Application of acoustic emission technology in monitoring structure integrity of bridges

Bridges are an important part of a nation’s infrastructure and reliable monitoring methods are necessary to ensure their safety and efficiency. Most bridges in use today were built decades ago and are now subjected to changes in load patterns that can cause localized distress, which can result in bridge failure if not corrected. Early detection of damage helps in prolonging lives of bridges and preventing catastrophic failures. This paper briefly reviews the various technologies currently used in health monitoring of bridge structures and in particular discusses the application and challenges of acoustic emission (AE) technology. Some of the results from laboratory experiments on a bridge model are also presented. The main objectives of these experiments are source localisation and assessment. The findings of the study can be expected to enhance the knowledge of acoustic emission process and thereby aid in the development of an effective bridge structure diagnostics system.

Monitoring and regulatory role of the balanced scorecard in controlling organisational identities

This thesis employs the theoretical fusion of disciplinary knowledge, interlacing an analysis from both functional and interpretive frameworks and applies these paradigms to three concepts—organisational identity, the balanced scorecard performance measurement system, and control. As an applied thesis, this study highlights how particular public sector organisations are using a range of multi-disciplinary forms of knowledge constructed for their needs to achieve practical outcomes. Practical evidence of this study is not bound by a single disciplinary field or the concerns raised by academics about the rigorous application of academic knowledge. The study’s value lies in its ability to explore how current communication and accounting knowledge is being used for practical purposes in organisational life. The main focus of this thesis is on identities in an organisational communication context. In exploring the theoretical and practical challenges, the research questions for this thesis were formulated as: 1. Is it possible to effectively control identities in organisations by the use of an integrated performance measurement system—the balanced scorecard—and if so, how? 2. What is the relationship between identities and an integrated performance measurement system—the balanced scorecard—in the identity construction process? Identities in the organisational context have been extensively discussed in graphic design, corporate communication and marketing, strategic management, organisational behaviour, and social psychology literatures. Corporate identity is the self-presentation of the personality of an organisation (Van Riel, 1995; Van Riel & Balmer, 1997), and organisational identity is the statement of central characteristics described by members (Albert & Whetten, 2003). In this study, identity management is positioned as a strategically complex task, embracing not only logo and name, but also multiple dimensions, levels and facets of organisational life. Responding to the collaborative efforts of researchers and practitioners in identity conceptualisation and methodological approaches, this dissertation argues that analysis can be achieved through the use of an integrated framework of identity products, patternings and processes (Cornelissen, Haslam, & Balmer, 2007), transforming conceptualisations of corporate identity, organisational identity and identification studies. Likewise, the performance measurement literature from the accounting field now emphasises the importance of ‘soft’ non-financial measures in gauging performance—potentially allowing the monitoring and regulation of ‘collective’ identities (Cornelissen et al., 2007). The balanced scorecard (BSC) (Kaplan & Norton, 1996a), as the selected integrated performance measurement system, quantifies organisational performance under the four perspectives of finance, customer, internal process, and learning and growth. Broadening the traditional performance measurement boundary, the BSC transforms how organisations perceived themselves (Vaivio, 2007). The rhetorical and communicative value of the BSC has also been emphasised in organisational self-understanding (Malina, Nørreklit, & Selto, 2007; Malmi, 2001; Norreklit, 2000, 2003). Thus, this study establishes a theoretical connection between the controlling effects of the BSC and organisational identity construction. Common to both literatures, the aspects of control became the focus of this dissertation, as ‘the exercise or act of achieving a goal’ (Tompkins & Cheney, 1985, p. 180). This study explores not only traditional technical and bureaucratic control (Edwards, 1981), but also concertive control (Tompkins & Cheney, 1985), shifting the locus of control to employees who make their own decisions towards desired organisational premises (Simon, 1976). The controlling effects on collective identities are explored through the lens of the rhetorical frames mobilised through the power of organisational enthymemes (Tompkins & Cheney, 1985) and identification processes (Ashforth, Harrison, & Corley, 2008). In operationalising the concept of control, two guiding questions were developed to support the research questions: 1.1 How does the use of the balanced scorecard monitor identities in public sector organisations? 