Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders

Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routine operations. The regression models used by these sensors often require a large number of labeled examples, yet obtaining the label information can be very expensive given the high time and cost required by quality inspections. In this context, active learning methods can be highly beneficial as they can suggest the most informative labels to query. However, most of the active learning strategies proposed for regression focus on the offline setting. In this work, we adapt some of these approaches to the stream-based scenario and show how they can be used to select the most informative data points. We also demonstrate how to use a semi-supervised architecture based on orthogonal autoencoders to learn salient features in a lower dimensional space. The Tennessee Eastman Process is used to compare the predictive performance of the proposed approaches.Comment: ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in the Real Worl

A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation

Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy

Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments-The Wastewater Treatment Plant Control Case

Altres ajuts: Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya i del Fons Social Europeu (2020 FI_B2 000)The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements-when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively

Clustering algorithm for D2D communication in next generation cellular networks : thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand

Next generation cellular networks will support many complex services for smartphones, vehicles, and other devices. To accommodate such services, cellular networks need to go beyond the capabilities of their previous generations. Device-to-Device communication (D2D) is a key technology that can help fulfil some of the requirements of future networks. The telecommunication industry expects a significant increase in the density of mobile devices which puts more pressure on centralized schemes and poses risk in terms of outages, poor spectral efficiencies, and low data rates. Recent studies have shown that a large part of the cellular traffic pertains to sharing popular contents. This highlights the need for decentralized and distributive approaches to managing multimedia traffic. Content-sharing via D2D clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. Different studies have established that D2D communication in clusters can improve spectral and energy efficiency, achieve low latency while increasing the capacity of the network. To achieve effective content-sharing among users, appropriate clustering strategies are required. Therefore, the aim is to design and compare clustering approaches for D2D communication targeting content-sharing applications. Currently, most of researched and implemented clustering schemes are centralized or predominantly dependent on Evolved Node B (eNB). This thesis proposes a distributed architecture that supports clustering approaches to incorporate multimedia traffic. A content-sharing network is presented where some D2D User Equipment (DUE) function as content distributors for nearby devices. Two promising techniques are utilized, namely, Content-Centric Networking and Network Virtualization, to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multi-factor clustering algorithm is proposed for grouping the DUEs sharing a common interest. Various performance parameters such as energy consumption, area spectral efficiency, and throughput have been considered for evaluating the proposed algorithm. The effect of number of clusters on the performance parameters is also discussed. The proposed algorithm has been further modified to allow for a trade-off between fairness and other performance parameters. A comprehensive simulation study is presented that demonstrates that the proposed clustering algorithm is more flexible and outperforms several well-known and state-of-the-art algorithms. The clustering process is subsequently evaluated from an individual user’s perspective for further performance improvement. We believe that some users, sharing common interests, are better off with the eNB rather than being in the clusters. We utilize machine learning algorithms namely, Deep Neural Network, Random Forest, and Support Vector Machine, to identify the users that are better served by the eNB and form clusters for the rest of the users. This proposed user segregation scheme can be used in conjunction with most clustering algorithms including the proposed multi-factor scheme. A comprehensive simulation study demonstrates that with such novel user segregation, the performance of individual users, as well as the whole network, can be significantly improved for throughput, energy consumption, and fairness

Building transformative framework for isolation and mitigation of quality defects in multi-station assembly systems using deep learning

The manufacturing industry is undergoing significant transformation towards electrification (e-mobility). This transformation has intensified critical development of new lightweight materials, structures and assembly processes supporting high volume and high variety production of Battery Electric Vehicles (BEVs). As new materials and processes get developed it is crucial to address quality defects detection, prediction, and prevention especially given that e-mobility products interlink quality and safety, for example, assembly of ‘live’ battery systems. These requirements necessitate the development of methodologies that ensure quality requirements of products are satisfied from Job 1. This means ensuring high right-first-time ratio during process design by reducing manual and ineffective trial-and-error process adjustments; and, then continuing this by maintaining near zero-defect manufacturing during production by reducing Mean-Time-to-Detection and Mean-Time-to-Resolution for critical quality defects. Current technologies for isolating and mitigating quality issues provide limited performance within complex manufacturing systems due to (i) limited modelling abilities and lack capabilities to leverage point cloud quality monitoring data provided by recent measurement technologies such as 3D scanners to isolate defects; (ii) extensive dependence on manual expertise to mitigate the isolated defects; and, (iii) lack of integration between data-driven and physics-based models resulting in limited industrial applicability, scalability and interpretability capabilities, hence constitute a significant barrier towards ensuring quality requirements throughout the product lifecycle. The study develops a transformative framework that goes beyond improving the accuracy and performance of current approaches and overcomes fundamental barriers for isolation and mitigation of product shape error quality defects in multi-station assembly systems (MAS). The proposed framework is based on three methodologies which explore MAS: (i) response to quality defects by isolating process parameters (root causes (RCs)) causing unaccepted shape error defects; (ii) correction of the isolated RCs by determining corrective actions (CA) policy to mitigate unaccepted shape error defects; and, (iii) training, scalability and interpretability of (i) and (ii) by establishing closed-loop in-process (CLIP) capability that integrates in-line point cloud data, deep learning approaches of (i) and (ii) and physics-based models to provide comprehensive data-driven defect identification and RC isolation (causality analysis). The developed methodologies include: (i) Object Shape Error Response (OSER) to isolate RCs within single- and multi-station assembly systems (OSER-MAS) by developing Bayesian 3D-convolutional neural network architectures that process point cloud data and are trained using physics-based models and have capabilities to relate complex product shape error patterns to RCs. It quantifies uncertainties and is applicable during the design phase when no quality monitoring data is available. (ii) Object Shape Error Correction (OSEC) to generate CAs that mitigate RCs and simultaneously account for cost and quality key performance indicators (KPIs), MAS reconfigurability, and stochasticity by developing a deep reinforcement learning framework that estimates effective and feasible CAs without manual expertise. (iii) Closed-Loop In-Process (CLIP) to enable industrial adoption of approaches (i) & (ii) by firstly enhancing the scalability by using (a) closed-loop training, and (b) continual/transfer learning. This is important as training deep learning models for a MAS is time-intensive and requires large amounts of labelled data; secondly providing interpretability and transparency for the estimated RCs that drive costly CAs using (c) 3D gradient-based class activation maps. The methods are implemented as independent kernels and then integrated within a transformative framework which is further verified, validated, and benchmarked using industrial-scale automotive sheet metal assembly case studies such as car door and cross-member. They demonstrate 29% better performance for RC isolation and 40% greater effectiveness for CAs than current statistical and engineering-based approaches