Learning detectors quickly with stationary statistics

Computer vision is increasingly becoming interested in the rapid estimation of object detectors. The canonical strategy of using Hard Negative Mining to train a Support Vector Machine is slow, since the large negative set must be traversed at least once per detector. Recent work has demonstrated that, with an assumption of signal stationarity, Linear Discriminant Analysis is able to learn comparable detectors without ever revisiting the negative set. Even with this insight, the time to learn a detector can still be on the order of minutes. Correlation filters, on the other hand, can produce a detector in under a second. However, this involves the unnatural assumption that the statistics are periodic, and requires the negative set to be re-sampled per detector size. These two methods differ chie y in the structure which they impose on the co- variance matrix of all examples. This paper is a comparative study which develops techniques (i) to assume periodic statistics without needing to revisit the negative set and (ii) to accelerate the estimation of detectors with aperiodic statistics. It is experimentally verified that periodicity is detrimental.

Cell image classification using histograms, higher order statistics and adaboost

A cell classification algorithm that uses first, second and third order statistics of pixel intensity distributions over pre-defined regions is implemented and evaluated. A cell image is segmented into 6 regions extending from a boundary layer to an inner circle. First, second and third order statistical features are extracted from histograms of pixel intensities in these regions. Third order statistical features used are one-dimensional bispectral invariants. 108 features were considered as candidates for Adaboost based fusion. The best 10 stage fused classifier was selected for each class and a decision tree constructed for the 6-class problem. The classifier is robust, accurate and fast by design.

Discussion of : constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

This is a discussion of the journal article: "Construcing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation". The article and discussion have appeared in the Journal of the Royal Statistical Society: Series B (Statistical Methodology).

RMAA safety performance : how does it compare with Greenfield projects?

Gross value of cons truction work in the repair , maintenance, minor alte ration and addition (RMAA) sector in Hong Kong has expanded dramatically by 58% from 1998 to 2007, accounting for over 53% of the whole construction market in 2007. Unfortunately, the portion of industrial accidents arising from this sector also increased substantially during the same period. It is important to improve the safety performance of the RMAA sector. This paper has set out the objectives to examine safety statistics of RMAA works; to compare them with those of green field projects; and more importantly, to highlight potential hurdles en countered in the process of comparison and finally to provide effective recommendations for overcoming these impediments. To strive for continuous safety improvement of RMAA works, comparable safety statistics should be compiled for this sector.

Provotypes: Teasing out tensions between industrial context and use context

What industrial organisations think people do and what they actually do are often two very different things. But exactly this tension can be a source of innovation: how can we give form to insights about what people do, to deliberately challenge industries' conceptions, and inspire new product and service development

Ethnography of software maintenance activities in an industrial engineering setup

New technical and procedural interventions are less likely to be adopted in industry, unless they are smoothly integrated into the existing practices of professionals. In this paper, we provide a case study of the use of ethnographic methods for studying software bug-fixing activities at an industrial engineering conglomerate. We aimed at getting an in-depth understanding of software developers' everyday practices in bug-fixing related projects and in turn inform the design of novel productivity tools. The use of ethnography has allowed us to look at the social side of software maintenance practices. In this paper, we highlight: 1) organizational issues that influence bug-fixing activities; 2) social role of bug tracking systems, and; 3) social issues specific to different phases of bug-fixing activities.

A game theoretical approach to construction safety in the Repair, Maintenance, Alteration and Addition (RMAA) sector

Safety has long been a problem in the construction industry. Repair, maintenance, alteration and addition (RMAA) sector has emerged to play an important role in the construction industry. It accounted for 53% of the total construction market in Hong Kong in 2007. Safety performance of the RMAA words has been alarming. Statistics indicate that the percentage of fatal industrial accidents arising from RMAA work in Hong Kong was over 56% in 2006 while the remaining 44% was from new works. Effective safety measures to address the safety problems and improve safety performance of the RMAA sector are urgently needed. Unsafe behaviour has been attributed to one of the major causes of accidents. Traditional cost-benefit analysis of workers' safety behaviour seems to be inadequate. This paper proposes to adopt a game theoretical approach to analyse safety behaviour of RMAA workers. Game theory is concerned with the decision-making process in situations where outcomes depend upon choices made by one or more players. A game theoretical model between contractor and worker has been proffered. Mathematical analysis of this game model has been done and implications of the analysis have been discussed.

