A hybrid of modified PSO and local search on a multi-robot search system

Particle swarm optimization (PSO), a new population based algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area.Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time.This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star.To validate the effectiveness and usefulness of algorithms,a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.

Modelling collision potentials in Port Anchorages: Application of the Navigational Traffic Conflict Technique (NTCT)

Despite the extent of works done on modelling port water collisions, not much research effort has been devoted to modelling collisions at port anchorages. This paper aims to fill this important gap in literature by applying the Navigation Traffic Conflict Technique (NTCT) for measuring the collision potentials in anchorages and for examining the factors contributing to collisions. Grounding on the principles of the NTCT, a collision potential measurement model and a collision potential prediction model were developed. These models were illustrated by using vessel movement data of the anchorages in Singapore port waters. Results showed that the measured collision potentials are in close agreement with those perceived by harbour pilots. Higher collision potentials were found in anchorages attached to shoreline and international fairways, but not at those attached to confined water. Higher operating speeds, larger numbers of isolated danger marks and day conditions were associated with reduction in the collision potentials.

A robust optimization approach for index tracking problem

Index tracking is an investment approach where the primary objective is to keep portfolio return as close as possible to a target index without purchasing all index components. The main purpose is to minimize the tracking error between the returns of the selected portfolio and a benchmark. In this paper, quadratic as well as linear models are presented for minimizing the tracking error. The uncertainty is considered in the input data using a tractable robust framework that controls the level of conservatism while maintaining linearity. The linearity of the proposed robust optimization models allows a simple implementation of an ordinary optimization software package to find the optimal robust solution. The proposed model of this paper employs Morgan Stanley Capital International Index as the target index and the results are reported for six national indices including Japan, the USA, the UK, Germany, Switzerland and France. The performance of the proposed models is evaluated using several financial criteria e.g. information ratio, market ratio, Sharpe ratio and Treynor ratio. The preliminary results demonstrate that the proposed model lowers the amount of tracking error while raising values of portfolio performance measures.

Creative adaptations: Integrating Feldenkrais principles in contemporary dance technique to facilitate the transition into tertiary dance education

As conservatoire-style dance teaching has traditionally utilised a hierarchical approach through which the student must conform to the ideal requirements of the conventional technique, current discourse is beginning to question how dance training can develop technical acuity without stifling students' ability to engage creatively. In recent years, there has been growing interest in the field of somatics and its relationship to tertiary dance training due to the understanding that this approach supports creative autonomy by radically repositioning the student's relationship to embodied learning, skill acquisition, enquiry and performance. This research addresses an observable disjuncture between the skills of dancers graduating from tertiary training and Australian dance industry needs, which increasingly demand the co-creative input of the dancer in choreographic practice. Drawing from Action Research, this paper will discuss a project which introduces somatic learning approaches, primarily from Feldenkrais Method and Hanna Somatics, to first-year dance students in their transition into tertiary education. This paper acknowledges previous research undertaken, most specifically the Somdance Manual by the University of Western Sydney, while directing focus to the first-year student transition from private dance studio training into the pre-professional arena.

Does the technique employed for skin temperature assessment alter outcomes? A systematic review

Skin temperature is an important physiological measure that can reflect the presence of illness and injury as well as provide insight into the localised interactions between the body and the environment. The aim of this systematic review was to analyse the agreement between conductive and infrared means of assessing skin temperature which are commonly employed in in clinical, occupational, sports medicine, public health and research settings. Full-text eligibility was determined independently by two reviewers. Studies meeting the following criteria were included in the review: 1) the literature was written in English, 2) participants were human (in vivo), 3) skin surface temperature was assessed at the same site, 4) with at least two commercially available devices employed—one conductive and one infrared—and 5) had skin temperature data reported in the study. A computerised search of four electronic databases, using a combination of 21 keywords, and citation tracking was performed in January 2015. A total of 8,602 were returned. Methodology quality was assessed by 2 authors independently, using the Cochrane risk of bias tool. A total of 16 articles (n = 245) met the inclusion criteria. Devices are classified to be in agreement if they met the clinically meaningful recommendations of mean differences within ±0.5 °C and limits of agreement of ±1.0 °C. Twelve of the included studies found mean differences greater than ±0.5 °C between conductive and infrared devices. In the presence of external stimulus (e.g. exercise and/or heat) five studies foundexacerbated measurement differences between conductive and infrared devices. This is the first review that has attempted to investigate presence of any systemic bias between infrared and conductive measures by collectively evaluating the current evidence base. There was also a consistently high risk of bias across the studies, in terms of sample size, random sequence generation, allocation concealment, blinding and incomplete outcome data. This systematic review questions the suitability of using infrared cameras in stable, resting, laboratory conditions. Furthermore, both infrared cameras and thermometers in the presence of sweat and environmental heat demonstrate poor agreement when compared to conductive devices. These findings have implications for clinical, occupational, public health, sports science and research fields.

