This paper presents a proof-of-concept for a lower-extremity rehabilitation device, called Rehab-bot, that would aid patients with lower-limb impairments in continuing their rehabilitation in its required intensity at home after inpatient care. This research focuses on developing the patient‘s muscle training feature using admittance control to generate resistance for isotonic exercise, particularly emphasizing the potential for progressive resistance training. The mechanical structure of the Rehab-bot was inspired by a continuous passive motion machine that can be optimized to be a light and compact device suitable for home-based use. Systems design, development, and experimental evaluation are presented. Experiments were performed with one healthy subject by monitoring two parameters: the forces exerted by leg muscles through a force sensor and the resulting position of the foot support that is actuated by the robot. Results have shown that Rehab-bot can demonstrate lower-limb isotonic exercise by generating a virtual load that can be progressively increased.
{"title":"Rehab-Bot: A home-based lower-extremity rehabilitation robot for muscle recovery","authors":"Sandro Mihradi , Edgar Buwana Sutawika , Vani Virdyawan , Rachmat Zulkarnain Goesasi , Masahiro Todoh","doi":"10.1016/j.cogr.2025.02.001","DOIUrl":"10.1016/j.cogr.2025.02.001","url":null,"abstract":"<div><div>This paper presents a proof-of-concept for a lower-extremity rehabilitation device, called Rehab-bot, that would aid patients with lower-limb impairments in continuing their rehabilitation in its required intensity at home after inpatient care. This research focuses on developing the patient‘s muscle training feature using admittance control to generate resistance for isotonic exercise, particularly emphasizing the potential for progressive resistance training. The mechanical structure of the Rehab-bot was inspired by a continuous passive motion machine that can be optimized to be a light and compact device suitable for home-based use. Systems design, development, and experimental evaluation are presented. Experiments were performed with one healthy subject by monitoring two parameters: the forces exerted by leg muscles through a force sensor and the resulting position of the foot support that is actuated by the robot. Results have shown that Rehab-bot can demonstrate lower-limb isotonic exercise by generating a virtual load that can be progressively increased.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 114-125"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601104","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}
Pub Date : 2025-01-01DOI: 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.
{"title":"Small target drone algorithm in low-altitude complex urban scenarios based on ESMS-YOLOv7","authors":"Yuntao Wei, Xiujia Wang, Chunjuan Bo, Zhan Shi","doi":"10.1016/j.cogr.2024.11.004","DOIUrl":"10.1016/j.cogr.2024.11.004","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 14-25"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143536","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}
Pub Date : 2025-01-01DOI: 10.1016/j.cogr.2025.07.001
Xuan Jin , Wen Zhou , Qinyou Zhu , Weijie Wang , Guoteng Xu
This paper employs text mining techniques, specifically Latent Dirichlet Allocation (LDA) and BERTopic topic models, to conduct an in-depth investigation of the supply and demand structure of regional scientific and technological achievements. The objective is to identify imbalances in supply and demand, thereby providing novel insights for enhancing the efficiency of technology transfer. The research findings indicate that the LDA model outperforms the BERTopic model in this study. Taking Guizhou Province, China, as a case study, the LDA model analysis categorizes the demand side into 16 domains and the supply side into 18 domains, both exhibiting a "long-tail distribution" characteristic. Further analysis reveals a structural imbalance in the supply and demand of scientific and technological achievements in Guizhou Province. For instance, there is a high demand in areas such as mineral extraction and utilization, as well as digital and intelligent applications, accounting for 20.3 % and 14.3 % respectively, yet the supply is insufficient, with only 5.1 % and 3.1 % respectively. Conversely, areas like mechanical processing, and bridge and building construction experience an oversupply, with the supply accounting for 17.9 % and 13.8 % respectively. Addressing the structural imbalance in the supply and demand of scientific and technological achievements, this study proposes development recommendations from three perspectives: policy and management systems, regional collaboration, and ecological construction. The aim is to optimize the supply and demand structure of scientific and technological achievements in Guizhou Province and promote the deep integration of technology and the economy.
{"title":"Research on the analysis and application of technological supply and demand structure based on LDA and BERTopic models","authors":"Xuan Jin , Wen Zhou , Qinyou Zhu , Weijie Wang , Guoteng Xu","doi":"10.1016/j.cogr.2025.07.001","DOIUrl":"10.1016/j.cogr.2025.07.001","url":null,"abstract":"<div><div>This paper employs text mining techniques, specifically Latent Dirichlet Allocation (LDA) and BERTopic topic models, to conduct an in-depth investigation of the supply and demand structure of regional scientific and technological achievements. The objective is to identify imbalances in supply and demand, thereby providing novel insights for enhancing the efficiency of technology transfer. The research findings indicate that the LDA model outperforms the BERTopic model in this study. Taking Guizhou Province, China, as a case study, the LDA model analysis categorizes the demand side into 16 domains and the supply side into 18 domains, both exhibiting a \"long-tail distribution\" characteristic. Further analysis reveals a structural imbalance in the supply and demand of scientific and technological achievements in Guizhou Province. For instance, there is a high demand in areas such as mineral extraction and utilization, as well as digital and intelligent applications, accounting for 20.3 % and 14.3 % respectively, yet the supply is insufficient, with only 5.1 % and 3.1 % respectively. Conversely, areas like mechanical processing, and bridge and building construction experience an oversupply, with the supply accounting for 17.9 % and 13.8 % respectively. Addressing the structural imbalance in the supply and demand of scientific and technological achievements, this study proposes development recommendations from three perspectives: policy and management systems, regional collaboration, and ecological construction. The aim is to optimize the supply and demand structure of scientific and technological achievements in Guizhou Province and promote the deep integration of technology and the economy.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 260-275"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694867","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.2025.06.001
Xiang Liu, Shuntian Xie
This research explores underwater robot applications in marine cable inspection and maintenance with solutions to accuracy, reliability, and efficiency challenges. Current methods using human divers and remotely operated vehicles (ROVs) are expensive, time-consuming, and involve safety hazards. The suggested AI-based robotic system incorporates sensor technology, predictive maintenance, and statistical validation to maximize marine cable inspections. A quantitative research method was employed, surveying data from 400 Marine Engineers and Underwater Robotics Specialists. Statistical analysis, such as reliability analysis, regression model, and hypothesis testing, determined the influence of technology adoption, environmental aspects, and predictive maintenance on inspection accuracy and cost savings. Model fit was confirmed through CFI , RMSEA , and . Results show that Maintenance Strategy & Cost Reduction is most influential. The research assures that AI-enhanced underwater robots provide a cost-efficient, guaranteed substitute to conventional approaches, promoting efficiency, safety, and long-term sustainability in marine cable operations.
