237-248 . |
| APA | Mokbal, Fawaz , Dan, Wang , Osman, Musa , Ping, Yang , Alsamhi, Saeed . An Efficient Intrusion Detection Framework Based on Embedding Feature Selection and Ensemble Learning Technique . | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY , 2022 , 19 (2) , 237-248 . |
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摘要 ：
Objective. Motor imagery-based brain-computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding. Approach. A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method channel group attention (CGA) to build a lightweight neural network Filter Bank CGA Network (FB-CGANet). Accompanied with FB-CGANet, the band exchange data augmentation method was proposed to generate training data for networks with filter bank structure. Main results. The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment. Significance. This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.
关键词 ：
channel attention channel attention motor imagery motor imagery data augmentation data augmentation deep learning deep learning brain-computer interface brain-computer interface
引用：
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| GB/T 7714 | Chen, Jiaming , Yi, Weibo , Wang, Dan et al. FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification [J]. | JOURNAL OF NEURAL ENGINEERING , 2022 , 19 (1) . |
| MLA | Chen, Jiaming et al. "FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification" . | JOURNAL OF NEURAL ENGINEERING 19 . 1 (2022) . |
| APA | Chen, Jiaming , Yi, Weibo , Wang, Dan , Du, Jinlian , Fu, Lihua , Li, Tong . FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification . | JOURNAL OF NEURAL ENGINEERING , 2022 , 19 (1) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
一种基于案例推理法的公共建筑节能改造方案确定方法，涉及公共建筑领域及节能改造领域。首先，选取目标建筑和案例建筑；其次，选取目标建筑和案例建筑的评价属性；再次，计算目标建筑和案例建筑各评价属性的相似度，然后计算目标建筑和案例建筑之间的相似度；最后，根据目标建筑和案例建筑的相似度结果，确定最相似案例建筑的节能改造方案为目标建筑的节能改造方案。
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| GB/T 7714 | 王丹 , 逄秀锋 , 王伟 et al. 一种基于案例推理法的公共建筑节能改造方案确定方法 : CN202110531847.2[P]. | 2021-05-14 . |
| MLA | 王丹 et al. "一种基于案例推理法的公共建筑节能改造方案确定方法" : CN202110531847.2. | 2021-05-14 . |
| APA | 王丹 , 逄秀锋 , 王伟 , 万川 , 孙甄淇 . 一种基于案例推理法的公共建筑节能改造方案确定方法 : CN202110531847.2. | 2021-05-14 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
一种基于熵权法COPARS模型的公共建筑节能改造技术决策方法，涉及公共建筑领域及节能改造领域。首先，基于目标建筑基本信息和机电系统运行数据确定待选节能改造技术；其次，选取节能量、静态投资回收期、减碳量为评价指标，并分别计算待选节能改造技术各评价指标大小；再次，建立COPARS多属性决策模型，并基于熵权法确定各评价指标权重系数；基于熵权法COPARS决策模型计算待选节能改造技术效用程度；最后，根据多属性决策模型效用程度计算结果，对待选节能改造技术进行优先排序。
引用：
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| GB/T 7714 | 王丹 , 逄秀锋 , 王伟 et al. 一种基于熵权法COPARS模型的公共建筑节能改造技术决策方法 : CN202110531725.3[P]. | 2021-05-14 . |
| MLA | 王丹 et al. "一种基于熵权法COPARS模型的公共建筑节能改造技术决策方法" : CN202110531725.3. | 2021-05-14 . |
| APA | 王丹 , 逄秀锋 , 王伟 , 万川 , 孙甄淇 . 一种基于熵权法COPARS模型的公共建筑节能改造技术决策方法 : CN202110531725.3. | 2021-05-14 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
To improve the accuracy of named entity recognition and reduce the cost of manual labeling, this study proposes a weakly supervised named entity recognition method based on the recurrent neural network (RNN), which utilizes the widely existing ontology in the medical field as the supplemental source of knowledge. In other words, a named entity recognition model is constructed by extracting semantic concept representation from medical ontology and integrating it with word and character embedding. First, the continuous bag of words model is utilized to extract semantic representation, including concept and word embedding. Then, the character-enhanced word embedding model is used to extract character representation. Finally, the tag sequence of Chinese medical text is obtained using a deep learning model RNN in combination with semantic and character embedding. The results of a comparative experiment on a true dataset of medical text show that the performance improvement of our proposed method compared with that of traditional methods reaches 2.2% to 6.1%, which verifies the effectiveness of our proposed method. © 2020, Editorial Department of Journal of HEU. All right reserved.