Balancing exploration and exploitation in particle swarm optimization on search tasking

In this study we present a combinatorial optimization method based on particle swarm optimization and local search algorithm on the multi-robot search system. Under this method, in order to create a balance between exploration and exploitation and guarantee the global convergence, at each iteration step if the distance between target and the robot become less than specific measure then a local search algorithm is performed. The local search encourages the particle to explore the local region beyond to reach the target in lesser search time. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.

Numerical optimization of soft-mold aided co-curing process of advanced grid-stiffened composite structures

The co-curing process for advanced grid-stiffened (AGS) composite structure is a promising manufacturing process, which could reduce the manufacturing cost, augment the advantages and improve the performance of AGS composite structure. An improved method named soft-mold aided co-curing process which replaces the expansion molds by a whole rubber mold is adopted in this paper. This co-curing process is capable to co-cure a typical AGS composite structure with the manufacturer’s recommended cure cycle (MRCC). Numerical models are developed to evaluate the variation of temperature and the degree of cure in AGS composite structure during the soft-mold aided co-curing process. The simulation results were validated by experimental results obtained from embedded temperature sensors. Based on the validated modeling framework, the cycle of cure can be optimized by reducing more than half the time of MRCC while obtaining a reliable degree of cure. The shape and size effects of AGS composite structure on the distribution of temperature and degree of cure are also investigated to provide insights for the optimization of soft-mold aided co-curing process.

Optimization of organic solar cells with thin film Au as anode

Optical transmittance and conductivity for thin metallic films, such as Au, are two inversely related and extremely important parameters for its application in organic photovoltaics as the front electrode. We report our findings on how these parameters have been optimized to attain maximum possible efficiencies by fabricating organic solar cells with thin Au film anodes of differing optical transmittances and consequently due to scaling at the nanolevel, varying electrical conductivities. There was an extraordinary improvement in the overall solar cell efficiency (to the order of 49%) when the Au thin film transmittance was increased from 38% to 54%. Surface morphologies of these thin films also have an effect on the critical parameters including, Voc, Jsc and FF.

Let’s talk about lex: Narrative analysis as both research method and teaching technique in law

Law is narration: it is narrative, narrator and the narrated. As a narrative, the law is constituted by a constellation of texts – from official sources such as statutes, treaties and cases, to private arrangements such as commercial contracts, deeds and parenting plans. All are a collection of stories: cases are narrative contests of facts and rights; statutes are recitations of the substantive and procedural bases for social, economic and political interactions; private agreements are plots for future relationships, whether personal or professional. As a narrator, law speaks in the language of modern liberalism. It describes its world in abstractions rather than in concrete experience, universal principles rather than individual subjectivity. It casts people into ‘parties’ to legal relationships; structures human interactions into ‘issues’ or ‘problems’; and tells individual stories within larger narrative arcs such as ‘the rule of law’ and ‘the interests of justice’. As the narrated, the law is a character in its own story. The scholarship of law, for example, is a type of story-telling with law as its central character. For positivists, still the dominant group in the legal genre, law is a closed system of formal rules with an “immanent rationality” and its own “structure, substantive content, procedure and tradition,” dedicated to finality of judgment. For scholars inspired by the interpretative tradition in the humanities, law is a more ambivalent character, susceptible to influences from outside its realm and masking a hidden ideological agenda under its cloak of universality and neutrality. For social scientists, law is a protagonist on a wider social stage, impacting on society, the economy and the polity is often surprising ways.

Graphene quantum dots and the resonance light scattering technique for trace analysis of phenol in different water samples

A novel, highly selective resonance light scattering (RLS) method was researched and developed for the analysis of phenol in different types of industrial water. An important aspect of the method involved the use of graphene quantum dots (GQDs), which were initially obtained from the pyrolysis of citric acid dissolved in aqueous solutions. The GQDs in the presence of horseradish peroxidase (HRP) and H2O2 were found to react quantitatively with phenol such that the RLS spectral band (310 nm) was quantitatively enhanced as a consequence of the interaction between the GQDs and the quinone formed in the above reaction. It was demonstrated that the novel analytical method had better selectivity and sensitivity for the determination of phenol in water as compared to other analytical methods found in the literature. Thus, trace amounts of phenol were detected over the linear ranges of 6.00×10−8–2.16×10−6 M and 2.40×10−6–2.88×10−5 M with a detection limit of 2.20×10−8 M. In addition, three different spiked waste water samples and two untreated lake water samples were analysed for phenol. Satisfactory results were obtained with the use of the novel, sensitive and rapid RLS method.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Linear programming for large-scale Markov decision problems

