Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information. We found, however, that there is still great room for improvement in how to preserve historical information in neural networks while avoiding overfitting to noise presented in the history. Addressing this allows better utilization of the capabilities of deep learning models. To this end, we design a \textbf{F}requency \textbf{i}mproved \textbf{L}egendre \textbf{M}emory model, or {\bf FiLM}: it applies Legendre Polynomials projections to approximate historical information, uses Fourier projection to remove noise, and adds a low-rank approximation to speed up computation. Our empirical studies show that the proposed FiLM significantly improves the accuracy of state-of-the-art models in multivariate and univariate long-term forecasting by (\textbf{20.3\%}, \textbf{22.6\%}), respectively. We also demonstrate that the representation module developed in this work can be used as a general plug-in to improve the long-term prediction performance of other deep learning modules. Code is available at https://github.com/tianzhou2011/FiLM/Comment: Accepted by The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022

Microfluidics and Neural Interfaces Development for the Safe Direct Current Stimulator

Safety of commercial neural implants fundamentally limits its working to the use of charge-balanced, biphasic pulses to interact with target neurons using metal electrodes. Short biphasic pulses are used to avoid toxic electrochemical reactions at the electrode-tissue interfaces. Biphasic pulses are effective at exciting neurons, but quite limited in inhibiting their activity. In contrast, direct current can both excite and inhibit neurons, however it leads to the formation of harmful, Faradaic reactions at the metal electrode/tissue interface. To address this challenge of safety over chronic use, we are developing the Safe Direct Current Stimulator (SDCS) technology, that generates an ionic direct current (iDC) from a biphasic input signal using a network of microfluidic channels and mechanical valves. This rectified iDC is applied to the target neural tissue through an ionically conductive neural interface. A key enabler towards transforming the SDCS concept from a benchtop design to an implantable neural prosthesis is the design of a miniature valve. Several valve architectures and actuation mechanism were studied for the development of the microfluidics in SDCS technology, before settling on the plunger-membrane microvalve design. This thesis characterizes a miniature polydimethylsiloxane (PDMS) based elastomeric normally closed (NC) mechanical valve actuated using a shape-memory alloy (SMA) wire through distinct tests and examines its current capability for iDC delivery. The analysis of the test outputs confirmed the feasibility of using this design for rectifying the charge-balanced alternating current (AC) into iDC. As metal electrodes are unsuitable for delivering iDC to the neural tissue safely, an ionic conductive neural lead is built. These gel-based, PDMS electrodes should be designed within the acceptable pressure limits that a nerve can handle safely. Preliminary experiments were conducted to verify the design and conductivity of the lead. While the results suggest that the lead design maintains the pressure below the maximum limit, its high impedance raises concerns. Although this thesis forms a basis for development of the SDCS device, further experimentation and progress is required for a reliable, safe, chronic, and fully functional device

Depth Estimation Using 2D RGB Images

Single image depth estimation is an ill-posed problem. That is, it is not mathematically possible to uniquely estimate the 3rd dimension (or depth) from a single 2D image. Hence, additional constraints need to be incorporated in order to regulate the solution space. As a result, in the first part of this dissertation, the idea of constraining the model for more accurate depth estimation by taking advantage of the similarity between the RGB image and the corresponding depth map at the geometric edges of the 3D scene is explored. Although deep learning based methods are very successful in computer vision and handle noise very well, they suffer from poor generalization when the test and train distributions are not close. While, the geometric methods do not have the generalization problem since they benefit from temporal information in an unsupervised manner. They are sensitive to noise, though. At the same time, explicitly modeling of a dynamic scenes as well as flexible objects in traditional computer vision methods is a big challenge. Considering the advantages and disadvantages of each approach, a hybrid method, which benefits from both, is proposed here by extending traditional geometric models’ abilities to handle flexible and dynamic objects in the scene. This is made possible by relaxing geometric computer vision rules from one motion model for some areas of the scene into one for every pixel in the scene. This enables the model to detect even small, flexible, floating debris in a dynamic scene. However, it makes the optimization under-constrained. To change the optimization from under-constrained to over-constrained while maintaining the model’s flexibility, ”moving object detection loss” and ”synchrony loss” are designed. The algorithm is trained in an unsupervised fashion. The primary results are in no way comparable to the current state of the art. Because the training process is so slow, it is difficult to compare it to the current state of the art. Also, the algorithm lacks stability. In addition, the optical flow model is extremely noisy and naive. At the end, some solutions are suggested to address these issues

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed

A Methodological Approach to Knowledge-Based Engineering Systems for Manufacturing

A survey of implementations of the knowledge-based engineering approach in different technological sectors is presented. The main objectives and techniques of examined applications are pointed out to illustrate the trends and peculiarities for a number of manufacturing field. Existing methods for the development of these engineering systems are then examined in order to identify critical aspects when applied to manufacturing. A new methodological approach is proposed to overcome some specific limitations that emerged from the above-mentioned survey. The aim is to provide an innovative method for the implementation of knowledge-based engineering applications in the field of industrial production. As a starting point, the field of application of the system is defined using a spatial representation. The conceptual design phase is carried out with the aid of a matrix structure containing the most relevant elements of the system and their relations. In particular, objectives, descriptors, inputs and actions are defined and qualified using categorical attributes. The proposed method is then applied to three case studies with different locations in the applicability space. All the relevant elements of the detailed implementation of these systems are described. The relations with assumptions made during the design are highlighted to validate the effectiveness of the proposed method. The adoption of case studies with notably different applications also reveals the versatility in the application of the method

The impact of news narrative on the economy and financial markets

This thesis investigates the impact of news narrative on socio-economic systems across four experiments. Recent years have witnessed a rise in the use of so-called alternative data sources to model and predict dynamics in socio-economic systems. Notably, sources such as newspaper text allow researchers to quantify the elusive concept of narrative, to incorporate text-based features into forecasting frameworks and thus to evaluate the impact of narrative on economic events. The first experiment proposes a new method of incorporating a wide array of sentiment scores from global newspaper articles into macroeconomic forecasts, attempting to forecast industrial production and consumer prices leveraging narrative and sentiment from global newspapers. I model industrial production and consumer prices across a diverse range of economies using an autoregressive framework. The second experiment uses narrative from global newspapers to construct themebased knowledge graphs about world events, demonstrating that features extracted from such graphs improve forecasts of industrial production in three large economies. The third experiment proposes a novel method of including news themes and their associated sentiment into predictions of changes in breakeven inflation rates (BEIR) for eight diverse economies with mature fixed income markets. I utilise five types of machine learning algorithms incorporating narrative-based features for each economy. In the above experiments, models incorporating narrative-based features generally outperform their benchmarks that do not contain such variables, demonstrating the predictive power of features derived from news narrative. The fourth experiment utilises GDELT data and the filtering methodology introduced in the first experiment to create a profitable systematic trading strategy based on the average tone scores for 15 diverse economies