DEVELOPMENT OF AN OFF-LINE RAINFLOW COUNTING ALGORITHM WITH TEMPORAL PARAMETERS AND DATA REDUCTION

Rainflow counting methods convert a complex load time history into a set of load reversals for use in fatigue damage modeling. Rainflow counting methods were originally developed to assess fatigue damage associated with mechanical cycling where creep of the material under load was not considered to be a significant contributor to failure. However, creep is a significant factor in some cyclic loading cases such as solder interconnects under temperature cycling. In this case, fatigue life models require the dwell time to account for stress relaxation and creep. This study develops a new version of the multi-parameter rainflow counting algorithm that provides a range-based dwell time estimation for use with time-dependent fatigue damage models. To show the applicability, the method is used to calculate the life of solder joints under a complex thermal cycling regime and is verified by experimental testing. An additional algorithm is developed in this study to provide data reduction in the results of the rainflow counting. This algorithm uses a damage model and a statistical test to determine which of the resultant cycles are statistically insignificant to a given confidence level. This makes the resulting data file to be smaller, and for a simplified load history to be reconstructed.

Measurement of in-service insulated rail joint tie plate response

This paper presents the results of a recent investigation into Insulated Rail Joint Tie Plate fatigue failures. In particular it focuses on the results of data obtained through field strain gauge and accelerometer measurements of in-service Insulated Rail Joint Tie Plates. These measurements have identified a significant variability in the strains present in similar joints operating under identical load conditions. This variability in stress invariably has a significant influence on the life of the joints. The results of rainflow counting and a fatigue analysis are also presented.

Análise dinâmica e verificação à fadiga de obras de arte rodoviárias mistas (aço-concreto) submetidas ao tráfego de veículos sobre o pavimento irregular.

As pontes rodoviárias metálicas e mistas (aço-concreto) são submetidas a um grande número de carregamentos repetitivos de diferentes magnitudes, ao longo do tempo. Estas ações dinâmicas podem causar a nucleação de fraturas ou mesmo a propagação destas sobre o sistema estrutural. A depender da magnitude, estes efeitos podem comprometer o sistema estrutural e a sua confiabilidade, além de reduzir a vida útil das pontes. Assim sendo, neste trabalho de pesquisa foi investigada a resposta dinâmica de uma ponte mista (aço-concreto), simplesmente apoiada, com vão de 40,0 m, submetida ao tráfego de veículos sobre a superfície irregular do pavimento. Para tal um modelo numérico representativo do sistema estrutural foi desenvolvido com base no emprego do programa ANSYS, por meio do uso de técnicas usuais de discretização, via método dos elementos finitos. Um estudo paramétrico foi desenvolvido para identificar, de forma qualitativa e quantitativa, o efeito das irregularidades do pavimento sobre o comportamento dinâmico da ponte mista investigada. Em seguida, a verificação do projeto à fadiga do sistema misto foi realizada, com base no emprego do algoritmo de contagem de ciclos Rainflow e em curvas S-N associadas às principais normas de projeto sobre o tema. As conclusões deste trabalho de pesquisa alertam aos engenheiros estruturais para a possibilidade concreta acerca do aumento do dano por fadiga, relacionado às ações dinâmicas de veículos trafegando sobre o tabuleiro de pontes em aço e mistas (aço-concreto).

Analysis of aircraft load spectrum by means of flight simulator

Damage tolerance analysis is a quite new methodology based on prescribed inspections. The load spectra used to derive results of these analysis strongly influence the final defined inspections programs that for this reason must be as much as possible representative of load acting on the considered structural component and at the same time, obtained reducing both cost and time. The principal purpose of our work is in improving the actual condition developing a complete numerical Damage Tolerance analysis, able to prescribe inspection programs on typical aircraft critical components, respecting DT regulations, starting from much more specific load spectrum then those actually used today. In particular, these more specific load spectrum to design against fatigue have been obtained through an appositively derived flight simulator developed in a Matlab/Simulink environment. This dynamic model has been designed so that it can be used to simulate typical missions performing manually (joystick inputs) or completely automatic (reference trajectory need to be provided) flights. Once these flights have been simulated, model’s outputs are used to generate load spectrum that are then processed to get information (peaks, valleys) to perform statistical and/or comparison consideration with other load spectrum. However, also much more useful information (loads amplitude) have been extracted from these generated load spectrum to perform the previously mentioned predictions (Rainflow counting method is applied for this purpose). The entire developed methodology works in a complete automatic way, so that, once some specified input parameters have been introduced and different typical flights have been simulated both, manually or automatically, it is able to relate the effects of these simulated flights with the reduction of residual strength of the considered component.

Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.

Scene invariant crowd counting for real-time surveillance

Automated crowd counting allows excessive crowding to be detected immediately, without the need for constant human surveillance. Current crowd counting systems are location specific, and for these systems to function properly they must be trained on a large amount of data specific to the target location. As such, configuring multiple systems to use is a tedious and time consuming exercise. We propose a scene invariant crowd counting system which can easily be deployed at a different location to where it was trained. This is achieved using a global scaling factor to relate crowd sizes from one scene to another. We demonstrate that a crowd counting system trained at one viewpoint can achieve a correct classification rate of 90% at a different viewpoint.

