Cognitive Robotics最新文献

{"title":"FB-YOLOv8s: A fire detection algorithm based on YOLOv8s","authors":"Yuhang Liu,&nbsp;Chunjuan Bo,&nbsp;Chong Feng","doi":"10.1016/j.cogr.2025.06.002","DOIUrl":"10.1016/j.cogr.2025.06.002","url":null,"abstract":"<div><div>The significance of fire detection lies in protecting public safety and safeguarding the lives and property of people. However, there exist some problems in traditional detection algorithms of fire, such as low accuracy, high miss rate, and low detection rate of small targets. To effectively solve these issues, a fire detection algorithm based on YOLOv8s is introduced in this paper, called FB-YOLOv8s. First, the FasterNet lightweight network is introduced into the YOLOv8s network, merging the FasterNet Block structure of FasterNet with the original C2f modules to reduce the number of model parameters. Second, the Bi-directional Feature Pyramid Network (BiFPN) is incorporated to replace the Path Aggregation Network (PANet) in the neck network to enhance the model’s feature fusion capability. Finally, we adopt the WIoUv3 loss function to optimize the training process and improve detection accuracy. The experimental results demonstrate that compared to the original algorithm, the mAP<span><math><msub><mrow></mrow><mrow><mn>0.5</mn></mrow></msub></math></span> of FB-YOLOv8s increases by 2.0 %, and the number of parameters decreases by 25.23 %. This method has better detection performance for fire targets.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 240-248"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification","authors":"Teng Wang ,&nbsp;Fenglian Li ,&nbsp;Jia Yang ,&nbsp;Wenhui Jia ,&nbsp;Fengyun Hu","doi":"10.1016/j.cogr.2025.05.002","DOIUrl":"10.1016/j.cogr.2025.05.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 211-225"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Zero-dynamics attack detection based on data association in feedback pathway","authors":"Zeyu Zhang ,&nbsp;Hongran Li ,&nbsp;Yuki Todo","doi":"10.1016/j.cogr.2025.03.003","DOIUrl":"10.1016/j.cogr.2025.03.003","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 126-139"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated model for segmentation of glomeruli in kidney images","authors":"Gurjinder Kaur,&nbsp;Meenu Garg,&nbsp;Sheifali Gupta","doi":"10.1016/j.cogr.2024.11.007","DOIUrl":"10.1016/j.cogr.2024.11.007","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 1-13"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks","authors":"Workeneh Geleta Negassa,&nbsp;Demissie J. Gelmecha,&nbsp;Ram Sewak Singh,&nbsp;Davinder Singh Rathee","doi":"10.1016/j.cogr.2025.01.002","DOIUrl":"10.1016/j.cogr.2025.01.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 61-76"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A transformation model for vision-based navigation of agricultural robots","authors":"Abdelkrim Abanay ,&nbsp;Lhoussaine Masmoudi ,&nbsp;Dirar Benkhedra ,&nbsp;Khalid El Amraoui ,&nbsp;Mouataz Lghoul ,&nbsp;Javier-Gonzalez Jimenez ,&nbsp;Francisco-Angel Moreno","doi":"10.1016/j.cogr.2025.03.002","DOIUrl":"10.1016/j.cogr.2025.03.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 140-151"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement of multi-parameter anomaly detection method: Addition of a relational token between parameters","authors":"Hironori Uchida ,&nbsp;Keitaro Tominaga ,&nbsp;Hideki Itai ,&nbsp;Yujie Li ,&nbsp;Yoshihisa Nakatoh","doi":"10.1016/j.cogr.2025.03.004","DOIUrl":"10.1016/j.cogr.2025.03.004","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 176-191"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-view graph neural network approach for magnetic resonance imaging-based diagnosis of knee injuries","authors":"Biyong Deng ,&nbsp;Jiashan Pan ,&nbsp;Xiaoyu Tang ,&nbsp;Haitao Fu ,&nbsp;Shushan Hu","doi":"10.1016/j.cogr.2025.05.001","DOIUrl":"10.1016/j.cogr.2025.05.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 201-210"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robotic terrain classification based on convolutional and long short-term memory neural networks","authors":"YiGe Hu","doi":"10.1016/j.cogr.2025.04.002","DOIUrl":"10.1016/j.cogr.2025.04.002","url":null,"abstract":"<div><div>Robotic mobility remains constrained by complex terrains and technological limitations, hindering real-world applications. This study presents a terrain classification framework integrating Fourier transform, adaptive filtering, and deep learning to enhance adaptability. Leveraging CNNs, LSTMs, and an attention mechanism, the approach improves feature fusion and classification accuracy. Evaluations on the Tampere University dataset demonstrate an 81 % classification accuracy, validating its effectiveness in terrain perception and autonomous navigation. The findings contribute to advancing robotic mobility in unstructured environments.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 166-175"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design cloud computing to monitor and controller for high voltage networks 400 KV","authors":"Hamed Khudair Khalil,&nbsp;Laith Ali Abdul Rahaim,&nbsp;Shamam Fadhil Alwash","doi":"10.1016/j.cogr.2025.03.005","DOIUrl":"10.1016/j.cogr.2025.03.005","url":null,"abstract":"<div><div>A high-voltage network (400 kV) is a system that has multiple control and communication elements and acts as a link between generating stations and transmission lines; it is considered one of the smart networks. The advantage of a smart grid over a traditional utility grid is that it uses a two-way communication mechanism. The monitoring and control system for this network utilizes SCADA and RTU, but it comes at a high cost. Nonetheless, it is preferable to have a system that is economical, intelligent, and dependable. In this research, we will design a remote monitoring and control system for high-voltage networks using cloud computing technology with IoT applications that support the above-mentioned systems and can be developed in case of any expansion in electrical networks. We use this system to remotely monitor smart network equipment and control the closing and opening of breakers using protection relays and sensors. This proposed system uses the ESP 32 microcontroller to send warning signals to remote operators via the Internet, utilizing the MQTT protocol. This system utilizes the Thing Board platform in conjunction with Quick Set (5030) software, enabling control via a laptop and smartphone.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 192-200"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}