关键词 ：
Embeddings Embeddings Information retrieval Information retrieval Semantics Semantics Ontology Ontology Recurrent neural networks Recurrent neural networks
引用：
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| GB/T 7714 | Zhao, Qing , Wang, Dan , Xu, Shushi et al. A weakly supervised Chinese medical named entity recognition method [J]. | Journal of Harbin Engineering University , 2020 , 41 (3) : 425-432 . |
| MLA | Zhao, Qing et al. "A weakly supervised Chinese medical named entity recognition method" . | Journal of Harbin Engineering University 41 . 3 (2020) : 425-432 . |
| APA | Zhao, Qing , Wang, Dan , Xu, Shushi , Zhang, Xiaotong , Wang, Xiaoxi . A weakly supervised Chinese medical named entity recognition method . | Journal of Harbin Engineering University , 2020 , 41 (3) , 425-432 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The accumulation and explosive growth of the electronic medical records (EMRs) make the name entity recognition (NER) technologies become critical for the meaningful use of EMR data and then the practice of evidence-based medicine. The dominate NER approaches use the distributed representation of the words and characters to build deep learning-based NER models. However, for the task of biomedical named entity recognition, there are a large amount of complicated medical terminologies that are composed of multiple words. Splitting these terminologies to learn the word and character embeddings might cause semantic ambiguities. In this paper, we treat each medical terminology as a concept and propose a concept-enhanced named entity recognition model (CNER), where the features from three different granularities (i.e., concept, word, and character) are combined together for bio-NER. The extensive experiments are conducted on two real-world corpora: fully labeled corpus and partially labeled corpus. CNER achieves the highest F1 score (fully labeled corpus: precision = 88.23, recall = 88.29, and F1 = 88.26; partially labeled corpus: precision = 87.03, recall = 88.19, and F1 = 87.61) by outperforming the baseline CW-BLSTM-CRF approach for 0.58% and 1.15% respectively, which demonstrates the effectiveness of the proposed approach.
关键词 ：
Semantic information analysis Semantic information analysis Concept feature Concept feature Deep neural network (DNN) Deep neural network (DNN) Named entity recognition (NER) Named entity recognition (NER)
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| GB/T 7714 | Zhao, Qing , Wang, Dan , Li, Jianqiang et al. Exploiting the concept level feature for enhanced name entity recognition in Chinese EMRs [J]. | JOURNAL OF SUPERCOMPUTING , 2020 , 76 (8) : 6399-6420 . |
| MLA | Zhao, Qing et al. "Exploiting the concept level feature for enhanced name entity recognition in Chinese EMRs" . | JOURNAL OF SUPERCOMPUTING 76 . 8 (2020) : 6399-6420 . |
| APA | Zhao, Qing , Wang, Dan , Li, Jianqiang , Akhtar, Faheem . Exploiting the concept level feature for enhanced name entity recognition in Chinese EMRs . | JOURNAL OF SUPERCOMPUTING , 2020 , 76 (8) , 6399-6420 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Local Outlier Factor (LOF) outlier detecting algorithm has good accuracy in detecting global and local outliers. However, the algorithm needs to traverse the entire dataset when calculating the local outlier factor of each data point, which adds extra time overhead and makes the algorithm execution inefficient. In addition, if the K -distance neighborhood of an outlier point P contains some outliers that are incorrectly judged by the algorithm as normal points, then P may be misidentified as normal point. To solve the above problems, this paper proposes a Neighbor Entropy Local Outlier Factor (NELOF) outlier detecting algorithm. Firstly, we improve the Self-Organizing Feature Map (SOFM) algorithm and use the optimized SOFM clustering algorithm to cluster the dataset. Therefore, the calculation of each data point's local outlier factor only needs to be performed inside the small cluster. Secondly, this paper replaces the K -distance neighborhood with relative K -distance neighborhood and utilizes the entropy of relative K neighborhood to redefine the local outlier factor, which improves the accuracy of outlier detection. Experiments results confirm that our optimized SOFM algorithm can avoid the random selection of neurons, and improve clustering effect of traditional SOFM algorithm. In addition, the proposed NELOF algorithm outperforms LOF algorithm in both accuracy and execution time of outlier detection.
关键词 ：
Canopy Canopy cluster cluster LOF LOF outlier detection outlier detection SOFM SOFM
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| GB/T 7714 | Yang, Ping , Wang, Dan , Wei, Zhuojun et al. An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm [J]. | IEEE ACCESS , 2019 , 7 : 115914-115925 . |
| MLA | Yang, Ping et al. "An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm" . | IEEE ACCESS 7 (2019) : 115914-115925 . |
| APA | Yang, Ping , Wang, Dan , Wei, Zhuojun , Dui, Xiaolin , Li, Tong . An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm . | IEEE ACCESS , 2019 , 7 , 115914-115925 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明涉及基于冷冻水回水温度最佳设定点的小型定频冷水机组变水温控制方法，首先建立小型定频冷水机组空调系统的模型，进行模拟仿真。再收集建筑的设计参数和所在地区的典型年气象数据，结合以上三者计算得到不同运行工况下的冷冻水回水最高允许温度。之后检验和修正冷冻水回水最高允许温度从而确定回水温度最佳设定点并建立不同的运行工况数据集。基于数据集，建立和验证定频冷水机组回水温度最佳设定点GRNN预测模型。最后借助预测模型确定机组回水温度最佳设定点进行适时调整。最后利用位式控制器，根据回水温度与最佳设定水温的差值控制冷水机组的启停。本发明能在保证室内热舒适的前提下提高小型定频冷水机组的效率，取得较好的节能效果。
引用：
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| GB/T 7714 | 王伟 , 孙育英 , 武尚将 et al. 基于冷冻水回水温度最佳设定点的小型定频冷水机组变水温控制方法 : CN201910359467.8[P]. | 2019-04-30 . |
| MLA | 王伟 et al. "基于冷冻水回水温度最佳设定点的小型定频冷水机组变水温控制方法" : CN201910359467.8. | 2019-04-30 . |
| APA | 王伟 , 孙育英 , 武尚将 , 洪阳 , 薛汇宇 , 林瑶 et al. 基于冷冻水回水温度最佳设定点的小型定频冷水机组变水温控制方法 : CN201910359467.8. | 2019-04-30 . |
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