A shape error metric for sheet metal forming and its application to springback

There are many variations within sheet metal forming, some of which are manifest in the final geometry of the formed component. It is important that this geometric variation be quantified and measured for use in a process or quality control system. The contribution of this paper is to propose a novel way of measuring the geometric difference between the desired shape and an actual formed "U" channel. The metric is based upon measuring errors in terms of the significant manufacturing variations. The metric accords with the manually measured errors of the channel set. The shape error metric is then extended to develop a simple empirical, whole-component, springback error measure. The springback error measure combines into one value all the angle springback and side wall curl geometric errors for a single channel. Two trends were observed: combined springback decreases when the blank holder force is increased; and the combined springback marginally decreases when the die radii is increased.

Evaluating parts-of-speech taggers for use in a text-to-scene conversion system

This paper presents parts-of-speech tagging as a first step towards an autonomous text-to-scene conversion system. It categorizes some freely available taggers, according to the techniques used by each in order to automatically identify word-classes. In addition, the performance of each identified tagger is verified experimentally. The SUSANNE corpus is used for testing and reveals the complexity of working with different tagsets, resulting in substantially lower accuracies in our tests than in those reported by the developers of each tagger. The taggers are then grouped to form a voting system to attempt to raise accuracies, but in no cases do the combined results improve upon the individual accuracies. Additionally a new metric, agreement, is tentatively proposed as an indication of confidence in the output of a group of taggers where such output cannot be validated.

E-commerce trust management system reliability

Various solutions have been proposed in managing trust relationship between trading partners in eCommerce environment. Determine the reliability of trust management systems in eCommerce is most difficult issue due to highly dynamic nature of eCommerce environments. As trust management systems depend on the feedback ratings provided by the trading partners, they are fallible to strategic manipulation of the feedback ratings attacks. This paper addressed the challenges of trust management systems. The requirements of a reliable trust management are also discussed. In particular, we introduce an adaptive credibility model that distinguishes between credible feedback ratings and malicious feedback ratings by considering transaction size, frequency of ratings and majority vote to form a feedback ratings verification metric. The approach has been validated by simulation result.

Experience report: anomaly detection of cloud application operations using log and cloud metric correlation analysis

Failure of application operations is one of the maincauses of system-wide outages in cloud environments. Thisparticularly applies to DevOps operations, such as backup,redeployment, upgrade, customized scaling, and migration that areexposed to frequent interference from other concurrent operations,configuration changes, and resources failure. However, currentpractices fail to provide a reliable assurance of correct execution ofthese kinds of operations. In this paper, we present an approach toaddress this problem that adopts a regression-based analysistechnique to find the correlation between an operation’s activity logsand the operation activity’s effect on cloud resources. Thecorrelation model is then used to derive assertion specifications,which can be used for runtime verification of running operations andtheir impact on resources. We evaluated our proposed approach onAmazon EC2 with 22 rounds of rolling upgrade operations whileother types of operations were running and random faults wereinjected. Our experiment shows that our approach successfullymanaged to raise alarms for 115 random injected faults, with aprecision of 92.3%.