潜器Stewart平台动力学仿真

建立了Stewart平台的动力学模型,对潜器的动力学特征进行分析,对其运动进行动力学仿真研究.以Stewart平台的任意支腿为研究对象,考虑各支腿的重力和惯性力,利用牛顿-欧拉方法建立了支腿及动平台的力和力矩平衡方程,推导出了Stewart平台的动力学方程及支腿所受的支反力.以潜艇模拟器并联机构为例,运用动力学模型进行了实际计算并绘制了支腿驱动力的变化曲线,将驱动力与支腿运动速度相乘得到支腿功率输出的变化曲线,为液压油缸的功率设计提供了依据,也为潜器的精确动力学解耦奠定了基础.     

让设计成为科学、艺术、时尚融合的载体——记北京服装学院教师作品展

本文结合北京服装学院2008年在中国美术馆举办的首届教师作品展,就学校的办学理念、办学特色、创新教育、优势互补和理论研讨等方面展开论述。不仅彰显了北京服装学院的学科优势,而且突出了学校在“奥运”和“神七”设计中贡献社会的浓重一笔。同时,也强调了学校对传统与时尚、艺术与科学、民族与世界融为一炉的设计理念的高度重视。     

Study on early warning index of spatial deformation for high concrete dam

Owing to the deficiency in early warning for high concrete dam,a formula was established to characterize the deformation behavior of high concrete dam as a whole.The early warning index of spatial deformation was calculated by low probability principle on the basis of the deformation entropy formula proposed.The steps were as follows.Firstly,the probability method was utilized to define the degree of order and degree of chaos for each survey point.Secondly,the weight distribution among the survey points was...     

Full-scaled deep metric learning for pedestrian re-identification

Multimedia Tools and Applications - The pedestrian re-identification problem (i.e., re-id) is essential and pre-requisite in multi-camera video surveillance studies, provided the fact that...     

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Reaction mechanism and microstructure evolution of reaction sintered <Emphasis Type="Italic">h</Emphasis>-BN

Hexagonal boron nitride ceramic (h-BN) based on the nitridation of B powders was obtained by reaction sintering method. The effects of sintering temperature on the mechanical properties and microstructure of the resultant products were investigated and the reaction mechanism was discussed. Results showed that the reaction between B and N₂ occurred vigorously at temperatures ranging from 1 000 °C to 1 300 °C, which resulted in the generation of t-BN. When the temperature exceeded 1 450 °C, transformation from t-BN to h-BN began to occur. As the sintering temperature increased, the spherical particles of t-BN gradually transformed into fine sheet particles of h-BN. These particles subsequently displayed a compact arrangement to achieve a more uniform microstructure, thereby increasing the strength.     

Estimation of access failure probability in D2D underlaid cellular networks

Device-to-Device (D2D) communication can reduce the mobile devices' energy consumption and increase the spectral efficiency in D2D underlaid cellular networks. However, D2D users will interfere with co-channel cellular users, which can lead to cellular communication access failures. There are two reasons for cellular communication access failures: (1)D2D interference and (2)insufficient spectrum resources. To address the absence of research on the performance of cellular services' access in D2D underlaid cellular networks, this paper defines the new services' access failure probability and handoff services' access failure probability to evaluate the effect of both D2D interference and limited resources on cellular communication access. Based on the stochastic geometry and stochastic process, a random network model is presented to estimate the access failure probabilities, which can provide guidelines for network design to ensure cellular services' access. The accuracy of the estimated access failure probability is validated through extensive simulations.     

综合物探方法在地面塌陷勘察中的应用

地面塌陷引发的地质灾害对国民经济建设的危害重大。目前,探测塌陷区的物探方法种类繁多,但没有一种方法能全面解决各种问题,需综合运用多种方法。以安徽某地面塌陷研究对象,运用高密度电法和瞬变电磁法探测重点塌陷区域,简要介绍了高密度电法、瞬变电磁法的工作原理、方法特点、数据采集和数据处理,并对数据进行了解释,通过此次探测研究,取得了明显的成果。结果表明,两种方法相互验证,相互参照,相互补充,查明了塌陷区及其附近的覆盖层厚度、地质构造和地层岩性变化,为后期地面塌陷灾害治理提供了科学依据。     

Improved Computational Model of Grid Cells Based on Column Structure

To simulate the firing pattern of biological grid cells, this paper presents an improved computational model of grid cells based on column structure. In this model, the displacement along different directions is processed by modulus operation, and the obtained remainder is associated with firing rate of grid cell. Compared with the original model, the improved parts include that: the base of modulus operation is changed, and the firing rate in firing field is encoded by Gaussian-like function. Simulation validates that the firing pattern generated by the improved computational model is more consistent with biological characteristic than original model. Besides, the firing pattern is badly influenced by the cumulative positioning error, but the computational model can also generate the regularly hexagonal firing pattern when the real-time positioning results are modified.