小型腔式微波离子源的研制

研制了１种用在离子束细胞刻蚀装置中的小型腔式微波离子源，它由石英放电管、同轴谐振腔和封装在管一端的二电极引出系统组成。该源由同轴腔激发起的表面波在石英管内产生等离子体柱。为了提高柱末端等离子体离子引出流密度，选用了在内径７．０ｍｍ直管的一端加上一内径５．０ｍｍ衬管的放电管。在微波频率为２．４５ＧＨｚ，输入功率为９３Ｗ下，氮的引出流密度分别为４８．４ｍＡ／ｃｍ２和９１．７ｍＡ／ｃｍ２。这种表面波放电等离子体源体积小、结构简单，可在较宽的压强范围内产生重复性好、工作稳定的等离子体柱。     

双层辉光离子渗镀钽

介绍了一种新型离子渗镀钽的工艺方法。较好解决了渗镀钽时供给活性钽粒子的总是比方法工艺简单，操作方便，效率高。为充分利用和节约使用昂贵的钽探索了一条实用可行的途径。     

复杂山区静校正方法研究及效果

对于山区地震勘探，开展静校正研究是资料处理的主要问题之一。静校正精度与剖面质量密切相关，准确的静校正取决于合理的方法和正确的应用。本文在分析了复杂山区特点以及静校正难点的基础上，指出要解决好山区静校正，仅限于普通的方法是远远不够的，必须从四个关键点人手，即风化层延迟时的求取、基准面的确定、校正速度的研究以及表层模型的建立等。本文对上述问题的研究做了介绍，展示了合成延迟时法十等效速度模型反演十变速浮动基准面（地震参考面）静校正等综合技术在太行山区（山西沁源、大寨）、伊J；1丘陵山区等复杂地表区应用的例子。显著的处理效果表明，严重的山区静校正问题得到了较好的解决，满足了地下构造的叠加成像，提高了勘探精度及其整体效果。     

基于CAE的模态综合法误差分析

为研究用模态综合法进行发动机舱前端模型NVH分析时的误差,用整体计算法和模态综合法分别进行截止频率为100,200,300,1 000,2 000和3 000 Hz的模态计算,并基于模态计算结果进行原点传递函数计算.结果表明:考虑静态补偿的模态综合法计算误差小于不考虑静态补偿的计算误差;子系统模态计算截止频率大于2倍原点传递函数频率时,模态综合法计算误差小于10%;当传递函数频率是截止频率的1/3时,误差可以降低到5%。     

SCS模型在山区小流域山洪灾害预报预警中的适用性分析

预报预警是山洪灾害防治中重要的非工程措施,是防灾减灾救灾的关键环节。选取分别代表秦巴山地水资源保护区、西南地震作用区、喀斯特地区、黄土高原超渗产流区、东南沿海台风影响区的湖北省丹江口官山河流域、四川省都江堰白沙河流域、贵州省望谟县望谟河流域、陕西省子洲县岔巴沟流域、广东省高州市马贵河流域这5个典型流域,通过构建改进的SCS模型进行历史山洪模拟,探讨该模型在小流域暴雨山洪预报预警中的广泛适用性。结果表明:基于SCS模型构建的小流域暴雨山洪预报方案中官山河、白沙河、望谟河、马贵河方案均为乙级方案,可用于正式发布预报;岔巴沟为丙级方案,可用于参考性预报。SCS模型在5个典型示范区的洪峰流量预报精度普遍在80%左右,高于我国山洪灾害洪峰流量预报平均水平(40%)。总体来说,SCS模型对各类典型流域的山洪模拟效果较好,能够用于山洪灾害预报预警业务。SCS模型结构简单,参数少,特别适用于无资料地区推广应用。     

高坝泄洪诱发场地振动响应分析及传播规律

为研究高坝泄洪诱发场地振动的问题,提出一种基于黏弹性人工边界的有限元数值模拟和水力学模型试验相结合的方法计算场地振动响应。以国内某工程为例,计算不同工况下场地振动响应,分析泄洪量和泄洪调度方式对场地振动的影响,揭示不同岩体条件下振动波的传播规律。结果表明:该工程各种泄洪方式都不会对周边居民的生活造成影响;泄洪量越大,下游场地振动响应越大,当3个溢洪洞泄洪条件一致时,采用2号洞泄洪最不利;同等泄洪条件下,采用4洞均分泄洪的振动响应小于按设计开度泄洪的振动响应;模型范围内放大振动波的主因是断层左右岩体性质的差异,断层的存在进一步加大了放大效应。     

基于LDRA Testbed的软件静态测试研究与实现

介绍了软件静态测试的基本概念与方法,以及如何利用测试工具LDRA Testbed进行软件静态测试,并对LDRA Testbed的静态测试原理进行了研究。最后结合实例实现了使用LDRA Testbed对C代码进行静态测试,并得到了静态测试质量报告、度量报告。     

中置式高压电容器柜改进设计

分析了南屯煤矿白马河风井变电所高压电容器柜原设计方案存在的问题,并对其进行了改进设计:选用适用于容性负载的真空断路器;添加放电线圈,并将放电线圈与电容器直接并联,使放电线圈与电容器组成一个完整的放电回路;添加接地开关,接地开关既有机械闭锁又有电气联锁,操作方便,安全性高。实际应用表明,改进设计后的高压电容器柜功能更加完善,更加安全、可靠。     

An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch

Economic dispatch is carried out at the energy control center to find out the optimal output of thermal generating units such that power balance criterion is met, unit operating limits are satisfied and the fuel cost is minimized. With growing environmental awareness and strict government regulations throughout the world, it has become essential to optimize not only the total fuel cost but also the harmful emissions, both, under static as well as dynamic conditions. The static environment economic dispatch finds the optimal output of generating units for a fixed load demand at a given time, while the dynamic environmental economic dispatch schedules the output of online generators with changing power demands over a certain time period (normally one day) so as to minimize these two conflicting objectives, simultaneously. In this paper, the price penalty factor approach is employed for simultaneous minimization of cost and emission. The generator ramp rate constraints, non-convex and discontinuous nature of cost function and the large number of generators in practical power plants, make this problem very difficult to solve. Here, a fuzzy ranking approach is employed to identify the solution which offers the best compromise between cost and emission objectives.     

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.