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学者姓名：冀俊忠
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Abstract ：
Causal discovery from river runoff data aids flood prevention and mitigation strategies, garnering attention in climate and earth science. However, most climate causal discovery methods rely on conditional independence approaches, overlooking the non-stationary characteristics of river runoff data and leading to poor performance. In this paper, we propose a river runoff causal discovery method based on deep reinforcement learning, called RCD-DRL, to effectively learn causal relationships from non-stationary river runoff time series data. The proposed method utilizes an actor-critic framework, which consists of three main modules: an actor module, a critic module, and a reward module. In detail, RCD-DRL first employs the actor module within the encoder-decoder architecture to learn latent features from raw river runoff data, enabling the model to quickly adapt to non-stationary data distributions and generating a causality matrix at different stations. Subsequently, the critic network with two fully connected layers is designed to estimate the value of the current encoded features. Finally, the reward module, based on the Bayesian information criterion (BIC), is used to calculate the reward corresponding to the currently generated causal matrix. Experimental results obtained on both synthetic and real datasets demonstrate the superior performance of the proposed method over the state-of-the-art methods.
Keyword ：
Non-stationary time series Non-stationary time series River runoff River runoff Deep reinforcement learning Deep reinforcement learning Climate causal discovery Climate causal discovery
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| GB/T 7714 | Ji, Junzhong , Wang, Ting , Liu, Jinduo et al. River runoff causal discovery with deep reinforcement learning [J]. | APPLIED INTELLIGENCE , 2024 , 54 (4) : 3547-3565 . |
| MLA | Ji, Junzhong et al. "River runoff causal discovery with deep reinforcement learning" . | APPLIED INTELLIGENCE 54 . 4 (2024) : 3547-3565 . |
| APA | Ji, Junzhong , Wang, Ting , Liu, Jinduo , Wang, Muhua , Tang, Wei . River runoff causal discovery with deep reinforcement learning . | APPLIED INTELLIGENCE , 2024 , 54 (4) , 3547-3565 . |
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Abstract ：
Dynamic effective connectivity (DEC) is the accumulation of effective connectivity in the time dimension, which can describe the continuous neural activities in the brain. Recently, learning DEC from functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data has attracted the attention of neuroinformatics researchers. However, the current methods fail to consider the gap between the fMRI and EEG modality, which can not precisely learn the DEC network from multimodal data. In this paper, we propose a multimodal causal adversarial network for DEC learning, named MCAN. The MCAN contains two modules: multimodal causal generator and multimodal causal discriminator. First, MCAN employs a multimodal causal generator with an attention-guided layer to produce a posterior signal and output a set of DEC networks. Then, the proposed method uses a multimodal causal discriminator to unsupervised calculate the joint gradient, which directs the update of the whole network. The experimental results on simulated data sets show that MCAN is superior to other state-of-the-art methods in learning the network structure of DEC and can effectively estimate the brain states. The experimental results on real data sets show that MCAN can better reveal abnormal patterns of brain activity and has good application potential in brain network analysis.
Keyword ：
adversarial training adversarial training Time series analysis Time series analysis Electroencephalography Electroencephalography Task analysis Task analysis Functional magnetic resonance imaging Functional magnetic resonance imaging multimodal causal learning multimodal causal learning Learning systems Learning systems functional magnetic resonance imaging functional magnetic resonance imaging electroencephalog electroencephalog Feature extraction Feature extraction Generators Generators Brain effective connectivity Brain effective connectivity
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| GB/T 7714 | Liu, Jinduo , Han, Lu , Ji, Junzhong . MCAN: Multimodal Causal Adversarial Networks for Dynamic Effective Connectivity Learning From fMRI and EEG Data [J]. | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2024 , 43 (8) : 2913-2923 . |
| MLA | Liu, Jinduo et al. "MCAN: Multimodal Causal Adversarial Networks for Dynamic Effective Connectivity Learning From fMRI and EEG Data" . | IEEE TRANSACTIONS ON MEDICAL IMAGING 43 . 8 (2024) : 2913-2923 . |
| APA | Liu, Jinduo , Han, Lu , Ji, Junzhong . MCAN: Multimodal Causal Adversarial Networks for Dynamic Effective Connectivity Learning From fMRI and EEG Data . | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2024 , 43 (8) , 2913-2923 . |
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Abstract ：
Automatic generation of medical reports for Brain Computed Tomography (CT) imaging is crucial for helping radiologists make more accurate clinical diagnoses efficiently. Brain CT imaging typically contains rich pathological information, including common pathologies that often co-occur in one report and rare pathologies that appear in medical reports with lower frequency. However, current research ignores the potential co-occurrence between common pathologies and pays insufficient attention to rare pathologies, severely restricting the accuracy and diversity of the generated medical reports. In this paper, we propose a Co-occurrence Relationship Driven Hierarchical Attention Network (CRHAN) to improve Brain CT report generation by mining common and rare pathologies in Brain CT imaging. Specifically, the proposed CRHAN follows a general encoder-decoder framework with two novel attention modules. In the encoder, a co-occurrence relationship guided semantic attention (CRSA) module is proposed to extract the critical semantic features by embedding the co-occurrence relationship of common pathologies into semantic attention. In the decoder, a common-rare topic driven visual attention (CRVA) module is proposed to fuse the common and rare semantic features as sentence topic vectors, and then guide the visual attention to capture important lesion features for medical report generation. Experiments on the Brain CT dataset demonstrate the effectiveness of the proposed method.
Keyword ：
Brain CT Brain CT medical report generation medical report generation Computed tomography Computed tomography Pathology Pathology Medical diagnostic imaging Medical diagnostic imaging Visualization Visualization hierarchical attention mechanism hierarchical attention mechanism Semantics Semantics Feature extraction Feature extraction Biomedical imaging Biomedical imaging Co-occurrence relationship Co-occurrence relationship
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| GB/T 7714 | Zhang, Xiaodan , Dou, Shixin , Ji, Junzhong et al. Co-Occurrence Relationship Driven Hierarchical Attention Network for Brain CT Report Generation [J]. | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2024 . |
| MLA | Zhang, Xiaodan et al. "Co-Occurrence Relationship Driven Hierarchical Attention Network for Brain CT Report Generation" . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2024) . |
| APA | Zhang, Xiaodan , Dou, Shixin , Ji, Junzhong , Liu, Ying , Wang, Zheng . Co-Occurrence Relationship Driven Hierarchical Attention Network for Brain CT Report Generation . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2024 . |
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Abstract ：
Diffusion models have been successfully applied to point cloud generation tasks recently. The main notion is using a forward process to progressively add noises into point clouds and then use a reverse process to generate point clouds by denoising these noises. However, since point cloud data is high-dimensional and exhibits complex structures, it is challenging to adequately capture the surface distribution of point clouds. Moreover, point cloud generation methods often resort to sampling methods and local operations to extract features, which inevitably ignores the global structures and overall shapes of point clouds. To address these limitations, we propose a latent diffusion model based on Transformers for point cloud generation. Instead of directly building a diffusion process based on the points, we first propose a latent compressor to convert original point clouds into a set of latent tokens before feeding them into diffusion models. Converting point clouds as latent tokens not only improves expressiveness, but also exhibits better flexibility since they can adapt to various downstream tasks. We carefully design the latent compressor based on an attention-based auto-encoder architecture to capture global structures in point clouds. Then, we propose to use transformers as the backbones of the latent diffusion module to maintain global structures. The powerful feature extraction ability of transformers guarantees the high quality and smoothness of generated point clouds. Experiments show that our method achieves superior performance in both unconditional generation on ShapeNet and multi-modal point cloud completion on ShapeNet-ViPC. Our code and samples are publicly available at https://github.com/Negai-98/LDT.
Keyword ：
3D 3D Diffusion model Diffusion model Point cloud generation Point cloud generation Transformers Transformers
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| GB/T 7714 | Ji, Junzhong , Zhao, Runfeng , Lei, Minglong . Latent diffusion transformer for point cloud generation [J]. | VISUAL COMPUTER , 2024 , 40 (6) : 3903-3917 . |
| MLA | Ji, Junzhong et al. "Latent diffusion transformer for point cloud generation" . | VISUAL COMPUTER 40 . 6 (2024) : 3903-3917 . |
| APA | Ji, Junzhong , Zhao, Runfeng , Lei, Minglong . Latent diffusion transformer for point cloud generation . | VISUAL COMPUTER , 2024 , 40 (6) , 3903-3917 . |
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Abstract ：
Meta-heuristic algorithms are popular for their efficiency in solving complex optimization problems. Although there are many known algorithms, identifying ways to improve their performance remains an important research area. This paper proposes a brain neuroscience-inspired meta-heuristic algorithm called the Neural Population Dynamics Optimization Algorithm (NPDOA). There are three strategies in NPDOA. (1) The attractor trending strategy drives neural populations towards optimal decisions, thereby ensuring exploitation capability. (2) The coupling disturbance strategy deviates neural populations from attractors by coupling with other neural populations, thus improving exploration ability. (3) The information projection strategy controls the communication between neural populations, enabling a transition from exploration to exploitation. The results of benchmark and practical problems verified the effectiveness of NPDOA.
Keyword ：
algorithm algorithm Neural population dynamics optimization Neural population dynamics optimization Information projection Information projection Attractor trending Attractor trending Coupling disturbance Coupling disturbance Meta-heuristic algorithms Meta-heuristic algorithms
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| GB/T 7714 | Ji, Junzhong , Wu, Tongxuan , Yang, Cuicui . Neural population dynamics optimization algorithm: A novel brain-inspired meta-heuristic method [J]. | KNOWLEDGE-BASED SYSTEMS , 2024 , 300 . |
| MLA | Ji, Junzhong et al. "Neural population dynamics optimization algorithm: A novel brain-inspired meta-heuristic method" . | KNOWLEDGE-BASED SYSTEMS 300 (2024) . |
| APA | Ji, Junzhong , Wu, Tongxuan , Yang, Cuicui . Neural population dynamics optimization algorithm: A novel brain-inspired meta-heuristic method . | KNOWLEDGE-BASED SYSTEMS , 2024 , 300 . |
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Abstract ：
Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi-parameter fluorescence-activated cell sorter dataset, indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.
Keyword ：
bioinformatics bioinformatics data mining data mining swarm intelligence swarm intelligence intelligent signal processing intelligent signal processing computational intelligence computational intelligence machine learning machine learning
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| GB/T 7714 | Liu, Jinduo , Zhai, Jihao , Ji, Junzhong . Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm [J]. | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY , 2024 . |
| MLA | Liu, Jinduo et al. "Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm" . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY (2024) . |
| APA | Liu, Jinduo , Zhai, Jihao , Ji, Junzhong . Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm . | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY , 2024 . |
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Abstract ：
Dynamic Constrained Multiobjective Optimization Problems (DCMOPs) are very difficult to solve because both of the objectives and constraints may change over time. The existing approaches for solving DCMOPs mainly develop dynamic response techniques and constraint handling techniques. But they do not focus on the search capability of the static optimizer in each environment, which ignores the intrinsic requirement of quickly locating Pareto-optimal Front (PF) in each environment when solving DCMOPs. To this end, this paper proposes a dual -population evolutionary algorithm for solving DCMOPs, called as DpEA, which maintains a population without considering constraints (called UP) for exploration and a population with considering constraints (called CP) for exploitation in each environment. In each iteration of a new environment, UP firstly adopts a stratified mutation strategy (SMS) and a dominated solution repairment strategy (DSR) to enhance the exploration ability of finding promising regions where the PF may reside. SMS uses solutions from different nondominated fronts to generate offspring, while DSR repairs the single -optimal variables of the dominated solutions by sampling from the distribution of those variables of nondominated solutions. Secondly, this paper uses an adaptive offspring ratio adjustment strategy to control the offspring number generated by UP and CP according to the normalized Hausdorff distance between nondominated solution sets from the two latest generations of UP. This strategy is helpful to balance the intensity between exploration and exploitation and thereby ensures efficient search. Experimental results on CEC 2023 DCF test suite show that DpEA has a superior performance over six state -of -the -art algorithms.
Keyword ：
Dynamic constrained multiobjective Dynamic constrained multiobjective Dominated solution repairment Dominated solution repairment optimization problems optimization problems Offspring generation ratio Offspring generation ratio Stratified mutation Stratified mutation Multiobjective evolutionary algorithms Multiobjective evolutionary algorithms
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| GB/T 7714 | Yang, Cuicui , Sui, Guangyuan , Ji, Junzhong et al. DpEA: A dual-population evolutionary algorithm for dynamic constrained multiobjective optimization [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 255 . |
| MLA | Yang, Cuicui et al. "DpEA: A dual-population evolutionary algorithm for dynamic constrained multiobjective optimization" . | EXPERT SYSTEMS WITH APPLICATIONS 255 (2024) . |
| APA | Yang, Cuicui , Sui, Guangyuan , Ji, Junzhong , Li, Xiang , Zhang, Xiaoyu . DpEA: A dual-population evolutionary algorithm for dynamic constrained multiobjective optimization . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 255 . |
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Abstract ：
本发明公开了一种基于超维计算辅助的车辆耐撞性多目标优化方法，首先将所有已被评估的车辆结构均匀划分为规模相同的“优质结构集合”和“劣质结构集合”，为构建分类模型提供平衡的训练数据。使用一种近似结构厚度值编码方式，将所有结构编码为相应的超向量。将这些超向量根据所属类别按位相加来构建分类模型，得到表示“优质结构”和“劣质结构”的类别超向量。最后，使用遗传算子新生成的候选结构以相同的方式编码为超向量，并使用余弦相似度计算与类别超向量的相似性来预测候选结构的类别。预测类别为“优质结构”的候选结构会被筛选出来并被真实的目标函数评估。本方法在求解标准测试问题集和车辆耐撞性优化问题时有着更好的效果。
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| GB/T 7714 | 冀俊忠 , 吴同轩 , 杨翠翠 . 一种基于超维计算辅助的车辆耐撞性多目标优化方法 : CN202310600838.3[P]. | 2023-05-25 . |
| MLA | 冀俊忠 et al. "一种基于超维计算辅助的车辆耐撞性多目标优化方法" : CN202310600838.3. | 2023-05-25 . |
| APA | 冀俊忠 , 吴同轩 , 杨翠翠 . 一种基于超维计算辅助的车辆耐撞性多目标优化方法 : CN202310600838.3. | 2023-05-25 . |
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Abstract ：
本发明公开了一种基于蚁群优化算法的电动汽车充电站选址方法，基于覆盖优先级的解构造策略，使蚁群获得解元素的全局构造顺序，从而合理安排选址对充电需求的覆盖顺序，避免显性选址重叠；采用基于轨迹覆盖关系的启发信息调整策略，在迭代中根据历史最优解来调整启发信息降低出现隐性选址重叠的概率；在每轮迭代中，通过CP‑SC构造充电设施选址问题的解，每构造一个可行解，都会进行一次局部信息素更新；在每轮迭代结束前，根据历史最优解的目标函数值即充电需求强度来进行全局信息素更新，并通过TCHA策略调整启发信息；迭代达到终止条件后，输出历史最优解。本方法不仅降低选址重叠率，还提高了充电需求的覆盖量。
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| GB/T 7714 | 冀俊忠 , 刘钺锋 , 杨翠翠 . 一种基于蚁群优化算法的电动汽车充电站选址方法 : CN202310759998.2[P]. | 2023-06-27 . |
| MLA | 冀俊忠 et al. "一种基于蚁群优化算法的电动汽车充电站选址方法" : CN202310759998.2. | 2023-06-27 . |
| APA | 冀俊忠 , 刘钺锋 , 杨翠翠 . 一种基于蚁群优化算法的电动汽车充电站选址方法 : CN202310759998.2. | 2023-06-27 . |
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Abstract ：
本发明公开了一种基于深度哈希互学习的脑网络分类方法，包括：数据预处理和脑功能网络构建；脑网络数据划分；基于深度哈希学习的个体特征提取；基于深度哈希学习的群体特征提取；基于哈希码的互学习；基于哈希码的分类。本发明首次考虑到群体脑网络中的表型标签差异，采用表型标签构建群体脑网络关系图，提出一种基于GCN的深度哈希学习模型提取脑网络的群体特征；并考虑到脑网络个体特征和群体特征间的关系，采用基于深度哈希互学习的脑网络分类方法通过个体特征和群体特征之间的互学习来增强特征的辨别能力。本方法与其他方法相比，分类性能更优。
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| GB/T 7714 | 冀俊忠 , 张雅琴 . 一种基于深度哈希互学习的脑网络分类方法 : CN202310522896.9[P]. | 2023-05-10 . |
| MLA | 冀俊忠 et al. "一种基于深度哈希互学习的脑网络分类方法" : CN202310522896.9. | 2023-05-10 . |
| APA | 冀俊忠 , 张雅琴 . 一种基于深度哈希互学习的脑网络分类方法 : CN202310522896.9. | 2023-05-10 . |
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