Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Neural interface systems : a new legal frontier?

In an age where digital innovation knows no boundaries, research in the area of brain-computer interface and other neural interface devices go where none have gone before. The possibilities are endless and as dreams become reality, the implications of these amazing developments should be considered. Some of these new devices have been created to correct or minimise the effects of disease or injury so the paper discusses some of the current research and development in the area, including neuroprosthetics. To assist researchers and academics in identifying some of the legal and ethical issues that might arise as a result of research and development of neural interface devices, using both non-invasive techniques and invasive procedures, the paper discusses a number of recent observations of authors in the field. The issue of enhancing human attributes by incorporating these new devices is also considered. Such enhancement may be regarded as freeing the mind from the constraints of the body, but there are legal and moral issues that researchers and academics would be well advised to contemplate as these new devices are developed and used. While different fact situation surround each of these new devices, and those that are yet to come, consideration of the legal and ethical landscape may assist researchers and academics in dealing effectively with matters that arise in these times of transition. Lawyers could seek to facilitate the resolution of the legal disputes that arise in this area of research and development within the existing judicial and legislative frameworks. Whether these frameworks will suffice, or will need to change in order to enable effective resolution, is a broader question to be explored.

Human neural cell interactions with orientated electrospun nanofibers in vitro

Aim: Electrospun nanofibers represent potent guidance substrates for nervous tissue repair. Development of nanofiber-based scaffolds for CNS repair requires, as a first step, an understanding of appropriate neural cell type-substrate interactions. Materials & methods: Astrocyte–nanofiber interactions (e.g., adhesion, proliferation, process extension and migration) were studied by comparing human neural progenitor-derived astrocytes (hNP-ACs) and a human astrocytoma cell line (U373) with aligned polycaprolactone (PCL) nanofibers or blended (25% type I collagen/75% PCL) nanofibers. Neuron–nanofiber interactions were assessed using a differentiated human neuroblastoma cell line (SH-SY5Y). Results & discussion: U373 cells and hNP-AC showed similar process alignment and length when associated with PCL or Type I collagen/PCL nanofibers. Cell adhesion and migration by hNP-AC were clearly improved by functionalization of nanofiber surfaces with type I collagen. Functionalized nanofibers had no such effect on U373 cells. Another clear difference between the U373 cells and hNP-AC interactions with the nanofiber substrate was proliferation; the cell line demonstrating strong proliferation, whereas the hNP-AC line showed no proliferation on either type of nanofiber. Long axonal growth (up to 600 µm in length) of SH-SY5Y neurons followed the orientation of both types of nanofibers even though adhesion of the processes to the fibers was poor. Conclusion: The use of cell lines is of only limited predictive value when studying cell–substrate interactions but both morphology and alignment of human astrocytes were affected profoundly by nanofibers. Nanofiber surface functionalization with collagen significantly improved hNP-AC adhesion and migration. Alternative forms of functionalization may be required for optimal axon–nanofiber interactions.

Using strategic movement to calibrate a neural compass : a spiking network for tracking head direction in rats and robots

The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that ‘grounding’ of modeled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex.

Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks

Rule extraction from neural network algorithms have been investigated for two decades and there have been significant applications. Despite this level of success, rule extraction from neural network methods are generally not part of data mining tools, and a significant commercial breakthrough may still be some time away. This paper briefly reviews the state-of-the-art and points to some of the obstacles, namely a lack of evaluation techniques in experiments and larger benchmark data sets. A significant new development is the view that rule extraction from neural networks is an interactive process which actively involves the user. This leads to the application of assessment and evaluation techniques from information retrieval which may lead to a range of new methods.

