信息工程最新文献

{"title":"A cooperative jamming decision-making method based on multi-agent reinforcement learning","authors":"Bingchen Cai,&nbsp;Haoran Li,&nbsp;Naimin Zhang,&nbsp;Mingyu Cao,&nbsp;Han Yu","doi":"10.1007/s43684-025-00090-4","DOIUrl":"10.1007/s43684-025-00090-4","url":null,"abstract":"<div><p>Electromagnetic jamming is a critical countermeasure in defense interception scenarios. This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-agent reinforcement learning-based cooperative jamming decision-making method (MA-CJD). The proposed approach achieves high-quality and efficient target allocation, jamming mode selection, and power control. Mathematical models for radar systems and active jamming are developed to represent a multi-jammer and multi-radar electromagnetic confrontation scenario. The cooperative jamming decision-making process is then modeled as a Markov game, where the QMix multi-agent reinforcement learning algorithm is innovatively applied to handle inter-jammer cooperation. To tackle the challenges of a parameterized action space, the MP-DQN network structure is adopted, forming the basis of the MA-CJD algorithm. Simulation experiments validate the effectiveness of the proposed MA-CJD algorithm. Results show that MA-CJD significantly reduces the time defense units are detected while minimizing jamming resource consumption. Compared with existing algorithms, MA-CJD achieves better solutions, demonstrating its superiority in cooperative jamming scenarios.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00090-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184710","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":"Chasing in virtual environment:Dynamic alignment for multi-user collaborative redirected walking","authors":"Tianyang Dong,&nbsp;Shuqian Lv,&nbsp;Hubin Kong,&nbsp;Huanbo Zhang","doi":"10.1016/j.vrih.2024.07.002","DOIUrl":"10.1016/j.vrih.2024.07.002","url":null,"abstract":"<div><h3>Background</h3><div>The redirected walking (RDW) method for multi-user collaboration requires maintaining the relative position between users in a virtual environment (VE) and physical environment (PE). A chasing game in a VE is a typical virtual reality game that entails multi-user collaboration. When a user approaches and interacts with a target user in the VE, the user is expected to approach and interact with the target user in the corresponding PE as well. Existing methods of multi-user RDW mainly focus on obstacle avoidance, which does not account for the relative positional relationship between the users in both VE and PE.</div></div><div><h3>Methods</h3><div>To enhance the user experience and facilitate potential interaction, this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking (DA-RDW) in a shared PE where the target user and other users are moving. This algorithm adopts improved artificial potential fields, where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic obstacles. For the best alignment, this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal direction. Moreover, this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent alignment. To balance obstacle avoidance and alignment, this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force vector.</div></div><div><h3>Results</h3><div>The efficacy of the proposed method was evaluated using a series of simulations and live-user experiments. The experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 1","pages":"Pages 26-46"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562755","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":"Optimizing wireless sensor network topology with node load consideration","authors":"Ruizhi Chen","doi":"10.1016/j.vrih.2024.08.003","DOIUrl":"10.1016/j.vrih.2024.08.003","url":null,"abstract":"<div><h3>Background</h3><div>With the development of the Internet, the topology optimization of wireless sensor networks has received increasing attention. However, traditional optimization methods often overlook the energy imbalance caused by node loads, which affects network performance.</div></div><div><h3>Methods</h3><div>To improve the overall performance and efficiency of wireless sensor networks, a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed. The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance, while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process. The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.</div></div><div><h3>Results</h3><div>The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59% and 94.55%, respectively, demonstrating good clustering performance. When calculating the node mortality rate and network load balancing standard deviation, the proposed algorithm showed dead nodes at approximately 50 iterations, with an average load balancing standard deviation of 1.7×10⁴, proving its contribution to extending the network lifespan.</div></div><div><h3>Conclusions</h3><div>This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan. The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring, healthcare, and agriculture.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 1","pages":"Pages 47-61"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562756","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":"Finger tracking for wearable VR glove using flexible rack mechanism","authors":"Roshan Thilakarathna,&nbsp;Maroay Phlernjai","doi":"10.1016/j.vrih.2024.03.001","DOIUrl":"10.1016/j.vrih.2024.03.001","url":null,"abstract":"<div><h3>Background</h3><div>With the increasing prominence of hand and finger motion tracking in virtual reality (VR) applications and rehabilitation studies, data gloves have emerged as a prevalent solution. In this study, we developed an innovative, lightweight, and detachable data glove tailored for finger motion tracking in VR environments.</div></div><div><h3>Methods</h3><div>The glove design incorporates a potentiometer coupled with a flexible rack and pinion gear system, facilitating precise and natural hand gestures for interaction with VR applications. Initially, we calibrated the potentiometer to align with the actual finger bending angle, and verified the accuracy of angle measurements recorded by the data glove. To verify the precision and reliability of our data glove, we conducted repeatability testing for flexion (grip test) and extension (flat test), with 250 measurements each, across five users. We employed the Gage Repeatability and Reproducibility to analyze and interpret the repeatable data. Furthermore, we integrated the gloves into a SteamVR home environment using the OpenGlove auto-calibration tool.