{"title":"Innovative strategy and practice of using underwater robot for marine cable inspection and operation and maintenance","authors":"Xiang Liu, Shuntian Xie","doi":"10.1016/j.cogr.2025.06.001","DOIUrl":"10.1016/j.cogr.2025.06.001","url":null,"abstract":"<div><div>This research explores underwater robot applications in marine cable inspection and maintenance with solutions to accuracy, reliability, and efficiency challenges. Current methods using human divers and remotely operated vehicles (ROVs) are expensive, time-consuming, and involve safety hazards. The suggested AI-based robotic system incorporates sensor technology, predictive maintenance, and statistical validation to maximize marine cable inspections. A quantitative research method was employed, surveying data from 400 Marine Engineers and Underwater Robotics Specialists. Statistical analysis, such as reliability analysis, regression model, and hypothesis testing, determined the influence of technology adoption, environmental aspects, and predictive maintenance on inspection accuracy and cost savings. Model fit was confirmed through CFI <span><math><mrow><mo>=</mo><mn>0.94</mn></mrow></math></span>, RMSEA <span><math><mrow><mo>=</mo><mn>0.047</mn></mrow></math></span>, and <span><math><mrow><mi>SRMR</mi><mo>=</mo><mn>0.052</mn></mrow></math></span>. Results show that Maintenance Strategy & Cost Reduction <span><math><mrow><mo>(</mo><mi>β</mi><mo>=</mo><mn>0.55</mn><mo>,</mo><mrow><mi>p</mi></mrow><mo><</mo><mn>0.01</mn><mo>)</mo></mrow></math></span> is most influential. The research assures that AI-enhanced underwater robots provide a cost-efficient, guaranteed substitute to conventional approaches, promoting efficiency, safety, and long-term sustainability in marine cable operations.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 226-239"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570295","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.2025.06.002
Yuhang Liu, Chunjuan Bo, Chong Feng
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 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.
{"title":"FB-YOLOv8s: A fire detection algorithm based on YOLOv8s","authors":"Yuhang Liu, Chunjuan Bo, 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}
Pub Date : 2025-01-01DOI: 10.1016/j.cogr.2025.08.001
Hui Chen , Runming Jiang , Fang Hu , Min Chen , Yin Zhang
In the context of natural scenes, traditional text recognition methods exhibit limitations when confronted with the substantial differences in characters and context among diverse languages. To address this challenge, we propose an approach LFEN for text recognition and correction in natural scenes. By directly embedding language features into the text recognition model, we effectively address the issue of accuracy in scene text recognition, reducing the potential risk of error accumulation compared to traditional language recognition-text recognition serial connections. Through a detailed analysis of global and local language features, this paper successfully achieves more accurate differentiation between languages with similar characters, thereby enhancing text recognition accuracy. Furthermore, by incorporating the intrinsic semantic relationships of text content, this paper employs a sequence-to-sequence (Seq2Seq) model based on convolutional neural networks for text correction. Through the integration of language information, different feature embeddings, and global residual connections, the paper provides a robust solution for text correction in scene text recognition. Compared to the baselines, the experimental results demonstrate that LFEN achieves superior performance in most evaluation metrics. Specifically, LFEN has around 2% in recall improved to BERT. This research contributes substantial support to the advancement of natural scene text recognition and correction.
{"title":"LFEN: A language feature enhanced network for scene text recognition","authors":"Hui Chen , Runming Jiang , Fang Hu , Min Chen , Yin Zhang","doi":"10.1016/j.cogr.2025.08.001","DOIUrl":"10.1016/j.cogr.2025.08.001","url":null,"abstract":"<div><div>In the context of natural scenes, traditional text recognition methods exhibit limitations when confronted with the substantial differences in characters and context among diverse languages. To address this challenge, we propose an approach LFEN for text recognition and correction in natural scenes. By directly embedding language features into the text recognition model, we effectively address the issue of accuracy in scene text recognition, reducing the potential risk of error accumulation compared to traditional language recognition-text recognition serial connections. Through a detailed analysis of global and local language features, this paper successfully achieves more accurate differentiation between languages with similar characters, thereby enhancing text recognition accuracy. Furthermore, by incorporating the intrinsic semantic relationships of text content, this paper employs a sequence-to-sequence (Seq2Seq) model based on convolutional neural networks for text correction. Through the integration of language information, different feature embeddings, and global residual connections, the paper provides a robust solution for text correction in scene text recognition. Compared to the baselines, the experimental results demonstrate that LFEN achieves superior performance in most evaluation metrics. Specifically, LFEN has around 2% in recall improved to BERT. This research contributes substantial support to the advancement of natural scene text recognition and correction.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"5 ","pages":"Pages 276-285"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880314","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}
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