Numerical investigation of mechanical and thermal dynamic properties of the industrial transformer

Industrial transformer is one of the most critical assets in the power and heavy industry. Failures of transformers can cause enormous losses. The poor joints of the electrical circuit on transformers can cause overheating and results in stress concentration on the structure which is the major cause of catastrophic failure. Few researches have been focused on the mechanical properties of industrial transformers under overheating thermal conditions. In this paper, both mechanical and thermal properties of industrial transformers are jointly investigated using Finite Element Analysis (FEA). Dynamic response analysis is conducted on a modified transformer FEA model, and the computational results are compared with experimental results from literature to validate this simulation model. Based on the FEA model, thermal stress is calculated under different temperature conditions. These analysis results can provide insights to the understanding of the failure of transformers due to overheating, therefore are significant to assess winding fault, especially to the manufacturing and maintenance of large transformers.

Preventing industrial scale using bauxite refinery residues

An Australian green power (AGP) company produces energy from burning biomass from the sugar industry and recycled wood waste, however alkali in biomass is released into a recirculating stream that forms a scale as it becomes more concentrated. This investigation has shown that the addition of Bayer liquor (alumina waste residue) successfully removes scale-forming species from the recirculating stream and thus has the potential to reduce the rate of scaling. Characterisation of the scale and Bayer precipitates has been performed using X-ray diffraction (XRD), infrared spectroscopy (IR) and inductively coupled plasma optical emission spectroscopy (ICP-OES).

Bluetooth vehicle trajectory by fusing Bluetooth and loops : motorway travel time statistics

Loop detectors are widely used on the motorway networks where they provide point speed and traffic volumes. Models have been proposed for temporal and spatial generalization of speed for average travel time estimation. Advancement in technology provides complementary data sources such as Bluetooth MAC Scanner (BMS), detecting the MAC ID of the Bluetooth devices transported by the traveller. Matching the data from two BMS stations provides individual vehicle travel time. Generally, on the motorways loops are closely spaced, whereas BMS are placed few kilometres apart. In this research, we fuse BMSs and loops data to define the trajectories of the Bluetooth vehicles. The trajectories are utilised to estimate the travel time statistics between any two points along the motorway. The proposed model is tested using simulation and validated with real data from Pacific motorway, Brisbane. Comparing the model with the linear interpolation based trajectory provides significant improvements.

Development of gas sensing technology for ground and airborne applications powered by solar energy : methodology and experimental results

Monitoring gases for environmental, industrial and agricultural fields is a demanding task that requires long periods of observation, large quantity of sensors, data management, high temporal and spatial resolution, long term stability, recalibration procedures, computational resources, and energy availability. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) are currently representing the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialised gas sensing systems, and offer the possibility of geo-located and time stamp samples. However, these technologies are not fully functional for scientific and commercial applications as their development and availability is limited by a number of factors: the cost of sensors required to cover large areas, their stability over long periods, their power consumption, and the weight of the system to be used on small UAVs. Energy availability is a serious challenge when WSN are deployed in remote areas with difficult access to the grid, while small UAVs are limited by the energy in their reservoir tank or batteries. Another important challenge is the management of data produced by the sensor nodes, requiring large amount of resources to be stored, analysed and displayed after long periods of operation. In response to these challenges, this research proposes the following solutions aiming to improve the availability and development of these technologies for gas sensing monitoring: first, the integration of WSNs and UAVs for environmental gas sensing in order to monitor large volumes at ground and aerial levels with a minimum of sensor nodes for an effective 3D monitoring; second, the use of solar energy as a main power source to allow continuous monitoring; and lastly, the creation of a data management platform to store, analyse and share the information with operators and external users. The principal outcomes of this research are the creation of a gas sensing system suitable for monitoring any kind of gas, which has been installed and tested on CH4 and CO2 in a sensor network (WSN) and on a UAV. The use of the same gas sensing system in a WSN and a UAV reduces significantly the complexity and cost of the application as it allows: a) the standardisation of the signal acquisition and data processing, thereby reducing the required computational resources; b) the standardisation of calibration and operational procedures, reducing systematic errors and complexity; c) the reduction of the weight and energy consumption, leading to an improved power management and weight balance in the case of UAVs; d) the simplification of the sensor node architecture, which is easily replicated in all the nodes. I evaluated two different sensor modules by laboratory, bench, and field tests: a non-dispersive infrared module (NDIR) and a metal-oxide resistive nano-sensor module (MOX nano-sensor). The tests revealed advantages and disadvantages of the two modules when used for static nodes at the ground level and mobile nodes on-board a UAV. Commercial NDIR modules for CO2 have been successfully tested and evaluated in the WSN and on board of the UAV. Their advantage is the precision and stability, but their application is limited to a few gases. The advantages of the MOX nano-sensors are the small size, low weight, low power consumption and their sensitivity to a broad range of gases. However, selectivity is still a concern that needs to be addressed with further studies. An electronic board to interface sensors in a large range of resistivity was successfully designed, created and adapted to operate on ground nodes and on-board UAV. The WSN and UAV created were powered with solar energy in order to facilitate outdoor deployment, data collection and continuous monitoring over large and remote volumes. The gas sensing, solar power, transmission and data management systems of the WSN and UAV were fully evaluated by laboratory, bench and field testing. The methodology created to design, developed, integrate and test these systems was extensively described and experimentally validated. The sampling and transmission capabilities of the WSN and UAV were successfully tested in an emulated mission involving the detection and measurement of CO2 concentrations in a field coming from a contaminant source; the data collected during the mission was transmitted in real time to a central node for data analysis and 3D mapping of the target gas. The major outcome of this research is the accomplishment of the first flight mission, never reported before in the literature, of a solar powered UAV equipped with a CO2 sensing system in conjunction with a network of ground sensor nodes for an effective 3D monitoring of the target gas. A data management platform was created using an external internet server, which manages, stores, and shares the data collected in two web pages, showing statistics and static graph images for internal and external users as requested. The system was bench tested with real data produced by the sensor nodes and the architecture of the platform was widely described and illustrated in order to provide guidance and support on how to replicate the system. In conclusion, the overall results of the project provide guidance on how to create a gas sensing system integrating WSNs and UAVs, how to power the system with solar energy and manage the data produced by the sensor nodes. This system can be used in a wide range of outdoor applications, especially in agriculture, bushfires, mining studies, zoology, and botanical studies opening the way to an ubiquitous low cost environmental monitoring, which may help to decrease our carbon footprint and to improve the health of the planet.

