Cognitive Robotics最新文献

英文中文

PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification PCCNN：一种集成脑电时频特征的CNN分类模型，用于脑卒中分类

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.05.002

Teng Wang , Fenglian Li , Jia Yang , Wenhui Jia , Fengyun Hu

Stroke classification is crucial for timely diagnosis and treatment, as it helps differentiate between hemorrhagic and ischemic strokes, which require distinct clinical interventions. This paper proposes a stroke classification method using multi-channel electroencephalography (EEG) data. Unlike single-channel data or simple multi-channel concatenation, our method processes EEG data as a channel matrix, significantly improving classification performance. We employ two complementary feature extraction techniques: discrete wavelet transform (DWT) and empirical mode decomposition (EMD). DWT extracts multi-scale wavelet coefficients from stroke-related frequency bands, while EMD decomposes EEG signals into intrinsic mode functions (IMFs), representing narrowband oscillation components. To enhance feature quality, we propose a hybrid selection method that integrates four metrics—information entropy, power spectral density (PSD) distance, statistical significance, and maximum information coefficient (MIC)—to comprehensively evaluate IMFs. This method accounts for both the intrinsic information content of EEG signals and the inter-class differences between hemorrhagic and ischemic stroke subjects. Furthermore, this paper designs a pyramid cascade convolutional neural network (PCCNN) model with multi-branch independent learning and hierarchical fusion. Each DWT and EMD feature is processed by an independent one-dimensional convolutional neural networks (1D-CNN) branch for targeted extraction. A pyramid fusion mechanism integrates branch outputs into a fused feature vector, enabling the feature interaction through a top-level fusion CNN. Experimental results demonstrate that the proposed method, which integrates channel matrix processing, high-quality DWT and EMD feature selection, and multi-branch feature fusion, significantly outperforms single-feature methods. The fusion feature achieves a classification accuracy of 99.48 %, effectively distinguishing EEG data of hemorrhagic and ischemic stroke.

中风分类对于及时诊断和治疗至关重要，因为它有助于区分出血性和缺血性中风，这需要不同的临床干预措施。提出了一种基于多通道脑电图数据的脑卒中分类方法。与单通道数据或简单的多通道拼接不同，我们的方法将脑电数据作为通道矩阵处理，显著提高了分类性能。我们采用了两种互补的特征提取技术：离散小波变换（DWT）和经验模态分解（EMD）。DWT从脑卒中相关频带提取多尺度小波系数，EMD将脑电信号分解为表征窄带振荡分量的内禀模态函数（IMFs）。为了提高特征质量，我们提出了一种综合信息熵、功率谱密度（PSD）距离、统计显著性和最大信息系数（MIC）四个指标的混合选择方法来综合评价imf。该方法既考虑了脑电图信号的固有信息量，又考虑了出血性脑卒中与缺血性脑卒中受试者的类间差异。在此基础上，设计了一种具有多分支独立学习和层次融合的金字塔级联卷积神经网络模型。每个DWT和EMD特征由一个独立的一维卷积神经网络（1D-CNN）分支进行处理，进行有针对性的提取。金字塔融合机制将分支输出整合为融合的特征向量，通过顶层融合CNN实现特征交互。实验结果表明，该方法集成了信道矩阵处理、高质量DWT和EMD特征选择以及多分支特征融合，显著优于单特征方法。该融合特征分类准确率达99.48%，可有效区分出血性脑卒中和缺血性脑卒中。

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引用次数: 0

Zero-dynamics attack detection based on data association in feedback pathway 基于反馈路径数据关联的零动态攻击检测

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.03.003

Zeyu Zhang , Hongran Li , Yuki Todo

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results.

