铝—钢双金属复合轧制层厚比及轧制力的研究

用实验方法研究了铝－钢双金属复合轧制时层厚比的变化规律。给出了双金属复合时轧制力的计算式。     

Cooling strategies for cryogenic machining from a materials viewpoint

This article discusses the cooling strategies for cryogenic machining from a materials viewpoint. It is argued that, because different materials respond to temperature and machining processes differently, different cooling strategies are needed to improve the machinabilities of materials by cryogenic machining. In this work, five workpiece materials such as AISI1010 low-carbon steel, AISI1070 high-carbon steel, AISIE52100 bearing steel, titanium alloy Ti-6Al-4V, and cast aluminum alloy A390 were studied experimentally at various temperatures. Based on the experimental results of the cryogenic properties of the materials and their known machining characteristics, the cooling strategies for cryogenic machining of these materials were analyzed.     

Camouflage texture evaluation using a saliency map

Multimedia Systems - Camouflage effect evaluation and examination is an important procedure in digital camouflage pattern design as it is helpful in improving the objectivity and effectiveness...     

国有企业计算机网络管理

本论文在简单了解了计算机网络的相关内容后,对国有企业的计算机网络进行了简单的介绍,并举例说明了某国有企业的网络管理结构及功能。     

光学薄膜元器件外观特性机器视觉检测系统

本系统利用工业级摄像头及开发包的机器视觉功能与计算软件编程控制技术相结合,通过与输入控制系统中的标准膜片模板外观品质进行对比,实现高速高效、准确识别检测光学薄膜器件中滤光片的外观缺陷。可在生产线上根据外观品质标准模板中设定的类别进行自动分类装盒。     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.