Automated control of a dragline using machine vision

The research described in this paper is directed toward increasing productivity of draglines through automation. In particular, it focuses on the swing-to-dump, dump, and return-to-dig phases of the dragline operational cycle by developing a swing automation system. In typical operation the dragline boom can be in motion for up to 80% of the total cycle time. This provides considerable scope for improving cycle time through automated or partially automated boom motion control. This paper describes machine vision based sensor technology and control algorithms under development to solve the problem of continuous real time bucket location and control. Incorporation of this capability into existing dragline control systems will then enable true automation of dragline swing and dump operations.

Post-tensioning and its effect on multi-level formwork load distribution

Multi-level concrete buildings requrre substantial temporary formwork structures to support the slabs during construction. The primary function of this formwork is to safely disperse the applied loads so that the slab being constructed, or the portion of the permanent structure already constructed, is not overloaded. Multi-level formwork is a procedure in which a limited number of formwork and shoring sets are cycled up the building as construction progresses. In this process, each new slab is supported by a number of lower level slabs. The new slab load is, essentially, distributed to these supporting slabs in direct proportion to their relative stiffness. When a slab is post-tensioned using draped tendons, slab lift occurs as a portion of the slab self-weight is balanced. The formwork and shores supporting that slab are unloaded by an amount equivalent to the load balanced by the post-tensioning. This produces a load distribution inherently different from that of a conventionally reinforced slab. Through , theoretical modelling and extensive on-site shore load measurement, this research examines the effects of post-tensioning on multilevel formwork load distribution. The research demonstrates that the load distribution process for post-tensioned slabs allows for improvements to current construction practice. These enhancements include a shortening of the construction period; an improvement in the safety of multi-level form work operations; and a reduction in the quantity of form work materials required for a project. These enhancements are achieved through the general improvement in safety offered by post-tensioning during the various formwork operations. The research demonstrates that there is generally a significant improvement in the factors of safety over those for conventionally reinforced slabs. This improvement in the factor of safety occurs at all stages of the multi-level formwork operation. The general improvement in the factors of safety with post-tensioned slabs allows for a shortening of the slab construction cycle time. Further, the low level of load redistribution that occurs during the stripping operations makes post-tensioned slabs ideally suited to reshoring procedures. Provided the overall number of interconnected levels remains unaltered, it is possible to increase the number of reshored levels while reducing the number of undisturbed shoring levels without altering the factors of safety, thereby, reducing the overall quantity of formwork and shoring materials.

Innovative planning approaches for generator interconnection group study

Today, a large number of wind generator interconnection requests have been queued and are being processed. The generator interconnection group study is a way to reduce the generator interconnection cycle time and increase interconnection certainty. However, it is very challenging to identify the “best” transmission upgrades for a large group of generator interconnections. It is also very important to differentiate the constraints caused by each generator interconnection request and identify their responsibilities for transmission upgrades. This paper outlines some innovative study approaches that can be used in a group study with large numbers of generator interconnection requests in a constrained area. Improved study methods are introduced, and a summary and conclusions are derived from the study.

Assembly line balancing the comparison of COMSOAL and MSNSH technique in Motorcycle manufacturing company

Today’s highly competitive market influences the manufacturing industry to improve their production systems to become the optimal system in the shortest cycle time as possible. One of most common problems in manufacturing systems is the assembly line balancing problem. The assembly line balancing problem involves task assignments to workstations with optimum line efficiency. The line balancing technique, namely “COMSOAL”, is an abbreviation of “Computer Method for Sequencing Operations for Assembly Lines”. Arcus initially developed the COMSOAL technique in 1966 [1], and it has been mainly applied to solve assembly line balancing problems [6]. The most common purposes of COMSOAL are to minimise idle time, optimise production line efficiency, and minimise the number of workstations. Therefore, this project will implement COMSOAL to balance an assembly line in the motorcycle industry. The new solution by COMSOAL will be used to compare with the previous solution that was developed by Multi‐Started Neighborhood Search Heuristic (MSNSH), which will result in five aspects including cycle time, total idle time, line efficiency, average daily productivity rate, and the workload balance. The journal name “Optimising and simulating the assembly line balancing problem in a motorcycle manufacturing company: a case study” will be used as the case study for this project [5].