1.2 How does the use of the balanced scorecard regulate identities in public sector organisations? This study adopts qualitative multiple case studies using ethnographic techniques. Data were gathered from interviews of 41 managers, organisational documents, and participant observation from 2003 to 2008, to inform an understanding of organisational practices and members’ perceptions in the five cases of two public sector organisations in Australia. Drawing on the functional and interpretive paradigms, the effective design and use of the systems, as well as the understanding of shared meanings of identities and identifications are simultaneously recognised. The analytical structure guided by the ‘bracketing’ (Lewis & Grimes, 1999) and ‘interplay’ strategies (Schultz & Hatch, 1996) preserved, connected and contrasted the unique findings from the multi-paradigms. The ‘temporal bracketing’ strategy (Langley, 1999) from the process view supports the comparative exploration of the analysis over the periods under study. The findings suggest that the effective use of the BSC can monitor and regulate identity products, patternings and processes. In monitoring identities, the flexible BSC framework allowed the case study organisations to monitor various aspects of finance, customer, improvement and organisational capability that included identity dimensions. Such inclusion legitimises identity management as organisational performance. In regulating identities, the use of the BSC created a mechanism to form collective identities by articulating various perspectives and causal linkages, and through the cascading and alignment of multiple scorecards. The BSC—directly reflecting organisationally valued premises and legitimised symbols—acted as an identity product of communication, visual symbols and behavioural guidance. The selective promotion of the BSC measures filtered organisational focus to shape unique identity multiplicity and characteristics within the cases. Further, the use of the BSC facilitated the assimilation of multiple identities by controlling the direction and strength of identifications, engaging different groups of members. More specifically, the tight authority of the BSC framework and systems are explained both by technical and bureaucratic controls, while subtle communication of organisational premises and information filtering is achieved through concertive control. This study confirms that these macro top-down controls mediated the sensebreaking and sensegiving process of organisational identification, supporting research by Ashforth, Harrison and Corley (2008). This study pays attention to members’ power of self-regulation, filling minor premises of the derived logic of their organisation through the playing out of organisational enthymemes (Tompkins & Cheney, 1985). Members are then encouraged to make their own decisions towards the organisational premises embedded in the BSC, through the micro bottom-up identification processes including: enacting organisationally valued identities; sensemaking; and the construction of identity narratives aligned with those organisationally valued premises. Within the process, the self-referential effect of communication encouraged members to believe the organisational messages embedded in the BSC in transforming collective and individual identities. Therefore, communication through the use of the BSC continued the self-producing of normative performance mechanisms, established meanings of identities, and enabled members’ self-regulation in identity construction. Further, this research establishes the relationship between identity and the use of the BSC in terms of identity multiplicity and attributes. The BSC framework constrained and enabled case study organisations and members to monitor and regulate identity multiplicity across a number of dimensions, levels and facets. The use of the BSC constantly heightened the identity attributes of distinctiveness, relativity, visibility, fluidity and manageability in identity construction over time. Overall, this research explains the reciprocal controlling relationships of multiple structures in organisations to achieve a goal. It bridges the gap among corporate and organisational identity theories by adopting Cornelissen, Haslam and Balmer’s (2007) integrated identity framework, and reduces the gap in understanding between identity and performance measurement studies. Parallel review of the process of monitoring and regulating identities from both literatures synthesised the theoretical strengths of both to conceptualise and operationalise identities. This study extends the discussion on positioning identity, culture, commitment, and image and reputation measures in integrated performance measurement systems as organisational capital. Further, this study applies understanding of the multiple forms of control (Edwards, 1979; Tompkins & Cheney, 1985), emphasising the power of organisational members in identification processes, using the notion of rhetorical organisational enthymemes. This highlights the value of the collaborative theoretical power of identity, communication and performance measurement frameworks. These case studies provide practical insights about the public sector where existing bureaucracy and desired organisational identity directions are competing within a large organisational setting. Further research on personal identity and simple control in organisations that fully cascade the BSC down to individual members would provide enriched data. The extended application of the conceptual framework to other public and private sector organisations with a longitudinal view will also contribute to further theory building.

