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。     

分布式光纤测温在动力电缆温度监测中的应用

动力电缆温度监测是可以及时发现电缆线路过热点位置和检测运行线路的绝缘状态,从而保障电缆安全稳定运行的技术措施。针对电缆过热事故的主要原因和特点,提出采用分布式光纤测温技术对电缆沿线温度进行监测预警,给出了分布式光纤测温系统的原理、组成和光缆布设方案,此系统对保证电力系统的安全运行有重要意义。     

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。     

基于假位置选择的位置隐私脱敏算法

传统位置隐私脱敏技术容易遭受背景知识攻击和单点攻击,造成用户位置隐私泄露,危及其生命财产安全。针对此问题,提出了一种基于假位置选择的位置隐私脱敏算法DLPD（dummy location privacy desensitization）。首先,通过衡量位置敏感程度、实行偏移优化、过滤位置查询概率来构造假位置候选集;其次,改善位置分布筛选假位置;最终,构造安全匿名集实现位置隐私脱敏。实验证明,该算法增强了隐私保护强度,提高了隐私保护的有效性,同时,降低了开销,具有实用性。     

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.

     

基于SPI的辽宁省庄河市近35年干旱演变特性研究

庄河市是辽南地区的重要水源地,分析其干旱演变特性有利于该地区水资源的合理开发与利用,对于防旱减灾、合理布局产业结构具有重要的现实意义。本文根据庄河市1984年1月1日至2018年12月31日的逐日降水数据计算了1个月、3个月、6个月、12个月共4个不同时间尺度下的标准化降水指数(SPI),并基于SPI对该地区近35年的年际与季节干旱演变特征进行了分析。结果表明:在年际变化方面,庄河市的SPI在2000年以前波动幅度较小,说明该地区的降水在时间上分布较为均匀;但自2000年以来,SPI序列出现较大的波峰与波谷,说明降雨的分布不均匀性有所增强;SPI的时间序列趋势线斜率为负值,说明该地区整体上呈现偏旱趋势,因此其水资源压力不断增大。在季节演变特征方面,夏季干旱和秋季干旱呈现增强趋势;秋旱的发生频率最高,而夏旱的出现频率最低。本研究结果可为庄河地区的抗旱减灾与水资源规划提供科学依据。     

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...