Cognitive Robotics最新文献

英文中文

Zero-shot intelligent fault diagnosis via semantic fusion embedding

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2024.12.001

Honghua Xu, Zijian Hu, Ziqiang Xu, Qilong Qian

Most fault diagnosis studies rely on the man-made data collected in laboratory where the operation conditions are under control and stable. However, they can hardly adapt to the practical conditions since the man-made data can hardly model the fault patterns across domains. Aiming to solve this problem, this paper proposes a novel deep fault semantic fusion embedding model (DFSFEM) to realize zero-shot intelligent fault diagnosis. The novelties of DFSFEM lie in two aspects. On the one hand, a novel semantic fusion embedding module is proposed to enhance the representability and adaptability of the feature learning across domains. On the other hand, a neural network-based metric module is designed to replace traditional distance measurements, enhancing the transferring capability between domains. These novelties jointly help DFSFEM provide prominent faithful diagnosis on unseen fault types. Experiments on bearing datasets are conducted to evaluate the zero-shot intelligent fault diagnosis performance. Extensive experimental results and comprehensive analysis demonstrate the superiority of the proposed DFSFEM in terms of diagnosis correctness and adaptability.

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引用次数: 0

DECTNet: A detail enhanced CNN-Transformer network for single-image deraining

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2024.12.002

Liping Wang , Guangwei Gao

Recently, Convolutional Neural Networks (CNN) and Transformers have been widely adopted in image restoration tasks. While CNNs are highly effective at extracting local information, they struggle to capture global context. Conversely, Transformers excel at capturing global information but often face challenges in preserving spatial and structural details. To address these limitations and harness both global and local features for single-image deraining, we propose a novel approach called the Detail Enhanced CNN-Transformer Network (DECTNet). DECTNet integrates two key components: the Enhanced Residual Feature Distillation Block (ERFDB) and the Dual Attention Spatial Transformer Block (DASTB). In the ERFDB, we introduce a mixed attention mechanism, incorporating channel information-enhanced layers within the residual feature distillation structure. This design facilitates a more effective step-by-step extraction of detailed information, enabling the network to restore fine-grained image details progressively. Additionally, in the DASTB, we utilize spatial attention to refine features obtained from multi-head self-attention, while the feed-forward network leverages channel information to enhance detail preservation further. This complementary use of CNNs and Transformers allows DECTNet to balance global context understanding with detailed spatial restoration. Extensive experiments have demonstrated that DECTNet outperforms some state-of-the-art methods on single-image deraining tasks. Furthermore, our model achieves competitive results on three low-light datasets and a single-image desnowing dataset, highlighting its versatility and effectiveness across different image restoration challenges.

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引用次数: 0

Attention-assisted dual-branch interactive face super-resolution network

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.01.001

Xujie Wan , Siyu Xu , Guangwei Gao

We propose a deep learning-based Attention-Assisted Dual-Branch Interactive Network (ADBINet) to improve facial super-resolution by addressing key challenges like inadequate feature extraction and poor multi-scale information handling. ADBINet features a multi-scale encoder-decoder architecture that captures and integrates features across scales, enhancing detail and reconstruction quality. The key to our approach is the Transformer and CNN Interaction Module (TCIM), which includes a Dual Attention Collaboration Module (DACM) for improved local and spatial feature extraction. The Channel Attention Guidance Module (CAGM) refines CNN and Transformer fusion, ensuring precise facial detail restoration. Additionally, the Attention Feature Fusion Unit (AFFM) optimizes multi-scale feature integration. Experimental results demonstrate that ADBINet outperforms existing methods in both quantitative and qualitative facial super-resolution metrics.

引用次数: 0

Small target drone algorithm in low-altitude complex urban scenarios based on ESMS-YOLOv7

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2024.11.004

Yuntao Wei, Xiujia Wang, Chunjuan Bo, Zhan Shi

The increasing use and militarization of UAV technology presents significant challenges to nations and societies. Notably, there is a deficit in anti- UAV technologies for civilian use, particularly in complex urban environments at low altitudes. This paper proposes the ESMS-YOLOv7 algorithm, which is specifically engineered to detect small target UAVs in such challenging urban landscapes. The algorithm focuses on the extraction of features from small target UAVs in urban contexts. Enhancements to YOLOv7 include the integration of the ELAN-C module, the SimSPPFCSPC-R module, and the MP-CBAM module, which collectively improve the network's ability to extract features and focus on small target UAVs. Additionally, the SIOU loss function is employed to increase the model's robustness. The effectiveness of the ESMS-YOLOv7 algorithm is validated through its performance on the DUT Anti-UAV dataset, where it exhibits superior capabilities relative to other leading algorithms.

