Pub Date : 2025-01-01DOI: 10.1016/j.cogr.2025.04.002
YiGe Hu
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.
{"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}
Pub Date : 2025-01-01DOI: 10.1016/j.cogr.2025.03.005
Hamed Khudair Khalil, Laith Ali Abdul Rahaim, Shamam Fadhil Alwash
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.
{"title":"Design cloud computing to monitor and controller for high voltage networks 400 KV","authors":"Hamed Khudair Khalil, Laith Ali Abdul Rahaim, 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}
Pub Date : 2025-01-01DOI: 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.
{"title":"LiPE: Lightweight human pose estimator for mobile applications towards automated pose analysis","authors":"Chengxiu Li , Ni Duan","doi":"10.1016/j.cogr.2024.11.005","DOIUrl":"10.1016/j.cogr.2024.11.005","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 26-36"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143537","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}
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