Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure’s damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach’s effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Visualisation of complex flows using texture-based techniques

Complex flow datasets are often difficult to represent in detail using traditional vector visualisation techniques such as arrow plots and streamlines. This is particularly true when the flow regime changes in time. Texture-based techniques, which are based on the advection of dense textures, are novel techniques for visualising such flows (i.e., complex dynamics and time-dependent). In this paper, we review two popular texture-based techniques and their application to flow datasets sourced from real research projects. The texture-based techniques investigated were Line Integral Convolution (LIC), and Image-Based Flow Visualisation (IBFV). We evaluated these techniques and in this paper report on their visualisation effectiveness (when compared with traditional techniques), their ease of implementation, and their computational overhead.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

OpenRatSLAM : an open source brain-based SLAM system

RatSLAM is a navigation system based on the neural processes underlying navigation in the rodent brain, capable of operating with low resolution monocular image data. Seminal experiments using RatSLAM include mapping an entire suburb with a web camera and a long term robot delivery trial. This paper describes OpenRatSLAM, an open-source version of RatSLAM with bindings to the Robot Operating System framework to leverage advantages such as robot and sensor abstraction, networking, data playback, and visualization. OpenRatSLAM comprises connected ROS nodes to represent RatSLAM’s pose cells, experience map, and local view cells, as well as a fourth node that provides visual odometry estimates. The nodes are described with reference to the RatSLAM model and salient details of the ROS implementation such as topics, messages, parameters, class diagrams, sequence diagrams, and parameter tuning strategies. The performance of the system is demonstrated on three publicly available open-source datasets.

Damage assessment in reinforced concrete flexural members using modal strain energy based method

Damage assessment (damage detection, localization and quantification) in structures and appropriate retrofitting will enable the safe and efficient function of the structures. In this context, many Vibration Based Damage Identification Techniques (VBDIT) have emerged with potential for accurate damage assessment. VBDITs have achieved significant research interest in recent years, mainly due to their non-destructive nature and ability to assess inaccessible and invisible damage locations. Damage Index (DI) methods are also vibration based, but they are not based on the structural model. DI methods are fast and inexpensive compared to the model-based methods and have the ability to automate the damage detection process. DI method analyses the change in vibration response of the structure between two states so that the damage can be identified. Extensive research has been carried out to apply the DI method to assess damage in steel structures. Comparatively, there has been very little research interest in the use of DI methods to assess damage in Reinforced Concrete (RC) structures due to the complexity of simulating the predominant damage type, the flexural crack. Flexural cracks in RC beams distribute non- linearly and propagate along all directions. Secondary cracks extend more rapidly along the longitudinal and transverse directions of a RC structure than propagation of existing cracks in the depth direction due to stress distribution caused by the tensile reinforcement. Simplified damage simulation techniques (such as reductions in the modulus or section depth or use of rotational spring elements) that have been extensively used with research on steel structures, cannot be applied to simulate flexural cracks in RC elements. This highlights a big gap in knowledge and as a consequence VBDITs have not been successfully applied to damage assessment in RC structures. This research will address the above gap in knowledge and will develop and apply a modal strain energy based DI method to assess damage in RC flexural members. Firstly, this research evaluated different damage simulation techniques and recommended an appropriate technique to simulate the post cracking behaviour of RC structures. The ABAQUS finite element package was used throughout the study with properly validated material models. The damaged plasticity model was recommended as the method which can correctly simulate the post cracking behaviour of RC structures and was used in the rest of this study. Four different forms of Modal Strain Energy based Damage Indices (MSEDIs) were proposed to improve the damage assessment capability by minimising the numbers and intensities of false alarms. The developed MSEDIs were then used to automate the damage detection process by incorporating programmable algorithms. The developed algorithms have the ability to identify common issues associated with the vibration properties such as mode shifting and phase change. To minimise the effect of noise on the DI calculation process, this research proposed a sequential order of curve fitting technique. Finally, a statistical based damage assessment scheme was proposed to enhance the reliability of the damage assessment results. The proposed techniques were applied to locate damage in RC beams and slabs on girder bridge model to demonstrate their accuracy and efficiency. The outcomes of this research will make a significant contribution to the technical knowledge of VBDIT and will enhance the accuracy of damage assessment in RC structures. The application of the research findings to RC flexural members will enable their safe and efficient performance.

