Monitoring the corrosion of concrete reinforcement using control curves

Corrosion testing (half-cell and LPR) was carried out on a number reinforced concrete panels which had been taken from the fascia of a twenty five year old high rise building in Melbourne, Australia. Corrosion, predominantly as a result of carbonation of the concrete, was associated with a limited amount of cracking. A monitoring technique was established in which probe electrodes (reference and counter) were retro-fitted into the concrete. The probe electrode setup was identical for all panels tested. It was found that the corrosion behaviour of all panels tested closely fitted a family of results when the corrosion potential is plotted against the polarisation resistance (Rp). This enabled the development of a so-called 'control curve' relating the corrosion potential to the Rp for all of the panels under investigation. This relationship was also confirmed on laboratory samples, indicating that for a fixed geometry and experimental conditions a relationship between the potential and polarisation resistance of steel can be established for the steel-concrete system. Experimental results will be presented which indicate that for a given monitoring cell geometry, it may be possible to propose criteria for the point at which remediation measures should be considered. The establishment of such a control curve has enabled the development of a powerful monitoring tool for the assessment of a number of proposed corrosion remediation techniques. The actual effect of any corrosion remediation technique becomes clearly apparent via the type and magnitude of deviation of post remediation data from the original (preremediation) control curve.

Action learning through the use of enterprise resource planning system in construction management

Although information communication technology (ICT) tool is long regard as very useful in today’s engineering, architectural and construction management, organizations cannot just only simply operate based upon its origin, but also requires the on-going observation, controlling measures, monitoring aids, adding features and fine-tuning actions to such tool before the desirable outcome can be achieved. However, it is a very common phenomenon that organizations purchase the licensed “off-the-shelf-software” ICT package and customize it to suit their own requirements. Due to the incapability and inefficiency of such software and customization, the possible result is making such tool becomes not user-friendly and obsolete.

The purpose of this paper therefore, as part of the doctoral research, is to review and report those actions taken by a construction organization to enhance the performance of its Enterprise Resource Planning (ERP) system upon launching it since December 2002. Such actions include: improving data inputting method; removing the transition bottleneck; introducing crystallization function; revising the organization’s “Delegation and Limits of Authority”; publishing the “League Table” amongst users; integrating the 3D Modeling into the system and adopting the “Resources Requirement Planning”.

Whilst the ultimate goals of this system are well beyond the time limit of the research project, an obvious interim result, achieved by this case studied organization, was winning a landmark project worth HK$5 billion after the ERP system was functioned effectively. The experience and success of this organization can be borrowed by those companies which are planning to adopt information technology (IT) strategy and use ICT tool in the architectural management system.

Reliability of a gymnastics vaulting feedback system

The current study assessed the intra- and inter-day reliability of a custom-built gymnastics vaulting feedback system. The system is a coach-friendly customized infra-red timing gate and contact timing mat system operated by the coach to augment the feedback provided to gymnasts on their vaulting performance during regular training practice. Thirteen Australian high performance gymnasts (eight males and five females) aged 11-23 years were assessed during two training sessions (Day 1 and Day 2) at their regular training centre. The approach velocity and board contact time measures were found to be reliable measures during vault training, with measures of pre-flight and table contact time less consistent. Future research should examine the validity of these measures as a tool for monitoring vault training.

Anaesthesia monitoring using fuzzy logic

Objective. Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. Method. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of offline tests. The training and testing data set are selected randomly from 30 sets of patients' data. Results. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. Conclusion. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.

Passenger monitoring in moving bus video

In this paper, we present a novel person detection system for public transport buses tackling the problem of changing illumination conditions. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modeling mechanism with a human shape model into a weighted Bayesian framework to detect passengers on-board buses. SIFT background modeling extracts local stable features on the pre-annotated background seat areas and tracks these features over time to build a global statistical background model for each seat. Since SIFT features are partially invariant to lighting, this background model can be used robustly to detect the seat occupancy status even under severe lighting changes. The human shape model further confirms the existence of a passenger when a seat is occupied. This constructs a robust passenger monitoring system which is resilient to illumination changes. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from bus cameras and the experimental results show that it is superior to state-of-art people detection systems.

Recognising and monitoring high-level behaviours in complex spatial environments

The recognition of activities from sensory data is important in advanced surveillance systems to enable prediction of high-level goals and intentions of the target under surveillance. The problem is complicated by sensory noise and complex activity spanning large spatial and temporal extents. This paper presents a system for recognising high-level human activities from multi-camera video data in complex spatial environments. The Abstract Hidden Markov mEmory Model (AHMEM) is used to deal with noise and scalability The AHMEM is an extension of the Abstract Hidden Markov Model (AHMM) that allows us to represent a richer class of both state-dependent and context-free behaviours. The model also supports integration with low-level sensory models and efficient probabilistic inference. We present experimental results showing the ability of the system to perform real-time monitoring and recognition of complex behaviours of people from observing their trajectories within a real, complex indoor environment.

Hierarchical monitoring of people's behaviors in complex environments using multiple cameras

We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. The results of the system demonstrate the ability to answer queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment.

Information needs and the implications for monitoring health systems : the Australian experience

This paper outlines the information needs underlying the health information system in Australia and the implications these have for the ability to monitor the performance of the health system. We discuss the use of indicators in performance monitoring and the role of information frameworks in providing a basis for their development. The major Australian data sources to support the development of performance indicators are outlined, and their current and likely futures uses for performance monitoring discussed.

Comonitoring of adenosine and dopamine using the wireless instantaneous neurotransmitter concentration system : proof of principle : laboratory investigation

Object

The authors of previous studies have demonstrated that local adenosine efflux may contribute to the therapeutic mechanism of action of thalamic deep brain stimulation (DBS) for essential tremor. Real-time monitoring of the neurochemical output of DBS-targeted regions may thus advance functional neurosurgical procedures by identifying candidate neurotransmitters and neuromodulators involved in the physiological effects of DBS. This would in turn permit the development of a method of chemically guided placement of DBS electrodes in vivo. Designed in compliance with FDA-recognized standards for medical electrical device safety, the authors report on the utility of the Wireless Instantaneous Neurotransmitter Concentration System (WINCS) for real-time comonitoring of electrical stimulation–evoked adenosine and dopamine efflux in vivo, utilizing fast-scan cyclic voltammetry (FSCV) at a polyacrylonitrile-based (T-650) carbon fiber microelectrode (CFM).
Methods

The WINCS was used for FSCV, which consisted of a triangle wave scanned between −0.4 and +1.5 V at a rate of 400 V/second and applied at 10 Hz. All voltages applied to the CFM were with respect to an Ag/AgCl reference electrode. The CFM was constructed by aspirating a single T-650 carbon fiber (r = 2.5 μm) into a glass capillary and pulling to a microscopic tip using a pipette puller. The exposed carbon fiber (the sensing region) extended beyond the glass insulation by ~ 50 μm. Proof of principle tests included in vitro measurements of adenosine and dopamine, as well as in vivo measurements in urethane-anesthetized rats by monitoring adenosine and dopamine efflux in the dorsomedial caudate putamen evoked by high-frequency electrical stimulation of the ventral tegmental area and substantia nigra.
Results

The WINCS provided reliable, high-fidelity measurements of adenosine efflux. Peak oxidative currents appeared at +1.5 V and at +1.0 V for adenosine, separate from the peak oxidative current at +0.6 V for dopamine. The WINCS detected subsecond adenosine and dopamine efflux in the caudate putamen at an implanted CFM during high-frequency stimulation of the ventral tegmental area and substantia nigra. Both in vitro and in vivo testing demonstrated that WINCS can detect adenosine in the presence of other easily oxidizable neurochemicals such as dopamine comparable to the detection abilities of a conventional hardwired electrochemical system for FSCV.
Conclusions

Altogether, these results demonstrate that WINCS is well suited for wireless monitoring of high-frequency stimulation-evoked changes in brain extracellular concentrations of adenosine. Clinical applications of selective adenosine measurements may prove important to the future development of DBS technology.

