升华再结晶法制备AIN晶须及其生长特性

以AIN粉体为原料，加入适量的CaO-B2O3矿化剂，采用升华再结晶法制备AIN晶须．初步探讨了反应器及其合成温度对产物种类的影响，研究了晶须的结构特征及其生长机理．结果表明，初期的合成产物包括AIN晶柱、晶须和非晶AIN纤维，以VLS机制生长：后期产物为AIN晶须，表现为VS生长机制：XRD及TEM分析表明，晶须大多呈现沿｛2110｝、｛101l｝和｛0001｝，l＝0、1、2、3的晶面生长．多数晶须宏观生长轴向平行于这些晶面的法线，而部分晶须由于发生斜生长，导致宏观生长轴向与这些晶面的法线斜交．     

The yeast spindle pole body component Spc72p interacts with Stu2p and is required for proper microtubule assembly

We have previously shown that Stu2p is a microtubule-binding protein and a component of the Saccharomyces cerevisiae spindle pole body (SPB). Here we report the identification of Spc72p, a protein that interacts with Stu2p. Stu2p and Spc72p associate in the two-hybrid system and can be coimmunoprecipitated from yeast extracts. Stu2p and Spc72p also interact with themselves, suggesting the possibility of a multimeric Stu2p-Spc72p complex. Spc72p is an essential component of the SPB and is able to associate with a preexisting SPB, indicating that there is a dynamic exchange between soluble and SPB forms of Spc72p. Unlike Stu2p, Spc72p does not bind microtubules in vitro, and was not observed to localize along microtubules in vivo. A temperature-sensitive spc72 mutation causes defects in SPB morphology. In addition, most spc72 mutant cells lack cytoplasmic microtubules; the few cytoplasmic microtubules that are observed are excessively long, and some of these are unattached to the SPB. spc72 cells are able to duplicate and separate their SPBs to form a bipolar spindle, but spindle elongation and chromosome segregation rarely occur. The chromosome segregation block does not arrest the cell cycle; instead, spc72 cells undergo cytokinesis, producing aploid cells and polyploid cells that contain multiple SPBs.     

小波变换在手写体汉字识别中的应用

汉字识别中的结构类特征具有抗变形的优点，合适做为手写体识别过程使用，但其中笔划特征的提取非常困难。本文用小波变换这一图象分析中有力工具对汉字图象分析处理之后，再进行笔划的提取，其过程简洁、明晰。     

电视监控系统云台控制器设计

电视监控系统是一种典型的分布式计算机信号采集系统。为了提高监控的范围和效果,云台控制器的优劣直接影响监控的范围和效果,因此对云台控制器的设计具有重要的意义。本系统采用串型通信,通过Modbus协议把控制信号输入单片机,经过对AT89S52单片机的编程,从而对八路继电器输出进行控制。系统不仅结构简单,而且还可以方便的对镜头、防护罩的控制进行扩展,功能强大,可以广泛应用于电视监控系统中的云台控制。     

海量数据库迁移与升级

目前数据库中数据越来越多,应用对数据库在线时间的要求也越来越高,使得数据库迁移和数据库升级成为了一件非常困难的事情.为此,介绍了几种迁移和升级的方法,用以实现以尽可能少的停机时间对TB级数据库完成数据库的迁移和升级.     

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.     

A conditional classification recurrent RBM for improved series mid-term forecasting

In the context of human-robot and robot-robot interactions, the better cooperation can be achieved by predicting the other party’s subsequent actions based on the current action of the other party. The time duration for adjustment is not sufficient provided by short term forecasting models to robots. A longer duration can by achieved by mid-term forecasting. But the mid-term forecasting models introduce the previous errors into the follow-up forecasting and amplified gradually, eventually invalidating the forecasting. A new mid-term forecasting with error suppression based on restricted Boltzmann machine(RBM) is proposed in this paper. The proposed model can suppress the error amplification by replacing the previous inputs with their features, which are retrieved by a deep belief network(DBN). Furthermore, a new mechanism is proposed to decide whether the forecasting result is accepted or not. The model is evaluated with several datasets. The reported experiments demonstrate the superior performance of the proposed model compared to the state-of-the-art approaches.

     

SESF-Fuse: an unsupervised deep model for multi-focus image fusion

Neural Computing and Applications - Muti-focus image fusion is the extraction of focused regions from different images to create one all-in-focus fused image. The key point is that only objects...     

用于自动测试系统继电器矩阵开关的设计

针对目前现有的矩阵开关在性价比、可靠性以及通用性等方面不尽如人意,无法满足中小型自动测试系统的实际要求这一问题;设计了一种用于自动测试系统的继电器矩阵开关,给出了其设计原理、硬件电路图以及主程序流程图;系统搭建完成后,进行8次独立重复试验,测试结果显示:该继电器矩阵开关测试数据的最大误差为±0.3％,同时具有性价比高、通用性好、可靠性较高等优点,特别适合于中小型测试系统的电源通道和信号通道的切换与控制.