Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend prelim- inary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean.

Cognitive processes and strategies employed by children to learn spatial representations

Children often have difficulties in learning spatial representations. This study investigated the effect of four different instructional formats on learning outcomes and strategies used when dealing with spatial tasks such as assembly procedures. It was hypothesised that instructional material that imposed least extraneous cognitive load would facilitate enhanced learning. Forty secondary students were presented with four types of instruction; orthographic drawing, isometric drawing, physical model and, isometric and physical model together. The findings provide evidence to suggest that working from physical models caused least extraneous cognitive load compared to the isometric and orthographic groups. The model group took less time, had more correctly completed models, required fewer extra looks, spent less time studying the instruction and made fewer errors. Problem decomposition, forward working and attending to information in the foreground of the graphical representation strategies were analysed.

Diffusion of business improvement processes in manufacturing companies in India

Manufacturing organisations spend more on Business Process Improvement initiatives to make them more competitive in growing global market. This paper presents a Rapid Improvement Workshop (RIW) framework which companies can used to identify the critical factors regulating the diffusion of business process improvement in their company. The framework can then be used address how process improvement can be efficiently implemented. We use the results from case studies at Caterpillar India. The paper identifies the critical factors that contribute to the successful implementation of process improvement programs in manufacturing organisations. We further identify certain technological and cultural barriers to the implementation of process improvement programs and how Indian manufacturing companies can overcome these barriers to attain competitive advantage in the global markets.

Queer/error : gay media systems and processes of abjection

Gay community media functions as a system with three nodes, in which the flows of information and capital theoretically benefit all parties: the gay community gains a sense of cohesion and citizenship through media; the gay media outlets profit from advertisers’ capital; and advertisers recoup their investments in lucrative ‘pink dollar’ revenue. But if a necessary corollary of all communication systems is error or noise, where—and what—are the errors in this system? In this paper we argue that the ‘error’ in the gay media system is Queerness, and that the gay media system ejects (in a process of Kristevan abjection) these Queer identities in order to function successfully. We examine the ways in which Queer identities are excluded from representation in such media through a discourse and content analysis of The Sydney Star Observer (Australia’s largest gay and lesbian paper). First, we analyse the way Queer bodies are excluded from the discourses that construct and reinforce both the ideal gay male body and the notions of homosexual essence required for that body to be meaningful. We then argue that abject Queerness returns in the SSO’s discourses of public health through the conspicuous absence of the AIDS-inflicted body (which we read as the epitome of the abject Queer), since this absence paradoxically conjures up a trace of that which the system tries to expel. We conclude by arguing that because the ‘Queer error’ is integral to the SSO, gay community media should practise a politics of Queer inclusion rather than exclusion.

Re-engineering project planning processes : a best practice model for managing project phase integration issues

Competitive markets are increasingly driving new initiatives for shorter cycle times resulting in increased overlapping of project phases. This, in turn, necessitates improving the interfaces between the different phases to be overlapped (integrated), thus allowing transfer of processes, information and knowledge from one individual or team to another. This transfer between phases, within and between projects, is one of the basic challenges to the philosophy of project management. To make the process transfer more transparent with minimal loss of momentum and project knowledge, this paper draws upon Total Quality Management (TQM) and Business Process Re-engineering (BPR) philosophies to develop a Best Practice Model for managing project phase integration. The paper presents the rationale behind the model development and outlines its two key parts; (1) Strategic Framework and (2) Implementation Plan. Key components of both the Strategic Framework and the Implementation Plan are presented and discussed.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Workflow support for scheduling in surgical care processes

Real-world business processes rely on the availability of scarce, shared resources, both human and non-human. Current workflow management systems support allocation of individual human resources to tasks but lack support for the full range of resource types used in practice, and the inevitable constraints on their availability and applicability. Based on past experience with resource-intensive workflow applications, we derive generic requirements for a workflow system which can use its knowledge of resource capabilities and availability to help create feasible task schedules. We then define the necessary architecture for implementing such a system and demonstrate its practicality through a proof-of-concept implementation. This work is presented in the context of a real-life surgical care process observed in a number of German hospitals.

Entrepreneurial discovery and exploitation processes : sequence or symbiosis?

This study examined the effect that temporal order within the entrepreneurial discovery-exploitation process has on the outcomes of venture creation. Consistent with sequential theories of discovery-exploitation, the general flow of venture creation was found to be directed from discovery toward exploitation in a random sample of nascent ventures. However, venture creation attempts which specifically follow this sequence derive poor outcomes. Moreover, simultaneous discovery-exploitation was the most prevalent temporal order observed, and venture attempts that proceed in this manner more likely become operational. These findings suggest that venture creation is a multi-scale phenomenon that is at once directional in time, and simultaneously driven by symbiotically coupled discovery and exploitation.

Human resource behaviour simulation in business processes

The structure and dynamics of a modern business environment are very hard to model using traditional methods. Such complexity raises challenges to effective business analysis and improvement. The importance of applying business process simulation to analyze and improve business activities has been widely recognized. However, one remaining challenge is the development of approaches to human resource behavior simulation. To address this problem, we describe a novel simulation approach where intelligent agents are used to simulate human resources by performing allocated work from a workflow management system. The behavior of the intelligent agents is driven a by state transition mechanism called a Hierarchical Task Network (HTN). We demonstrate and validate our simulator via a medical treatment process case study. Analysis of the simulation results shows that the behavior driven by the HTN is consistent with design of the workflow model. We believe these preliminary results support the development of more sophisticated agent-based human resource simulation systems.

Sustainable reuse of storm/waste-water resources : a review on solar photocatalytic purification processes

In recent years, there has been a significant amount of research and development in the area of solar photocatalysis. This paper reviews and summarizes the mechanism of photocatalytic oxidation process, types of photocatalyst, and the factors influencing the photoreactor efficiency and the most recent findings related to solar detoxification and disinfection of water contaminants. Various solar reactors for photocatlytic water purification are also briefly described. The future potential of solar photocatlysis for storm water treatment and reuse is also discussed to ensure sustainable use of solar energy and storm water resources.

History-aware, real-time risk detection in business processes (Extended Version)

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.

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.

Mother and child sharing through drawing: Intergenerational collaborative processes for making artworks

This paper presents a preliminary study into collaborated processes for art-making, undertaken by a young child and an adult. The study explores collaborative drawing in the context of sociocultural research into early childhood education. The study particularly examines whether childhood techniques for making marks, creative processing and art-making could be ‘re-learned’ by the adult, while new opportunities for expanding on extant repertoire could be available to the child. In this context the child teaches and learns from the adult, and the adult teaches and learns from the child. The study utilised video-data-recording to facilitate microanalysis of the researchers in action, enabling the adult researcher to present a discourse into the dynamics of how the visual, mark-making repertoires of an adult and child can be co-developed. Preliminary findings help contribute to the various discourses available into sociocultural research that supports processes for exploring and making art, and which allows a challenge to the role of the adult educator as a provider or director of what is learned.