我国"入世"对石油石化业的承诺及主要应对措施

介绍了我国加入WTO后，与石油石化业直接相关的具体承诺，重点研究了中国石油石化业为应对这些承诺所采取的主要措施。     

中压汽包法兰断裂失效原因分析及改进措施

本文针对中压汽包法兰断裂失效原因进行分析，指出汽包上两个法兰同时断裂的原因在于安全阀排汽管路设计上的失误，并提出对安全阀排汽系统的改进措施。     

管壳式换热器管束振动分析及防振措施

针对管壳式换热器常见的振动破坏形式 ,全面分析了管束振动的机理 ,并提出了相应的抗振措施     

数显仪表在生产中的抗干扰措施探讨

着重分析了在生产中使用数显仪表干扰产生的原因，列举了抗干扰的措施和在生产实践中取得的成绩。     

我国二氧化硫氧化制硫酸的钒催化剂现状和展望

从钒催化剂的生产、市场、开发、质量等方面分析我国二氧化硫氧化制硫酸催化剂的现状，指出我国钒催化剂与国外产品存在的差距和原因，提出发展钒催化剂的对策。     

浅论食品化学污染及其防治策略

分析了食品化学污染的主要途径,以及这些污染物对人体健康的危害性,对食品化学污染的防治策略进行了探讨。     

A general non-local denoising model using multi-kernel-induced measures

Noises are inevitably introduced in digital image acquisition processes, and thus image denoising is still a hot research problem. Different from local methods operating on local regions of images, the non-local methods utilize non-local information (even the whole image) to accomplish image denoising. Due to their superior performance, the non-local methods have recently drawn more and more attention in the image denoising community. However, these methods generally do not work well in handling complicated noises with different levels and types. Inspired by the fact in machine learning field that multi-kernel methods are more robust and effective in tackling complex problems than single-kernel ones, we establish a general non-local denoising model based on multi-kernel-induced measures (GNLMKIM for short), which provides us a platform to analyze some existing and design new filters. With the help of GNLMKIM, we reinterpret two well-known non-local filters in the united view and extend them to their novel multi-kernel counterparts. The comprehensive experiments indicate that these novel filters achieve encouraging denoising results in both visual effect and PSNR index.     

Centroid index: Cluster level similarity measure

In clustering algorithm, one of the main challenges is to solve the global allocation of the clusters instead of just local tuning of the partition borders. Despite this, all external cluster validity indexes calculate only point-level differences of two partitions without any direct information about how similar their cluster-level structures are. In this paper, we introduce a cluster level index called centroid index. The measure is intuitive, simple to implement, fast to compute and applicable in case of model mismatch as well. To a certain extent, we expect it to generalize other clustering models beyond the centroid-based k-means as well.     

Linear reconstruction measure steered nearest neighbor classification framework

The linear reconstruction measure (LRM), which determines the nearest neighbors of the query sample in all known training samples by sorting the minimum L₂-norm error linear reconstruction coefficients, is introduced in this paper. The intuitive interpretation and mathematical proofs are presented to reveal the efficient working mechanism of LRM. Through analyzing the physical meaning of coefficients and regularization items, we find that LRM provides more useful information and advantages than the conventional similarity measure model which calculates the distance between two entities (i.e. conventional point-to-point, C-PtP). Inspired by the advantages of LRM, the linear reconstruction measure steered nearest neighbor classification framework (LRM-NNCF) is designed with eight classifiers according to different decision rules and models of LRM. Evaluation on several face databases and the experimental results demonstrate that these proposed classifiers can achieve greater performance than the C-PtP based 1-NNs and competitive recognition accuracy and robustness compared with the state-of-the-art classifiers.