引用次数: 0

Integrated model for segmentation of glomeruli in kidney images

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.

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引用次数: 0

Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks

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.

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引用次数: 0

LiPE: Lightweight human pose estimator for mobile applications towards automated pose analysis

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2024.11.005

Chengxiu Li , Ni Duan

Current human pose estimation models adopt heavy backbones and complex feature enhance- ment modules to pursue higher accuracy. However, they ignore the need for model efficiency in real-world applications. In real-world scenarios such as sports teaching and automated sports analysis for better preservation of traditional folk sports, human pose estimation often needs to be performed on mobile devices with limited computing resources. In this paper, we propose a lightweight human pose estimator termed LiPE. LiPE adopts a lightweight MobileNetV2 backbone for feature extraction and lightweight depthwise separable deconvolution modules for upsampling. Predictions are made at a high resolution with a lightweight prediction head. Compared with the baseline, our model reduces MACs by 93.2 %, and reduces the number of parameters by 93.9 %, while the accuracy drops by only 3.2 %. Based on LiPE, we develop a real- time human pose estimation and evaluation system for automated pose analysis. Experimental results show that our LiPE achieves high computational efficiency and good accuracy for application on mobile devices.

引用次数: 0

Mobile robot path planning using deep deterministic policy gradient with differential gaming (DDPG-DG) exploration 利用深度确定性策略梯度与微分博弈（DDPG-DG）探索移动机器人路径规划

Cognitive Robotics

Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.08.002

Shripad V. Deshpande , Harikrishnan R , Babul Salam KSM Kader Ibrahim , Mahesh Datta Sai Ponnuru

Mobile robot path planning involves decision-making in uncertain, dynamic conditions, where Reinforcement Learning (RL) algorithms excel in generating safe and optimal paths. The Deep Deterministic Policy Gradient (DDPG) is an RL technique focused on mobile robot navigation. RL algorithms must balance exploitation and exploration to enable effective learning. The balance between these actions directly impacts learning efficiency.

This research proposes a method combining the DDPG strategy for exploitation with the Differential Gaming (DG) strategy for exploration. The DG algorithm ensures the mobile robot always reaches its target without collisions, thereby adding positive learning episodes to the memory buffer. An epsilon-greedy strategy determines whether to explore or exploit. When exploration is chosen, the DG algorithm is employed. The combination of DG strategy with DDPG facilitates faster learning by increasing the number of successful episodes and reducing the number of failure episodes in the experience buffer. The DDPG algorithm supports continuous state and action spaces, resulting in smoother, non-jerky movements and improved control over the turns when navigating obstacles. Reward shaping considers finer details, ensuring even small advantages in each iteration contribute to learning.

Through diverse test scenarios, it is demonstrated that DG exploration, compared to random exploration, results in an average increase of 389% in successful target reaches and a 39% decrease in collisions. Additionally, DG exploration shows a 69% improvement in the number of episodes where convergence is achieved within a maximum of 2000 steps.

移动机器人路径规划涉及在不确定的动态条件下进行决策，而强化学习（RL）算法在生成安全和最优路径方面表现出色。深度确定性策略梯度（DDPG）是一种专注于移动机器人导航的 RL 技术。RL 算法必须兼顾利用和探索，才能实现有效学习。本研究提出了一种方法，将用于开发的 DDPG 策略与用于探索的差分博弈（DG）策略相结合。DG 算法可确保移动机器人始终在无碰撞的情况下到达目标，从而为记忆缓冲区增加积极的学习事件。ε-贪婪策略决定是探索还是利用。当选择探索时，则采用 DG 算法。将 DG 策略与 DDPG 算法相结合，可以增加经验缓冲区中成功事件的数量，减少失败事件的数量，从而加快学习速度。DDPG 算法支持连续的状态和动作空间，从而使动作更平滑、不生涩，并改善了导航障碍物时对转弯的控制。奖励塑造考虑到了更精细的细节，确保每次迭代中的微小优势也能促进学习。通过各种测试场景证明，与随机探索相比，DG 探索使成功到达目标的次数平均增加了 389%，碰撞次数减少了 39%。此外，DG探索在最多2000步内实现收敛的次数提高了69%。