Image-based flow visualisation (IBFV) to enhance interpretation of complex flow patterns within a shallow tidal barrier estuary

We applied a texture-based flow visualisation technique to a numerical hydrodynamic model of the Pumicestone Passage in southeast Queensland, Australia. The quality of the visualisations using our flow visualisation tool, are compared with animations generated using more traditional drogue release plot and velocity contour and vector techniques. The texture-based method is found to be far more effective in visualising advective flow within the model domain. In some instances, it also makes it easier for the researcher to identify specific hydrodynamic features within the complex flow regimes of this shallow tidal barrier estuary as compared with the direct and geometric based methods.

Histogram of weighted local directions for gait recognition

In this paper, we explore the effectiveness of patch-based gradient feature extraction methods when applied to appearance-based gait recognition. Extending existing popular feature extraction methods such as HOG and LDP, we propose a novel technique which we term the Histogram of Weighted Local Directions (HWLD). These 3 methods are applied to gait recognition using the GEI feature, with classification performed using SRC. Evaluations on the CASIA and OULP datasets show significant improvements using these patch-based methods over existing implementations, with the proposed method achieving the highest recognition rate for the respective datasets. In addition, the HWLD can easily be extended to 3D, which we demonstrate using the GEV feature on the DGD dataset, observing improvements in performance.

Online self-supervised multi-instance segmentation of dynamic objects

This paper presents a method for the continuous segmentation of dynamic objects using only a vehicle mounted monocular camera without any prior knowledge of the object’s appearance. Prior work in online static/dynamic segmentation is extended to identify multiple instances of dynamic objects by introducing an unsupervised motion clustering step. These clusters are then used to update a multi-class classifier within a self-supervised framework. In contrast to many tracking-by-detection based methods, our system is able to detect dynamic objects without any prior knowledge of their visual appearance shape or location. Furthermore, the classifier is used to propagate labels of the same object in previous frames, which facilitates the continuous tracking of individual objects based on motion. The proposed system is evaluated using recall and false alarm metrics in addition to a new multi-instance labelled dataset to evaluate the performance of segmenting multiple instances of objects.

An evaluation of crowd counting methods, features and regression models

Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models. A K-fold cross validation protocol is followed to evaluate the performance across five public datasets: UCSD, PETS 2009, Fudan, Mall and Grand Central datasets. Image features are categorised into five types: size, shape, edges, keypoints and textures. The regression models evaluated are: Gaussian process regression (GPR), linear regression, K nearest neighbours (KNN) and neural networks (NN). The results demonstrate that local features outperform equivalent holistic and histogram based features; optimal performance is observed using all image features except for textures; and that GPR outperforms linear, KNN and NN regression

Unsupervised online learning of condition-invariant images for place recognition

This paper presents an online, unsupervised training algorithm enabling vision-based place recognition across a wide range of changing environmental conditions such as those caused by weather, seasons, and day-night cycles. The technique applies principal component analysis to distinguish between aspects of a location’s appearance that are condition-dependent and those that are condition-invariant. Removing the dimensions associated with environmental conditions produces condition-invariant images that can be used by appearance-based place recognition methods. This approach has a unique benefit – it requires training images from only one type of environmental condition, unlike existing data-driven methods that require training images with labelled frame correspondences from two or more environmental conditions. The method is applied to two benchmark variable condition datasets. Performance is equivalent or superior to the current state of the art despite the lesser training requirements, and is demonstrated to generalise to previously unseen locations.

Copula-based spatial modelling of geometallurgical variables

The most important aspect of modelling a geological variable, such as metal grade, is the spatial correlation. Spatial correlation describes the relationship between realisations of a geological variable sampled at different locations. Any method for spatially modelling such a variable should be capable of accurately estimating the true spatial correlation. Conventional kriged models are the most commonly used in mining for estimating grade or other variables at unsampled locations, and these models use the variogram or covariance function to model the spatial correlations in the process of estimation. However, this usage assumes the relationships of the observations of the variable of interest at nearby locations are only influenced by the vector distance between the locations. This means that these models assume linear spatial correlation of grade. In reality, the relationship with an observation of grade at a nearby location may be influenced by both distance between the locations and the value of the observations (ie non-linear spatial correlation, such as may exist for variables of interest in geometallurgy). Hence this may lead to inaccurate estimation of the ore reserve if a kriged model is used for estimating grade of unsampled locations when nonlinear spatial correlation is present. Copula-based methods, which are widely used in financial and actuarial modelling to quantify the non-linear dependence structures, may offer a solution. This method was introduced by Bárdossy and Li (2008) to geostatistical modelling to quantify the non-linear spatial dependence structure in a groundwater quality measurement network. Their copula-based spatial modelling is applied in this research paper to estimate the grade of 3D blocks. Furthermore, real-world mining data is used to validate this model. These copula-based grade estimates are compared with the results of conventional ordinary and lognormal kriging to present the reliability of this method.