本文研究了非最小相位系统的安全性问题，这是一种典型的网络物理系统。非最小相位系统的特征是其传递函数中存在不稳定的零，这使得它们特别容易受到干扰和攻击。非最小相位系统比最小相位系统更容易受到零动态攻击（ZDA）。ZDA是一种隐蔽的攻击策略，它利用系统的内部动态，在导致系统逐渐不稳定的同时保持不可检测。最近的网络事件表明，在电网和交通系统等关键基础设施中，这种隐性攻击的风险越来越大。本文首先论证了现有的零动态攻击算法存在陷入局部收敛的局限性，然后提出了一种增强的零动态攻击算法（EZDA），该算法通过发散系统数据来克服局部收敛问题。在此基础上，提出了一种自回归模型，可以在原始数据和伪造数据之间建立数据关联。通过观察状态值的波动，该模型不仅可以检测到ZDA，还可以检测到EZDA。最后通过数值模拟和应用实例对理论结果进行了验证。

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引用次数: 0

Cognitive Robotics

Pub Date : 2025-01-01

引用次数: 0

Cognitive Robotics

Pub Date : 2025-01-01

引用次数: 0

Cognitive Robotics

Pub Date : 2025-01-01

引用次数: 0

A transformation model for vision-based navigation of agricultural robots 农业机器人视觉导航的转换模型

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.03.002

Abdelkrim Abanay , Lhoussaine Masmoudi , Dirar Benkhedra , Khalid El Amraoui , Mouataz Lghoul , Javier-Gonzalez Jimenez , Francisco-Angel Moreno

This paper presents a Top-view Transformation Model (TTM) for a vision-based autonomous navigation of an agricultural mobile robot. The TTM transforms images captured by an onboard camera into a virtual Top-view, eliminating perspective distortions such as the vanishing point effect and ensuring uniform pixel distribution. The transformed images are analyzed to ensure an autonomous navigation of the robot between crop rows. The navigation method involves real-time estimation of the robot's position relative to crop rows and the control low is derived from the estimated robot's heading and lateral offset for steering the robot along the crop rows. A simulated scenario has been generated in Gazebo in order to implement the developed approach using the Robot Operating System (ROS), while an evaluation on a real agricultural mobile robot has also been performed. The experimental results demonstrate the feasibility of the TTM approach and its implementation for autonomous navigation, reaching good performance.

提出了一种基于视觉的农业移动机器人自主导航俯视图转换模型。TTM将机载摄像机拍摄的图像转换为虚拟顶视图，消除了视角失真，如消失点效应，并确保均匀的像素分布。对变换后的图像进行分析，以确保机器人在作物行之间自主导航。导航方法包括实时估计机器人相对于作物行的位置，并且根据估计的机器人的航向和横向偏移量推导出控制低，以便沿着作物行的方向操纵机器人。为了利用机器人操作系统（ROS）实现所开发的方法，在Gazebo中生成了一个模拟场景，同时对一个真实的农业移动机器人进行了评估。实验结果证明了TTM方法及其在自主导航中的可行性，取得了良好的性能。

引用次数: 0

A multi-view graph neural network approach for magnetic resonance imaging-based diagnosis of knee injuries 基于磁共振成像的膝关节损伤诊断的多视图神经网络方法

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.05.001

Biyong Deng , Jiashan Pan , Xiaoyu Tang , Haitao Fu , Shushan Hu

The knee plays a pivotal role in the human anatomy, serving as a cornerstone for support, mobility, shock attenuation, and balance. Currently, magnetic resonance imaging (MRI) remains the preferred method for diagnosing knee injuries, including anterior cruciate ligament (ACL) tears and meniscal tears, due to its efficiency and accuracy in medical imaging. However, the interpretation and understanding of knee MRI images are time-consuming, laborious, require sufficient expertise, and are also prone to diagnostic errors. Thus, it is imperative to devise a computational method employing knee MRI for intelligent diagnosis of knee injuries, as this could expedite medical assessments by physicians, reduce costs, and substantially reduce the risk of misdiagnosis. Although several computational methods have been proposed to diagnose knee injuries, most rely heavily on local features in MRI images and exhibit low prediction accuracy. In this paper, we proposed a novel multi-view graph neural network, abbreviated as MVGNN, to identify knee injuries (specifically ACL tears and meniscal tears) by leveraging graph representations derived from multiple MRI views. Comprehensive experiments demonstrate that MVGNN achieves state-of-the-art results for diagnosing knee injuries, with a 5.9% improvement in accuracy on ACL data and a 6.5% improvement on Men data, compared to the second-best method, MVCNN.