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引用次数: 0

Emerging trends in human upper extremity rehabilitation robot 人体上肢康复机器人的新趋势

Cognitive Robotics

Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.09.001

Sk. Khairul Hasan, Subodh B. Bhujel, Gabrielle Sara Niemiec

Stroke is a leading cause of neurological disorders that result in physical disability, particularly among the elderly. Neurorehabilitation plays a crucial role in helping stroke patients recover from physical impairments and regain mobility. Physical therapy is one of the most effective forms of neurorehabilitation, but the growing number of patients requires a large workforce of trained therapists, which is currently insufficient. Robotic rehabilitation offers a promising alternative, capable of supplementing or even replacing human-assisted physical therapy through the use of rehabilitation robots. To design effective robotic devices for rehabilitation, a solid foundation of knowledge is essential. This article provides a comprehensive overview of the key elements needed to develop human upper extremity rehabilitation robots. It covers critical aspects such as upper extremity anatomy, joint range of motion, anthropometric parameters, disability assessment techniques, and robot-assisted training methods. Additionally, it reviews recent advancements in rehabilitation robots, including exoskeletons, end-effector-based robots, and planar robots. The article also evaluates existing upper extremity rehabilitation robots based on their mechanical design and functionality, identifies their limitations, and suggests future research directions for further improvement.

中风是导致身体残疾的神经系统疾病的主要原因，尤其是在老年人中。神经康复在帮助脑卒中患者从肢体损伤中康复并恢复行动能力方面发挥着至关重要的作用。物理治疗是最有效的神经康复方式之一，但由于患者人数不断增加，需要大量训练有素的治疗师，而目前这方面的人才还很缺乏。机器人康复提供了一种前景广阔的替代方案，通过使用康复机器人，能够补充甚至取代人类辅助物理治疗。要设计出有效的康复机器人设备，扎实的知识基础必不可少。本文全面概述了开发人类上肢康复机器人所需的关键要素。它涵盖了上肢解剖、关节活动范围、人体测量参数、残疾评估技术和机器人辅助训练方法等关键方面。此外，文章还回顾了康复机器人的最新进展，包括外骨骼、基于末端执行器的机器人和平面机器人。文章还根据机械设计和功能评估了现有的上肢康复机器人，指出了它们的局限性，并提出了进一步改进的未来研究方向。

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引用次数: 0

Fourier Hilbert: The input transformation to enhance CNN models for speech emotion recognition 傅里叶·希尔伯特：输入变换增强CNN模型的语音情感识别

Cognitive Robotics

Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.11.002

Bao Long Ly

Signal processing in general, and speech emotion recognition in particular, have long been familiar Artificial Intelligence (AI) tasks. With the explosion of deep learning, CNN models are used more frequently, accompanied by the emergence of many signal transformations. However, these methods often require significant hardware and runtime. In an effort to address these issues, we analyze and learn from existing transformations, leading us to propose a new method: Fourier Hilbert Transformation (FHT). In general, this method applies the Hilbert curve to Fourier images. The resulting images are small and dense, which is a shape well-suited to the CNN architecture. Additionally, the better distribution of information on the image allows the filters to fully utilize their power. These points support the argument that FHT provides an optimal input for CNN. Experiments conducted on popular datasets yielded promising results. FHT saves a large amount of hardware usage and runtime while maintaining high performance, even offers greater stability compared to existing methods. This opens up opportunities for deploying signal processing tasks on real-time systems with limited hardware.

一般来说，信号处理，特别是语音情感识别，一直是人们熟悉的人工智能（AI）任务。随着深度学习的爆炸式发展，CNN模型的使用越来越频繁，伴随着许多信号变换的出现。然而，这些方法通常需要大量的硬件和运行时。为了解决这些问题，我们分析并学习了现有的变换，从而提出了一种新的方法：傅里叶希尔伯特变换（FHT）。一般来说，这种方法将希尔伯特曲线应用于傅里叶图像。生成的图像小而密集，这是一种非常适合CNN架构的形状。此外，图像上信息的更好分布允许滤波器充分利用它们的功率。这些观点支持了FHT为CNN提供最佳输入的论点。在流行的数据集上进行的实验产生了令人鼓舞的结果。FHT在保持高性能的同时节省了大量的硬件使用和运行时间，甚至比现有方法提供了更高的稳定性。这为在硬件有限的实时系统上部署信号处理任务提供了机会。

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