Monitoring batch treatment inhibitor performance continuously using electrochemical noise analysis

Electrochemical noise analysis (ENA) was used to monitor continuously the formation and deterioration processes of a commercial batch treatment inhibitor film of the type used for protecting against CO₂ corrosion in oilfields; ENA was shown to be able to follow effectively the formation and deterioration processes of batch treatment inhibitor films. As an inhibitor film formed, the current noise amplitude decreased rapidly and the noise resistance R_n, which is deducible from the voltage and current noise records, was found to increase sharply. Conversely, as the inhibitor film deteriorated, the current noise amplitude increased rapidly and R_n decreased rapidly. In the corrosion inhibition system studied, the noise resistance was confirmed to be similar to the linear polarisation resistance. Based on the calculation of R_n on a continuous basis, a technique is proposed to study fast corrosion processes.

A mutation-based evolving neural network model and its application to condition monitoring

Data analysis using intelligent systems is a key solution to many industrial problems. In this paper, a mutation-based evolving artificial neural network, which is based on an integration of the Fuzzy ARTMAP (FAM) neural network and evolutionary programming (EP), is proposed. The proposed FAMEP model is applied to detect and classify possible faults from a number of sensory signals of a circulating water system in a power generation plant. The efficiency of FAM-EP is assessed and compared with that of the original FAM network in terms of classification accuracy as well as network complexity. In addition, the bootstrap method is used to quantify the performance statistically. The results positively demonstrate the usefulness of FAM-EP in tackling data classification problems.

Condition monitoring and fault prediction via an adaptive neural network

This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum' setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant.

Processing multiple image streams for real-time monitoring of parking lots

We present a system to detect parked vehicles in a typical parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. Performance enhancements are made on the algorithm so that it operates well in the context of multiple image streams. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.

Security risks/vulnerability in a RFID system and possible defenses

Remote technologies are changing our way of life. The radio frequency identification (RFJD) system is a new technology which uses the open air to transmit information. This information transmission needs to be protected to provide user safety and privacy. Business will look for a 5ystem that hasfraud resilience to prevent the misuse of information to take dishonest advantage. The business and the user need to be assured that the transmitted information has no content which is capable of undertaking malicious activities. Public awareness of RFID security will help users and organizations to understand the need for security protection. Publishing a security guideline from the regulating body and monitoring implementation of that guideline in RFID 5ystems will ensure that businesses and users are protected. This chapter explains the importance of security in a RFID system and will outline the protective measures. It also points out the research direction of RFID 5ystems.

Monitoring the early stage self-assembly of enzyme-assisted peptide hydrogels

The early stages of the self-assembly of peptide hydrogels largely determine their final material properties. Here we discuss experimental methodologies for monitoring the self-assembly kinetics which underpin peptide hydrogel formation. The early stage assembly of an enzyme-catalysed Fmoc-trileucine based self-assembled hydrogel was examined using spectroscopic techniques (circular dichroism, CD, and solution NMR) as well as chromatographic (HPLC) and mechanical (rheology) techniques. Optimal conditions for enzyme-assisted hydrogel formation were identified and the kinetics examined. A lag time associated with the formation and accumulation of the self-assembling peptide monomer was observed and a minimum hydrogelator concentration required for gelation was identified. Subsequent formation of well defined nano-and microscale structures lead to self-supporting hydrogels at a range of substrate and enzyme concentrations. 1H NMR monitoring of the early self-assembly process revealed trends that were well in agreement with those identified using traditional methods (i.e. HPLC, CD, rheology) demonstrating 1H NMR spectroscopy can be used to non-invasively monitor the self-assembly of peptide hydrogels without damaging or perturbing the system.