Mapping equity and diversity : from counting to culture change

This paper investigates the links between various approaches to managing equity and diversity and their effectiveness in changing the measures of inclusivity of women in organisations as a means of auditing and mapping managing diversity outcomes in Australia. The authors argue that managing diversity is more than changing systems and counting numbers it is also about managing the substantive culture change required in order to achieve inclusivity particularly intercultural inclusivity. Research in one sector of the education industry that investigated the competency skills required for culture change is offered as a model or guide for understanding and reflecting upon intercultural competency and its sequential development.

Crowd counting using group tracking and local features

In public venues, crowd size is a key indicator of crowd safety and stability. In this paper we propose a crowd counting algorithm that uses tracking and local features to count the number of people in each group as represented by a foreground blob segment, so that the total crowd estimate is the sum of the group sizes. Tracking is employed to improve the robustness of the estimate, by analysing the history of each group, including splitting and merging events. A simplified ground truth annotation strategy results in an approach with minimal setup requirements that is highly accurate.

Hospital beds : A primer for counting and comparing

There is considerable public, political and professional debate about the need for additional hospital beds in Australia. However, there is no clarity in regard to the definition, meaning and significance of hospital bed counts. Relative to population, there has been a total decline in bed availability in Australia over the past 15 years of 14.6% (22.9% for public hospital beds). This decline is partly offset by reductions in length of stay and changes to models of care; however, the net effect is increased bed occupancy which has in turn resulted in system-wide congestion. Future bed capability needs to be better planned to meet growing demands while at the same time continuing trends for more efficient use. Future planning should be based in part on weighted bed capability matched to need.

A fiber Bragg grating sensor system for train axle counting

Railway signaling facilitates two main functions, namely, train detection and train control, in order to maintain safe separations among the trains. Track circuits are the most commonly used train detection means with the simple open/close circuit principles; and subsequent adoption of axle counters further allows the detection of trains under adverse track conditions. However, with electrification and power electronics traction drive systems, aggravated by the electromagnetic interference in the vicinity of the signaling system, railway engineers often find unstable or even faulty operations of track circuits and axle counting systems, which inevitably jeopardizes the safe operation of trains. A new means of train detection, which is completely free from electromagnetic interference, is therefore required for the modern railway signaling system. This paper presents a novel optical fiber sensor signaling system. The sensor operation, field setup, axle detection solution set, and test results of an installation in a trial system on a busy suburban railway line are given.

Scene invariant crowd counting

This paper describes a scene invariant crowd counting algorithm that uses local features to monitor crowd size. Unlike previous algorithms that require each camera to be trained separately, the proposed method uses camera calibration to scale between viewpoints, allowing a system to be trained and tested on different scenes. A pre-trained system could therefore be used as a turn-key solution for crowd counting across a wide range of environments. The use of local features allows the proposed algorithm to calculate local occupancy statistics, and Gaussian process regression is used to scale to conditions which are unseen in the training data, also providing confidence intervals for the crowd size estimate. A new crowd counting database is introduced to the computer vision community to enable a wider evaluation over multiple scenes, and the proposed algorithm is tested on seven datasets to demonstrate scene invariance and high accuracy. To the authors' knowledge this is the first system of its kind due to its ability to scale between different scenes and viewpoints.

A visual description of early-number counting, addition and subtraction knowledge

Early-number is a rich fabric of interconnected ideas that is often misunderstood and thus taught in ways that do not lead to rich understanding. In this presentation, a visual language is used to describe the organisation of this domain of knowledge. This visual language is based upon Piaget’s notion of reflective abstraction (Dubinsky, 1991; Piaget, 1977/2001), and thus captures the epistemological associations that link the problems, concepts and representations of the domain. The constructs of this visual language are introduced and then applied to the early-number domain. The introduction to this visual language may prompt reflection upon its suitability and significance to the description of other domains of knowledge. Through such a process of analysis and description, the visual language may serve as a scaffold for enhancing pedagogical content knowledge and thus ultimately improve learning outcomes.

Scene invariant crowd counting and crowd occupancy analysis

In public places, crowd size may be an indicator of congestion, delay, instability, or of abnormal events, such as a fight, riot or emergency. Crowd related information can also provide important business intelligence such as the distribution of people throughout spaces, throughput rates, and local densities. A major drawback of many crowd counting approaches is their reliance on large numbers of holistic features, training data requirements of hundreds or thousands of frames per camera, and that each camera must be trained separately. This makes deployment in large multi-camera environments such as shopping centres very costly and difficult. In this chapter, we present a novel scene-invariant crowd counting algorithm that uses local features to monitor crowd size. The use of local features allows the proposed algorithm to calculate local occupancy statistics, scale to conditions which are unseen in the training data, and be trained on significantly less data. Scene invariance is achieved through the use of camera calibration, allowing the system to be trained on one or more viewpoints and then deployed on any number of new cameras for testing without further training. A pre-trained system could then be used as a ‘turn-key’ solution for crowd counting across a wide range of environments, eliminating many of the costly barriers to deployment which currently exist.