MRPII系统开发中物料需求计划算法的探讨

探讨了MRPII系统开发中最关键的问题之一,即物料需求计划算法的解决方案。     

数据库在多媒体系统中的应用与安全

提出了处理库中多媒体数据的技术和方法，讨论了数据库的安全问题。     

基于细菌觅食机理改进粒子群算法的研究

粒子群算法与细菌觅食算法在优化问题中均体现了较好的性能,但由于各自特定的进化机制,也都存在缺点。粒子群优化（PSO）算法在优化过程中过快陷入局部极值,为了避免这个缺陷,提出了一种新的混合算法。通过PSO算法完成整个空间的全局搜索,通过细菌觅食算法（BFOA）中的趋向性运动算子完成局部搜索的功能,再通过典型函数进行测试,结果表明新算法可以有效弥补细菌觅食算法速度不快和粒子群算法精度不高的缺陷,同时部分地避免了局部收敛的问题,从而适用于解决复杂函数的优化问题。     

MPEG-4视频解码器中IDCT模块的MMA前端设计

面向移动多媒体终端SOC设计了一种二维IDCT硬件加速器,基于快速LLM算法实现了8点IDCT运算,使用单RAM转置存储器进行行列分解,采用流水线技术,提高了处理速度,同时降低了芯片面积。     

基于超声导引的药品自动搬移系统的研制

阐述基于超声导引的药品智能搬移系统的研制方案,并给出主要设计电路。该系统用主从结构的两块TIMSP430F149单片机作为控制核心。从单片机控制搬移设备上的超声发送模块发送超声,坐标系上的三个超声接收器采集超声信号并做放大、滤波、单稳处理,处理后的信号发送给主单片机进行分析并发送控制命令给从单片机,从而实现对搬移设备的控制与定位。     

Evaluating Particle Deposition in the Artificial Groundwater Recharge Process by Physical and CT Imaging Experiments

Particle clogging in the artificial groundwater recharge process is one of the main factors influencing the artificial groundwater recharge efficiency, and particle deposition is the microscopic mechanism of the occurrence and development of particle clogging. Particle deposition in porous media changes the pore structure. The computed tomography (CT) scanning technique is a nondestructive testing method and determines the spatial distribution of pores in porous media. This study combines physical and CT scanning experiments to identify the change process of the pore structure in the artificial groundwater recharge process and compares the pore changes during recharge experiments between two columns containing different media. Porous media changes are observed with the CT scanning technique. The fractal theory is applied in the analysis of CT scan images and physical experiment results. The results of this study indicate that particle deposition can be examined by using CT scan images to obtain pore-related parameters, the internal pore structure of porous media determined through CT scan images can be applied in numerical simulation, and a mathematical model for particle deposition calculation in porous media is established. Compared to the physical experiment measurements, the spatial particle deposition information acquired with the CT scanning technique exhibits a higher accuracy and contains much more relevant data. Not only does this research reveal more clearly the particle clogging mechanism which is based on particle deposition, but also characterize, simulate and predict more accurately the development tendency of particle clogging during artificial groundwater recharge.

     

Monthly Streamflow Forecasting Using Convolutional Neural Network

Monthly streamflow forecasting is vital for managing water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In this study, the feasibility of the convolutional neural network (CNN), a deep learning method, is explored for monthly streamflow forecasting. CNN can automatically extract critical features from numerous inputs with its convolution–pooling mechanism, which is a distinct advantage compared with other AI models. Hydrological and large-scale atmospheric circulation variables, including rainfall, streamflow, and atmospheric circulation factors are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The artificial neural network (ANN) and extreme learning machine (ELM) with inputs identified based on cross-correlation and mutual information analyses are established for comparative analyses. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN outperforms ANN and ELM in all statistical measures. Moreover, CNN shows better stability in forecasting accuracy.

     

Dual local learning regularized nonnegative matrix factorization and its semi-supervised extension for clustering

Neural Computing and Applications - Nonnegative matrix factorization (NMF) has received considerable attention in data representation due to its strong interpretability. However, traditional NMF...     

基于暂态零序能量的配电网故障选线方法研究

针对中性点经消弧线圈接地系统发生单相接地故障时因故障边界复杂、随机,故障稳态分量小,零序瞬时功率倒相而导致传统的单一特征量选线方法失效问题,提出了一种新的基于暂态零序能量的选线方法。该方法通过一种数字滤波器来滤除暂态零序电流中的不对称分量和稳态工频分量,得到各条线路零序电流和母线零序电压的暂态纯故障分量,由此构造暂态零序能量,并根据暂态零序能量的大小和方向判断故障类型及线路,即发生单相接地故障时,非故障线路暂态零序能量的方向为正,故障线路暂态零序能量的方向为负,且其绝对值为其余线路暂态零序能量之和;发生母线接地故障时,母线暂态零序能量的大小为其余线路暂态零序能量之和。针对母线暂态零序能量受噪声影响大而导致选线灵敏度低的问题,提出将自动重合闸技术应用到该选线方法中的方案。Matlab仿真结果表明,该选线方法的准确性、可靠性较高。     

基于物联传感网络的管道内爆炸风险监控系统设计

天然气在封闭的管道中传输,信息难以获取,采集数据不完整,导致无法准确地对管道内的密封不严、腐蚀穿孔等情况及时感知而造成燃气泄漏发生爆炸;将物联传感网络的思想引入到管道内爆炸风险监控中,在管道中布置压力传感器与气敏传感器对管内数据进行采集与处理;针对燃气爆炸模型在系统的软件设计中,用Zigbee网络协议构造物联传感网络,使用多目标优化搜索技术对爆炸危险点进行搜索;实验结果表明,该系统在不同的环境下可以准确地对管道爆炸进行预警,误差在3%左右。