Modeling resource management in the building design process by information constraint Petri nets

The effect of resource management on the building design process directly influences the development cycle time and success of construction projects. This paper presents the information constraint net (ICN) to represent the complex information constraint relations among design activities involved in the building design process. An algorithm is developed to transform the information constraints throughout the ICN into a Petri net model. A resource management model is developed using the ICN to simulate and optimize resource allocation in the design process. An example is provided to justify the proposed model through a simulation analysis of the CPN Tools platform in the detailed structural design. The result demonstrates that the proposed approach can obtain the resource management and optimization needed for shortening the development cycle and optimal allocation of resources.

Reduction in resting plasma granulysin as a marker of increased training load

Granulysin is a cytolytic granule protein released by natural killer cells and activated cytotoxic T lymphocytes. The influence of exercise training on circulating granulysin concentration is unknown, as is the relationship between granulysin concentration, natural killer cell number and natural killer cell cytotoxicity. We examined changes in plasma granulysin concentration, natural killer cell number and cytotoxicity following acute exercise and different training loads. Fifteen highly trained male cyclists completed a baseline 40-km cycle time trial (TT401) followed by five weeks of normal training and a repeat time trial (TT402). The cyclists then completed four days of high intensity training followed by another time trial (TT403) on day five. Following one final week of normal training cyclists completed another time trial (TT404). Fasting venous blood was collected before and after each time trial to determine granulysin concentration, natural killer cell number and natural killer cell cytotoxicity. Granulysin concentration increased significantly after each time trial (P<0.001). Pre-exercise granulysin concentration for TT403 was significantly lower than pre-exercise concentration for TT401 (-20.3 +/- 7.5%, P<0.026), TT402 (-16.7 +/- 4.3%, P<0.003) and 7T404 (-21 +/- 4.2%, P<0.001). Circulating natural killer cell numbers also increased significantly post-exercise for each time trial (P<0.001), however there was no significant difference across TT40 (P>0.05). Exercise did not significantly alter natural killer cell cytotoxicity on a per cell basis, and there were no significant differences between the four time trials. In conclusion, plasma granulysin concentration increases following moderate duration, strenuous exercise and is decreased in response to a short-term period of intensified training.

Real-time monitoring of nucleation-growth cycle of carbon nanoparticles in acetylene plasmas

Quantum cascade laserabsorption spectroscopy was used to measure the absolute concentration of acetylene in situ during the nanoparticle growth in Ar + C2H2 RF plasmas. It is demonstrated that the nanoparticle growth exhibits a periodical behavior, with the growth cycle period strongly dependent on the initial acetylene concentration in the chamber. Being 300 s at 7.5% of acetylene in the gas mixture, the growth cycle period decreases with the acetylene concentration increasing; the growth eventually disappears when the acetylene concentration exceeds 32%. During the nanoparticle growth, the acetylene concentration is small and does not exceed 4.2% at radio frequency (RF) power of 4 W, and 0.5% at RF power of 20 W. An injection of a single acetylene pulse into the discharge also results in the nanoparticlenucleation and growth. The absorption spectroscopy technique was found to be very effective for the time-resolved measurement of the hydrocarbon content in nanoparticle-generatingplasmas.