</div></div><div><h3>Conclusions</h3><div>The repeatability analysis revealed an aggregate error of 1.45 degrees in both the gripped and flat hand positions. This outcome was notably favorable when compared with the findings from assessments of nine alternative data gloves that employed similar protocols. In these experiments, users navigated and engaged with virtual objects, underlining the glove's exact tracking of finger motion. Furthermore, the proposed data glove exhibited a low response time of 17–34 ms and back-drive force of only 0.19 N. Additionally, according to a comfort evaluation using the Comfort Rating Scales, the proposed glove system is wearable, placing it at the WL1 level.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 1","pages":"Pages 1-25"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562754","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":"FDCPNet:feature discrimination and context propagation network for 3D shape representation","authors":"Weimin SHI,&nbsp;Yuan XIONG,&nbsp;Qianwen WANG,&nbsp;Han JIANG,&nbsp;Zhong ZHOU","doi":"10.1016/j.vrih.2024.06.001","DOIUrl":"10.1016/j.vrih.2024.06.001","url":null,"abstract":"<div><h3>Background</h3><div>Three-dimensional (3D) shape representation using mesh data is essential in various applications, such as virtual reality and simulation technologies. Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas, which affects the overall precision. To address these issues, we propose the Feature Discrimination and Context Propagation Network (FDCPNet), which is a novel approach that synergistically integrates local and global features in mesh datasets.</div></div><div><h3>Methods</h3><div>FDCPNet is composed of two modules: (1) the Feature Discrimination Module, which employs an attention mechanism to enhance the identification of key local features, and (2) the Context Propagation Module, which enriches key local features by integrating global contextual information, thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model.</div></div><div><h3>Results</h3><div>Experiments on popular datasets validated the effectiveness of FDCPNet, showing an improvement in the classification accuracy over the baseline MeshNet. Furthermore, even with reduced mesh face numbers and limited training data, FDCPNet achieved promising results, demonstrating its robustness in scenarios of variable complexity.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 1","pages":"Pages 83-94"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562654","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 haptic feedback glove for virtual piano interaction","authors":"Yifan FU,&nbsp;Jialin LIU,&nbsp;Xu LI,&nbsp;Xiaoying SUN","doi":"10.1016/j.vrih.2024.07.001","DOIUrl":"10.1016/j.vrih.2024.07.001","url":null,"abstract":"<div><h3>Background</h3><div>Haptic feedback plays a crucial role in virtual reality (VR) interaction, helping to improve the precision of user operation and enhancing the immersion of the user experience. Instrumental haptic feedback in virtual environments is primarily realized using grounded force or vibration feedback devices. However, improvements are required in terms of the active space and feedback realism.</div></div><div><h3>Methods</h3><div>We propose a lightweight and flexible haptic feedback glove that can haptically render objects in VR environments via kinesthetic and vibration feedback, thereby enabling users to enjoy a rich virtual piano-playing experience. The kinesthetic feedback of the glove relies on a cable-pulling mechanism that rotates the mechanism and pulls the two cables connected to it, thereby changing the amount of force generated to simulate the hardness or softness of the object. Vibration feedback is provided by small vibration motors embedded in the bottom of the fingertips of the glove. We designed a piano-playing scenario in the virtual environment and conducted user tests. The evaluation metrics were clarity, realism, enjoyment, and satisfaction.</div></div><div><h3>Results</h3><div>A total of 14 subjects participated in the test, and the results showed that our proposed glove scored significantly higher on the four evaluation metrics than the no-feedback and vibration feedback methods.</div></div><div><h3>Conclusions</h3><div>Our proposed glove significantly enhances the user experience when interacting with virtual objects.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 1","pages":"Pages 95-110"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562655","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":"YGC-SLAM:A visual SLAM based on improved YOLOv5 and geometric constraints for dynamic indoor environments","authors":"Juncheng ZHANG ,&nbsp;Fuyang KE ,&nbsp;Qinqin TANG ,&nbsp;Wenming YU ,&nbsp;Ming ZHANG","doi":"10.1016/j.vrih.2024.05.001","DOIUrl":"10.1016/j.vrih.2024.05.001","url":null,"abstract":"<div><h3>Background</h3><div>As visual simultaneous localization and mapping (SLAM) is primarily based on the assumption of a static scene, the presence of dynamic objects in the frame causes problems such as a deterioration of system robustness and inaccurate position estimation. In this study, we propose a YGC-SLAM for indoor dynamic environments based on the ORB-SLAM2 framework combined with semantic and geometric constraints to improve the positioning accuracy and robustness of the system.</div></div><div><h3>Methods</h3><div>First, the recognition accuracy of YOLOv5 was improved by introducing the convolution block attention model and the improved EIOU loss function, whereby the prediction frame converges quickly for better detection. The improved YOLOv5 was then added to the tracking thread for dynamic target detection to eliminate dynamic points. Subsequently, multi-view geometric constraints were used for re-judging to further eliminate dynamic points while enabling more useful feature points to be retained and preventing the semantic approach from over-eliminating feature points, causing a failure of map building. The K-means clustering algorithm was used to accelerate this process and quickly calculate and determine the motion state of each cluster of pixel points. Finally, a strategy for drawing keyframes with de-redundancy was implemented to construct a clear 3D dense static point-cloud map.