Injection velocity control of thermoplastic injection molding via a double controller scheme

Injection velocity has been recognized as a key variable in thermoplastic injection molding. Its closed-loop control is, however, difficult due to the complexity of the process dynamic characteristics. The basic requirements of the control system include tracking of a pre-determined injection velocity curve defined in a profile, load rejection and robustness. It is difficult for a conventional control scheme to meet all these requirements. Injection velocity dynamics are first analyzed in this paper. Then a novel double-controller scheme is adopted for the injection velocity control. This scheme allows an independent design of set-point tracking and load rejection and has good system robustness. The implementation of the double-controller scheme for injection velocity control is discussed. Special techniques such as profile transformation and shifting are also introduced to improve the velocity responses. The proposed velocity control has been experimentally demonstrated to be effective for a wide range of processing conditions.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Design, construction and evaluation of systems to predict risk in obstetrics

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy. We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Feature selection using expected attainable discrimination

We propose expected attainable discrimination (EAD) as a measure to select discrete valued features for reliable discrimination between two classes of data. EAD is an average of the area under the ROC curves obtained when a simple histogram probability density model is trained and tested on many random partitions of a data set. EAD can be incorporated into various stepwise search methods to determine promising subsets of features, particularly when misclassification costs are difficult or impossible to specify. Experimental application to the problem of risk prediction in pregnancy is described.

Building indifferentiable compression functions from the PGV compression functions

Preneel, Govaerts and Vandewalle (PGV) analysed the security of single-block-length block cipher based compression functions assuming that the underlying block cipher has no weaknesses. They showed that 12 out of 64 possible compression functions are collision and (second) preimage resistant. Black, Rogaway and Shrimpton formally proved this result in the ideal cipher model. However, in the indifferentiability security framework introduced by Maurer, Renner and Holenstein, all these 12 schemes are easily differentiable from a fixed input-length random oracle (FIL-RO) even when their underlying block cipher is ideal. We address the problem of building indifferentiable compression functions from the PGV compression functions. We consider a general form of 64 PGV compression functions and replace the linear feed-forward operation in this generic PGV compression function with an ideal block cipher independent of the one used in the generic PGV construction. This modified construction is called a generic modified PGV (MPGV). We analyse indifferentiability of the generic MPGV construction in the ideal cipher model and show that 12 out of 64 MPGV compression functions in this framework are indifferentiable from a FIL-RO. To our knowledge, this is the first result showing that two independent block ciphers are sufficient to design indifferentiable single-block-length compression functions.

The clinical utility of the Cornell Scale for Depression in Dementia as a routine assessment in nursing homes

Objective To examine the clinical utility of the Cornell Scale for Depression in Dementia (CSDD) in nursing homes. Setting 14 nursing homes in Sydney and Brisbane, Australia. Participants 92 residents with a mean age of 85 years. Measurements Consenting residents were assessed by care staff for depression using the CSDD as part of their routine assessment. Specialist clinicians conducted assessment of depression using the Semi-structured Clinical Diagnostic Interview for DSM-IV-TR Axis I Disorders for residents without dementia or the Provisional Diagnostic Criteria for Depression in Alzheimer Disease for residents with dementia to establish expert clinical diagnoses of depression. The diagnostic performance of the staff completed CSDD was analyzed against expert diagnosis using receiver operating characteristic (ROC) curves. Results The CSDD showed low diagnostic accuracy, with areas under the ROC curve being 0.69, 0.68 and 0.70 for the total sample, residents with dementia and residents without dementia, respectively. At the standard CSDD cutoff score, the sensitivity and specificity were 71% and 59% for the total sample, 69% and 57% for residents with dementia, and 75% and 61% for residents without dementia. The Youden index (for optimizing cut-points) suggested different depression cutoff scores for residents with and without dementia. Conclusion When administered by nursing home staff the clinical utility of the CSDD is highly questionable in identifying depression. The complexity of the scale, the time required for collecting relevant information, and staff skills and knowledge of assessing depression in older people must be considered when using the CSDD in nursing homes.

Protecting encrypted cookies from compression side-channel attacks

Compression is desirable for network applications as it saves bandwidth; however, when data is compressed before being encrypted, the amount of compression leaks information about the amount of redundancy in the plaintext. This side channel has led to successful CRIME and BREACH attacks on web traffic protected by the Transport Layer Security (TLS) protocol. The general guidance in light of these attacks has been to disable compression, preserving confidentiality but sacrificing bandwidth. In this paper, we examine two techniques - heuristic separation of secrets and fixed-dictionary compression|for enabling compression while protecting high-value secrets, such as cookies, from attack. We model the security offered by these techniques and report on the amount of compressibility that they can achieve.