Intelligent Evaluation Method of Human Cervical Vertebra Rehabilitation Based on Computer Vision

With the changes in human work and lifestyle, the incidence of cervical spondylosis is increasing substantially, especially for adolescents. Cervical spine exercises are an important means to prevent and rehabilitate cervical spine diseases, but no mature unmanned evaluating and monitoring system for cervical spine rehabilitation training has been proposed. Patients often lack the guidance of a physician and are at risk of injury during the exercise process. In this paper, we first propose a cervical spine exercise assessment method based on a multi-task computer vision algorithm, which can replace physicians to guide patients to perform rehabilitation exercises and evaluations. The model based on the Mediapipe framework is set up to construct a face mesh and extract features to calculate the head pose angles in 3-DOF (three degrees of freedom). Then, the sequential angular velocity in 3-DOF is calculated based on the angle data acquired by the computer vision algorithm mentioned above. After that, the cervical vertebra rehabilitation evaluation system and index parameters are analyzed by data acquisition and experimental analysis of cervical vertebra exercises. A privacy encryption algorithm combining YOLOv5 and mosaic noise mixing with head posture information is proposed to protect the privacy of the patient’s face. The results show that our algorithm has good repeatability and can effectively reflect the health status of the patient’s cervical spine

Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine

Multiparameter optimization of complex electromechanical systems in a physical space is a challenging task. CPS (Cyberphysical system) technology can speed up the solution of the problem based on data interaction and collaborative optimization of physical space and cyberspace. This paper proposed a general multiparameter optimization framework by combining physical process simulation and clustering genetic algorithm for the CPS application. The utility of this approach is demonstrated in the instance of automobile engine energy-saving in this paper. A 1.8-L turbocharged GDI (gasoline direct injection) engine model was established and calibrated according to the test data and physical entity. A joint simulation program combining CGA (Clustering Genetic Algorithm) with the GDI engine simulation model was set up for the engine multiparameter optimization and performance prediction in cyberspace; then, the influential mechanism of multiple factors on engine energy-saving optimization was analyzed at 2000 RPM (Revolutions Per Minute) working condition. A multiparameter optimization with clustering genetic algorithm was introduced for multiparameter optimization among physical and digital data. The trade-off between fuel efficiency, dynamic performance, and knock risk was discussed. The results demonstrated the effectiveness of the proposed method and that it can contribute to develop a novel automotive engine control strategy in the future

A Survey of Multi-Agent Cross Domain Cooperative Perception

Intelligent unmanned systems for ground, sea, aviation, and aerospace application are important research directions for the new generation of artificial intelligence in China. Intelligent unmanned systems are also important carriers of interactive mapping between physical space and cyberspace in the process of the digitization of human society. Based on the current domestic and overseas development status of unmanned systems for ground, sea, aviation, and aerospace application, this paper reviewed the theoretical problems and research trends of multi-agent cross-domain cooperative perception. The scenarios of multi-agent cooperative perception tasks in different areas were deeply investigated and analyzed, the scientific problems of cooperative perception were analyzed, and the development direction of multi-agent cooperative perception theory research for solving the challenges of the complex environment, interactive communication, and cross-domain tasks was expounded

A Survey of Multi-Agent Cross Domain Cooperative Perception

Intelligent unmanned systems for ground, sea, aviation, and aerospace application are important research directions for the new generation of artificial intelligence in China. Intelligent unmanned systems are also important carriers of interactive mapping between physical space and cyberspace in the process of the digitization of human society. Based on the current domestic and overseas development status of unmanned systems for ground, sea, aviation, and aerospace application, this paper reviewed the theoretical problems and research trends of multi-agent cross-domain cooperative perception. The scenarios of multi-agent cooperative perception tasks in different areas were deeply investigated and analyzed, the scientific problems of cooperative perception were analyzed, and the development direction of multi-agent cooperative perception theory research for solving the challenges of the complex environment, interactive communication, and cross-domain tasks was expounded

Safety assessment for autonomous vehicles: A reference driver model for highway merging scenarios

Driver models are crucial for the safety assessment of autonomous vehicles (AVs) because of their role as reference models. Specifically, an AV is expected to achieve at least the same level of safety performance as a careful and competent driver model. To make this comparison possible, quantitative modeling of careful and competent driver models is essential. Thus, the UNECE Regulation No. 157 proposes two driver models as benchmarks for AVs, enabling safety assessment of AV longitudinal behaviors. However, these two driver models are unable to be applied in non-car-following scenarios, limiting their applications in scenarios such as highway merging. To this end, we propose a careful and competent driver model for highway merging (CCDM2) scenarios using interpretable reinforcement learning-based decision-making and safety constraint control. We compare our model's safe driving capabilities with human drivers in challenging merging scenarios and demonstrate the "careful" and "competent" characteristics of our model while ensuring its interpretability. The results indicate the model's capability to handle merging scenarios with even better safety performance than human drivers. This model is of great value for AV safety assessment in merging scenarios and contributes to future reference driver models to be included in AV safety regulations

FedPRM:Federated Personalized Mixture Representation for Driver Intention Prediction

Driver intention prediction has the potential to greatly improve the ability of autonomous vehicles (AVs) to effectively handle risky driving behaviors, thereby ensuring driving safety. Conventional data-driven approaches for driver intention prediction models typically involve gathering extensive driver-related data, which raises significant privacy concerns. With the development of the Internet of Vehicles (IoV), federated learning (FL) has emerged as a prominent privacy-preserving learning paradigm, garnering considerable attention. However, FL encounters challenges in driver intention prediction due to the heterogeneity of driver client data and the limited computational resources of vehicles. To address these challenges, this paper proposes the FedPMR framework, comprising a computationally efficient model for predicting driver intentions. Moreover, to tackle the problem of data heterogeneity, it leverages personalized mixture representation to provide a personalized model adapted to the local data distribution of each driver client. We conducted extensive experiments on the Brain4Cars dataset, achieving an F1-score of 95.24&amp;#x0025; and a comprehensive evaluation metric of 0.9663, exceeding state-of-the-art. The experimental results demonstrate that the proposed FedPMR effectively addresses the challenges encountered when applying FL to driver intention prediction.</p

Development and Research of a Multi-Medium Motion Capture System for Underwater Intelligent Agents

A multi-medium motion capture system based on markers&rsquo; visual detection is developed and experimentally demonstrated for monitoring underwater intelligent agents such as fish biology and bionic robot-fish. Considering the refraction effect between air and water, a three-dimensional (3D) reconstruction model is established, which can be utilized to reconstruct the 3D coordinate of markers underwater from 2D data. Furthermore, the process of markers matching is undertaken through the multi-lens fusion perception prediction combined K-Means clustering algorithm. Subsequently, in order to track the marker of being occluded, according to the kinematics information of fish, an improved Kalman filtering algorithm is proposed. Finally, the feasibility and effectiveness of proposed system are verified through experimental results. The main models and methods in this paper can provide a reference and inspiration for measurement of underwater intelligent agents

Research on framework of reliability analysis on common rail system for diesel engines

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