As an important part of guard system, intrusion detection systems have great significance to the security of industrial control systems. Cause the industrial control systems of the on-site environment is often very co...
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With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelong learning approaches potentially of...
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This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene ...
This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects.
Empowered by the advanced 3D sensing, computer vision and AI algorithm, autonomous robotics provide an unprecedented possibility for close-up infrastructure environment inspection in an efficient and reliable fashion....
Empowered by the advanced 3D sensing, computer vision and AI algorithm, autonomous robotics provide an unprecedented possibility for close-up infrastructure environment inspection in an efficient and reliable fashion. Deep neural network (DNN) learning algorithms, pretrained on the large database can empower real-time object detection as well as fully autonomous, safe robotic navigation in unstructured environments while avoiding the potential obstacle. However, the development and deployment of the robots, inspection planning and operation procedures are still tedious and segmented with tremendous manual intervention during environmental inspection and anomaly monitoring. The proposed digital twin approach is able to provide a virtual representation model of the target environment either from a build-design or from 3D scanning of the real-world physical assets at high resolution in the Unity simulation environment, a transverse drone robot model and test its robotics Operating System(ROS) autonomous navigation and obstacle avoidance software stack, and the hardware-in-the-loop test can thus be conducted for the flight control algorithm effectiveness and real-time object detection performance evaluation. The preliminary result shows that VGG16-UNet deep learning algorithm was able to use only a small amount of guidance and time from experienced inspection pilots to successfully identify the critical elements and defects and real-time navigate around the unstructured environment. The proposed digital twin framework and methodology is promising to be utilized for developing and testing fully autonomous inspection robots and its path planning and navigation and detection operation with greater cost- and time-efficiency.
We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is character...
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Human-machine work system (HMWS) is a composite system that combines the accuracy and strength of machines in repetitive work with the advantages of humans in cognitive ability, flexibility, and adaptability to perfor...
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Direct stimulation of peripheral nerves with implantable electrodes successfully provided sensory feedback to amputees while using hand *** of the electrodes is key to success,which we have improved for the polyimide-...
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Direct stimulation of peripheral nerves with implantable electrodes successfully provided sensory feedback to amputees while using hand *** of the electrodes is key to success,which we have improved for the polyimide-based transverse intrafascicular multichannel electrode(TIME).The TIMEs were implanted in the median and ulnar nerves of three trans-radial amputees for up to six *** present a comprehensive assessment of the electrical properties of the thin-film metallization as well as material status post *** TIMEs stayed within the electrochemical safe limits while enabling consistent and precise amplitude *** lead to a reliable performance in terms of eliciting *** signs of corrosion or morphological change to the thin-film metallization of the probes was observed by means of electrochemical and optical *** presented longevity demonstrates that thin-film electrodes are applicable in permanent implant systems.
Magnetic continuum robots (MCRs) show promising potential for applications in narrow lumens, such as vascular interventions. Accurate shape sensing of these robots is crucial for the success of interventional surgerie...
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ISBN:
(数字)9798331509644
ISBN:
(纸本)9798331509651
Magnetic continuum robots (MCRs) show promising potential for applications in narrow lumens, such as vascular interventions. Accurate shape sensing of these robots is crucial for the success of interventional surgeries. However, the submillimeter dimensions of MCRs poses significant challenges for integrating additional sensors. An alternative approach is shape estimation from images, yet the lack of precise MCR kinematics can make this process time-consuming. In this work, we propose a novel MCR design featuring a rectangular tip capable of specific planar bending, along with a corresponding kinematic model and a mechanical model. Additionally, we introduce a model-based shape estimation method that enables the estimation of the MCR's shape from a single-view image without the need for external markers. Experimental results demonstrate that our method can estimate an MCR's shape with a mean absolute error (MAE) of $\mathbf{0.58}\pm \mathbf{0.41}\mathbf{mm}$ within 85ms, facilitating real-time application. Furthermore, closed-loop control based on this method has achieved an end position error below 11% of the tip's total length, highlighting the accuracy and practicality of our approach for surgical interventions.
To handle the detrimental effects brought by leakage of radioactive gases at nuclear power station, we propose a bus based evacuation optimization problem. The proposed model incorporates the following four constraint...
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To handle the detrimental effects brought by leakage of radioactive gases at nuclear power station, we propose a bus based evacuation optimization problem. The proposed model incorporates the following four constraints, 1) the maximum dose of radiation per evacuee, 2) the limitation of bus capacity, 3) the number of evacuees at demand node（bus pickup stop）,4) evacuees balance at demand and shelter nodes, which is formulated as a mixed integer nonlinear programming（MINLP）problem. Then, to eliminate the difficulties of choosing a proper M value in Big-M method, a Big-M free method is employed to linearize the nonlinear terms of the MINLP problem. Finally, the resultant mixed integer linear program（MILP） problem is solvable with efficient commercial solvers such as CPLEX or Gurobi, which guarantees the optimal evacuation plan *** evaluate the effectiveness of proposed evacuation model, we test our model on two different scenarios（a random one and a practical scenario）. For both scenarios, our model attains executable evacuation plan within given 3600 seconds computation time.
Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions i...
Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.
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