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。     

自适应多采样扫描转换算法

随着片元着色器的可编程能力不断提高和新型图元的出现,扫描转换必须加强多采样时的处理能力.经典的边方程扫描转换算法便于实现多采样,但采样点测试的计算量大并且存在冗余测试,为此提出一种自适应多采样扫描转换算法.该算法的多采样集中在"边界片元",并结合点采样和区域采样技术根据自适应细分规则细分边界片元进行采样点的测试;避免了边方程算法中进行采样点测试时的乘法运算,减少了采样点的冗余测试,提高了多采样时扫描转换的性能.实验结果表明,文中算法在较小代价下达到或接近有相同采样点数的超采样反走样能力.     

FY-3气象卫星MERSI数据快速预处理的IDL实现

作为新型卫星数据源,FY\|3/MERSI(风云三号中分辨率光谱成像仪)影像的快速预处理方法与模块目前较少见。采用基于三角网的几何校正算法,根据研究区Shapefile文件和FY\|3/MERSI自带定位数据提取处理区域,实现对应区域全部20个通道几何校正,并利用后向映射重采样输出各通道图像纠正后像元点DN值,随后进行辐射定标、太阳高度角订正等预处理。该系列过程用IDL编写相关程序实现了用户界面化交互操作。运行结果表明:设计的算法流程占用内存小,计算速度快。对于一个面积约45万km²的区域,所有通道处理耗时仅450 s,且校正后影像质量和精度均较好。相较于相同区域借助ENVI软件下地理位置查找表法(GLT)仅实现单通道几何校正耗时超30 min,本文开发的模块大大提高了FY\|3/MERSI数据处理效率,为其在城市热环境、积雪等领域的应用做好准备工作。     

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.     

结构正则化多视图非负矩阵分解

现存非负矩阵分解（non-negative matrix factorization,NMF）研究多考虑单一视图分解数据,忽略了数据信息的全面性。此外,NMF限制其获取数据的内在几何结构。针对以上问题,提出一个结构正则化多视图非负矩阵分解算法（structure regularized multi-view nonnegative matrix factorization,SRMNMF）。首先,通过主成分分析来对数据进行全局结构的判别式学习;其次,利用流形学习来捕获数据的局部结构;然后,通过利用多视图数据的多样性和差异性来学习表征。模型提升了算法聚类的整体性能,更加有效地挖掘数据的结构信息。此外,采用高效的交替迭代算法优化目标函数得到最优的因子矩阵。在六个数据集上与现存的代表性方法比较,所提出的SRMNMF的准确率、NMI和Purity分别最大提高4.4%、6.1%和4.05%。     

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.     

坦克炮控系统摩擦扰动补偿研究

针对坦克水平向炮控伺服系统中存在的摩擦不确定因素及未建模动态,首先建立水平向炮控伺服系统的数学模型,将摩擦扰动和未建模动态视为一个综合扰动项,然后利用扩张状态观测器对综合扰动项进行观测和补偿,并将扩张状态观测器(ESO)补偿回路作为经典PID控制方法的内回路,实现ESO-PID的复合控制;最后对所用方法进行仿真,结果表明,在原有控制方法的基础上设计控制器,集合了PID控制算法的"被动抗扰"和自抗扰控制方法中扩张状态观测器的"主动抗扰"特性,能够在保持控制系统原有特性的基础上大幅度提高炮控系统的稳定精度以及低速性能。