Monitoring energy performance of air conditioners in classrooms

Air quality and temperatures in classrooms are important factors influencing the student learning process. To improve the thermal comfort of classrooms for Queensland State Schools, Queensland Government initiated the "Cooler Schools Program". One of the key objectives under this program was to develop low energy cooling systems as an alternative to high energy demand conventioanl split system of air conditioning (AC) systems. In order to compare and evaluate the energy performance of different types of air conditioners installed in classrooms, monitoring systems were installed in a state primary school located in the greater outer urban area of Brisbane, Australia. It was found that the installation of monitoring systems could have a significant impact on the accuracy of the data being collected. By comparing the estimated energy efficiency ratio (EER)for four qualified air conditioners included in this study, it was also found that AC6, a hybrid air conditioner newly developed by the Queensland Department of Public Works (DPW), had the best energy performance, although the current data were not able to show the full advantages of the system.

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.

How can soil monitoring networks be used to improve predictions of organic carbon pool dynamics and CO2 fluxes in agricultural soils?

Abstract As regional and continental carbon balances of terrestrial ecosystems become available, it becomes clear that the soils are the largest source of uncertainty. Repeated inventories of soil organic carbon (SOC) organized in soil monitoring networks (SMN) are being implemented in a number of countries. This paper reviews the concepts and design of SMNs in ten countries, and discusses the contribution of such networks to reducing the uncertainty of soil carbon balances. Some SMNs are designed to estimate country-specific land use or management effects on SOC stocks, while others collect soil carbon and ancillary data to provide a nationally consistent assessment of soil carbon condition across the major land-use/soil type combinations. The former use a single sampling campaign of paired sites, while for the latter both systematic (usually grid based) and stratified repeated sampling campaigns (5–10 years interval) are used with densities of one site per 10–1,040 km². For paired sites, multiple samples at each site are taken in order to allow statistical analysis, while for the single sites, composite samples are taken. In both cases, fixed depth increments together with samples for bulk density and stone content are recommended. Samples should be archived to allow for re-measurement purposes using updated techniques. Information on land management, and where possible, land use history should be systematically recorded for each site. A case study of the agricultural frontier in Brazil is presented in which land use effect factors are calculated in order to quantify the CO2 fluxes from national land use/management conversion matrices. Process-based SOC models can be run for the individual points of the SMN, provided detailed land management records are available. These studies are still rare, as most SMNs have been implemented recently or are in progress. Examples from the USA and Belgium show that uncertainties in SOC change range from 1.6–6.5 Mg C ha−1 for the prediction of SOC stock changes on individual sites to 11.72 Mg C ha−1 or 34% of the median SOC change for soil/land use/climate units. For national SOC monitoring, stratified sampling sites appears to be the most straightforward attribution of SOC values to units with similar soil/land use/climate conditions (i.e. a spatially implicit upscaling approach). Keywords Soil monitoring networks - Soil organic carbon - Modeling - Sampling design

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Toward automated power line corridor monitoring using advanced aircraft control and multisource feature fusion

The conventional manual power line corridor inspection processes that are used by most energy utilities are labor-intensive, time consuming and expensive. Remote sensing technologies represent an attractive and cost-effective alternative approach to these monitoring activities. This paper presents a comprehensive investigation into automated remote sensing based power line corridor monitoring, focusing on recent innovations in the area of increased automation of fixed-wing platforms for aerial data collection, and automated data processing for object recognition using a feature fusion process. Airborne automation is achieved by using a novel approach that provides improved lateral control for tracking corridors and automatic real-time dynamic turning for flying between corridor segments, we call this approach PTAGS. Improved object recognition is achieved by fusing information from multi-sensor (LiDAR and imagery) data and multiple visual feature descriptors (color and texture). The results from our experiments and field survey illustrate the effectiveness of the proposed aircraft control and feature fusion approaches.

Impact of a data warehouse model for improved decision-making process in healthcare

