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学者姓名：闫健卓
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Abstract ：
Multivariate Time Series (MTS) acquisition processes often exhibit irregularities, making accurate MTS forecasting challenging. Previous researches focused on interpolation approaches to address data completeness in irregular MTS, but these approaches may introduce noise, thereby altering the feature distributions of irregular MTS. Recent researches trend advocate embedding the missing temporal information through position encoding for forecasting irregular MTS. However, these position encodings were typically designed for text sequences and assumed fixed time intervals, which lead to the loss or distortion of temporal information when applied to irregular MTS. Moreover, they struggled to capture the temporal dynamic information in irregular MTS. To address these challenges, we propose a novel approach called TITD (Time Interval and Temporal Decay), which utilizes time interval and temporal decay information to enhance irregular MTS forecasting. TITD optimizes position encoding to effectively capture both local time interval features and long-term temporal decay patterns, breaking the limitations of static and fixed interval position encoding on time dynamic representation. Simultaneously, TITD integrates multi-view input information from irregular MTS to enhance the representation learning of the relationships across different views, thereby achieving superior forecasting performance without interpolation. Extensive experiments on three real-world time series datasets have demonstrated that TITD provides significant improvements over state-of-the-art methods in irregular MTS forecasting.
Keyword ：
Temporal decay Temporal decay Position encoding Position encoding Time interval Time interval Multi-head attention Multi-head attention Irregular time series Irregular time series
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| GB/T 7714 | Ji, Jinquan , Cao, Yu , Ma, Yukun et al. TITD: enhancing optimized temporal position encoding with time intervals and temporal decay in irregular time series forecasting [J]. | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
| MLA | Ji, Jinquan et al. "TITD: enhancing optimized temporal position encoding with time intervals and temporal decay in irregular time series forecasting" . | APPLIED INTELLIGENCE 55 . 6 (2025) . |
| APA | Ji, Jinquan , Cao, Yu , Ma, Yukun , Yan, Jianzhuo . TITD: enhancing optimized temporal position encoding with time intervals and temporal decay in irregular time series forecasting . | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
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| GB/T 7714 | Jabbar, Muhammad Kashif , Yan, Jianzhuo , Xu, Hongxia et al. Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images (vol 12, 535, 2022) [J]. | BRAIN SCIENCES , 2024 , 14 (8) . |
| MLA | Jabbar, Muhammad Kashif et al. "Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images (vol 12, 535, 2022)" . | BRAIN SCIENCES 14 . 8 (2024) . |
| APA | Jabbar, Muhammad Kashif , Yan, Jianzhuo , Xu, Hongxia , Rehman, Zaka Ur , Jabbar, Ayesha . Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images (vol 12, 535, 2022) . | BRAIN SCIENCES , 2024 , 14 (8) . |
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Abstract ：
With the proliferation of time series data across diverse domains, achieving accurate and high-performance time series representation has become paramount. The intricacies of large-scale datasets characterized by nonlinear and heterogeneous distributions have prompted the exploration of attention models, such as the Transformer architecture, in time series analysis. However, these models often neglect temporal positional relationships among sequences, leading to suboptimal capture of long-term dependencies. This paper introduces an innovative approach that integrates temporal decay positional encoding with ordinary differential equations (ODEs) to formulate decaying position encodings, thereby enhancing time series representation. Additionally, a Spatial Dynamic Correlation Subgraph Module (SDCSM) is proposed to dynamically capture evolving interactions among observations. The presented research significantly boosts representation performance, while also addressing training time and memory space constraints, making it applicable across domains reliant on precise time series representation.
