Coordination of UAV path planning in 2D and 3D in environmental/bio sensing applications

This paper is concerned with the optimal path planning and initialization interval of one or two UAVs in presence of a constant wind. The method compares previous literature results on synchronization of UAVs along convex curves, path planning and sampling in 2D and extends it to 3D. This method can be applied to observe gas/particle emissions inside a control volume during sampling loops. The flight pattern is composed of two phases: a start-up interval and a sampling interval which is represented by a semi-circular path. The methods were tested in four complex model test cases in 2D and 3D as well as one simulated real world scenario in 2D and one in 3D.

Adaptive dynamic path re-planning RRT algorithms with game theory for UAVs

The main aim of this paper is to describe an adaptive re-planning algorithm based on a RRT and Game Theory to produce an efficient collision free obstacle adaptive Mission Path Planner for Search and Rescue (SAR) missions. This will provide UAV autopilots and flight computers with the capability to autonomously avoid static obstacles and No Fly Zones (NFZs) through dynamic adaptive path replanning. The methods and algorithms produce optimal collision free paths and can be integrated on a decision aid tool and UAV autopilots.

A variable step-size FxLMS algorithm for feedforward active noise control systems based on a new online secondary path modelling technique

Several approaches have been introduced in literature for active noise control (ANC) systems. Since FxLMS algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of FxLMS algorithm. In many ANC applications an online secondary path modelling method using a white noise as a training signal is required to ensure convergence of the system. This paper also proposes a new approach for online secondary path modelling in feedfoward ANC systems. The proposed algorithm stops injection of the white noise at the optimum point and reactivate the injection during the operation, if needed, to maintain performance of the system. Benefiting new version of FxLMS algorithm and not continually injection of white noise makes the system more desirable and improves the noise attenuation performance. Comparative simulation results indicate effectiveness of the proposed approach.

A new online secondary path modelling method for feedforward active noise control systems

An online secondary path modelling method using a white noise as a training signal is required in many applications of active noise control (ANC) to ensure convergence of the system. Not continually injection of white noise during system operation makes the system more desirable. The purposes of the proposed method are two folds: controlling white noise by preventing continually injection, and benefiting white noise with a larger variance. The modelling accuracy and the convergence rate increase when a white noise with larger variance is used, however larger the variance increases the residual noise, which decreases performance of the system. This paper proposes a new approach for online secondary path modelling in feedfoward ANC systems. The proposed algorithm uses the advantages of the white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the system. Comparative simulation results shown in this paper indicate effectiveness of the proposed approach in controlling active noise.

An optimized online secondary path modeling method for single-channel feedback ANC systems

This paper proposes a new method for online secondary path modeling in feedback active noise control (ANC) systems. In practical cases, the secondary path is usually time varying. For these cases, online modeling of secondary path is required to ensure convergence of the system. In literature the secondary path estimation is usually performed offline, prior to online modeling, where in the proposed system there is no need for using offline estimation. The proposed method consists of two steps: a noise controller which is based on an FxLMS algorithm, and a variable step size (VSS) LMS algorithm which is used to adapt the modeling filter with the secondary path. In order to increase performance of the algorithm in a faster convergence and accurate performance, we stop the VSS-LMS algorithm at the optimum point. The results of computer simulation shown in this paper indicate effectiveness of the proposed method.

Integrating agency and resource dependence theory : firm profitability, industry regulation, and board task performance

Boards of directors are key governancemechanisms in organizations and fulfill twomain tasks:monitoringmanagers and firm performance, and providing advice and access to resources. In spite of a wealth of researchmuch remains unknown about how boards attend to the two tasks. This study investigates whether organizational (firm profitability) and environmental factors (industry regulation) affect board task performance. The data combine CEOs' responses to a questionnaire, and archival data from a sample of large Italian firms. Findings show that past firm performance is negatively associatedwith board monitoring and advice tasks; greater industry regulation enhances perceived board task performance; board monitoring and advice tasks tend to reinforce each other, despite their theoretical and practical distinction.

The temperature dependence of the momentum distribution of beryllium measured by neutron Compton scattering

In this paper we report a new neutron Compton scattering (NCS) measurement of the ground state single atom kinetic energy of polycrystalline beryllium at momentum transfers in the range 27}104 As ~1 and temperatures in the range 110}1150 K. The measurements have been made with the electron Volt spectrometer (eVS) at the ISIS facility and the measured kinetic energies are shown to be &10% higher than calculations made in the harmonic approximation.

Impact energy dependence of Al13 cluster deposition on Ni(001) surface

In this paper, the influence of the impact energy on the initial fabrication of thin films formed by low energy cluster deposition was investigated by molecular dynamics simulation of All 3 clusters depositing on Ni(0 0 1) substrate. In the case of soft-landing, (0.01 eV/atom), clusters are rearranged from I-h symmetry into fcc-like clusters on the surface. Then they aggregate each other, which result in thin film growing in 3D island mode. While, growth will be in layer-by-layer mode at the impact energy of a few electron volt due to the transient lateral spread of cluster atoms induced by dense collision cascade. This effect has been traced to collision cascade inside the cluster. which is enhanced by collision with a hard Ni substrate. (C) 2002 Elsevier Science B.V. All rights reserved.

