Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Autonomous helicopter hover using an artificial neural network

Details the developments to date of an unmanned air vehicle (UAV) based on a standard size 60 model helicopter. The design goal is to have the helicopter achieve stable hover with the aid of an INS and stereo vision. The focus of the paper is on the development of an artificial neural network (ANN) that makes use of only the INS data to generate hover commands, which are used to directly manipulate the flight servos. Current results show that networks incorporating some form of recurrency (state history) offer little advantage over those without. At this stage, the ANN has partially maintained periods of hover even with misaligned sensors.

Buckling behaviour and design of tapered steel lighting masts

Tapered tubular steel masts are commonly used to support floodlights in a range of applications. The design of these slender tapered masts requires a rational elastic flexural buckling analysis as the thickness also varies with height. Therefore a series of finite element analyses of tapered masts with varying geometry parameters was conducted to develop an elastic flexural buckling load formula. This paper briefly discusses the design methods, and then presents the details of the finite element analyses and the results. 1–Associate Professor of Civil Engineering, and Director, Physical Infrastructure Centre 2–Former BE (Civil) Student, QUT

LED lighting design strategies to enhance window appearance and increase energy savings in daylit office spaces

Vertical windows are the most common and simplest method to introduce daylight to interior spaces of office buildings, while also providing a view and connection to the outside. However, high contrast ratios between windows and surrounding surfaces can cause visual discomfort for occupants and can negatively influence their health and productivity. Consequently, building occupants may try to adapt their working environment through closing blinds and turning on lights in order to improve indoor visual comfort. Such interventions defeat the purpose of daylight harvesting systems and can increase the forecast electric lighting consumption in buildings that include such systems. A simple strategy to prevent these problematic consequences is to reduce the luminance contrasts presented by the window wall by increasing the luminance of areas surrounding the window through the sparing use of energy-efficient supplementary lighting, such light emitting diodes (LEDs). This paper presents the result of a pilot study in typical office in Brisbane, Australia that tests the effectiveness of a supplementary LED lighting system. The study shows an improvement in the appraisal of the visual environment is achieved using the supplementary system, along with up to 88% reductions in luminance contrast at the window wall. Also observed is a 36% reduction in the likelihood of user interventions that would increase energy usage. These results are used as the basis of an annual energy simulation of the test office and indicate that supplementary systems could be used to save energy beyond what is typically realised in side lit office spaces.

Improving the impact of luminance contrast on the window appearance in a conventional office room: Using supplementary lighting strategies

High contrast ratios between windows and surrounding surfaces could cause reduced visibility or discomfort for occupants. Consequently, building users may choose to intervene in lighting conditions through closing blinds and turning on the lamps in order to enhance indoor visual comfort. Such interventions increase projected electric lighting use in buildings. One simple method to prevent these problematic issues is increasing the luminance of the areas surrounding to the bright surface of windows through the use of energy-efficient supplementary lighting, such Light Emitting Diodes (LEDs). This paper reports on the results of a pilot study in conventional office in Brisbane, Australia. The outcomes of this study indicated that a supplementary LED system of approximately 18 W could reduce the luminance contrast on the window wall from values in the order of 117:1 to 33:1. In addition, the results of this experiment suggested that this supplementary strategy could increase the subjective scale appraisal of window appearance by approximately 33%, as well as reducing the likelihood of users’ intention to turn on the ceiling lights by about 27%. It could also diminish the likelihood of occupants’ intention to move the blind down by more than 90%.

Integrating LED lighting design strategies with side daylighting systems to improve interior lighting design of office buildings

A high contrast ratio between windows and surrounding walls may lead to office workers visual discomfort that could negatively affect their satisfaction and productivity. Consequently, occupants may try to adapt their working environment by closing blinds and/ or turning on the lights to enhance indoor visual comfort, which can reduce predicted energy savings. The hypothesis of this study is that reducing luminance contrast ratio on the window wall will improve window appearance which potentially will reduce visual discomfort and decrease workers interventions. Thus, this PhD research proposes a simple strategy to diminish the luminance contrast on the window wall by increasing the luminance of the areas surrounding the windows using supplementary light emitting diode (LED) systems. To test the hypothesis, this investigation will involve three experiments in different office layouts with various window types and orientations in Brisbane, Australia. It will assess user preferences for different luminance patterns in windowed offices featuring flexible, lowpower LED lighting installations that allows multiple lighting design options on the window wall. Detailed luminance and illuminance measures will be used to match quantitative lighting design assessment to user preferences.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Feasibility of a stepped wedge cluster RCT and concurrent observational sub-study to evaluate the effects of modified ward night lighting on inpatient fall rates and sleep quality: A protocol for a pilot trial

