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学者姓名：贾克斌
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Abstract ：
Depression has become the prevailing global mental health concern. The accuracy of traditional depression diagnosis methods faces challenges due to diverse factors, making primary identification a complex task. Thus, the imperative lies in developing a method that fulfills objectivity and effectiveness criteria for depression identification. Current research underscores notable disparities in brain activity between individuals with depression and those without. The Electroencephalogram (EEG), as a biologically reflective and easily accessible signal, is widely used to diagnose depression. This article introduces an innovative depression prediction strategy that merges time-frequency complexity and electrode spatial topology to aid in depression diagnosis. Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network. Subsequently, leveraging channel correlation, the brain network adjacency matrix is employed and calculated. The final depression classification is achieved by training and validating a graph convolutional network with graph node features and a brain network adjacency matrix based on channel correlation. The proposed strategy has been validated using two publicly available EEG datasets, MODMA and PRED+CT, achieving notable accuracy rates of 98.30 and 96.51%, respectively. These outcomes affirm the reliability and utility of our proposed strategy in predicting depression using EEG signals. Additionally, the findings substantiate the effectiveness of EEG time-frequency complexity characteristics as valuable biomarkers for depression prediction.
Keyword ：
brain network brain network depression prediction depression prediction EEG signal EEG signal time-frequency complexity time-frequency complexity spatial topology spatial topology graph convolutional network graph convolutional network
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| GB/T 7714 | Liu, Wei , Jia, Kebin , Wang, Zhuozheng . Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology [J]. | FRONTIERS IN NEUROSCIENCE , 2024 , 18 . |
| MLA | Liu, Wei 等. "Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology" . | FRONTIERS IN NEUROSCIENCE 18 (2024) . |
| APA | Liu, Wei , Jia, Kebin , Wang, Zhuozheng . Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology . | FRONTIERS IN NEUROSCIENCE , 2024 , 18 . |
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Abstract ：
As skeleton data becomes increasingly available, Graph Convolutional Networks (GCNs) are popularly adapted to extract the spatial and temporal features for skeleton-based action recognition. However, there are still limitations to be addressed in GCN-based methods. First, the multi-level semantic features fail to be connected, making fine-grained information loss as the network deepens. Second, the cross-scale spatiotempral features fail to be simultaneously considered and refined to focus on informative areas. These limitations lead to the challenge in distinguishing the confusing actions. To address these issues, we propose a cross-scale connection (CSC) structure and a spatiotemporal refinement focus (STRF) module. The CSC aims to bridge the gap between multi-level semantic features. The STRF module refines the cross-scale spatiotemporal features to focus on informative joints in each frame. Both are embedded into the standard GCNs to form the cross-scale spatiotemporal refinement network (CSR-Net). Our proposed CSR-Net explicitly models the cross-scale spatiotemporal information among multi-level semantic representations to boost the distinguishing capability for ambiguous actions. We conduct extensive experiments to demonstrate the effectiveness of our proposed method and it outperforms state-of-the-art methods on the NTU RGB+D 60, NTU-RGB+D 120 and NW-UCLA datasets.
Keyword ：
Skeleton-based action recognition Skeleton-based action recognition cross-scale fusion cross-scale fusion graph convolutional network graph convolutional network
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| GB/T 7714 | Zhang, Yu , Sun, Zhonghua , Dai, Meng et al. Cross-Scale Spatiotemporal Refinement Learning for Skeleton-Based Action Recognition [J]. | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 : 441-445 . |
| MLA | Zhang, Yu et al. "Cross-Scale Spatiotemporal Refinement Learning for Skeleton-Based Action Recognition" . | IEEE SIGNAL PROCESSING LETTERS 31 (2024) : 441-445 . |
| APA | Zhang, Yu , Sun, Zhonghua , Dai, Meng , Feng, Jinchao , Jia, Kebin . Cross-Scale Spatiotemporal Refinement Learning for Skeleton-Based Action Recognition . | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 , 441-445 . |
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Abstract ：
Vehicle passing angles are critical metrics for evaluating the geometric passability of vehicles. The accurate measurement of these angles is essential for route planning in complex terrain and in guiding the production of specialized vehicles. However, the current measurement methods cannot meet the requirements of efficiency, convenience and robustness. This paper presents a novel measurement method by building and measuring the point cloud of a vehicle chassis. Based on this method, a novel measurement system is designed and its effectiveness is verified. In the system, a wheeled robot acquires and processes data after passing underneath the vehicle. Then, we introduce a new approach to reduce the main sources of error when building point clouds beneath the vehicle, achieved by modifying the extraction algorithm and the proportion of different feature points in each frame. Additionally, we present a fast geometric calculation algorithm for calculating the passing angles. The simulation experiment results demonstrate deviations of 0.06252%, 0.01575%, and 0.003987% when comparing the calculated angles to those of the simulated vehicle. The experimental results show that the method and system are effective at acquiring the point cloud of the vehicle and calculating the parameters of passing angles with good data consistency, exhibiting variances of 0.12407, 0.12407, and 0.69804.
Keyword ：
passing angles passing angles information processing information processing LIDAR LIDAR data acquisition data acquisition point cloud point cloud
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| GB/T 7714 | Chen, Jiaping , Jia, Kebin , Wang, Zhiju et al. An Intelligent Measurement Method and System for Vehicle Passing Angles [J]. | APPLIED SCIENCES-BASEL , 2023 , 13 (11) . |
| MLA | Chen, Jiaping et al. "An Intelligent Measurement Method and System for Vehicle Passing Angles" . | APPLIED SCIENCES-BASEL 13 . 11 (2023) . |
| APA | Chen, Jiaping , Jia, Kebin , Wang, Zhiju , Sun, Zhonghua . An Intelligent Measurement Method and System for Vehicle Passing Angles . | APPLIED SCIENCES-BASEL , 2023 , 13 (11) . |
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Abstract ：
The majority of the optical observations collected via spaceborne optical satellites are corrupted by clouds or haze, restraining further applications of Earth observation; thus, exploring an ideal method for cloud removal is of great concern. In this paper, we propose a novel probabilistic generative model named sequential-based diffusion models (SeqDMs) for the cloud-removal task in a remote sensing domain. The proposed method consists of multi-modal diffusion models (MmDMs) and a sequential-based training and inference strategy (SeqTIS). In particular, MmDMs is a novel diffusion model that reconstructs the reverse process of denosing diffusion probabilistic models (DDPMs) to integrate additional information from auxiliary modalities (e.g., synthetic aperture radar robust to the corruption of clouds) to help the distribution learning of main modality (i.e., optical satellite imagery). In order to consider the information across time, SeqTIS is designed to integrate temporal information across an arbitrary length of both the main modality and auxiliary modality input sequences without retraining the model again. With the help of MmDMs and SeqTIS, SeqDMs have the flexibility to handle an arbitrary length of input sequences, producing significant improvements only with one or two additional input samples and greatly reducing the time cost of model retraining. We evaluate our method on a public real-world dataset SEN12MS-CR-TS for a multi-modal and multi-temporal cloud-removal task. Our extensive experiments and ablation studies demonstrate the superiority of the proposed method on the quality of the reconstructed samples and the flexibility to handle arbitrary length sequences over multiple state-of-the-art cloud removal approaches.
Keyword ：
cloud removal cloud removal diffusion models diffusion models multi-modal multi-modal synthetic aperture radar (SAR)-optical synthetic aperture radar (SAR)-optical multi-temporal multi-temporal
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| GB/T 7714 | Zhao, Xiaohu , Jia, Kebin . Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models [J]. | REMOTE SENSING , 2023 , 15 (11) . |
| MLA | Zhao, Xiaohu et al. "Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models" . | REMOTE SENSING 15 . 11 (2023) . |
| APA | Zhao, Xiaohu , Jia, Kebin . Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models . | REMOTE SENSING , 2023 , 15 (11) . |
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Abstract ：
Sleep is crucial for human health. Automatic sleep stage classification based on polysomnogram (PSG) is meaningful for the diagnosis of sleep disorders, which has attracted extensive attention in recent years. Most existing methods could not fully consider the different transitions of sleep stages and fit the visual inspection of sleep experts simultaneously. To this end, we propose a temporal multi-scale hybrid attention network, namely TMHAN, to automatically achieve sleep staging. The temporal multi-scale mechanism incorporates short-term abrupt and long-term periodic transitions of the successive PSG epochs. Furthermore, the hybrid attention mechanism includes 1-D local attention, 2-D global attention, and 2-D contextual sparse multi-head self-attention for three kinds of sequence-level representations. The concatenated representation is subsequently fed into a softmax layer to train an end-to-end model. Experimental results on two benchmark sleep datasets show that TMHAN obtains the best performance compared with several baselines, demonstrating the effectiveness of our model. In general, our work not only provides good classification performance, but also fits the actual sleep staging processes, which makes contribution for the combination of deep learning and sleep medicine.
