Development of a Motion Prediction System by Logistic Regression for the Kinect-Based Upper-Limb Assistive Device
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To enable the user move freely and stably without operating any control interfaces, we developed a motion prediction system based on logistic regression method for Kinect-based upper-limb assistive device. A probabilistic output is provided by classifying spatial vectors and performing the pre-trajectory with the highest probability, 85% of classification accuracy can be achieved.Trajectory planning is key component for autonomous vehicles. However, most existing trajectory planning methods which utilized a set of fixed weights to evaluate an optimal trajectory from a set of trajectory candidates may change abruptly in complex dynamic scenarios. In this study, a robust trajectory planning was proposed based on historical information. The developed trajectory planning is mainly consisting of candidate trajectories generation module, the collision detection module, the stability detection module and the speed planning module. Firstly, the candidate trajectories are generated according to the vehicle kinematics model. And then the collision detection module is used to remove the dangerous trajectories. Then, the optimal trajectory is selected by the multi-attribute indicators evaluation, and the selected trajectory is checked by the stability detection module based on the lateral deviation of the optimal trajectory from the historical trajectory. Based on its result, the current optimal trajectory or the historical trajectory is chosen as the output trajectory. Finally, based on the output trajectory, the speed planning module is executed to generate a smooth trajectory. Simulation and field experiments were conducted to evaluate the effectiveness of the proposed method.
Trajectory Optimization
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Autonomous trajectory generation for UAVs is to decide vehicles motion and find a smooth trajectory in complex environments.A trajectory generation approach is proposed,which is based on tactical trajectory data base.Using this approach,according to environments and task requirements,chooses appropriate maneuver and generates the proper real-time trajectory autonomously.Simulation results show that the real-time of this approach is good,and the generated trajectory is easy to be followed.
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In order to research the PV module characterization it was necessary to develop the Prototype PV characterization station. Among other possibilities derived from the measured results of Prototype PV characterization station it is also possible to model the Sun trajectory at installation location. Such measured results can be used to model Sun trajectory. Also, the Sun trajectory generated using presented model is verified with conventional analytical model Sun trajectory. Some conclusions for new approach are presented and discussed.
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In order to effectively match the target vehicle with the trajectory of the passenger's mobile phone to be matched, this research proposed a trajectory similarity matching model of spatiotemporal trajectory big data. The model mainly adopts two advanced measurement methods: structural measurement of spatiotemporal trajectory and motion state measurement of spatiotemporal trajectory. The GPS trajectory of a vehicle traveling on a certain road within a time interval was used as the target trajectory, and the similarity between the trajectories was calculated. Finally, the number of mobile phone trajectories matching the target vehicle was determined, and then the number of mobile phones matching the target vehicle was obtained. Experimental results verify that the model could effectively match the target trajectory with the mobile phone trajectory, which was in line with the expected effect.
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Nowadays, people pay more and more attention to privacy protection with the continuous occurrence of data breaches. In location-based services, dummy trajectory generation is a popular location privacy protection method. However, this method is used by some malicious users for benefits, which results in economic losses and the waste of resources of the locationbased services provider. For this problem, dummy trajectory identification has been proposed by researchers. Nevertheless, with the continuous development of dummy trajectory generation algorithms, the existing dummy trajectory identification methods are unsuitable. In this paper, we propose a hybrid neural network framework for dummy trajectory identification, called trajectory similarity hybrid networks (TSHN). The main idea of TSHN is to identify whether a target trajectory is a virtual trajectory according to the similarity score between historical trajectories and the target trajectory. For each historical trajectory of the user, the mobility and individual features are extracted to train TSHN. The trajectory similarity score generated by the TSHN is used to identify dummy trajectories. The experimental results show that our proposed TSHN can identify the dummy trajectory with an accuracy of 0.97, which significantly outperforms the existing dummy trajectory identification methods.
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The air trajectory was designed for a mine water-exit attack,and a mathematic model of attack trajectory was established.The attitude stabilization control for vertically upward trajectory,the restriction of turn angular rate,the setting of dive angle for fast turn trajectory,and the guidance of dive attack trajectory were analyzed.In addition,the dynamic characteristic of each trajectory was simulated,and the results show that the preset trajectory is stable and con-trollable,and can be used to accurately strike target.
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This paper addresses the problem of representing and recognizing motion trajectories. We first propose to derive scene-related equipotential lines for points in a motion trajectory and concatenate them to construct a 3D tube for representing the trajectory. Based on this 3D tube, a droplet-based method is further proposed which derives a "water droplet" from the 3D tube and recognizes trajectory activities accordingly. Our proposed 3D tube can effectively embed both motion and scene-related information of a motion trajectory while the proposed droplet- based method can suitably catch the characteristics of the 3D tube for activity recognition. Experimental results demonstrate the effectiveness of our approach.
Equipotential
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