Keyword ：
Multi-Head Attention Mechanism Multi-Head Attention Mechanism Dynamic Correlation Subgraphs Dynamic Correlation Subgraphs Temporal Position Encoding Temporal Position Encoding Time Series Representation Learning Time Series Representation Learning Temporal Decay Temporal Decay
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| GB/T 7714 | Ji, Jinquan , Yan, Jianzhuo , Cao, Yu . Enhancing Time Series Representation Learning with Spatiotemporal Dynamic Encoding [J]. | 2023 IEEE INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WI-IAT , 2023 : 508-512 . |
| MLA | Ji, Jinquan et al. "Enhancing Time Series Representation Learning with Spatiotemporal Dynamic Encoding" . | 2023 IEEE INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WI-IAT (2023) : 508-512 . |
| APA | Ji, Jinquan , Yan, Jianzhuo , Cao, Yu . Enhancing Time Series Representation Learning with Spatiotemporal Dynamic Encoding . | 2023 IEEE INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WI-IAT , 2023 , 508-512 . |
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Abstract ：
Diabetic retinopathy (DR) is a visual obstacle caused by diabetic disease, which forms because of long-standing diabetes mellitus, which damages the retinal blood vessels. This disease is considered one of the principal causes of sightlessness and accounts for more than 158 million cases all over the world. Since early detection and classification could diminish the visual impairment, it is significant to develop an automated DR diagnosis method. Although deep learning models provide automatic feature extraction and classification, training such models from scratch requires a larger annotated dataset. The availability of annotated training datasets is considered a core issue for implementing deep learning in the classification of medical images. The models based on transfer learning are widely adopted by the researchers to overcome annotated data insufficiency problems and computational overhead. In the proposed study, features are extracted from fundus images using the pre-trained network VGGNet and combined with the concept of transfer learning to improve classification performance. To deal with data insufficiency and unbalancing problems, we employed various data augmentation operations differently on each grade of DR. The results of the experiment indicate that the proposed framework (which is evaluated on the benchmark dataset) outperformed advanced methods in terms of accurateness. Our technique, in combination with handcrafted features, could be used to improve classification accuracy.
Keyword ：
transfer learning transfer learning computer-aided diagnosis computer-aided diagnosis fundus images fundus images diabetic retinopathy diabetic retinopathy convolutional neural network convolutional neural network annotated data insufficiency annotated data insufficiency
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| GB/T 7714 | Jabbar, Muhammad Kashif , Yan, Jianzhuo , Xu, Hongxia et al. Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images [J]. | BRAIN SCIENCES , 2022 , 12 (5) . |
| MLA | Jabbar, Muhammad Kashif et al. "Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images" . | BRAIN SCIENCES 12 . 5 (2022) . |
| APA | Jabbar, Muhammad Kashif , Yan, Jianzhuo , Xu, Hongxia , Ur Rehman, Zaka , Jabbar, Ayesha . Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images . | BRAIN SCIENCES , 2022 , 12 (5) . |
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Abstract ：
Epilepsy is a chronic and recurrent brain dysfunction disease. An acute epileptic attack will interfere with a patient's normal behavior and consciousness, having a great impact on their life. The purpose of this study was to design a seizure prediction model to improve the quality of patients' lives and assist doctors in making diagnostic decisions. This paper presents a transformer-based seizure prediction model. Firstly, the time-frequency characteristics of electroencephalogram (EEG) signals were extracted by short-time Fourier transform (STFT). Secondly, a three transformer tower model was used to fuse and classify the features of the EEG signals. Finally, when combined with the attention mechanism of transformer networks, the EEG signal was processed as a whole, which solves the problem of length limitations in deep learning models. Experiments were conducted with a Children's Hospital Boston and the Massachusetts Institute of Technology database to evaluate the performance of the model. The experimental results show that, compared with previous EEG classification models, our model can enhance the ability to use time, frequency, and channel information from EEG signals to improve the accuracy of seizure prediction.
