Skilled master studio draws lessons from studio which is a produciton organization form.It plays an important role in innovation and skill inheritance.It is an effective method for solving the culitivation of highly skilled talents.This paper explores the definition,operation and support of skilled master studio in order to perfect and popularize it.
Aiming at measuring tree barriers in transmission line corridors, a binocular vision ranging method is proposed to measure the distance between the transmission lines and trees. Based on the principle of binocular vision ranging, the binocular camera is calibrated using a marked checkerboard as a calibration board. Then, the SAD region matching algorithm is applied to the preprocessed images, and the disparity map is acquired. Finally, the distance between the tree and the transmission line can obtain according to the three-dimensional coordinate information of two target points. The results of the experiments show that the proposed binocular vision-based method can achieve the measurement error within ±30cm. The precision can meet the need of measuring the distance between trees and the transmission lines, which is an effective way for warning tree barriers in transmission line corridors.
On the basis of daily precipitation data of 48 stations in Xinjiang during 1961-2011,the extreme precipitation threshold value were defined in different stations according to percentile method and variation characteristics of extreme precipitation events(EPE)in Xinjiang are studied.The inner-annual inhomogeneity characteristics of extreme precipitation events are analyzed by introducing the new parameters that reflect the temporal distribution of precipitation including concentration degree and concentration period.Results shows as follows.1)The spatial distribution of the extreme precipitation threshold value is almost same to the annual average extreme precipitation,which shows that the maximum locates in Tianshan Mountain area,the value in northern Xinjiang is larger than that in the south as same as the extreme precipitation frequency.2) There are some differences in spatial distribution between concentration degree and concentration period of extreme precipitation events.The range of the extreme precipitation events concentration degree is 0.62-0.9,which shows that the extreme precipitation appears intensively in Xinjiang.The concentration degree of extreme precipitation events in northern Xinjiang is smaller than that in the southern and eastern.The extreme precipitation appears mainly between early July and the middle of August.3)The concentration degree of extreme precipitation events shows decrease trends in the last 51 years,however,the trends in concentration period Increase,That means that the extreme precipitation appears increasingly dispersively.4)The concentration degree and concentration period of extreme precipitation events shows a positive correlation with the amounts of extreme precipitation.The amounts of extreme precipitation becomes less with the concentration degree gets higher and the concentration period gets earlier especially in northern Xinjiang,vice versa.
In moving objects information management, Motion state model (MSM) is based on sample method and suits to most applications. Because of its characters, 2n index tree is proposed to organize the motion data. In order to increase the efficiency of indexing, some issues such as late updating of leaf node splitting and merging, pre-query before the query of objects relation and the reference of motion vectors in index tree are carefully studied. Comparing to other indexing method, 2n index tree works better with MSM.
Abstract The online transmission and real‐time rendering of complex 3D models have always been a bottleneck which limits the performance of Web 3D simulation systems. To improve the efficiency of data transmission and mesh reconstruction, this article proposes a novel progressive mesh structure. In the first stage of progressive visualization, the base data and the base index generated by vertex clustering simplification are transmitted to the client for the fundamental rendering. Then the incremental data and corresponding indexes at higher levels are transmitted, as the viewpoint approaches the simulation object. The multi‐scale incremental data organization benefits the performance and efficiency of the Web 3D simulation system by separately transmitting and reconstructing the corresponding level of mesh details. To demonstrate the adaptability and reliability of this algorithm, we developed an experimental prototype system to conduct a series of experiments. The results of experiments show that the improved progressive mesh structure described in this article takes good advantage of the vertex clustering simplification scheme to increase the efficiency of online transmission and mesh reconstruction, and the average frame rate of the progressive visualization has been increased to some extent, especially for massive data in large scale scenes.
Trajectory prediction is one of the core functions of autonomous driving. Modeling spatial-aware interactions and temporal motion patterns for observed vehicles are critical for accurate trajectory prediction. Most recent works on trajectory prediction utilize recurrent neural networks (RNNs) to model temporal patterns and usually need convolutional neural networks (CNNs) additionally to capture spatial interactions. Although Transformer, a multi-head attention-based network, has shown its notable ability in many sequence-modeling tasks (e.g., machine translation in natural language processing), it has not been explored much in trajectory prediction. This paper presents a Spatial Interaction-aware Transformer-based model, which uses the multi-head self-attention mechanism to capture both interactions of neighbor vehicles and temporal dependencies of trajectories. This model applies a GRU-based encoder-decoder module to make the prediction. Besides, different from methods considering the spatial interactions only among observed trajectories in both encoding and decoding stages, our model will also consider the potential spatial interactions between future trajectories in decoding. The proposed model was evaluated on the NGSIM dataset. Compared with other baselines, our model exhibited better prediction precision, especially for long-term prediction.
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