VIP Barcoding: Composition vector-based software for rapid species identification based on DNA barcoding

Species identification based on short sequences of DNA markers, that is, DNA barcoding, has emerged as an integral part of modern taxonomy. However, software for the analysis of large and multilocus barcoding data sets is scarce. The Basic Local Alignment Search Tool (BLAST) is currently the fastest tool capable of handling large databases (e.g. >5000 sequences), but its accuracy is a concern and has been criticized for its local optimization. However, current more accurate software requires sequence alignment or complex calculations, which are time-consuming when dealing with large data sets during data preprocessing or during the search stage. Therefore, it is imperative to develop a practical program for both accurate and scalable species identification for DNA barcoding. In this context, we present VIP Barcoding: a user-friendly software in graphical user interface for rapid DNA barcoding. It adopts a hybrid, two-stage algorithm. First, an alignment-free composition vector (CV) method is utilized to reduce searching space by screening a reference database. The alignment-based K2P distance nearest-neighbour method is then employed to analyse the smaller data set generated in the first stage. In comparison with other software, we demonstrate that VIP Barcoding has (i) higher accuracy than Blastn and several alignment-free methods and (ii) higher scalability than alignment-based distance methods and character-based methods. These results suggest that this platform is able to deal with both large-scale and multilocus barcoding data with accuracy and can contribute to DNA barcoding for modern taxonomy. VIP Barcoding is free and available at http://msl.sls.cuhk.edu.hk/vipbarcoding/.

Importance of amino-terminus in maintenance of oligomeric structure of cytosolic serine hydroxymethyltransferase

The role of the amino and carboxyl-terminal regions of cytosolic serine hydroxymethyltransferase (SHMT) in subunit assembly and catalysis was studied using six amino-terminal (lacking the first 6, 14, 30, 49, 58, and 75 residues) and two carboxyl-terminal (lacking the last 49 and 185 residues) deletion mutants. These mutants were constructed from a full length cDNA clone using restriction enzyme/PCR-based methods and overexpressed in Escherichia coli. The overexpressed proteins, des-(A1-K6)-SHMT and des-(A1- W14)-SHMT were present in the soluble fraction and they were purified to homogeneity. The deletion clones, for des-(A1–V30)-SHMT and des-(A1–L49)-SHMT were expressed at very low levels, whereas des-(A1–R58)-SHMT, des-(A1–G75)-SHMT, des-(Q435–F483)-SHMT and des-(L299-F483)-SHMT mutant proteins were not soluble and formed inclusion bodies. Des-(A1–K6)-SHMT and des-(A1–W14)-SHMT catalyzed both the tetrahydrofolate-dependent and tetrahydrofolate-independent reactions, generating characteristic spectral intermediates with glycine and tetrahydrofolate. The two mutants had similar kinetic parameters to that of the recombinant SHMT (rSHMT). However, at 55 °C, the des-(A1–W14)-SHMT lost almost all the activity within 5 min, while at the same temperature rSHMT and des-(A1–K6)-SHMT retained 85% and 70% activity, respectively. Thermal denaturation studies showed that des-(A1–W14)-SHMT had a lower apparent melting temperature (52°C) compared to rSHMT (56°C) and des-(A1–K6)-SHMT (55 °C), suggesting that N-terminal deletion had resulted in a decrease in the thermal stability of the enzyme. Further, urea induced inactivation of the enzymes revealed that 50% inactivation occurred at a lower urea concentration (1.2 ± 0.1 M) in the case of des-(A1–W14)-SHMT compared to rSHMT (1.8 ±0.1 M) and des-(A1–K6)-SHMT (1.7 ±0.1 M). The apoenzyme of des-(A1- W14)-SHMT was present predominantly in the dimer form, whereas the apoenzymes of rSHMT and des-(A1–K6)-SHMT were a mixture of tetramers (≈75% and ≈65%, respectively) and dimers. While, rSHMT and des-(A1–K6)-SHMT apoenzymes could be reconstituted upon the addition of pyridoxal-5'-phosphate to 96% and 94% enzyme activity, respectively, des-(A1–W14)-SHMT apoenzyme could be reconstituted only upto 22%. The percentage activity regained correlated with the appearance of visible CD at 425 nm and with the amount of enzyme present in the tetrameric form upon reconstitution as monitored by gel filtration. These results demonstrate that, in addition to the cofactor, the N-terminal arm plays an important role in stabilizing the tetrameric structure of SHMT.

