An evolutionary-based similarity reasoning scheme for monotonic multi-input fuzzy inference systems

In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models.

Analysis of a patchwork-based audio watermarking scheme

Recently, a patchwork-based audio watermarking scheme has been proposed in [1], which embeds watermarks by modifying the means of absolute-valued discrete cosine transform (DCT) coefficients corresponding to suitable fragments. This audio watermarking scheme is more robust to common attacks than the existing counterparts. In this paper, we presents a detailed analysis of this audio watermarking scheme. We first derive a probability density function (pdf) of a random variable corresponding to the mean of an absolute-valued DCT fragment. Then, based on the obtained pdf, we show how watermarking parameters affect the performance of the concerned audio watermarking scheme. The analysis result provides a guideline for the selection of watermarking parameters. The effectiveness of our analysis is verified by simulations using a large number of real-world audio segments.

A new energy efficient cluster-head and backup selection scheme in WSN

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Proper organization of nodes (clustering) is one of the major techniques to expand the lifespan of the whole network through aggregating data at the cluster head. The cluster head is the backbone of the entire cluster. That means if a cluster head fails to accomplish its function, the received and collected data by cluster head can be lost. Moreover, the energy consumption following direct communications from sources to base stations will be increased. In this paper, we propose a type-2 fuzzy based self-configurable cluster head selection (SCCH) approach to not only consider the selection criterion of the cluster head but also present the cluster backup approach. Thus, in case of cluster failure, the system still works in an efficient way. The novelty of this protocol is the ability of handling communication uncertainty, which is an inherent operational aspect of sensor networks. The experiment results indicate SCCH performs better than other recently developed methods.

Using feature selection and classification scheme for automating phishing email detection

Email has become the critical communication medium for most organizations. Unfortunately, email-born attacks in computer networks are causing considerable economic losses worldwide. Exiting phishing email blocking appliances have little effect in weeding out the vast majority of phishing emails. At the same time, online criminals are becoming more dangerous and sophisticated. Phishing emails are more active than ever before and putting the average computer user and organizations at risk of significant data, brand and financial loss. In this paper, we propose a hybrid feature selection approach based combination of content-based and behaviour-based. The approach could mine the attacker behaviour based on email header. On a publicly available test corpus, our hybrid features selection is able to achieve 94% accuracy rate.

Policy and practice impacts of applied research: a case study analysis of the New South Wales Health Promotion Demonstration Research Grants Scheme 2000-2006

Background
Intervention research provides important information regarding feasible and effective interventions for health policy makers, but few empirical studies have explored the mechanisms by which these studies influence policy and practice. This study provides an exploratory case series analysis of the policy, practice and other related impacts of the 15 research projects funded through the New South Wales Health Promotion Demonstration Research Grants Scheme during the period 2000 to 2006, and explored the factors mediating impacts.

Methods
Data collection included semi-structured interviews with the chief investigators (n = 17) and end-users (n = 29) of each of the 15 projects to explore if, how and under what circumstances the findings had been used, as well as bibliometric analysis and verification using documentary evidence. Data analysis involved thematic coding of interview data and triangulation with other data sources to produce case summaries of impacts for each project. Case summaries were then individually assessed against four impact criteria and discussed at a verification panel meeting where final group assessments of the impact of research projects were made and key influences of research impact identified.

Results
Funded projects had variable impacts on policy and practice. Project findings were used for agenda setting (raising awareness of issues), identifying areas and target groups for interventions, informing new policies, and supporting and justifying existing policies and programs across sectors. Reported factors influencing the use of findings were: i) nature of the intervention; ii) leadership and champions; iii) research quality; iv) effective partnerships; v) dissemination strategies used; and, vi) contextual factors.

Conclusions
The case series analysis provides new insights into how and under what circumstances intervention research is used to influence real world policy and practice. The findings highlight that intervention research projects can achieve the greatest policy and practice impacts if they address proximal needs of the policy context by engaging end-users from the inception of projects and utilizing existing policy networks and structures, and using a range of strategies to disseminate findings that go beond traditional peer review publications.

Establishment of temperature control scheme for microbioreactor operation using integrated microheater

This paper presents design and fabrication of a microbioreactor platform, and implementation of two temperature control methods (i.e. on-off and PID) and their performance evaluation on the microbioreactor platform (working volume ~300 μL). The temperature of the microbioreactor content is controlled by using a subminiature heater placed underneath the microbioreactor and is measured with a miniature Pt 100 sensor. The microbioreactor is also integrated with a magnetic stirring capacity and a water evaporation control scheme. Programs for the two temperature control methods are written in LabVIEW software and implemented by interfacing them with a data acquisition card. It is shown that by implementing on–off and PID temperature control methods, the temperature of the microbioreactor content can be tightly controlled with an accuracy of approximately ±0.5 °C of the set point values. Both control methods also provide a good response and settling time values (i.e. around 2 min). Contrary to the on/off control method, the PID control method requires no adjustments whenever the set-point values are modified. The PID temperature control method works well for the entire tested range.

Generalised damage identification scheme via sparse representation

Structures inevitably deteriorate during their service lives. Therefore, the methods capable of identifying and assessing various damages in a structure timely and accurately have drawn increasing attention. From a broader perspective, structural damage identification problem can be regarded as a pattern recognition problem by using sparse representation techniques. The unknown signal/feature from a damaged structure can be associated to a known type of signal/feature in a “dictionary”, leading to damage identification. From this new angle, an innovative damage identification scheme has been proposed by the authors. In this paper, two important techniques of this scheme are further discussed, namely the construction of dictionary and the choice of parameters. The numerical simulated soil-pipe system is used for verifying the performance of the proposed method. The results demonstrate that this damage identification scheme will be a promising tool for structural health monitoring.