</div></div><div><h3>Results</h3><div>Through testing on TUM dataset and a real environment, the experimental results show that our algorithm reduces the absolute trajectory error by 98.22% and the relative trajectory error by 97.98% compared with the original ORB-SLAM2, which is more accurate and has better real-time performance than similar algorithms, such as DynaSLAM and DS-SLAM.</div></div><div><h3>Conclusions</h3><div>The YGC-SLAM proposed in this study can effectively eliminate the adverse effects of dynamic objects, and the system can better complete positioning and map building tasks in complex environments.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 1","pages":"Pages 62-82"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562653","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":"Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP","authors":"Zhongtian Jin,&nbsp;Chong Chen,&nbsp;Aris Syntetos,&nbsp;Ying Liu","doi":"10.1007/s43684-024-00088-4","DOIUrl":"10.1007/s43684-024-00088-4","url":null,"abstract":"<div><p>Bearings are critical components in machinery, and accurately predicting their remaining useful life (RUL) is essential for effective predictive maintenance. Traditional RUL prediction methods often rely on manual feature extraction and expert knowledge, which face specific challenges such as handling non-stationary data and avoiding overfitting due to the inclusion of numerous irrelevant features. This paper presents an approach that leverages Continuous Wavelet Transform (CWT) for feature extraction, a Channel-Temporal Mixed MLP (CT-MLP) layer for capturing intricate dependencies, and a dynamic attention mechanism to adjust its focus based on the temporal importance of features within the time series. The dynamic attention mechanism integrates multi-head attention with innovative enhancements, making it particularly effective for datasets exhibiting non-stationary behaviour. An experimental study using the XJTU-SY rolling bearings dataset and the PRONOSTIA bearing dataset revealed that the proposed deep learning algorithm significantly outperforms other state-of-the-art algorithms in terms of RMSE and MAE, demonstrating its robustness and accuracy.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00088-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939427","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":"An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles","authors":"Zhengqin Liu,&nbsp;Jinlong Lei,&nbsp;Peng Yi,&nbsp;Yiguang Hong","doi":"10.1007/s43684-024-00087-5","DOIUrl":"10.1007/s43684-024-00087-5","url":null,"abstract":"<div><p>Lately, there has been a lot of interest in game-theoretic approaches to the trajectory planning of autonomous vehicles (AVs). But most methods solve the game independently for each AV while lacking coordination mechanisms, and hence result in redundant computation and fail to converge to the same equilibrium, which presents challenges in computational efficiency and safety. Moreover, most studies rely on the strong assumption of knowing the intentions of all other AVs. This paper designs a novel autonomous vehicle trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated trajectory planning by exploiting vehicle-to-everything (V2X) technology. Firstly, the trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define the interaction fairness of the planned trajectories and prove that interaction-fair trajectories correspond to the variational equilibrium (VE) of this game. Subsequently, we propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, in which each CAV optimizes its individual trajectory based on neighboring CAVs’ information shared through V2X, and the roadside unit takes the role of updating multipliers for collision avoidance constraints. The approach can significantly improve computational efficiency through parallel computing among CAVs, and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, we conduct Monte Carlo experiments in multiple situations at an intersection, where the empirical results show the advantages of SVEP, including the fast computation speed, a small communication payload, high scalability, equilibrium concordance, and safety, making it a promising solution for trajectory planning in connected traffic scenarios. To the best of our knowledge, this is the first study to achieve semi-distributed solving of a game with coupled constraints in a CAV trajectory planning problem.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00087-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912803","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":"Network synchronizability enhancement via adding antagonistic interactions","authors":"Yue Song,&nbsp;Xiaoqin Liu,&nbsp;Dingmei Wang,&nbsp;Pengfei Gao,&nbsp;Mengqi Xue","doi":"10.1007/s43684-024-00086-6","DOIUrl":"10.1007/s43684-024-00086-6","url":null,"abstract":"<div><p>We discover a “less-is-more” effect that adding local antagonistic interactions (negative edge weights) can enhance the overall synchronizability of a dynamical network system. To explain this seemingly counterintuitive phenomenon, a condition is established to identify those edges the weight reduction of which improves the synchronizability index of the underlying network. We further reveal that this condition can be interpreted from the perspective of resistance distance and network community structure. The obtained result is also verified via numerical experiments on a 14-node network and a 118-node network. Our finding brings new thoughts and inspirations to the future directions of optimal network design problems.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00086-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906061","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}