Importance of the amino terminus in maintenance of oligomeric structure of sheep liver cytosolic serine hydroxymethyltransferase

The Role Of The Amino And Carboxyl-Terminal Regions Of Cytosolic Serine Hydroxymethyltransferase (SHMT) In Subunit Assembly And Catalysis Was Studied Using Sis Amino-Terminal (Lacking The First 6, 14, 30, 49, 58, And 75 Residues) And Two Carboxyl-Terminal (Lacking The Last 49 And 185 Residues) Deletion Mutants. These Mutants Were Constructed From A Full Length Cdna Clone Using Restriction Enzyme/PCR-Based Methods And Overexpressed In Escherichia Coli. The Overexpressed Proteins, Des-(A1-K6) SHMT And Des-(A1-W14)-SHMT Were Present In The Soluble Fraction And They Were Purified To Homogeneity. The Deletion Clones, For Des-(A1-V30)-SHMT And Des-(A1-L49)-SHMT Were Expressed At Very Low Levels, Whereas Des-(A1-R58)-SHMT, Des-/A1-G75)-SHMT, Des-(Q435-F483)-SHMT And Des-(L299-F483)-SHMT Mutant Proteins Were Not Soluble And Formed Inclusion Bodies. Des-(A1-K6)-SHMT And Des-(A1-W14)-SHMT Catalyzed Both The Tetrahydrofolate-Dependent And Tetrahydrofolate-Independent Reactions, Generating Characteristic Spectral Intermediates With Glycine And Tetrahydrofolate. The Two Mutants Had Similar Kinetic Parameters To That Of The Recombinant SHMT (Rshmt). However, At 55 Degrees C, The Des-(A1-W14)-SHMT Lost Almost All The Activity Within 5 Min, While At The Same Temperature Rshmt And Des-(A1-K6)-SHMT Retained 85% And 70% Activity, Respectively. Thermal Denaturation Studies Showed That Des-(A1-W14)-SHMT Had A Lower Apparent Melting Temperature (52 Degrees C) Compared To Rshmt (56 Degrees C) And Des-(A1-K6)-SHMT (55 Degrees C), Suggesting That N-Terminal Deletion Had Resulted In A Decrease In The Thermal Stability Of The Enzyme. Further Urea Induced Inactivation Of The Enzymes Revealed That 50% Inactivation Occurred At A Lower Urea Concentration (1.2+/-0.1 M) In The Case Of Des-(A1-W14)-SHMT Compared To Rshmt (1.8+/-0.1 M) And Des-(A1 -K6)-SHMT (1.7+/-0.1 M). The Apoenzyme Of Des-/A1-K6)-SHMT Was Present Predominantly In The Dimer Form, Whereas The Apoenzymes Of Rshmt And Des-(A1-K6)-SHMT Were A Mixture Of Tetramers (Approximate To 75% And Approximate To 65%, Respectively) And Dimers. While, Rshmt And Des-(A1-K6)-SHMT Apoenzymes Could Be Reconstituted Upon The Addition Of Pyridoxal-5'-Phosphate To 96% And 94% Enzyme Activity, Respectively Des-(A1-W14)-SHMT Apoenzyme Could Be Reconstituted Only Upto 22%. The Percentage Activity Refined Correlated With The Appearance Of Visible CD At 425 Nm And With The Amount Of Enzyme Present In The Tetrameric Form Upon Reconstitution As Monitored By Gel Filtration. These Results Demonstrate That, In Addition To The Cofactor, The N-Terminal Arm Plays An Important Role In Stabilizing The Tetrameric Structure Of SHMT.

Comparison Of If Estimation Methods For Noise Robustness

Non-stationary signal modeling is a well addressed problem in the literature. Many methods have been proposed to model non-stationary signals such as time varying linear prediction and AM-FM modeling, the later being more popular. Estimation techniques to determine the AM-FM components of narrow-band signal, such as Hilbert transform, DESA1, DESA2, auditory processing approach, ZC approach, etc., are prevalent but their robustness to noise is not clearly addressed in the literature. This is critical for most practical applications, such as in communications. We explore the robustness of different AM-FM estimators in the presence of white Gaussian noise. Also, we have proposed three new methods for IF estimation based on non-uniform samples of the signal and multi-resolution analysis. Experimental results show that ZC based methods give better results than the popular methods such as DESA in clean condition as well as noisy condition.