3 research outputs found
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Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome
We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of a post-analysis of a genome-wide association study (GWAS); they include a novel accuracy scoring metric (average rank, or AVGRANK) and a novel cross-validation strategy (locus-based sampling) that both correctly account for the ``sparse positive bag'' nature of the GWAS post-analysis rSNP recognition problem. We trained and validated the CERENKOV series models using a set of reference SNPs whose composition is based on selection criteria (linkage disequilibrium and minor allele frequency) that we designed to ensure relevance to GWAS post-analysis. The CERENKOV models are! based on a machine-learning algorithm (gradient boosted decision trees) incorporating various SNP annotation features that are from genomic, epigenomic, phylogenetic, and chromatin data. CERENKOV2 includes features based on the geometry of the annotation features in data-space, and the CERENKOV3 models include features derived from SNP clustering, molecular network and convolutional output on genomic signals. We compared the validation performance of CERENKOV to nine other methods for rSNP recognition (including GWAVA, RSVP, DeltaSVM, DeepSEA, EIGEN, and DANQ), and found that CERENKOV's validation performance is the strongest out of all of the classifiers that we tested, by both traditional global rank-based measures (AUPRC, AUROC) and AVGRANK. From the performance comparison between CERENKOV and its successors, we found that rSNP recognition performance benefits from data-space geometry, SNP clustering and molecular network-derived features
A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification
The vibration of a wing structure in the air reflects coupled aerodynamic–mechanical responses under varying flight states that are defined by the angle of attack and airspeed. It is of great challenge to identify the flight state from the complex vibration signals. In this paper, a novel one-dimension convolutional neural network (CNN) is developed, which is able to automatically extract useful features from the structural vibration of a recently fabricated self-sensing wing through wind-tunnel experiments. The obtained signals are firstly decomposed into various subsignals with different frequency bands via dual-tree complex-wavelet packet transformation. Then, the reconstructed subsignals are selected to form the best combination for multichannel inputs of the CNN. A swarm-based evolutionary algorithm called grey-wolf optimizer is utilized to optimize a set of key parameters of the CNN, which saves considerable human efforts. Two case studies demonstrate the high identification accuracy and robustness of the proposed method over standard deep-learning methods in flight-state identification, thus providing new perspectives in self-awareness toward the next generation of intelligent air vehicles
Enhancing remanufacturing automation using deep learning approach
In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces.In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces