Prediction of Hardness and Residual Stress in Orthogonal Cutting of Inconel 718

Due to its high strength in high temperatures, Inconel 718 is widely used in the aerospace industry. However, Inconel 718 is a difficult-to-cut alloy with poor machinability. For instance, the cutting force is high in cutting Inconel 718, resulting in work-hardening of the machined surface and high residual stress in the machined surface. When residual stress releases, the part deforms and scrapes with error beyond tolerance. Therefore, it is necessary to predict the residual stress in the machined surface under a set of machining conditions. By modifying the machining conditions, the residual stress in the machined surface is under control, and the part deformation is limited. In this research, an analytical approach to the hardness and the residual stress in the machined surface in orthogonal cutting is proposed. This research has advantages over the experiment, the conventional approach and the FEA methods. With this approach, the cutting parameters can be optimized to minimize the residual stress in the machined surface and improve the surface integrity

Interpreting and exploiting narrative as a sketch design generator for application in VE

The research in this paper focusses on how a narrative text can be the generator of an architectural drawing, or other architectural representation, such as an Architectural Virtual Environment. The drawn physical sketch has traditionally played that role. A particular approach to narrative has been important for some notable architects and their architecture. Ian Ritchie (2014), for instance, celebrates the use of poetry to describe the essential spirit of a scheme before any drawing is done. The work in the paper here describes the proposition to capture such narrative text in a systematic and structured way. We describe foundational work on how the captured narrative text has been translated into a contemporary, computer-mediated, design development environment. Different narrative accounts recalling a now demolished house form the focus case study. This case study is the vehicle through which the initial principles establishing how best to move from narrative to virtual representation are established and tested

A Fast and Accurate Pitch Estimation Algorithm Based on the Pseudo Wigner-Ville Distribution

Estimation of fundamental frequency (F0) in voiced segments of speech signals, also known as pitch tracking, plays a crucial role in pitch synchronous speech analysis, speech synthesis, and speech manipulation. In this paper, we capitalize on the high time and frequency resolution of the pseudo Wigner-Ville distribution (PWVD) and propose a new PWVD-based pitch estimation method. We devise an efficient algorithm to compute PWVD faster and use cepstrum-based pre-filtering to avoid cross-term interference. Evaluating our approach on a database with speech and electroglottograph (EGG) recordings yields a state-of-the-art mean absolute error (MAE) of around 4Hz. Our approach is also effective at voiced/unvoiced classification and handling sudden frequency changes

Multi-modal fusion methods for robust emotion recognition using body-worn physiological sensors in mobile environments

High-accuracy physiological emotion recognition typically requires participants to wear or attach obtrusive sensors (e.g., Electroencephalograph). To achieve precise emotion recognition using only wearable body-worn physiological sensors, my doctoral work focuses on researching and developing a robust sensor fusion system among different physiological sensors. Developing such fusion system has three problems: 1) how to pre-process signals with different temporal characteristics and noise models, 2) how to train the fusion system with limited labeled data and 3) how to fuse multiple signals with inaccurate and inexact ground truth. To overcome these challenges, I plan to explore semi-supervised, weakly supervised and unsupervised machine learning methods to obtain precise emotion recognition in mobile environments. By developing such techniques, we can measure the user engagement with larger amounts of participants and apply the emotion recognition techniques in a variety of scenarios such as mobile video watching and online education

Strengthening and weakening of methane hydrate by water vacancies

Gas clathrate hydrates show promising applications in sustainable technologies such as future energy resources, gas capture and storage. The stability of clathrate hydrates under external load is of great crucial to those important applications, but remains unknown. Water vacancy is a common structural defect in clathrate hydrates. Herein, the mechanical characteristics of sI methane hydrates containing three types of water vacancy are investigated by molecular dynamics simulations with four different water forcefields. Mechanical properties of methane hydrates such as tensile strength are dictated not only by the density but also the type of water vacancy. Surprisingly, the tensile strength of methane hydrates can be weakened or strengthened, depending on the adopted water model and water vacancy density. Strength enhancement mainly results from the formation of new water cages. This work provides critical insights into the mechanics and microstructural properties of methane clathrate hydrates under external load, which is of primary importance in the recovery of natural gas from methane hydrate reservoirs.Cited as: Lin, Y., Liu, Y., Xu, K., Li, T., Zhang, Z., Wu, J. Strengthening and weakening of methane hydrate by water vacancies. Advances in Geo-Energy Research, 2022, 6(1): 23-37. https://doi.org/10.46690/ager.2022.01.0

SAFE: An EEG dataset for stable affective feature selection

An affective brain-computer interface (aBCI) is a direct communication pathway between human brain and computer, via which the computer tries to recognize the affective states of its user and respond accordingly. As aBCI introduces personal affective factors into human-computer interaction, it could potentially enrich the user’s experience during the interaction. Successful emotion recognition plays a key role in such a system. The state-of-the-art aBCIs leverage machine learning techniques which consist in acquiring affective electroencephalogram (EEG) signals from the user and calibrating the classifier to the affective patterns of the user. Many studies have reported satisfactory recognition accuracy using this paradigm. However, affective neural patterns are volatile over time even for the same subject. The recognition accuracy cannot be maintained if the usage of aBCI prolongs without recalibration. Existing studies have overlooked the performance evaluation of aBCI during long-term use. In this paper, we propose SAFE—an EEG dataset for stable affective feature selection. The dataset includes multiple recording sessions spanning across several days for each subject. Multiple sessions across different days were recorded so that the long-term recognition performance of aBCI can be evaluated. Based on this dataset, we demonstrate that the recognition accuracy of aBCIs deteriorates when re-calibration is ruled out during long-term usage. Then, we propose a stable feature selection method to choose the most stable affective features, for mitigating the accuracy deterioration to a lesser extent and maximizing the aBCI performance in the long run. We invite other researchers to test the performance of their aBCI algorithms on this dataset, and especially to evaluate the long-term performance of their methods

Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction

Data S1: Data

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device

GEMv2 : Multilingual NLG benchmarking in a single line of code

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe