Process monitoring, modelling and fault diagnosis in aluminium reduction cells

Recent years have seen an increasing number of applications in the Hall-Heroult process that utilise anode current signals as additional measurements for process monitoring and fault diagnosis. The measurement of anode current signals is attractive because it provides information of the cell conditions in the vicinity of each anode, which cannot be obtained from the conventionally measured line current and cell voltage. However, the existing approaches of incorporating the measurement of individual anode current signals into aluminium reduction cell operation have limitations. Therefore, these approaches need to be refined and new methods are required to achieve improved process efficiency. This work investigates the practical uses of individual anode current signals in the aluminium reduction cells for localised process monitoring and fault diagnosis. It includes the development of a robust data acquisition system that has the ability to accurately measure anode currents continuously at desired frequencies without being interrupted by cell control actions such as the anode replacement. In this work, based on the collected anode current signals in industrial aluminium reduction cells, three different applications are developed: signal-based process monitoring and fault diagnosis based on a new variable calculated from the anode current; online estimation of localised process variables using a novel multi-level state observer that is applied to a dynamic model with anode current signals as model inputs; and statistical process monitoring and fault diagnosis using anode current signals. Inspired by the uses of cell pseudo-resistance in cell monitoring and control, the pseudo-resistance for each anode-cathode path is used to represent the local cell conditions. The path pseudo-resistances, calculated from the anode current signals acquired from industrial cells, are used to study the spatial variations in the cells under different conditions. It is found that the path pseudo-resistance is more useful than the anode current itself in reflecting the cell conditions in the vicinity of each anode. Based on the analysis results, simple and effective local process monitoring and fault diagnosis schemes are proposed for classification of different abnormalities considered in this work. A multi-level state observer is developed for the online estimation of spatial alumina concentration distribution and local ACD based on individual anode current measurements. One of the key difficulties of the state estimation in the Hall-Heroult process is that the localised mass transfer rates are unknown. To overcome this, the robust extended Kalman filter is applied to a dynamic model of the cell that is discretised subsequently level by level, where the estimated process variables at each level are used to estimate more detailed alumina concentration distribution and ACD at the next one. The proposed approach is validated in experimental studies, which confirms the stability of the state observer and the accuracy of the estimation. This work also proposes a method for statistical process monitoring and fault diagnosis with the measurements of individual anode current. The method is based on the Kernel Principal Component Analysis, which is ideal for the Hall-Heroult process as the variables in the process are nonlinearly correlated. The approach is applied to datasets that represent each anode, which contain the path pseudoresistance of the respective anode and the currents carried by its neighbouring anodes, to achieve coupled local process monitoring and fault diagnosis. Validation of the approach using the data collected in an industrial cell shows that it is able to pinpoint the location of the faults and be used to evaluate the effect of the localised faults on other anodes

Topological Insulators-Based Magnetic Heterostructure

The combination of magnetism and topology in magnetic topological insulators (MTIs) has led to unprecedented advancements of time reversal symmetry-breaking topological quantum physics in the past decade. Compared with the uniform films, the MTI heterostructures provide a better framework to manipulate the spin-orbit coupling and spin properties. In this review, we summarize the fundamental mechanisms related to the physical orders host in (Bi,Sb)2(Te,Se)3-based hybrid systems. Besides, we provide an assessment on the general strategies to enhance the magnetic coupling and spin-orbit torque strength through different structural engineering approaches and effective interfacial interactions. Finally, we offer an outlook of MTI heterostructures-based spintronics applications, particularly in view of their feasibility to achieve room-temperature operation.Comment: 33 pages, 11 figure

Unsupervised Traffic Accident Detection in First-Person Videos

Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems. However, most work on video anomaly detection suffers from two crucial drawbacks. First, they assume cameras are fixed and videos have static backgrounds, which is reasonable for surveillance applications but not for vehicle-mounted cameras. Second, they pose the problem as one-class classification, relying on arduously hand-labeled training datasets that limit recognition to anomaly categories that have been explicitly trained. This paper proposes an unsupervised approach for traffic accident detection in first-person (dashboard-mounted camera) videos. Our major novelty is to detect anomalies by predicting the future locations of traffic participants and then monitoring the prediction accuracy and consistency metrics with three different strategies. We evaluate our approach using a new dataset of diverse traffic accidents, AnAn Accident Detection (A3D), as well as another publicly-available dataset. Experimental results show that our approach outperforms the state-of-the-art.Comment: Accepted to IROS 201

SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection

Despite significant progress in shadow detection, current methods still struggle with the adverse impact of background color, which may lead to errors when shadows are present on complex backgrounds. Drawing inspiration from the human visual system, we treat the input shadow image as a composition of a background layer and a shadow layer, and design a Style-guided Dual-layer Disentanglement Network (SDDNet) to model these layers independently. To achieve this, we devise a Feature Separation and Recombination (FSR) module that decomposes multi-level features into shadow-related and background-related components by offering specialized supervision for each component, while preserving information integrity and avoiding redundancy through the reconstruction constraint. Moreover, we propose a Shadow Style Filter (SSF) module to guide the feature disentanglement by focusing on style differentiation and uniformization. With these two modules and our overall pipeline, our model effectively minimizes the detrimental effects of background color, yielding superior performance on three public datasets with a real-time inference speed of 32 FPS.Comment: Accepted by ACM MM 202

CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer

Contour based scene text detection methods have rapidly developed recently, but still suffer from inaccurate frontend contour initialization, multi-stage error accumulation, or deficient local information aggregation. To tackle these limitations, we propose a novel arbitrary-shaped scene text detection framework named CT-Net by progressive contour regression with contour transformers. Specifically, we first employ a contour initialization module that generates coarse text contours without any post-processing. Then, we adopt contour refinement modules to adaptively refine text contours in an iterative manner, which are beneficial for context information capturing and progressive global contour deformation. Besides, we propose an adaptive training strategy to enable the contour transformers to learn more potential deformation paths, and introduce a re-score mechanism that can effectively suppress false positives. Extensive experiments are conducted on four challenging datasets, which demonstrate the accuracy and efficiency of our CT-Net over state-of-the-art methods. Particularly, CT-Net achieves F-measure of 86.1 at 11.2 frames per second (FPS) and F-measure of 87.8 at 10.1 FPS for CTW1500 and Total-Text datasets, respectively.Comment: This paper has been accepted by IEEE Transactions on Circuits and Systems for Video Technolog

Immunogenic Cell Death Amplified by Co-localized Adjuvant Delivery for Cancer Immunotherapy

Despite their potential, conventional whole-cell cancer vaccines prepared by freeze-thawing or irradiation have shown limited therapeutic efficacy in clinical trials. Recent studies have indicated that cancer cells treated with certain chemotherapeutics, such as mitoxantrone, can undergo immunogenic cell death (ICD) and initiate antitumor immune responses. However, it remains unclear how to exploit ICD for cancer immunotherapy. Here, we present a new material-based strategy for converting immunogenically dying tumor cells into a powerful platform for cancer vaccination and demonstrate their therapeutic potential in murine models of melanoma and colon carcinoma. We have generated immunogenically dying tumor cells surface-modified with adjuvant-loaded nanoparticles. Dying tumor cells laden with adjuvant nanodepots efficiently promote activation and antigen cross-presentation by dendritic cells in vitro and elicit robust antigen-specific CD8α+ T-cells in vivo. Furthermore, whole tumor-cell vaccination combined with immune checkpoint blockade leads to complete tumor regression in 78% of CT26 tumor-bearing mice and establishes long-term immunity against tumor recurrence. Our strategy presented here may open new doors to "personalized" cancer immunotherapy tailored to individual patient's tumor cells. Keywords: cancer immunotherapy; cancer vaccine; Cell engineering; innunogenic cell death; nanoparticl

Orecchio: Extending Body-Language through Actuated Static and Dynamic Auricular Postures

In this paper, we propose using the auricle – the visible part of the ear – as a means of expressive output to extend body language to convey emotional states. With an initial exploratory study, we provide an initial set of dynamic and static auricular postures. Using these results, we examined the relationship between emotions and auricular postures, noting that dynamic postures involving stretching the top helix in fast (e.g., 2Hz) and slow speeds (1Hz) conveyed intense and mild pleasantness while static postures involving bending the side or top helix towards the center of the ear were associated with intense and mild unpleasantness. Based on the results, we developed a prototype (called Orrechio) with miniature motors, custommade robotic arms and other electronic components. A preliminary user evaluation showed that participants feel more comfortable using expressive auricular postures with people they are familiar with, and that it is a welcome addition to the vocabulary of human body language