Hyperdimensional Computing-based Multimodality Emotion Recognition with Physiological Signals

To interact naturally and achieve mutual sympathy between humans and machines, emotion recognition is one of the most important function to realize advanced human-computer interaction devices. Due to the high correlation between emotion and involuntary physiological changes, physiological signals are a prime candidate for emotion analysis. However, due to the need of a huge amount of training data for a high-quality machine learning model, computational complexity becomes a major bottleneck. To overcome this issue, brain-inspired hyperdimensional (HD) computing, an energy-efficient and fast learning computational paradigm, has a high potential to achieve a balance between accuracy and the amount of necessary training data. We propose an HD Computing-based Multimodality Emotion Recognition (HDC-MER). HDCMER maps real-valued features to binary HD vectors using a random nonlinear function, and further encodes them over time, and fuses across different modalities including GSR, ECG, and EEG. The experimental results show that, compared to the best method using the full training data, HDC-MER achieves higher classification accuracy for both valence (83.2% vs. 80.1%) and arousal (70.1% vs. 68.4%) using only 1/4 training data. HDC-MER also achieves at least 5% higher averaged accuracy compared to all the other methods in any point along the learning curve

Residual stress generation in tungsten-copper brazed joint using brazing alloy

Understanding the residual stress state in brazed joints is crucial for operational design and life time performance of the part in service. High magnitudes residual stresses are expected in the joined materials following cooling from brazing temperatures (≈950°C) due to large mismatches in material properties such as coefficient of thermal expansion and Young’s modulus. This study aims at further understanding of the generation and distribution of residual stresses when brazing tungsten to copper using a eutectic gold-copper brazing alloy. This configuration is potentially useful for future divertor designs. Finite Element Analysis (FEM) has been used to predict the brazing induced stresses and residual stress measurements were carried out on the brazed joint by X-ray diffraction (XRD) to validate the prediction model. Large residual stresses are predicted and measured in the tungsten; however there is disagreement in the sign of the stress. Predicted stresses are highly tensile in nature close to the brazing interface, whereas the measured stresses are highly compressive. The disagreement is believed to be caused by the model not accurately simulating the complex brazing process. Residual stress measurements on the copper were not possible due to texturing during brazing, grain growth and significant inelastic strains and deformations. Misalignment of parent materials was also observed to significantly affect residual stresses

Residual stress generations in brazed tungsten dissimilar joints

Understanding the residual stress state in brazed joints is crucial for the operational design and lifetime performance of the part in service. High-magnitude residual stresses are expected in the joined materials following cooling from brazing temperatures (≈950 °C) due to large mismatches in the thermal and mechanical properties. This paper aims at further understanding of the residual stresses caused when brazing tungsten to copper and tungsten to 316L austenitic steel using a eutectic gold-copper brazing alloy. These configurations are potentially useful for future diverter designs. Finite element analysis has been used to predict the brazing-induced stresses and residual stress measurements were carried out on the brazed joint by X-ray diffraction to validate the prediction model. Large residual stresses are predicted and measured in the tungsten; however, there is disagreement in the nature of the stress in the tungsten-copper configuration. Predicted stresses are highly tensile in nature close to the brazing interface, whereas the measured stresses are highly compressive. The disagreement is believed to be caused by the model not accurately simulating the complex brazing process. Residual stress measurements on the copper were not possible due to texturing during brazing, grain growth, and significant inelastic strains. There is excellent correlation between the measured and predicted stresses in the tungsten-316L configuration. High-tensile stresses were predicted in the tungsten (magnitude approximately 1000 MPa close to the braze interface) and high tensile stresses were measured (magnitude approximately 800 MPa in the same region). Joint misalignment of parent materials was also observed to significantly affect the residual stresses

An interpretable machine learning framework for measuring urban perceptions from panoramic street view images

The proliferation of street view images (SVIs) and the constant advancements in deep learning techniques have enabled urban analysts to extract and evaluate urban perceptions from large-scale urban streetscapes. However, many existing analytical frameworks have been found to lack interpretability due to their end-to-end structure and "black-box" nature, thereby limiting their value as a planning support tool. In this context, we propose a five-step machine learning framework for extracting neighborhood-level urban perceptions from panoramic SVIs, specifically emphasizing feature and result interpretability. By utilizing the MIT Place Pulse data, the developed framework can systematically extract six dimensions of urban perceptions from the given panoramas, including perceptions of wealth, boredom, depression, beauty, safety, and liveliness. The practical utility of this framework is demonstrated through its deployment in Inner London, where it was used to visualize urban perceptions at the Output Area (OA) level and to verify against real-world crime rate

Polydisperse Aerosol Transport and Deposition in Upper Airways of Age-Specific Lung

A comprehensive understanding of airflow characteristics and particle transport in the human lung can be useful in modelling to inform clinical diagnosis, treatment, and management, including prescription medication and risk assessment for rehabilitation. One of the difficulties in clinical treatment of lung disorders lies in the patients' variable physical lung characteristics caused by age, amongst other factors, such as different lung sizes. A precise understanding of the comparison between different age groups with various flow rates is missing in the literature, and this study aims to analyse the airflow and aerosol transport within the age-specific lung. ANSYS Fluent solver and the large-eddy simulation (LES) model were employed for the numerical simulation. The numerical model was validated with the available literature and the computational results showed airway size-reduction significantly affected airflow and particle transport in the upper airways. This study reports higher deposition at the mouth-throat region for larger diameter particles. The overall deposition efficiency (DE) increased with airway size reduction and flow rate. Lung aging effected the pressure distribution and a higher pressure drop was reported for the aged lung as compared to the younger lung. These findings could inform medical management through individualised simulation of drug-aerosol delivery processes for the patient-specific lung

Systolic blood pressure, chronic obstructive pulmonary disease and cardiovascular risk

\ua9 2023 Author(s) (or their employer(s)). Objective: In individuals with complex underlying health problems, the association between systolic blood pressure (SBP) and cardiovascular disease is less well recognised. The association between SBP and risk of cardiovascular events in patients with chronic obstructive pulmonary disease (COPD) was investigated. Methods: and analysis In this cohort study, 39 602 individuals with a diagnosis of COPD aged 55-90 years between 1990 and 2009 were identified from validated electronic health records (EHR) in the UK. The association between SBP and risk of cardiovascular end points (composite of ischaemic heart disease, heart failure, stroke and cardiovascular death) was analysed using a deep learning approach. Results: In the selected cohort (46.5% women, median age 69 years), 10 987 cardiovascular events were observed over a median follow-up period of 3.9 years. The association between SBP and risk of cardiovascular end points was found to be monotonic; the lowest SBP exposure group of <120 mm Hg presented nadir of risk. With respect to reference SBP (between 120 and 129 mm Hg), adjusted risk ratios for the primary outcome were 0.99 (95% CI 0.93 to 1.05) for SBP of <120 mm Hg, 1.02 (0.97 to 1.07) for SBP between 130 and 139 mm Hg, 1.07 (1.01 to 1.12) for SBP between 140 and 149 mm Hg, 1.11 (1.05 to 1.17) for SBP between 150 and 159 mm Hg and 1.16 (1.10 to 1.22) for SBP ≥160 mm Hg. Conclusion: Using deep learning for modelling EHR, we identified a monotonic association between SBP and risk of cardiovascular events in patients with COPD