The health system is one sector dealing with a deluge of complex data. Many healthcare organisations struggle to utilise these volumes of health data effectively and efficiently. Also, there are many healthcare organisations, which still have stand-alone systems, not integrated for management of information and decision-making. This shows, there is a need for an effective system to capture, collate and distribute this health data. Therefore, implementing the data warehouse concept in healthcare is potentially one of the solutions to integrate health data. Data warehousing has been used to support business intelligence and decision-making in many other sectors such as the engineering, defence and retail sectors. The research problem that is going to be addressed is, "how can data warehousing assist the decision-making process in healthcare". To address this problem the researcher has narrowed an investigation focusing on a cardiac surgery unit. This research used the cardiac surgery unit at the Prince Charles Hospital (TPCH) as the case study. The cardiac surgery unit at TPCH uses a stand-alone database of patient clinical data, which supports clinical audit, service management and research functions. However, much of the time, the interaction between the cardiac surgery unit information system with other units is minimal. There is a limited and basic two-way interaction with other clinical and administrative databases at TPCH which support decision-making processes. The aims of this research are to investigate what decision-making issues are faced by the healthcare professionals with the current information systems and how decision-making might be improved within this healthcare setting by implementing an aligned data warehouse model or models. As a part of the research the researcher will propose and develop a suitable data warehouse prototype based on the cardiac surgery unit needs and integrating the Intensive Care Unit database, Clinical Costing unit database (Transition II) and Quality and Safety unit database [electronic discharge summary (e-DS)]. The goal is to improve the current decision-making processes. The main objectives of this research are to improve access to integrated clinical and financial data, providing potentially better information for decision-making for both improved from the questionnaire and by referring to the literature, the results indicate a centralised data warehouse model for the cardiac surgery unit at this stage. A centralised data warehouse model addresses current needs and can also be upgraded to an enterprise wide warehouse model or federated data warehouse model as discussed in the many consulted publications. The data warehouse prototype was able to be developed using SAS enterprise data integration studio 4.2 and the data was analysed using SAS enterprise edition 4.3. In the final stage, the data warehouse prototype was evaluated by collecting feedback from the end users. This was achieved by using output created from the data warehouse prototype as examples of the data desired and possible in a data warehouse environment. According to the feedback collected from the end users, implementation of a data warehouse was seen to be a useful tool to inform management options, provide a more complete representation of factors related to a decision scenario and potentially reduce information product development time. However, there are many constraints exist in this research. For example the technical issues such as data incompatibilities, integration of the cardiac surgery database and e-DS database servers and also, Queensland Health information restrictions (Queensland Health information related policies, patient data confidentiality and ethics requirements), limited availability of support from IT technical staff and time restrictions. These factors have influenced the process for the warehouse model development, necessitating an incremental approach. This highlights the presence of many practical barriers to data warehousing and integration at the clinical service level. Limitations included the use of a small convenience sample of survey respondents, and a single site case report study design. As mentioned previously, the proposed data warehouse is a prototype and was developed using only four database repositories. Despite this constraint, the research demonstrates that by implementing a data warehouse at the service level, decision-making is supported and data quality issues related to access and availability can be reduced, providing many benefits. Output reports produced from the data warehouse prototype demonstrated usefulness for the improvement of decision-making in the management of clinical services, and quality and safety monitoring for better clinical care. However, in the future, the centralised model selected can be upgraded to an enterprise wide architecture by integrating with additional hospital units’ databases.

Condition monitoring and diagnosis of injector faults in a diesel engine using in-cylinder pressure and acoustic emission techniques

Failing injectors are one of the most common faults in diesel engines. The severity of these faults could have serious effects on diesel engine operations such as engine misfire, knocking, insufficient power output or even cause a complete engine breakdown. It is thus essential to prevent such faults from occurring by monitoring the condition of these injectors. In this paper, the authors present the results of an experimental investigation on identifying the signal characteristics of a simulated incipient injector fault in a diesel engine using both in-cylinder pressure and acoustic emission (AE) techniques. A time waveform event driven synchronous averaging technique was used to minimize or eliminate the effect of engine speed variation and amplitude fluctuation. It was found that AE is an effective method to detect the simulated injector fault in both time (crank angle) and frequency (order) domains. It was also shown that the time domain in-cylinder pressure signal is a poor indicator for condition monitoring and diagnosis of the simulated injector fault due to the small effect of the simulated fault on the engine combustion process. Nevertheless, good correlations between the simulated injector fault and the lower order components of the enveloped in-cylinder pressure spectrum were found at various engine loading conditions.