膝关节在人体解剖学中扮演着关键的角色，是支撑、活动、减震和平衡的基石。目前，磁共振成像（MRI）仍然是诊断膝关节损伤的首选方法，包括前交叉韧带（ACL）撕裂和半月板撕裂，由于其在医学成像中的效率和准确性。然而，膝关节MRI图像的解释和理解是费时费力的，需要足够的专业知识，也容易出现诊断错误。因此，设计一种利用膝关节MRI对膝关节损伤进行智能诊断的计算方法势在必行，因为这可以加快医生的医疗评估，降低成本，并大大降低误诊的风险。虽然已经提出了几种计算方法来诊断膝关节损伤，但大多数方法严重依赖于MRI图像中的局部特征，预测精度较低。在本文中，我们提出了一种新的多视图图神经网络，简称为MVGNN，通过利用来自多个MRI视图的图表示来识别膝关节损伤（特别是ACL撕裂和半月板撕裂）。综合实验表明，与第二好的方法MVCNN相比，MVGNN在诊断膝关节损伤方面取得了最先进的结果，ACL数据的准确率提高了5.9%，Men数据的准确率提高了6.5%。

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引用次数: 0

Integrated model for segmentation of glomeruli in kidney images 肾脏图像中肾小球分割的集成模型

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2024.11.007

Gurjinder Kaur, Meenu Garg, Sheifali Gupta

Kidney diseases, especially those that affect the glomeruli, have become more common worldwide in recent years. Accurate and early detection of glomeruli is critical for accurately diagnosing kidney problems and determining the most effective treatment options. Our study proposed an advanced model, FResMRCNN, an enhanced version of Mask R-CNN, for automatically detecting and segmenting the glomeruli in PAS-stained human kidney images. The model integrates the power of FPN with a ResNet101 backbone, which was selected after assessing seven different backbone architectures. The integration of FPN and ResNet101 into the FResMRCNN model improves glomeruli detection, segmentation accuracy and stability by representing multi-scale features. We trained and tested our model using the HuBMAP Kidney dataset, which contains high-resolution PAS-stained microscopy images. During the study, the effectiveness of our proposed model is examined by generating bounding boxes and predicted masks of glomeruli. The performance of the FResMRCNN model is evaluated using three performance metrics, including the Dice coefficient, Jaccard index, and binary cross-entropy loss, which show promising results in accurately segmenting glomeruli.

肾脏疾病，特别是影响肾小球的疾病，近年来在世界范围内变得越来越常见。准确和早期发现肾小球对于准确诊断肾脏问题和确定最有效的治疗方案至关重要。我们的研究提出了一种先进的模型，FResMRCNN，一种增强版的Mask R-CNN，用于自动检测和分割pas染色的人肾脏图像中的肾小球。该模型将FPN的功能与ResNet101骨干网集成在一起，在评估了七种不同的骨干网架构后选择了ResNet101骨干网。将FPN和ResNet101集成到FResMRCNN模型中，通过表示多尺度特征，提高了肾小球的检测、分割精度和稳定性。我们使用HuBMAP肾脏数据集来训练和测试我们的模型，该数据集包含高分辨率pas染色显微镜图像。在研究过程中，我们提出的模型的有效性是通过生成边界框和肾小球的预测掩膜来检验的。使用Dice系数、Jaccard指数和二元交叉熵损失三个性能指标来评估FResMRCNN模型的性能，这些指标在准确分割肾小球方面显示出很好的效果。

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引用次数: 0

Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks 基于混合机器学习的无人机辅助无线网络三维无人机节点定位

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.01.002

Workeneh Geleta Negassa, Demissie J. Gelmecha, Ram Sewak Singh, Davinder Singh Rathee

This paper presents a hybrid machine-learning framework for optimizing 3-Dimensional (3D) Unmanned Aerial Vehicles (UAV) node localization and resource distribution in UAV-assisted THz 6G networks to ensure efficient coverage in dynamic, high-density environments. The proposed model efficiently managed interference, adapted to UAV mobility, and ensured optimal throughput by dynamically optimizing UAV trajectories. The hybrid framework combined the strengths of Graph Neural Networks (GNN) for feature aggregation, Deep Neural Networks (DNN) for efficient resource allocation, and Double Deep Q-Networks (DDQN) for distributed decision-making. Simulation results demonstrated that the proposed model outperformed traditional machine learning models, significantly improving energy efficiency, latency, and throughput. The hybrid model achieved an optimized energy efficiency of 90 Tbps/J, reduced latency to 0.0105 ms, and delivered a network throughput of approximately 96 Tbps. The model adapts to varying link densities, maintaining stable performance even in high-density scenarios. These findings underscore the framework's potential to address key challenges in UAV-assisted 6G networks, paving the way for scalable and efficient communication in next-generation wireless systems.

本文提出了一种混合机器学习框架，用于优化无人机辅助太赫兹6G网络中的三维（3D）无人机（UAV）节点定位和资源分配，以确保在动态、高密度环境中有效覆盖。该模型有效地管理了干扰，适应了无人机的移动性，并通过动态优化无人机轨迹来保证最优吞吐量。该混合框架结合了用于特征聚合的图神经网络（GNN）、用于有效资源分配的深度神经网络（DNN）和用于分布式决策的双深度q网络（DDQN）的优势。仿真结果表明，该模型优于传统的机器学习模型，显著提高了能量效率、延迟和吞吐量。该混合模型实现了90 Tbps/J的优化能效，将延迟降低到0.0105 ms，并提供了约96 Tbps的网络吞吐量。该模型可以适应不同的链路密度，即使在高密度场景下也能保持稳定的性能。这些发现强调了该框架在解决无人机辅助6G网络关键挑战方面的潜力，为下一代无线系统的可扩展和高效通信铺平了道路。

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引用次数: 0

Improvement of multi-parameter anomaly detection method: Addition of a relational token between parameters 改进多参数异常检测方法：在参数之间添加关系标记

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.03.004

Hironori Uchida , Keitaro Tominaga , Hideki Itai , Yujie Li , Yoshihisa Nakatoh

In the continuous development of systems, the increasing volume and complexity of data that engineers must analyze have become significant challenges. To address this issue, extensive research has been conducted on automated anomaly detection in logs. However, due to the limited variety of available datasets, most studies have focused on sequence-based anomalies in logs, with relatively little attention paid to parameter-based anomaly detection. To bridge this gap, we prepared a labeled dataset specifically designed for parameter-based anomaly detection and propose a novel method utilizing BERTMaskedLM. Since continuously changing logs in system development are difficult to label, we also propose a method that enables learning without labeled data. Previous studies have employed BERTMaskedLM to capture relationships between parameters in multi-parameter logs for anomaly detection. However, a known issue arises when the ranges of numerical parameters overlap, resulting in reduced detection accuracy. To mitigate this, we introduced tokens that encode the relationships between parameters, improving the independence of parameter combinations and enhancing anomaly detection accuracy (increasing the F1-score by more than 0.002). In this study, we employed a simple yet effective approach by using the total value of each token as the added token. Since only the parameter portions vary within the same log template structure, these proposed tokens effectively capture the relationships between parameters. Additionally, we visualized the influence of the added tokens and conducted experiments using a new dataset to assess the reliability of our proposed method.

在系统的不断发展中，工程师必须分析的数据量和复杂性的增加已经成为重大挑战。为了解决这个问题，人们对日志中的自动异常检测进行了广泛的研究。然而，由于可用数据集的种类有限，大多数研究都集中在基于序列的测井异常上，而对基于参数的异常检测的关注相对较少。为了弥补这一差距，我们准备了一个专门用于基于参数的异常检测的标记数据集，并提出了一种利用BERTMaskedLM的新方法。由于系统开发中不断变化的日志很难标记，我们还提出了一种方法，可以在没有标记数据的情况下进行学习。以前的研究使用BERTMaskedLM捕获多参数日志中参数之间的关系，用于异常检测。然而，当数值参数的范围重叠时，会出现一个已知的问题，导致检测精度降低。为了缓解这种情况，我们引入了对参数之间的关系进行编码的令牌，提高了参数组合的独立性，提高了异常检测的准确性（将f1分数提高了0.002以上）。在本研究中，我们采用了一种简单而有效的方法，即使用每个令牌的总价值作为添加的令牌。由于在相同的日志模板结构中只有参数部分不同，因此这些建议的令牌有效地捕获了参数之间的关系。此外，我们可视化了添加令牌的影响，并使用新的数据集进行了实验，以评估我们提出的方法的可靠性。

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