Identification of eusynstyelamide B as a potent cell cycle inhibitor following the generation and screening of an ascidian-derived extract library using a real time cell analyzer

Ascidians are marine invertebrates that have been a source of numerous cytotoxic compounds. Of the first six marine-derived drugs that made anticancer clinical trials, three originated from ascidian specimens. In order to identify new anti-neoplastic compounds, an ascidian extract library (143 samples) was generated and screened in MDA-MB-231 breast cancer cells using a real-time cell analyzer (RTCA). This resulted in 143 time-dependent cell response profiles (TCRP), which are read-outs of changes to the growth rate, morphology, and adhesive characteristics of the cell culture. Twenty-one extracts affected the TCRP of MDA-MB-231 cells and were further investigated regarding toxicity and specificity, as well as their effects on cell morphology and cell cycle. The results of these studies were used to prioritize extracts for bioassay-guided fractionation, which led to the isolation of the previously identified marine natural product, eusynstyelamide B (1). This bis-indole alkaloid was shown to display an IC50 of 5 μM in MDA-MB-231 cells. Moreover, 1 caused a strong cell cycle arrest in G2/M and induced apoptosis after 72 h treatment, making this molecule an attractive candidate for further mechanism of action studies.

Time-driven life cycle cost estimation system for product family design

This research develops a design support system, which is able to estimate the life cycle cost of different product families at the early stage of product development. By implementing the system, a designer is able to develop various cost effective product families in a shorter lead-time and minimise the destructive impact of the product family on the environment.

A framework for fully decentralised cycle stealing

Ordinary desktop computers continue to obtain ever more resources – in-creased processing power, memory, network speed and bandwidth – yet these resources spend much of their time underutilised. Cycle stealing frameworks harness these resources so they can be used for high-performance computing. Traditionally cycle stealing systems have used client-server based architectures which place significant limits on their ability to scale and the range of applica-tions they can support. By applying a fully decentralised network model to cycle stealing the limits of centralised models can be overcome. Using decentralised networks in this manner presents some difficulties which have not been encountered in their previous uses. Generally decentralised ap-plications do not require any significant fault tolerance guarantees. High-performance computing on the other hand requires very stringent guarantees to ensure correct results are obtained. Unfortunately mechanisms developed for traditional high-performance computing cannot be simply translated because of their reliance on a reliable storage mechanism. In the highly dynamic world of P2P computing this reliable storage is not available. As part of this research a fault tolerance system has been created which provides considerable reliability without the need for a persistent storage. As well as increased scalability, fully decentralised networks offer the ability for volunteers to communicate directly. This ability provides the possibility of supporting applications whose tasks require direct, message passing style communication. Previous cycle stealing systems have only supported embarrassingly parallel applications and applications with limited forms of communication so a new programming model has been developed which can support this style of communication within a cycle stealing context. In this thesis I present a fully decentralised cycle stealing framework. The framework addresses the problems of providing a reliable fault tolerance sys-tem and supporting direct communication between parallel tasks. The thesis includes a programming model for developing cycle stealing applications with direct inter-process communication and methods for optimising object locality on decentralised networks.

Marketplace forces and the history of retailing : The cycle of control

This paper traces the history of store (retailer-controlled) and national (manufacture controlled)brands; identifies the key historical characteristics of the past 200 years of marketing history;describes the four main time periods of U.S. retail marketing (1800 - 2000); and comments on the most likely developments within the current phases of brand marketing. Will the future focus on technology and new forms of communications? The Internet exemplifies an unconventional retailing environment, with etailer numbers growing rapidly. The central proposition of this paper is that a "cycle of control" - a pattern of marketing developments within the history of retailing and national marketing communications - Can indicate the success of marketing strategies in the future.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

Risk assessment in life-cycle costing for road asset management

Queensland Department of Main Roads, Australia, spends approximately A$ 1 billion annually for road infrastructure asset management. To effectively manage road infrastructure, firstly road agencies not only need to optimise the expenditure for data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. This paper presents the results of case studies in using the probability-based method for an integrated approach (i.e. assessing optimal costs of pavement strength data collection; calibrating deterioration prediction models that suit local condition and assessing risk-adjusted budget estimates for road maintenance and rehabilitation for assessing life-cycle budget estimates). The probability concept is opening the path to having the means to predict life-cycle maintenance and rehabilitation budget estimates that have a known probability of success (e.g. produce budget estimates for a project life-cycle cost with 5% probability of exceeding). The paper also presents a conceptual decision-making framework in the form of risk mapping in which the life-cycle budget/cost investment could be considered in conjunction with social, environmental and political issues.