We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost. Since it is intractable to compete with the optimal policy for large scale problems, we pursue the more modest goal of competing with a low-dimensional family of policies. We use the dual linear programming formulation of the MDP average cost problem, in which the variable is a stationary distribution over state-action pairs, and we consider a neighborhood of a low-dimensional subset of the set of stationary distributions (defined in terms of state-action features) as the comparison class. We propose a technique based on stochastic convex optimization and give bounds that show that the performance of our algorithm approaches the best achievable by any policy in the comparison class. Most importantly, this result depends on the size of the comparison class, but not on the size of the state space. Preliminary experiments show the effectiveness of the proposed algorithm in a queuing application.

Studies of water and electrolyte movement from oral rehydration solutions (rice- and glucose-based) across a normal and secreting gut using a dual isotope tracer technique in a rat perfusion model

Aims: To establish a model to measure bidirectional flow of water from a glucose oral rehydration solution (G-ORS) and a newly developed rice-based oral rehydration solution (R-ORS) using a dual isotope tracer technique in a rat perfusion model. To measure net water, sodium and potassium absorption from the ORS. Methods: In viva steady-state perfusion studies were carried out in normal and secreting (induced by cholera toxin) rat small intestine (n = 11 in each group). To determine bidirectional flow of water from the ORS the animals were initially labelled with tritium, and deuterium was added to the perfusion solution. Sequential perfusate and blood samples were collected after attainment of steady-state conditions and analysed for water and electrolyte content. Results: There was a significant increase in net water absorption from the R-ORS compared to the G-ORS in both the normal (P < 0.02) and secreting intestine (P < 0.05). Water efflux was significantly reduced in the R-ORS group compared to the G-ORS group in both the normal (P < 0.01) and the secreting intestine (P < 0.01). There was an increase in sodium absorption in the R-ORS group compared to the G-ORS. The G-ORS produced a significantly greater blood glucose level at 75 min compared to the R-ORS (P < 0.03) in the secreting intestine. Conclusions: This study demonstrates the improved water absorption from a rice-based ORS in both the normal and secreting intestine. Evidence that the absorption of water may be influenced by the osmolality of the ORS was also demonstrated.

Simulated systems optimization through response surface techniques [Optimización de sistemas simulados a través de técnicas de superficie de respuesta]

The purpose of this article is to show the applicability and benefits of the techniques of design of experiments as an optimization tool for discrete simulation models. The simulated systems are computational representations of real-life systems; its characteristics include a constant evolution that follows the occurrence of discrete events along the time. In this study, a production system, designed with the business philosophy JIT (Just in Time) is used, which seeks to achieve excellence in organizations through waste reduction in all the operational aspects. The most typical tool of JIT systems is the KANBAN production control that seeks to synchronize demand with flow of materials, minimize work in process, and define production metrics. Using experimental design techniques for stochastic optimization, the impact of the operational factors on the efficiency of the KANBAN / CONWIP simulation model is analyzed. The results show the effectiveness of the integration of experimental design techniques and discrete simulation models in the calculation of the operational parameters. Furthermore, the reliability of the methodologies found was improved with a new statistical consideration.

Sampling accuracy of reef resource inventory technique

A new technique called the reef resource inventory (RRI) was developed to map the distribution and abundance of benthos and substratum on reefs. The rapid field sampling technique uses divers to visually estimate the percentage cover of categories of benthos and substratum along 2x20 in plotless strip-transects positioned randomly over the tops, and systematically along the edge of reefs. The purpose of this study was to compare the relative sampling accuracy of the RRI against the line intercept transect technique (LIT), an international standard for sampling reef benthos and substratum. Analysis of paired sampling with LIT and RRI at 51 sites indicated sampling accuracy was not different (P > 0.05) for 8 of the 12 benthos and substratum categories used in the study. Significant differences were attributed to small-scale patchiness and cryptic coloration of some benthos; effects associated with sampling a sparsely distributed animal along a line versus an area; difficulties in discriminating some of the benthos and substratum categories; and differences due to visual acuity since LIT measurements were taken by divers close to the seabed whereas RRI measurements were taken by divers higher in the water column. The relative cost efficiency of the RRI technique was at least three times that of LIT for all benthos and substratum categories and as much as 10 times higher for two categories. These results suggest that the RRI can be used to obtain reliable and accurate estimates of relative abundance of broad categories of reef benthos and substratum.