Acoustic emission for diesel engine monitoring: A review and preliminary

Vibration analysis has been a prime tool in condition monitoring of rotating machines, however, its application to internal combustion engines remains a challenge because engine vibration signatures are highly non-stationary that are not suitable for popular spectrum-based analysis. Signal-to-noise ratio is a main concern in engine signature analysis due to severe background noise being generated by consecutive mechanical events, such as combustion, valve opening and closing, especially in multi-cylinder engines. Acoustic Emission (AE) has been found to give excellent signal-to-noise ratio allowing discrimination of fine detail of normal or abnormal events during a given cycle. AE has been used to detect faults, such as exhaust valve leakage, fuel injection behaviour, and aspects of the combustion process. This paper presents a review of AE application to diesel engine monitoring and preliminary investigation of AE signature measured on an 18-cylinder diesel engine. AE is compared with vibration acceleration for varying operating conditions: load and speed. Frequency characteristics of AE from those events are analysed in time-frequency domain via short time Fourier trasform. The result shows a great potential of AE analysis for detection of various defects in diesel engines.

A Bayesian‐Markov process for reliability prediction

Accurate reliability prediction for large-scale, long lived engineering is a crucial foundation for effective asset risk management and optimal maintenance decision making. However, a lack of failure data for assets that fail infrequently, and changing operational conditions over long periods of time, make accurate reliability prediction for such assets very challenging. To address this issue, we present a Bayesian-Marko best approach to reliability prediction using prior knowledge and condition monitoring data. In this approach, the Bayesian theory is used to incorporate prior information about failure probabilities and current information about asset health to make statistical inferences, while Markov chains are used to update and predict the health of assets based on condition monitoring data. The prior information can be supplied by domain experts, extracted from previous comparable cases or derived from basic engineering principles. Our approach differs from existing hybrid Bayesian models which are normally used to update the parameter estimation of a given distribution such as the Weibull-Bayesian distribution or the transition probabilities of a Markov chain. Instead, our new approach can be used to update predictions of failure probabilities when failure data are sparse or nonexistent, as is often the case for large-scale long-lived engineering assets.

A software framework for risk-aware business process management

With the large diffusion of Business Process Managemen (BPM) automation suites, the possibility of managing process-related risks arises. This paper introduces an innovative framework for process-related risk management and describes a working implementation realized by extending the YAWL system. The framework covers three aspects of risk management: risk monitoring, risk prevention, and risk mitigation. Risk monitoring functionality is provided using a sensor-based architecture, where sensors are defined at design time and used at run-time for monitoring purposes. Risk prevention functionality is provided in the form of suggestions about what should be executed, by who, and how, through the use of decision trees. Finally, risk mitigation functionality is provided as a sequence of remedial actions (e.g. reallocating, skipping, rolling back of a work item) that should be executed to restore the process to a normal situation.

Assessment of “change in flexibility method” in structural health monitoring systems

The modern structural diagnosis process is rely on vibration characteristics to assess safer serviceability level of the structure. This paper examines the potential of change in flexibility method to use in damage detection process and two main practical constraints associated with it. The first constraint addressed in this paper is reduction in number of data acquisition points due to limited number of sensors. Results conclude that accuracy of the change in flexibility method is influenced by the number of data acquisition points/sensor locations in real structures. Secondly, the effect of higher modes on damage detection process has been studied. This addresses the difficulty of extracting higher order modal data with available sensors. Four damage indices have been presented to identify their potential of damage detection with respect to different locations and severity of damage. A simply supported beam with two degrees of freedom at each node is considered only for a single damage cases throughout the paper.

Estimating the effects of environmental exposures using a weighted mean of monitoring stations

The health effects of environmental hazards are often examined using time series of the association between a daily response variable (e.g., death) and a daily level of exposure (e.g., temperature). Exposures are usually the average from a network of stations. This gives each station equal importance, and negates the opportunity for some stations to be better measures of exposure. We used a Bayesian hierarchical model that weighted stations using random variables between zero and one. We compared the weighted estimates to the standard model using data on health outcomes (deaths and hospital admissions) and exposures (air pollution and temperature) in Brisbane, Australia. The improvements in model fit were relatively small, and the estimated health effects of pollution were similar using either the standard or weighted estimates. Spatial weighted exposures would be probably more worthwhile when there is either greater spatial detail in the health outcome, or a greater spatial variation in exposure.

Monitoring charged particle and ion emissions from a laser printer

While the emission rate of ultrafine particles has been measured and quantified, there is very little information on the emission rates of ions and charged particles from laser printers. This paper describes a methodology that can be adopted for measuring the surface charge density on printed paper and the ion and charged particle emissions during operation of a high-emitting laser printer and shows how emission rates of ultrafine particles, ions and charged particles may be quantified using a controlled experiment within a closed chamber.