ConDTC: Contrastive Deep Trajectory Clustering for Fine-grained Mobility Pattern Mining
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Trajectory clustering is a cornerstone task in the field of trajectory mining. With the proliferation of deep learning, deep trajectory clustering has been widely researched to mine mobility patterns from massive unlabeled trajectories. Nevertheless, existing methods mostly ignore trajectories' temporal regularities, which are essential for mining fine-grained mobility patterns for applications including traveling group identification, transportation mode discovering, social security emergency, etc. To fill this gap, we propose ConDTC, a contrastive deep trajectory clustering method targeting for fine-grained mobility pattern mining. Specifically, we first design a spatial-temporal trajectory representation learning method which can capture both spatial and temporal regularities of trajectories synchronously. The proposed trajectory representation model can be used as a pre-trained model to serve various downstream trajectory mining tasks. Then, we construct a contrastive trajectory clustering module which optimizes trajectory representations and clustering performance simultaneously. Experimental results on three datasets validate that ConDTC can identify fine-grained mobility patterns by clustering trajectories with similar spatial-temporal mobility patterns together while separating those with different mobility patterns apart. Actually, ConDTC outperforms all state-of-the-art competitors substantially in terms of effectiveness, efficiency and robustness.The algorithm GDBSCAN only needs two parameters.It can discover clusters of any shape.But it is very sensitive to the parameter Eps.This paper puts forward a kind of method to make sure the Eps,which is based on datagrid.The method can achieve better result on the case of data distributing unevenly and shapes of clusters reaching each other.
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We present a method to classify objects in video streams using a brain-inspired Hierarchical Temporal Memory (HTM) algorithm. Object classification is a challenging task where humans still significantly outperform machine learning algorithms due to their unique capabilities. We have implemented a system which achieves very promising performance in terms of recognition accuracy. Unfortunately, conducting more advanced experiments is very computationally demanding; some of the trials run on a standard CPU may take as long as several days for 960x540 video streams frames. Therefore we have decided to accelerate selected parts of the system using OpenCL. In particular, we seek to determine to what extent porting selected and computationally demanding parts of a core may speed up calculations. The classification accuracy of the system was examined through a series of experiments and the performance was given in terms of F1 score as a function of the number of columns, synapses, $min\_overlap$ and $winners\_set\_size$. The system achieves the highest F1 score of 0.95 and 0.91 for $min\_overlap=4$ and 256 synapses, respectively. We have also conduced a series of experiments with different hardware setups and measured CPU/GPU acceleration. The best kernel speed-up of 632x and 207x was reached for 256 synapses and 1024 columns. However, overall acceleration including transfer time was significantly lower and amounted to 6.5x and 3.2x for the same setup.
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Abstract This paper presents an assessment of the steps involved in trajectory synthesis (e.g., trajectory layout, trajectory generation, trajectory spacing, trajectory postprocessing, and trajectory physics) for shape creating manufacturing operations, e.g., rapid prototyping, milling, electrical discharge machining. The rationale for this paper is that the trajectory plays an important role in determining the productivity as explained below. Shape creating manufacturing operations are those that make use of a “tool” which operates on a “workpiece” to create the desired shape. The tool operates on the workpiece as dictated by the trajectory of the tool, resulting in the manufactured shape. Often, the geometric and functional properties of the end product are dependent on the trajectory of the tool. Moreover, the trajectory dictates the accuracy of the end product and the time taken to manufacture the product. Recognizing the important contribution of the trajectory in shape creating operations, this paper focuses on trajectory synthesis.
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The innovation of this paper is that a mathematical model of 3-D trajectory prediction is proposed and studied, based on 2-D trajectory prediction algorithm.And 3-D trajectory prediction algorithm is applied to object tracking system, in order to fast and efficient tracking.A large number of experiments are done by MALTAB.By comparing the test results, prediction trajectory is consistent with actual trajectory.
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