某抽水蓄能电站高面板堆石坝坝体分区优化

针对某抽水蓄能电站上水库主坝最大坝高达182.3 m,上水库土石料料源紧张,坝基地质条件复杂,岸坡起伏差大,大坝变形及防渗结构可靠性要求高等问题,为提高大坝安全性能,研究主坝断面分区优化方案。坝体分区优化充分考虑利用上水库石料厂开挖的石料筑坝;上、下游堆石区分界线坡比采用倾向下游1∶0.4;在坝顶以下部位设置增模区;连接板向库内平移并减小转弯段曲率,并将基础垫层优化为特殊垫层及过渡料。实验表明该优化方案有效保证了结构的安全。     

Defects restoration during cooling and annealing in PTC type barium titanate ceramics

The PTC-type BaTiO₃ ceramics of similar microstructure are obtained by careful control of a sintering scheme. The defect chemistry of them is modified by varying the cooling rate and annealing conditons. In addition to applying Heywang and Jonker models for explaining the resistivity anomaly of these samples, the outward diffusion of oxygen vacancies (V_O), which left excess barium vacancies (V_Ba) behind, is proposed to be the formation mechanism of surface states. The formation of defect complex consisting of a V _O ^.. - _Ba pair is assumed to be the cause of small diffusivity of V_O in these materials. The phenomena, in which the slower cooling rate raises the resistivity of samples, is ascribed to the higher concentration of excess barium vacancies (V_Ba) contained in these samples. However, the maximum potential barrier height (_max) of the samples is the same, irrespective of the amount of surface state concentration (N _s) and is estimated to be _max = 0.66eV, from the In (_max)-1/T _max plot.     

电位滴定法研究~(99)Tc-HMDP和~(99)Tc-HEDP中Tc的氧化态

~(99)Tc-HMDP和~(99)Tc-HEDP溶液是在相应的螫合剂存在下,用过量SnCl_2还原~(99)TcO_4-制得。用标准I_2液对SnCl_2进行了剩余量电位滴定,确定了在pH～3的介质中,~(99)Tc-HMDP和~(99)Tc-HE-DP中~(99)Tc的氧化态均为+4。全部实验在N_2气流中进行。~(99)Tc-HMDP和~(99)Tc-HEDP的紫外光谱表明:在用I_2滴定的过程中,~(99)Tc只处于一种氧化态+4;将它们放置在空气中,一小时后仍较稳定。     

碳纤维的表面性质——气-固色谱法研究

用气—固色谱法(反气相色谱法)研究了表面处理对碳纤维表面性质的影响。通过观察被测碳纤维表面与正烷烃及极性有机液体探针的相互作用,测定在“零复盖”条件下淋洗过程的保留体积,计算了吸附热力学函数。结果表明,经苯乙烯—马来酸酐共聚物电沉积处理的碳纤维表面与未处理碳纤维相比,表面能的色散分量差别不大。烷烃蒸汽在处理过的碳纤维表面上吸附时有较大的熵变,这表明烷烃分子在这种表面上吸附后构象变化更大,即探针分子活动性降低的程度比未处理表面来得大。极性探针与两种碳纤维表面的相互作用有很明显的差别,处理后的碳纤维表面与电子给体探针有强的相互作用,表明共聚物电沉积使碳纤维表面增加了电子受体基因,因而能有效地改善碳纤维表面与复合材料基体树脂的粘合性能。     

Improvement on dynamic elastic interpolation technique for reconstructing 3-D objects from serial cross sections [biomedical application

An improved method for automatically reconstructing a three-dimensional object from serial cross sections is presented. The method improves the dynamic elastic contour interpolation technique. There are two major improvements: (1) in the case of branching that involves concave contours, instead of pairwise interpolation between the start contour and each goal contour, the goal image is considered globally and local constraints are imposed on the forces exerting upon the vertices; and (2) it takes the continuity of high-order derivatives into consideration and incorporates the schemes of spline theory, quadratic-variation-based surface interpolation, and finite-element-based multilevel surface interpolation to create smoother surfaces of the reconstructed object. Based on the output from the preliminary processing, two alternatives, a quadratic-variation-based surface interpolation algorithm and a finite-element-based multilevel surface interpolation algorithm, can be adopted to obtain the final surface representation     

HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG

Knowledge and Information Systems - In the past decade, human activity recognition (HAR) has grown in popularity due to its applications in security and entertainment. As recent years have...     

Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models

Engineering with Computers - The indirect and accurate determination of blast-induced rock movement has important significance in the reduction of ore loss and dilution and in the protection of...     

Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.     

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.