Keyword ：
STFT STFT electroencephalogram electroencephalogram epilepsy epilepsy transformer transformer seizure prediction seizure prediction
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| GB/T 7714 | Yan, Jianzhuo , Li, Jinnan , Xu, Hongxia et al. Seizure Prediction Based on Transformer Using Scalp Electroencephalogram [J]. | APPLIED SCIENCES-BASEL , 2022 , 12 (9) . |
| MLA | Yan, Jianzhuo et al. "Seizure Prediction Based on Transformer Using Scalp Electroencephalogram" . | APPLIED SCIENCES-BASEL 12 . 9 (2022) . |
| APA | Yan, Jianzhuo , Li, Jinnan , Xu, Hongxia , Yu, Yongchuan , Xu, Tianyu . Seizure Prediction Based on Transformer Using Scalp Electroencephalogram . | APPLIED SCIENCES-BASEL , 2022 , 12 (9) . |
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Abstract ：
Water quality prediction is an important research focus in smart water and can provide the support to control and reduce water pollution. However, existing water quality prediction models are mainly data-driven and only rely on various sensor data. This paper proposes a new water quality prediction modeling approach integrating data and knowledge. We develop a water quality prediction framework that combines knowledge graph and deep adversarial networks. The knowledge extraction and management compound extracts the water quality knowledge graph from different knowledge sources by using the deep adversarial joint model. The fusing parameter importance learning compound calculates the contribution of parameters in water quality prediction by taking into account both knowledge and data levels of correlation. Finally, a water quality prediction model combining weighted CNN-LSTM with adversarial learning predicts the values of total nitrogen based on real-time monitoring data. The experimental results on monitoring data from the Juhe River of China show that the proposed model can greatly improve the effect of water quality prediction.
Keyword ：
Knowledge graph Knowledge graph Parameter importance learning Parameter importance learning Water quality prediction Water quality prediction CNN-LSTM CNN-LSTM Adversarial learning Adversarial learning
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| GB/T 7714 | Yan, Jianzhuo , Gao, Qingcai , Yu, Yongchuan et al. Combining knowledge graph with deep adversarial network for water quality prediction [J]. | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2022 , 30 (4) : 10360-10376 . |
| MLA | Yan, Jianzhuo et al. "Combining knowledge graph with deep adversarial network for water quality prediction" . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 30 . 4 (2022) : 10360-10376 . |
| APA | Yan, Jianzhuo , Gao, Qingcai , Yu, Yongchuan , Chen, Lihong , Xu, Zhe , Chen, Jianhui . Combining knowledge graph with deep adversarial network for water quality prediction . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2022 , 30 (4) , 10360-10376 . |
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Abstract ：
With the rapid development of the internet and social media, extracting emergency events from online news reports has become an urgent need for public safety. However, current studies on the text mining of emergency information mainly focus on text classification and event recognition, only obtaining a general and conceptual cognition about an emergency event, which cannot effectively support emergency risk warning, etc. Existing event extraction methods of other professional fields often depend on a domain-specific, well-designed syntactic dependency or external knowledge base, which can offer high accuracy in their professional fields, but their generalization ability is not good, and they are difficult to directly apply to the field of emergency. To address these problems, an end-to-end Chinese emergency event extraction model, called EmergEventMine, is proposed using a deep adversarial network. Considering the characteristics of Chinese emergency texts, including small-scale labelled corpora, relatively clearer syntactic structures, and concentrated argument distribution, this paper simplifies the event extraction with four subtasks as a two-stage task based on the goals of subtasks, and then develops a lightweight heterogeneous joint model based on deep neural networks for realizing end-to-end and few-shot Chinese emergency event extraction. Moreover, adversarial training is introduced into the joint model to alleviate the overfitting of the model on the small-scale labelled corpora. Experiments on the Chinese emergency corpus fully prove the effectiveness of the proposed model. Moreover, this model significantly outperforms other existing state-of-the-art event extraction models.
Keyword ：
text mining text mining event extraction event extraction deep adversarial training deep adversarial training
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| GB/T 7714 | Yan, Jianzhuo , Chen, Lihong , Yu, Yongchuan et al. EmergEventMine: End-to-End Chinese Emergency Event Extraction Using a Deep Adversarial Network [J]. | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION , 2022 , 11 (6) . |
| MLA | Yan, Jianzhuo et al. "EmergEventMine: End-to-End Chinese Emergency Event Extraction Using a Deep Adversarial Network" . | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 11 . 6 (2022) . |
| APA | Yan, Jianzhuo , Chen, Lihong , Yu, Yongchuan , Xu, Hongxia , Gao, Qingcai , Cao, Kunpeng et al. EmergEventMine: End-to-End Chinese Emergency Event Extraction Using a Deep Adversarial Network . | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION , 2022 , 11 (6) . |
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Abstract ：
Water quality prediction is an important means to control and reduce water pollution. Exist water quality prediction models are mainly data driven and only depend on various sensor data. At present, the integration of machine learning, especially deep learning, and the knowledge graph (KG) has become a research hotspot. Many studies have proved that the introduction of domain knowledge can effectively improve the data-driven models. This paper proposes a water quality prediction model integrating KG and deep adversarial network. The KG of water quality is extracted by the joint extraction of entities and relations, and introduced into prediction modeling as prior knowledge for parameter importance learning. A FreeAT-based adversarial learning framework is combined with the deep prediction model to improve the generalization ability of model in a few-slot learning scenario. The experimental results on monitoring data from the Juhe River show that the proposed model can greatly improve the robustness of the model and reduce the prediction error.
