Fast buffering for FPGA implementation of vision-based object recognition systems

Real-time frame rate is an important factor for practical deployment of computer vision systems. Field programmable gate array (FPGA) technology has been considered for many applications due to its parallel computing capability. FPGA implementations of computer vision algorithms normally involve buffering data on external memory devices, which could slow down the whole system. This paper proposes a buffering scheme suitable for implementing real-time vision-based systems on an FPGA that does not require external memory to buffer data. A stop sign detection system implemented on an FPGA employing the proposed buffering scheme is presented as an example system. This system is capable of processing over 200?fps at the frame size of 480?×?752 pixels.     

微温传感器系统的研制

为评价云南天文台抚仙湖太阳望远镜的圆顶视宁度,研制一种能测量常温、微温的仪器。在天文建筑物附近,安放两个微温传感器,可以测量影响天文观测的湍流强度的分布情况。介绍微温传感器的工作原理、硬件设计、软件设计以及仪器的测试。实验结果表明,该传感器具有较高的分辨率和较快的热响应时间,它将对望远镜建设物的设计具有实际意义。     

The parameterized level set method for structural topology optimization with shape sensitivity constraint factor

In recent years, the parameterized level set method (PLSM) has attracted widespread attention for its good stability, high efficiency and the smooth result of topology optimization compared with the conventional level set method. In the PLSM, the radial basis functions (RBFs) are often used to perform interpolation fitting for the conventional level set equation, thereby transforming the iteratively updating partial differential equation (PDE) into ordinary differential equations (ODEs). Hence, the RBFs play a key role in improving efficiency, accuracy and stability of the numerical computation in the PLSM for structural topology optimization, which can describe the structural topology and its change in the optimization process. In particular, the compactly supported radial basis function (CS-RBF) has been widely used in the PLSM for structural topology optimization because it enjoys considerable advantages. In this work, based on the CS-RBF, we propose a PLSM for structural topology optimization by adding the shape sensitivity constraint factor to control the step length in the iterations while updating the design variables with the method of moving asymptote (MMA). With the shape sensitivity constraint factor, the updating step length is changeable and controllable in the iterative process of MMA algorithm so as to increase the optimization speed. Therefore, the efficiency and stability of structural topology optimization can be improved by this method. The feasibility and effectiveness of this method are demonstrated by several typical numerical examples involving topology optimization of single-material and multi-material structures.

     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

银基电接触材料改性及制备工艺

概述了银基电接触材料的发展,介绍了Ag-Ni、Ag-C、Ag-WC和Ag-MO4个主要系列的银基电接触材料的特点及其改性方法,比较了不同银基电接触材料改性前后的优缺点。同时,叙述了粉末冶金法、合金内氧化法及预氧化合粉末法等银基电接触材料的制备工艺。最后指出,提高银基电接触材料的性能、发展新型的节银触头材料和复合触头材料是今后银基电接触材料的发展方向。     

关于输气管线内衬聚乙烯管可防止其腐蚀的商榷

本文参照德国一实例，即利用多种类型PE衬层，在一定温差及有气体扩散阻挡层和无气体扩散阻挡层的条件下，详细地叙述了高压酸性气体对其进行长期渗透作用的试验。结果显示：水蒸气和腐蚀性气体（如H2S）的渗透作用是不能忽视的，甚至当PE层为5mm时亦有一定渗透性；此外，在衬层底部尚产生了化学腐蚀作用，从而促使人们必须考虑须正确选择PE下边的防腐底层。     

基于建筑能源系统的混合储能技术研究现状

由于用户负荷需求的多元化和不确定性,单一类型的储能技术已不能满足高品质的建筑供能需要.通过耦合不同类型储能设备,实现多能源协调互补的混合储能技术应运而生.本文在建筑能源应用背景下,首先介绍了混合储能技术的原理,从建筑用能需求角度梳理了混合储能技术的研究进程,指出了现阶段混合储能的主要研究方向.其次基于混合储能的几种常见匹配方式,综述了热能、燃气化学能和电能等多类型能源混合存储技术的应用现状,并根据典型案例介绍了相应混合储能的系统组成、运行策略和系统特点,说明建筑用户的多能用能需求如何得到满足.最后对混合储能系统性能和经济性情况进行分析,提出了评估混合储能系统性能的重要指标和影响其经济性的主要因素.     

Flow stress and softening behavior of wrought magnesium alloy AZ31B at elevated temperature

1　INTRODUCTIONThewroughtmagnesiumalloyshaveexcellentspecificstrengthandstiffness ,machinability ,dampcapacity ,dimensionalstability ,lowmeltingcostsandare ,hence ,veryattractiveinsuchapplicationsasau tomobile ,aviation ,electronicandcommunicationin dustry[16 ] .Investigationsontheflowstressandsofteningbehaviorofmagnesiumalloysathigherformingtem peratureandstrainratehavebeenanimportantsub jectinwroughtmagnesiumalloysforming[710 ] .InthispapertheflowstressandsofteningbehaviorofAZ31Bdeform…     

550 MPa级中厚板矫直过程中表面凹坑缺陷的成因与对策

针对550 MPa级高强中厚板在矫直过程中极易出现氧化铁皮压入凹坑缺陷的问题，根据现场矫直工艺，研究了在不同温度下进行热变形时试样表面和横截面的形貌，钢基体与氧化铁皮界面处平直度的变化规律。结果表明，热变形温度为450 ℃时，表面氧化铁皮开始出现裂纹，且随着变形温度的降低，裂纹的数量逐渐增加；此外，氧化铁皮与钢基体之间的界面长度随变形温度的降低而呈直线增加，表明氧化铁皮与钢基体界面平直度随着热变形温度的降低越发粗糙、易脱落而形成凹坑缺陷。为此，应适当提高终轧温度以确保矫直时钢板表面温度大于450 ℃，同时提高钢板的运行速率，防止氧化铁皮在低温变形发生破碎和脱落的同时降低氧化铁皮厚度，从而减小因氧化铁皮脱落而造成凹坑缺陷的发生几率。