带有流化床盐酸再生装置的高效无废液排放的酸洗设备

新开发的具有高速，浅酸洗槽和控制湍流状态的酸洗工艺，能缩短酸洗时间，获得均匀酸洗表面并过酸洗。增加的流化床酸再生系统提供的无废液排放操作使得酸洗每吨材料的盐酸消耗低于０．２ｋｇ。     

速度模型对叠前深度偏移的敏感性

为了从复杂构造地区的地震资料中得到正确的地下图像,必须采用叠前深度偏移方法。获取正确的图像的必要条件是要用精确的速度模型进行迭前深度偏移。在需要作叠前深度偏移的情况下,如果常规方法得到的模型就得不到令人满意的结果。因此人们已研制出新的确定速度模型的迭代法。这些方法的收敛性通过对速度模型的限定约束,以加快其收敛速度。这些限定的方法只是寻找影响偏移图像的实际地层速度的场分量。为了确定这些分量,用复杂的已知其精确模型的合成数据集(Marmousi数据集)对叠前     

ON THE CALCULATION OF FLOW OVER AND THROUGH A ROCK-FILL DAM

1.INTRODUCTIONInthedesignofanovertoppedrock--filldam,oneofthemajortasksistodecidethesizeofthedownstreamprotection.Theovertoppedrock--filldamismadeofporousmedia,thereforethewaterwillpenetrateintothedamfromtheupperfaceandemergeoutofitfromthelowerpart,whileitflowsoverthedam.Hence,theprotectionissubjectedtotheactionofboththeoverflowandtheseepageflow,andneitherofthemcanbenegligible.Inthiscase,theflowfieldconsistsoftwoparts:theopenflowandtheseepageflow,whichhavedifferentproperties.Infact,forthe…     

各向异性旅行时层析成像

利用地震数据估算速度时常常是以与方向性无关的时距曲线为基础的。尽管我们已经了解到许多岩石存在较强的各向异性,但多年来人们已根据各向同性估值得到了一些有益的结果。如果速度明显随方向变化,则各向同性假设将导致严重的构造解释误差。除此之外,与角度有关的信息将有助于更好地解释所测量的岩性。为了估算出每一波型的两个速度参数,VSP和井间数据的孔径是不够的。如果数据有一个对称的同相轴,我们可选择直达波速度(根据沿轴向的     

用微地震法实现无源地震成像

我们研究了一种微地震信号处理方法,以实现记录站之下的地下构造的局部成像。该方法用垂向旅行的地震信号的自相关构成虚拟反射地震记录,以便对地下构造进行解释。对取自夏威夷岛微地震记录进行处理后得到的虚拟反射数据。显示出一致的反射系数模式,它可解释成表层水平特性。对虚拟反射数据     

Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.     

Example-based automatic music-driven conventional dance motion synthesis

We introduce a novel method for synthesizing dance motions that follow the emotions and contents of a piece of music. Our method employs a learning-based approach to model the music to motion mapping relationship embodied in example dance motions along with those motions' accompanying background music. A key step in our method is to train a music to motion matching quality rating function through learning the music to motion mapping relationship exhibited in synchronized music and dance motion data, which were captured from professional human dance performance. To generate an optimal sequence of dance motion segments to match with a piece of music, we introduce a constraint-based dynamic programming procedure. This procedure considers both music to motion matching quality and visual smoothness of a resultant dance motion sequence. We also introduce a two-way evaluation strategy, coupled with a GPU-based implementation, through which we can execute the dynamic programming process in parallel, resulting in significant speedup. To evaluate the effectiveness of our method, we quantitatively compare the dance motions synthesized by our method with motion synthesis results by several peer methods using the motions captured from professional human dancers' performance as the gold standard. We also conducted several medium-scale user studies to explore how perceptually our dance motion synthesis method can outperform existing methods in synthesizing dance motions to match with a piece of music. These user studies produced very positive results on our music-driven dance motion synthesis experiments for several Asian dance genres, confirming the advantages of our method.     

An algorithm for three-dimensional reconstruction incorporating cross-plane rays

Conventional multislice positron cameras reconstruct a three-dimensional distribution of a positron-emitting radioscope as a set of two-dimensional transverse sections. Consequently, annihilation photons which cross two or more planes are eliminated from the data. Such an approach makes efficient use of the emitted photon flux. A method is proposed which makes more efficient use of the available photons by including both oblique and transverse section in the reconstruction. The implementation of the method consists of centering a scaled convolution filter on each detected coincidence event line and backprojecting the filter values through the three-dimensional reconstruction volume. The final image is normalized to allow for the different number of oblique and transverse sections that contribute to each point in the imaging volume. The method has been evaluated using both simulated data and measured data obtained with a routing area detector positron camera.