Artificial neural network and its applications in quality process control, document recognition and biomedical imaging

In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques and higher computational costs. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels. Therefore, thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In this thesis, we have studied the effectiveness of using artificial neural network (ANN) in pharmaceutical, document recognition and biomedical imaging applications for image thresholding and classification purposes. Finally, we have developed edge-based, ANN-based and region-growing based image thresholding techniques to extract low contrast objects of interest and classify them into respective classes in those applications. Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry\u27s productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this thesis, we have developed an image processing system using edge-based image thresholding techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications to pass the accepted and rejected capsules. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this thesis, optimal parameters for ANN-based local thresholding approach for gray scale composite document image with non-uniform background is proposed. An exhaustive search was conducted to select the optimal features and found that pixel value, mean and entropy are the most significant features at window size 3x3 in this application. For other applications, it might be different, but the procedure to find the optimal parameters is same. The average recognition rate 99.25% shows that the proposed 3 features at window size 3x3 are optimal in terms of recognition rate and PSNR compare to the ANN-based thresholding technique with different parameters presented in the literature. In biomedical imaging application, breast cancer continues to be a public health problem. In this thesis we presented a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. Three layers ANN with seven features is proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist\u27s sensitivity 75%

Incorporating automated rail fatigue damage detection algorithms with crack growth modelling

This thesis examines the feasibility of incorporating Non Destructive Testing (NDT) of rail surface damage by means of combining image processing with damage prediction models. As rail traffic and adherence to safety measures become increasingly strict on the network, the associated maintenance cost of rail infrastructure must be kept at a minimum. Proactive maintenance is crucial to maintaining the competitive advantage of rail transport. A considerable amount of research has been done on improving the practical tediousness associated with popular condition monitoring techniques in rail industry e.g. Ultrasonic, and Eddy current method. This thesis aims to fill the gap of yet to be explored benefit, of combining detection and prediction of RCF damage. This research project will contribute to the rail industry by simplifying maintenance operations and support decision making. In this thesis, a summary of existing image-based NDT and crack growth models is presented as a foundation on which the novel application is built.It could be said that similar research mainly focuses on quantifying severity of damage without predicting crack behaviour. The simulated results of the proposed image processing algorithm confirm superiority of local illumination invariant enhancement, multi-window segmentation, and cascaded feature extraction. The influential parameters of these methods are consistent within each image data set but differ across all sets. This is observed to be as a result of difference in environmental and reflection properties of acquired images.A sensitivity analysis of the proposed algorithm on data set 2 suggests a non-linear relationship between severity of damage and pixel mean intensity including variance. Taking to account fracture mechanics aspect of this thesis, the influence of crack geometry on growth rate and path has been established by case study of newly initiated and critically grown cracks. It was further established that larger cracks are observed to grow faster than smaller ones. In addition, the influence of track curve radius and supporting structures on wheel rail contact dynamics is well understood from the structural mechanic’s tests related to contact forces and bending moment. These translate to increase or decrease in contact stresses, strains, and the propagation rate of defects. Unlike other predictive models, the method developed in this thesis focuses on replicating the actual surface condition of the rail prior to estimating the fracture parameters (using detailed 3D Finite Element model) that dictate residual life of the rail asset. The model makes it possible to combine two separate maintenance activities i.e. detection and prediction without inducing down time of the service. A direct impact of this novel application is the utilisation of the actual crack boundary for prediction of fracture behaviour. It is insinuated that stress distribution of actual crack boundary differs from elliptical equivalent assumptions. Further work would include improving detection aspect of the novel application to avoid intersecting boundary coordinates, which are not readily imported into the Linear Elastic Fracture Mechanics (LEFM) prediction model. It is also beneficial to expand the prediction aspect of the research work to include influence of neighbouring cracks and fluid entrapment for more flexible analysis of other environmental and contact conditions. To improve on current work, it will be useful to conduct laboratory investigations on the influence of Image Acquisition System (IAS) light source in relation to illumination inequality within the captured image. Also fracture mechanics experimental validation can be used to assert the accuracy of the metho

Automatic Detection of Road Cracks using EfficientNet with Residual U-Net-based Segmentation and YOLOv5-based Detection

The main factor affecting road performance is pavement damage. One of the difficulties in maintaining roads is pavement cracking. Credible and reliable inspection of heritage structural health relies heavily on crack detection on road surfaces. To achieve intelligent operation and maintenance, intelligent crack detection is essential to traffic safety. The detection of road pavement cracks using computer vision has gained popularity in recent years. Recent technological breakthroughs in general deep learning algorithms have resulted in improved results in the discipline of crack detection. In this paper, two techniques for object identification and segmentation are proposed. The EfficientNet with residual U-Net technique is suggested for segmentation, while the YOLO v5 algorithm is offered for crack detection. To correctly separate the pavement cracks, a crack segmentation network is used. Road crack identification and segmentation accuracy were enhanced by optimising the model's hyperparameters and increasing the feature extraction structure. The suggested algorithm's performance is compared to state-of-the-art algorithms. The suggested work achieves 99.35% accuracy

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

From cellular vulnerability to altered circuit activity:a systems biology approach to study amyotrophic lateral sclerosis

The devastating effects of the brain losing its ability to control voluntary body movement are illustrated by diseases such as amyotrophic lateral sclerosis (ALS) - where the nerve cells that allow the brain to effectively communicate with muscles are progressively lost. Most of the ALS research traditionally revolves around the affected nerve cells, known as motoneurons, and aims to rescue their decline in function. Motoneurons are however part of larger networks in the nervous system and constantly receive, process and transmit signals. Therefore, even the smallest alteration of a single motoneuron will likely leave a mark on its connecting neurons and vice versa. Could it be that solely targeting the function of diseased neurons has unexpected effects in an already (mal)adapted network? To mimic ALS, we used the worm Caenorhabditis elegans engineered to express the human gene TDP-43. Dysregulated TDP-43 is considered a uniform hallmark of ALS and its expression in C. elegans causes severe paralysis. By developing and combining numerous technology-driven, mostly unbiased screening approaches we show that TDP-43 impedes neuronal function and causes an imbalance between stimulatory and inhibitory signals in the motor circuit. While functional output of repressed motoneurons could be restored via modulation of their activity, these interventions did not result in improved locomotion. Rebalancing the derailed motor circuit dynamics by combining multiple treatments, however, effectively restored movement. Because of the high degree of similarity in genetic alterations and pathology between ALS worms and patients, similar therapeutic strategies may eventually be valuable for ALS patients

Assessing motor-related phenotypes of Caenorhabditis elegans with the wide field-of-view nematode tracking platform

Caenorhabditis elegans is a valuable model organism in biomedical research that has led to major discoveries in the fields of neurodegeneration, cancer and aging. Because movement phenotypes are commonly used and represent strong indicators of C. elegans fitness, there is an increasing need to replace manual assessments of worm motility with automated measurements to increase throughput and minimize observer biases. Here, we provide a protocol for the implementation of the improved wide field-of-view nematode tracking platform (WF-NTP), which enables the simultaneous analysis of hundreds of worms with respect to multiple behavioral parameters. The protocol takes only a few hours to complete, excluding the time spent culturing C. elegans, and includes (i) experimental design and preparation of samples, (ii) data recording, (iii) software management with appropriate parameter choices and (iv) post-experimental data analysis. We compare the WF-NTP with other existing worm trackers, including those having high spatial resolution. The main benefits of WF-NTP relate to the high number of worms that can be assessed at the same time on a whole-plate basis and the number of phenotypes that can be screened for simultaneously