Keyword ：
Attention mechanism Attention mechanism Temporal multi-scale mechanism Temporal multi-scale mechanism Polysomnogram Polysomnogram Sleep stage classification Sleep stage classification Biomedical signal processing Biomedical signal processing
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| GB/T 7714 | Jin, Zheng , Jia, Kebin . A temporal multi-scale hybrid attention network for sleep stage classification [J]. | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2023 , 61 (9) : 2291-2303 . |
| MLA | Jin, Zheng et al. "A temporal multi-scale hybrid attention network for sleep stage classification" . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 61 . 9 (2023) : 2291-2303 . |
| APA | Jin, Zheng , Jia, Kebin . A temporal multi-scale hybrid attention network for sleep stage classification . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2023 , 61 (9) , 2291-2303 . |
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Abstract ：
Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately.
Keyword ：
data augmentation data augmentation remote sensing remote sensing image segmentation image segmentation deep learning deep learning multi-scale features extraction multi-scale features extraction
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| GB/T 7714 | He, Yan , Jia, Kebin , Wei, Zhihao . Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique [J]. | REMOTE SENSING , 2023 , 15 (9) . |
| MLA | He, Yan et al. "Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique" . | REMOTE SENSING 15 . 9 (2023) . |
| APA | He, Yan , Jia, Kebin , Wei, Zhihao . Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique . | REMOTE SENSING , 2023 , 15 (9) . |
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Abstract ：
Depression has gradually become the most common mental disorder in the world. The accuracy of its diagnosis may be affected by many factors, while the primary diagnosis seems to be difficult to define. Finding a way to identify depression by satisfying both objective and effective conditions is an urgent issue. In this paper, a strategy for predicting depression based on spatiotemporal features is proposed, and is expected to be used in the auxiliary diagnosis of depression. Firstly, electroencephalogram (EEG) signals were denoised through the filter to obtain the power spectra of the three corresponding frequency ranges, Theta, Alpha and Beta. Using orthogonal projection, the spatial positions of the electrodes were mapped to the brainpower spectrum, thereby obtaining three brain maps with spatial information. Then, the three brain maps were superimposed on a new brain map with frequency domain and spatial characteristics. A Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) were applied to extract the sequential feature. The proposed strategy was validated with a public EEG dataset, achieving an accuracy of 89.63% and an accuracy of 88.56% with the private dataset. The network had less complexity with only six layers. The results show that our strategy is credible, less complex and useful in predicting depression using EEG signals.
Keyword ：
spatiotemporal features spatiotemporal features EEG signals EEG signals neural network neural network deep learning deep learning depression prediction depression prediction
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| GB/T 7714 | Liu, Wei , Jia, Kebin , Wang, Zhuozheng et al. A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal [J]. | BRAIN SCIENCES , 2022 , 12 (5) . |
| MLA | Liu, Wei et al. "A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal" . | BRAIN SCIENCES 12 . 5 (2022) . |
| APA | Liu, Wei , Jia, Kebin , Wang, Zhuozheng , Ma, Zhuo . A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal . | BRAIN SCIENCES , 2022 , 12 (5) . |
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Abstract ：
本实用新型专利特别涉及一种MRI兼容的插拔式组织血流检测探头，用于解决目前扩散相关光谱技术组织血流仪测量过程中探头固定的问题。具体包括：探测光纤、光源光纤和柔性探头座。探头柔韧性高，可实现与被测组织的紧密贴合；探测光纤、光源光纤可与柔性固定探头座之间以插拔方式实现安装，探测光纤和光源光纤垂直于柔性探头底面；不同光源和探测间距，可实现不同深度的组织血流检测；探头各组成部分材制均不涉及金属，核磁共振(MRI)兼容。本实用新型专利以插拔方式解决了组织血流测量过程中探测光纤与光源光纤的固定问题，满足扩散相关光谱技术组织血流测量的理论模型要求，提高了组织血流测量的准确性和稳定性。
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| GB/T 7714 | 李哲 , 姜敏楠 , 冯金超 et al. 一种MRI兼容的插拔式组织血流检测探头 : CN202220215504.5[P]. | 2022-01-26 . |
| MLA | 李哲 et al. "一种MRI兼容的插拔式组织血流检测探头" : CN202220215504.5. | 2022-01-26 . |
| APA | 李哲 , 姜敏楠 , 冯金超 , 贾克斌 . 一种MRI兼容的插拔式组织血流检测探头 : CN202220215504.5. | 2022-01-26 . |
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Abstract ：
本实用新型专利特别涉及一种MRI兼容的贴附式组织血流检测探头，用于解决目前扩散相关光谱技术组织血流测量过程中探头固定的问题。具体包括：探测光纤、光源光纤和柔性探头座。探头柔韧性高，可实现与被测组织的紧密贴合；探测光纤和光源光纤平行于柔性探头底面，易于固定；不同光源和探测间距，可实现不同深度的组织血流检测；探头各组成部分材制均不涉及金属，核磁共振(MRI)兼容。本实用新型专利以贴附方式解决了组织血流测量过程中探测光纤与光源光纤的固定问题，满足扩散相关光谱技术组织血流测量的理论模型要求，提高了组织血流测量的准确性和稳定性。
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| GB/T 7714 | 李哲 , 姜敏楠 , 冯金超 et al. 一种MRI兼容的贴附式组织血流检测探头 : CN202220217251.5[P]. | 2022-01-26 . |
| MLA | 李哲 et al. "一种MRI兼容的贴附式组织血流检测探头" : CN202220217251.5. | 2022-01-26 . |
| APA | 李哲 , 姜敏楠 , 冯金超 , 贾克斌 . 一种MRI兼容的贴附式组织血流检测探头 : CN202220217251.5. | 2022-01-26 . |
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Abstract ：
Three Dimensional-High Efficiency Video Coding ( 3D-HEVC) standard is the latest ThreeDimensional (3D) video coding standard, but the coding complexity increases greatly due to the introduction of depth map coding technology. Among them, the quad-tree partition of depth map intra-frame Coding Unit (CU) accounts for more than 90% of the coding complexity in 3D-HEVC. Therefore, for the intra-frame coding of depth map in 3D-HEVC, considering the high complexity of CU quad-tree partition, a fast prediction scheme of CU partition structure based on deep learning is proposed. Firstly, the dataset of CU partition structure information for learning depth map is constructed. Secondly, a Multi- Branch Convolutional Neural Network (MB-CNN) model for predicting the CU partition structure is built. Then, the MB-CNN model is trained by using the built dataset. Finally, the MB- CNN model is embedded into the 3D-HEVC test platform, which reduces greatly the complexity of CU partition by predicting the partition structure of CU in depth map intraframe coding. Experimental results show that the proposed algorithm reduces effectively the coding complexity of 3D-HEVC without significant synthesized view quality distortion. Specifically, compared to the standard method, the coding complexity on the standard test sequence is reduced by 37.4%.
Keyword ：
Three Dimensional-High Efficiency Video Coding(3D-HEVC) Three Dimensional-High Efficiency Video Coding(3D-HEVC) Coding Unit (CU) partition Coding Unit (CU) partition Intra-frame coding Intra-frame coding Depth map Depth map Deep learning Deep learning
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| GB/T 7714 | Liu Chang , Jia Kebin , Liu Pengyu . Fast Partition Algorithm in Depth Map Intra-frame Coding Unit Based on Multi-branch Network [J]. | JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY , 2022 , 44 (12) : 4357-4366 . |
| MLA | Liu Chang et al. "Fast Partition Algorithm in Depth Map Intra-frame Coding Unit Based on Multi-branch Network" . | JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY 44 . 12 (2022) : 4357-4366 . |
| APA | Liu Chang , Jia Kebin , Liu Pengyu . Fast Partition Algorithm in Depth Map Intra-frame Coding Unit Based on Multi-branch Network . | JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY , 2022 , 44 (12) , 4357-4366 . |
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