Energy dependence of methyl-radical adsorption on diamond (001)-(2×1) surface

The deposition of hyperthermal CH3 on diamond (001)-(2×1) surface at room temperature has been studied by means of molecular dynamics simulation using the many-body hydrocarbon potential. The energy threshold effect has been observed. That is, with fixed collision geometry, chemisorption can occur only when the incident energy of CH3 is above a critical value (Eth). Increasing the incident energy, dissociation of hydrogen atoms from the incident molecule was observed. The chemisorption probability of CH3 as a function of its incident energy was calculated and compared with that of C2H2. We found that below 10 eV, the chemisorption probability of C2H2 is much lower than that of CH3 on the same surface. The interesting thing is that it is even lower than that of CH3 on a hydrogen covered surface at the same impact energy. It indicates that the reactive CH3 molecule is the more important species than C2H2 in diamond synthesis at low energy, which is in good agreement with the experimental observation.

Career-path analysis using optimal matching and self-organizing maps

This paper is devoted to the analysis of career paths and employability. The state-of-the-art on this topic is rather poor in methodologies. Some authors propose distances well adapted to the data, but are limiting their analysis to hierarchical clustering. Other authors apply sophisticated methods, but only after paying the price of transforming the categorical data into continuous, via a factorial analysis. The latter approach has an important drawback since it makes a linear assumption on the data. We propose a new methodology, inspired from biology and adapted to career paths, combining optimal matching and self-organizing maps. A complete study on real-life data will illustrate our proposal.

Dynamic robot path planning using an enhanced simulated annealing approach

Evolutionary computation is an effective tool for solving optimization problems. However, its significant computational demand has limited its real-time and on-line applications, especially in embedded systems with limited computing resources, e.g., mobile robots. Heuristic methods such as the genetic algorithm (GA) based approaches have been investigated for robot path planning in dynamic environments. However, research on the simulated annealing (SA) algorithm, another popular evolutionary computation algorithm, for dynamic path planning is still limited mainly due to its high computational demand. An enhanced SA approach, which integrates two additional mathematical operators and initial path selection heuristics into the standard SA, is developed in this work for robot path planning in dynamic environments with both static and dynamic obstacles. It improves the computing performance of the standard SA significantly while giving an optimal or near-optimal robot path solution, making its real-time and on-line applications possible. Using the classic and deterministic Dijkstra algorithm as a benchmark, comprehensive case studies are carried out to demonstrate the performance of the enhanced SA and other SA algorithms in various dynamic path planning scenarios.

The gender wage gap in Australia - The path of future convergence

The paper attempts to project the future trend of the gender wage gap in Australia up to 2031. The empirical analysis utilises the Income Distribution Survey (1996) together with Australian Bureau of Statistics (ABS) demographic projections. The methodology combines the ABS projections with assumptions relating to the evolution of educational attainment in order to project the future distribution of human capital skills and consequently the future size of the gender wage gap. The analysis suggests that female relative pay will continue to rise up to 2031. However, gender wage convergence will be relatively slow, with a substantial gap remaining in 2031.

Projections of the future path of the gender wage gap in Great Britain

Purpose – The paper attempts to project the future trend of the gender wage gap in Great Britain up to 2031. Design/methodology/approach – The empirical analysis utilises the British Household Panel Study Wave F together with Office for National Statistics (ONS) demographic projections. The methodology combines the ONS projections with assumptions relating to the evolution of educational attainment in order to project the future distribution of human capital skills and consequently the future size of the gender wage gap. Findings – The analysis suggests that gender wage convergence will be slow, with little female progress by 2031 unless there is a large rise in returns to female experience. Originality/value – The paper has projected the pattern of male and female skill acquisition together with the associated trend in wages up to 2031.

Investigation of the intensity dependence of amplitude noise in electro-optic phase modulators

This thesis studied the source of instability in optical phase modulators used in high accuracy laser measurement systems. The nonlinear origin of the amplitude noise helped further reducing this instability in applications that rely on phase modulators to function. This outcome will have positive impacts on the development of new methods in the amplitude noise suppression.

Baseline study of alcohol dependence among general drivers and drunk driving offenders in Guangzhou, China

This study aimed to investigate drink driving in a sample of general drivers and convicted drunk driving offenders in Guangzhou, China. The study also aimed to explore some potential factors that impact on alcohol-related driving behaviour. Samples of 406 general drivers and 101 drunk driving offenders were recruited between May and October 2012. A survey was used to collect information about demographic characteristics, knowledge, attitudes and practices related to drink driving. The Alcohol Use Disorders Identification Test (AUDIT) was used to assess possible drinking problems. The average age reported for starting to drink alcohol for both groups of participants was around 19 years old. The mean AUDIT score of general drivers was 7.4 (SD = 5.4) representing a low level of alcohol problems, and for convicted drunk driving offenders was 11.1 (SD = 5.9) representing a medium level of alcohol problems (significant difference between means, t = 5.75, p < 0.001). AUDIT scores indicated that a substantial proportion (65%) of the offenders had medium to high levels of alcohol use disorders, compared with 38.5% among general drivers. Offenders who knew the drunk driving legal limit had a lower AUDIT score (M = 9.8, SD = 5.16) than those who did not know it (M = 12.2, SD = 6.257, t = -1.987. p = 0.05). In addition, offenders who were novice drivers (licensed less than 2 years) had a higher AUDIT score (M = 16.4, SD = 7.6) than the other three driver experience categories used.