Heterogeneity in auditory alarm sets makes them easier to learn

The primary objective of the experiments reported here was to demonstrate the effects of opening up the design envelope for auditory alarms on the ability of people to learn the meanings of a set of alarms. Two sets of alarms were tested, one already extant and one newly-designed set for the same set of functions, designed according to a rationale set out by the authors aimed at increasing the heterogeneity of the alarm set and incorporating some well-established principles of alarm design. For both sets of alarms, a similarity-rating experiment was followed by a learning experiment. The results showed that the newly-designed set was judged to be more internally dissimilar, and easier to learn, than the extant set. The design rationale outlined in the paper is useful for design purposes in a variety of practical domains and shows how alarm designers, even at a relatively late stage in the design process, can improve the efficacy of an alarm set.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

ACE research vignette 001 : benefits and drawbacks of start-up team heterogeneity

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. In this vignette, Dr. Micheal Stuetzer considers some of the benefits and drawbacks associated with team start-up.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

4000 years of phenotypic change in an island bird: heterogeneity of selection over three microevolutionary timescales.

Pronounced phenotypic shifts in island populations are typically attributed to natural selection, but reconstructing heterogeneity in long-term selective regimes remains a challenge. We examined a scenario of divergence proposed for species colonizing a new environment, involving directional selection with a rapid shift to a new optimum and subsequent stabilization. We provide some of the first empirical evidence for this model of evolution using morphological data from three timescales in an island bird, Zosterops lateralis chlorocephalus. In less than four millennia since separation from its mainland counterpart, a substantial increase in body size has occurred and was probably achieved in fewer than 500 generations after colonization. Over four recent decades, morphological traits have fluctuated in size but showed no significant directional trends, suggesting maintenance of a relatively stable phenotype. Finally, estimates of contemporary selection gradients indicated generally weak directional selection. These results provide a rare description of heterogeneity in long-term natural regimes, and caution that observations of current selection may be of limited value in inferring mechanisms of past adaptation due to a lack of constancy even over short time-frames.

Neural differentiation of mouse embryonic stem cells on conductive nanofiber scaffolds

Nerve tissue engineering requires suitable precursor cells as well as the necessary biochemical and physical cues to guide neurite extension and tissue development. An ideal scaffold for neural regeneration would be both fibrous and electrically conductive. We have contrasted the growth and neural differentiation of mouse embryonic stem cells on three different aligned nanofiber scaffolds composed of poly L: -lactic acid supplemented with either single- or multi-walled carbon-nanotubes. The addition of the nanotubes conferred conductivity to the nanofibers and promoted mESC neural differentiation as evidenced by an increased mature neuronal markers expression. We propose that the conductive scaffold could be a useful tool for the generation of neural tissue mimics in vitro and potentially as a scaffold for the repair of neural defects in vivo.

Revealing performance heterogeneity through knowledge management maturity evaluation : a capability-based approach

The dynamic capabilities view (DCV) focuses on renewal of firms’ strategic knowledge resources so as to sustain competitive advantage within turbulent markets. Within the context of the DCV, the focus of knowledge management (KM) is to develop the KMC through deploying knowledge governance mechanisms that are conducive to facilitating knowledge processes so as to produce superior business performance over time. The essence of KM performance evaluation is to assess how well the KMC is configured with knowledge governance mechanisms and processes that enable a firm to achieve superior performance through matching its knowledge base with market needs. However, little research has been undertaken to evaluate KM performance from the DCV perspective. This study employed a survey study design and adopted hypothesis-testing approaches to develop a capability-based KM evaluation framework (CKMEF) that upholds the basic assertions of the DCV. Under the governance of the framework, a KM index (KMI) and a KM maturity model (KMMM) were derived not only to indicate the extent to which a firm’s KM implementations fulfill its strategic objectives, and to identify the evolutionary phase of its KMC, but also to bench-mark the KMC in the research population. The research design ensured that the evaluation framework and instruments have statistical significance and good generalizabilty to be applied in the research population, namely construction firms operating in the dynamic Hong Kong construction market. The study demonstrated the feasibility of quantitatively evaluating the development of the KMC and revealing the performance heterogeneity associated with the development.

Dealing with heterogeneity problems and causal effect estimation in entrepreneurship research

This paper deals with causal effect estimation strategies in highly heterogeneous empirical settings such as entrepreneurship. We argue that the clearer used of modern tools developed to deal with the estimation of causal effects in combination with our analysis of different sources of heterogeneity in entrepreneurship can lead to entrepreneurship with higher internal validity. We specifically lend support from the counterfactual logic and modern research of estimation strategies for causal effect estimation.