Background: Falls among hospitalised patients impose a considerable burden on health systems globally and prevention is a priority. Some patient-level interventions have been effective in reducing falls, but others have not. An alternative and promising approach to reducing inpatient falls is through the modification of the hospital physical environment and the night lighting of hospital wards is a leading candidate for investigation. In this pilot trial, we will determine the feasibility of conducting a main trial to evaluate the effects of modified night lighting on inpatient ward level fall rates. We will test also the feasibility of collecting novel forms of patient level data through a concurrent observational sub-study. Methods/design: A stepped wedge, cluster randomised controlled trial will be conducted in six inpatient wards over 14 months in a metropolitan teaching hospital in Brisbane (Australia). The intervention will consist of supplementary night lighting installed across all patient rooms within study wards. The planned placement of luminaires, configurations and spectral characteristics are based on prior published research and pre-trial testing and modification. We will collect data on rates of falls on study wards (falls per 1000 patient days), the proportion of patients who fall once or more, and average length of stay. We will recruit two patients per ward per month to a concurrent observational sub-study aimed at understanding potential impacts on a range of patient sleep and mobility behaviour. The effect on the environment will be monitored with sensors to detect variation in light levels and night-time room activity. We will also collect data on possible patient-level confounders including demographics, pre-admission sleep quality, reported vision, hearing impairment and functional status. Discussion: This pragmatic pilot trial will assess the feasibility of conducting a main trial to investigate the effects of modified night lighting on inpatient fall rates using several new methods previously untested in the context of environmental modifications and patient safety. Pilot data collected through both parts of the trial will be utilised to inform sample size calculations, trial design and final data collection methods for a subsequent main trial.

Robust automatic face clustering in news video

Clustering identities in a video is a useful task to aid in video search, annotation and retrieval, and cast identification. However, reliably clustering faces across multiple videos is challenging task due to variations in the appearance of the faces, as videos are captured in an uncontrolled environment. A person's appearance may vary due to session variations including: lighting and background changes, occlusions, changes in expression and make up. In this paper we propose the novel Local Total Variability Modelling (Local TVM) approach to cluster faces across a news video corpus; and incorporate this into a novel two stage video clustering system. We first cluster faces within a single video using colour, spatial and temporal cues; after which we use face track modelling and hierarchical agglomerative clustering to cluster faces across the entire corpus. We compare different face recognition approaches within this framework. Experiments on a news video database show that the Local TVM technique is able effectively model the session variation observed in the data, resulting in improved clustering performance, with much greater computational efficiency than other methods.

Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Illuminating the glass box: The lighting designs of Richard Kelly

A promotional brochure celebrating the completion of the Seagram Building in spring 1957 features on its cover intense portraits of seven men bisected by a single line of bold text that asks, “Who are these Men?” The answer appears on the next page: “They Dreamed of a Tower of Light” (Figures 1, 2). Each photograph is reproduced with the respective man’s name and project credit: architects, Mies van der Rohe and Philip Johnson; associate architect, Eli Jacques Kahn; electrical contractor, Harry F. Fischbach; lighting consultant, Richard Kelly; and electrical engineer, Clifton E. Smith. To the right, a rendering of the new Seagram Tower anchors the composition, standing luminous against a star-speckled night sky; its glass walls and bronze mullions are transformed into a gossamer skin that reveals the tower’s structural skeleton. Lightolier, the contract lighting manufacturer, produced the brochure to promote its role in the lighting of the Seagram Building, but Lightolier’s promotional copy was not far from the truth.

Decreasing luminance contrast on window walls as well as users’ interventions: Using LED lighting system

Mr Mehdi Amirkhani presented his research and case studies on improved lighting design in commercial spaces through daylight control and innovative lighting placement. This technical meeting was organised by IESANZ Queensland Chapter on April 7, 2016.

The language of lighting design

From 23 August until 2 October 2010, the successful Richard Kelly exhibition and symposium held at the Yale Architecture School in New Haven, US drew attention to the many contributions of this pioneering designer. Luminous opens the debate with five international lighting designers on the role and relevance of Kelly's vocabulary today.