Keyword ：
adversarial learning adversarial learning knowledge graph knowledge graph water quality prediction water quality prediction CNN-LSTM CNN-LSTM parameter importance learning parameter importance learning
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| GB/T 7714 | Yan, Jianzhuo , Gao, Qingcai , Chen, Jianhui . A Water Quality Prediction Model Based on Knowledge-enhanced Deep Adversarial Network [J]. | SPECIAL SESSION 2021) , 2021 : 284-289 . |
| MLA | Yan, Jianzhuo et al. "A Water Quality Prediction Model Based on Knowledge-enhanced Deep Adversarial Network" . | SPECIAL SESSION 2021) (2021) : 284-289 . |
| APA | Yan, Jianzhuo , Gao, Qingcai , Chen, Jianhui . A Water Quality Prediction Model Based on Knowledge-enhanced Deep Adversarial Network . | SPECIAL SESSION 2021) , 2021 , 284-289 . |
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Abstract ：
Epilepsy is a common neurological disease characterized by recurrent seizures. Electroencephalography (EEG), which records neural activity, is commonly used to diagnose epilepsy. This paper proposes an Empirical Mode Decomposition (EMD) and Deep Convolutional Neural Network epileptic seizure prediction method. First, the original EEG signals are segmented using 30s sliding windows, and the segmented EEG signal is decomposed into Intrinsic Mode Functions (IMF) and residuals. Then, the entropy features which can better express the signal are extracted from the decomposed components. Finally, a deep convolutional neural network is used to construct the epileptic seizure prediction model. This experiment was conducted on the CHB-MIT Scalp EEG dataset to evaluate the performance of our proposed EMD-CNN epileptic EEG seizure detection model. The experimental results show that, compared with some previous EEG classification models, this model is helpful to improving the accuracy of epileptic seizure prediction.
Keyword ：
Empirical Mode Decomposition Empirical Mode Decomposition Epilepsy Epilepsy Convolutional neural network Convolutional neural network EEG EEG
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| GB/T 7714 | Yan, Jianzhuo , Li, Jinnan , Xu, Hongxia et al. EEG Seizure Prediction Based on Empirical Mode Decomposition and Convolutional Neural Network [J]. | BRAIN INFORMATICS, BI 2021 , 2021 , 12960 : 463-473 . |
| MLA | Yan, Jianzhuo et al. "EEG Seizure Prediction Based on Empirical Mode Decomposition and Convolutional Neural Network" . | BRAIN INFORMATICS, BI 2021 12960 (2021) : 463-473 . |
| APA | Yan, Jianzhuo , Li, Jinnan , Xu, Hongxia , Yu, Yongchuan , Pan, Lexin , Cheng, Xuerui et al. EEG Seizure Prediction Based on Empirical Mode Decomposition and Convolutional Neural Network . | BRAIN INFORMATICS, BI 2021 , 2021 , 12960 , 463-473 . |
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Abstract ：
The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R-2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.
Keyword ：
isolation forest isolation forest one-dimensional residual convolutional neural networks one-dimensional residual convolutional neural networks bi-directional gated recurrent units bi-directional gated recurrent units water quality prediction water quality prediction
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| GB/T 7714 | Yan, Jianzhuo , Liu, Jiaxue , Yu, Yongchuan et al. Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model [J]. | WATER , 2021 , 13 (9) . |
| MLA | Yan, Jianzhuo et al. "Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model" . | WATER 13 . 9 (2021) . |
| APA | Yan, Jianzhuo , Liu, Jiaxue , Yu, Yongchuan , Xu, Hongxia . Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model . | WATER , 2021 , 13 (9) . |
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