Effective routing with utilisation of cross-layer information for industrial wireless sensor networks

This thesis focuses on providing reliable data transmissions in large-scale industrial wireless sensor networks through improving network layer protocols. It addresses three major problems: scalability, dynamic industrial environments and coexistence of multiple types of data traffic in a network. Theoretical developments are conducted, followed by simulation studies for verification of theoretic results. The approach proposed in this thesis has been shown to be effective for large-scale network implementation and to provide improved data transmission reliability for both periodic and sporadic traffic.

Decomposition analysis of air pollution abatement in China: empirical study for ten industrial sectors from 1998 to 2009

This study analyzes the management of air pollutant substance in Chinese industrial sectors from 1998 to 2009. Decomposition analysis applying the logarithmic mean divisia index is used to analyze changes in emissions of air pollutants with a focus on the following five factors: coal pollution intensity (CPI), end-of-pipe treatment (EOP), the energy mix (EM), productive efficiency change (EFF), and production scale changes (PSC). Three pollutants are the main focus of this study: sulfur dioxide (SO2), dust, and soot. The novelty of this paper is focusing on the impact of the elimination policy on air pollution management in China by type of industry using the scale merit effect for pollution abatement technology change. First, the increase in SO2 emissions from Chinese industrial sectors because of the increase in the production scale is demonstrated. However, the EOP equipment that induced this change and improvements in energy efficiency has prevented an increase in SO2 emissions that is commensurate with the increase in production. Second, soot emissions were successfully reduced and controlled in all industries except the steel industry between 1998 and 2009, even though the production scale expanded for these industries. This reduction was achieved through improvements in EOP technology and in energy efficiency. Dust emissions decreased by nearly 65% between 1998 and 2009 in the Chinese industrial sectors. This successful reduction in emissions was achieved by implementing EOP technology and pollution prevention activities during the production processes, especially in the cement industry. Finally, pollution prevention in the cement industry is shown to result from production technology development rather than scale merit. © 2013 Elsevier Ltd. All rights reserved.

Determinants of eco-efficiency in the Chinese industrial sector

This study measures productive inefficiency within the context of multi-environmental pollution (eco-efficiency) in the Chinese industrial sector. The weighted Russell directional distance model is applied to measure eco-efficiency using production technology. The objective is to clarify how external factors affect eco-efficiency. The major findings are that both foreign direct investment and investment for pollution abatement improve eco-efficiency as measured by air pollutant substances. A levy system for wastewater discharge improves eco-efficiency as measured by wastewater pollutant substances. However, an air pollutant levy does not significantly affect eco-efficiency as measured by air pollutants.

Understanding the quantitative skill base on introductory statistics : a case study from business statistics

Basic mathematical skills are critical to a student’s ability to successfully undertake an introductory statistics course. Yet in business education this vitally important area of mathematics and statistics education is under-researched. The question therefore arises as to what level of mathematical skill a typical business studies student will possess as they enter the tertiary environment, and whether there are any common deficiencies that we can identify with a view to tackling the problem. This paper will focus on a study designed to measure the level of mathematical ability of first year business students. The results provide timely insight into a growing problem faced by many tertiary educators in this field.