Application of selftuning regulator algorithm to generating unit controllers

The processing capability of computers makes it feasible to implement sophisticated techniques for process control. However, to minimize the software development costs, it is desirable to develop algorithms that can have a wider application. In keeping with this, an AVR and speed governor for a generating unit have been designed using the same algorithm, called the selftuning regulator. The selftuning AVR and speed governor have been implemented using a microprocessor and a minicomputer online. The feasibility of developing unified algorithms is demonstrated by means of realtime tests, in which the two controllers are used simultaneously for a generating unit physical model.     

Results of test on microprocessor-based voltage and speed regulators carried out on a large physical model complex

Results of experimental tests carried out with microprocessor-based voltage and speed regulators using a digital-analogue-physical model complex of a large power system at the Siberian Power Institute of the USSR Academy of Sciences are described. The physical test facility, and the test objectives and procedures are also outlined. Tests were carried out on a Soviet-built microprocessor AVR and a Canadian-built microprocessor AVR and speed governor. The work described shows that the development of speed and voltage regulators using microprocessors is a feasible and promising proposition.     

废电子电气产品塑料可持续再生过程中的能量回收(上)

故障现象：制冷正常，但制热时烧熔丝。分析与检测：该空调安装好后，试机时只试了制冷状态，制冷工作正常，却未进行制热试机。后来用户想试一下制热，拨到制热挡，熔断器一开机就跳闸，等合上闸后整机就无任何反应了。最初分析也许是巧合，检查主控板上的熔丝已经烧了，随即更换上了一块线路板，试机制冷正常，但在制热状态立刻烧熔断，判断制热存在严重短路现象。即然制冷正常，制热状态只多一个四通阀线圈用电，检查室外机的接线，原来是安装工错将三跟导线中的黄线接到了地上。     

延长采用微硬盘并由电池供电的便携产品的工作时间

就由电池供电并以微硬盘（HDD）作为主要存储介质的手持产品而言，目前有3种市场驱动因素汇聚在一起，可能导致在这些便携式产品的电池工作时间方面产生极大的突破。这类便携式产品包括便携式播放器（PMP）、MP3播放器、数码相机（DSC）、数字录像机（DVR）、智能电话和GPS系统等。     

Incorporating social anxiety into a model of college problem drinking: Replication and extension.

Although research has found an association between social anxiety and alcohol use in noncollege samples, results have been mixed for college samples. College students face many novel social situations in which they may drink to reduce social anxiety. In the current study, the authors tested a model of college problem drinking, incorporating social anxiety and related psychosocial variables among 228 undergraduate volunteers. According to structural equation modeling (SEM) results, social anxiety was unrelated to alcohol use and was negatively related to drinking consequences. Perceived drinking norms mediated the social anxiety-alcohol use relation and was the variable most strongly associated with problem drinking. College students appear to be unique with respect to drinking and social anxiety. Although the notion of social anxiety alone as a risk factor for problem drinking was unsupported, additional research is necessary to determine whether there is a subset of socially anxious students who have high drinking norms and are in need of intervention. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Error Analysis for Image Inpainting

Image inpainting refers to restoring a damaged image with missing information. In recent years, there have been many developments on computational approaches to image inpainting problem [2, 4, 6, 9, 11–13, 27, 28]. While there are many effective algorithms available, there is still a lack of theoretical understanding on under what conditions these algorithms work well. In this paper, we take a step in this direction. We investigate an error bound for inpainting methods, by considering different image spaces such as smooth images, piecewise constant images and a particular kind of piecewise continuous images. Numerical results are presented to validate the theoretical error bounds. Tony F. Chan received the B.S. degree in engineering and the M.S. degree in aerospace engineering in 1973, from the California Institute of Technology, and the Ph.D. degree in computer science from Stanford University in 1978. He is Professor of Mathematics and currently also Dean of the division of Physical science at University of California, Los Angeles, where he has been a Professor since 1986. His research interests include mathematical and computational methods in image processing, multigrid, domain decomposition algorithms, iterative methods, Krylov subspace methods, and parallel algorithms. Sung Ha Kang received the Ph.D. degree in mathematics in 2002, from University of California, Los Angeles, and currently is Assistant Professor of Mathematics at University of Kentucky since 2002. Her research interests include mathematical and computational methods in image processing and computer vision.     

Total Variation Wavelet Inpainting

We consider the problem of filling in missing or damaged wavelet coefficients due to lossy image transmission or communication. The task is closely related to classical inpainting problems, but also remarkably differs in that the inpainting regions are in the wavelet domain. New challenges include that the resulting inpainting regions in the pixel domain are usually not geometrically well defined, as well as that degradation is often spatially inhomogeneous. We propose two related variational models to meet such challenges, which combine the total variation (TV) minimization technique with wavelet representations. The associated Euler-Lagrange equations lead to nonlinear partial differential equations (PDE’s) in the wavelet domain, and proper numerical algorithms and schemes are designed to handle their computation. The proposed models can have effective and automatic control over geometric features of the inpainted images including sharp edges, even in the presence of substantial loss of wavelet coefficients, including in the low frequencies. Existence and uniqueness of the optimal inpaintings are also carefully investigated. Research supported in part by grants ONR-N00014-03-1-0888, NSF DMS-9973341, DMS-0202565 and DMS-0410062, and NIH contract P 20 MH65166.     

Classification-based objective functions

Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, an approach to training artificial neural networks on classification problems. Classification-based learning attempts to guide the network directly to correct pattern classification rather than using common error minimization heuristics, such as sum-squared error (SSE) and cross-entropy (CE), that do not explicitly minimize classification error. CB1 is presented here as a novel objective function for learning classification problems. It seeks to directly minimize classification error by backpropagating error only on misclassified patterns from culprit output nodes. CB1 discourages weight saturation and overfitting and achieves higher accuracy on classification problems than optimizing SSE or CE. Experiments on a large OCR data set have shown CB1 to significantly increase generalization accuracy over SSE or CE optimization, from 97.86% and 98.10%, respectively, to 99.11%. Comparable results are achieved over several data sets from the UC Irvine Machine Learning Database Repository, with an average increase in accuracy from 90.7% and 91.3% using optimized SSE and CE networks, respectively, to 92.1% for CB1. Analysis indicates that CB1 performs a fundamentally different search of the feature space than optimizing SSE or CE and produces significantly different solutions. Editor: Risto Miikkulainen