温度,压力对漂移室气体增益的影响

研究了温度及气体压力对漂移室（工作于正比区）气体放大倍数的影响，温度和气体压力的变化范围分别是５￣４０℃及９０￣１０５ｋＰａ。用实验数据拟合得到了函数关系并测量了几种饱和度下函数中的参数值。     

RADIOACTIVE OR NATURAL TRACER。TECHNIQUES FOR LEAK DETERMINING OF DAM ABUTMENT

Infiltration and localization of preferential infiltration zones at the dam abutment are measured using radioactive tracer tests of flow in boreholes,meanwhile interconnection between boreholes and the energing water points is analysed.The theory and practice of radioactive tracer synthetic detective method are described to give methods and calculation formulae used under the condition of stable flow in single well to measure permeability coefficient and hydrostatic heads.Major single hole techniques including measurement for seepage line,velocity,rate of seepage flow and relationship of recharge of groundwater in aquifers are introduced briefly.The possibilities offered by natureal tracers are analysed,uincluding electric-conduct,pH-value and temperature of water as well as stable isotopes(D,^18O) and tritium.Furthermore,the sensibilities of this theory and methods were confirmed by detecting seepage flow field of Xinanjiang Dam.     

服务器虚拟化带来的信息安全风险以及影响策略

服务器虚拟化大大提高了服务器的资源利用率,对客户来说是十分有利的。但是,也带来了一些诸如破坏正常的网络架构、可能致使系统服务器超载等信息安全风险,需要采取一定的影响策略予以解决。能采取的影响策略有:安装兼容性杀毒软件、不间断地进行服务器硬件的容量分析等。     

An ET-Based Two-Phase Method for the Calibration and Application of Distributed Hydrological Models

Water Resources Management - Parameter calibration is a core process for the application of distributed hydrological models, which depends strongly on river runoff records. However, the sparse...     

Maximizing learning without sacrificing the fun: Stealth assessment,adaptivity and learning supports in educational games

In this study, we investigated the validity of a stealth assessment of physics understanding in an educational game, as well as the effectiveness of different game-level delivery methods and various in-game supports on learning. Using a game called Physics Playground, we randomly assigned 263 ninth- to eleventh-grade students into four groups: adaptive, linear, free choice and no-treatment control. Each condition had access to the same in-game learning supports during gameplay. Results showed that: (a) the stealth assessment estimates of physics understanding were valid—significantly correlating with the external physics test scores; (b) there was no significant effect of game-level delivery method on students' learning; and (c) physics animations were the most effective (among eight supports tested) in predicting both learning outcome and in-game performance (e.g. number of game levels solved). We included student enjoyment, gender and ethnicity in our analyses as moderators to further investigate the research questions.     

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.     

GRU-based capsule network with an improved loss for personnel performance prediction

Applied Intelligence - Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human...     

An improved generative adversarial network for translating clothes from the human body to tiled image

Neural Computing and Applications - Nowadays, purchasing the products similar to the styles of stars’ products has become a new trend in e-commerce platforms. Clothing transaction constitutes...     

An iterative gradient convolutional neural network and its application in endoscopic photoacoustic image formation from incomplete acoustic measurement

Neural Computing and Applications - Endoscopic photoacoustic tomography (EPAT) is a rapidly developing catheter-based imaging technique to provide cross-sectional images of anatomical, functional...     

基于计算机视觉的油封尺寸检测系统设计

针对油封的大批量、多品种、流水线作业的生产特点以及"零废品率"的检测要求,利用机器视觉检测和计算机图象处理技术,研究开发了适应大批量生产和在线实时检测的油封尺寸机器视觉检测系统;建立了系统软硬件结构,分析了系统的组成和工作原理及工作过程,并对图像处理算法进行了设计;系统标定后的实验检测结果证明,该检测系统能实现产品100%检测的生产目标,具有非接触、在线实时、精度适中、成本低等特点,具有较好的生产通用性。