An Improved Masking Strategy for Self-supervised Masked Reconstruction in Human Activity Recognition

Masked reconstruction serves as a fundamental pretext task for self-supervised learning, enabling the model to enhance its feature extraction capabilities by reconstructing the masked segments from extensive unlabeled data. In human activity recognition, this pretext task employed a masking strategy centered on the time dimension. However, this masking strategy fails to fully exploit the inherent characteristics of wearable sensor data and overlooks the inter-channel information coupling, thereby limiting its potential as a powerful pretext task. To address these limitations, we propose a novel masking strategy called Channel Masking. It involves masking the sensor data along the channel dimension, thereby compelling the encoder to extract channel-related features while performing the masked reconstruction task. Moreover, Channel Masking can be seamlessly integrated with masking strategies along the time dimension, thereby motivating the self-supervised model to undertake the masked reconstruction task in both the time and channel dimensions. Integrated masking strategies are named Time-Channel Masking and Span-Channel Masking. Finally, we optimize the reconstruction loss function to incorporate the reconstruction loss in both the time and channel dimensions. We evaluate proposed masking strategies on three public datasets, and experimental results show that the proposed strategies outperform prior strategies in both self-supervised and semi-supervised scenarios

Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition

Contrastive learning has been applied to Human Activity Recognition (HAR) based on sensor data owing to its ability to achieve performance comparable to supervised learning with a large amount of unlabeled data and a small amount of labeled data. The pre-training task for contrastive learning is generally instance discrimination, which specifies that each instance belongs to a single class, but this will consider the same class of samples as negative examples. Such a pre-training task is not conducive to human activity recognition tasks, which are mainly classification tasks. To address this problem, we follow SimCLR to propose a new contrastive learning framework that negative selection by clustering in HAR, which is called ClusterCLHAR. Compared with SimCLR, it redefines the negative pairs in the contrastive loss function by using unsupervised clustering methods to generate soft labels that mask other samples of the same cluster to avoid regarding them as negative samples. We evaluate ClusterCLHAR on three benchmark datasets, USC-HAD, MotionSense, and UCI-HAR, using mean F1-score as the evaluation metric. The experiment results show that it outperforms all the state-of-the-art methods applied to HAR in self-supervised learning and semi-supervised learning.Comment: 11 pages, 5 figure

Probing the fractional quantum Hall phases in valley-layer locked bilayer MoS2_{2}

Semiconducting transition-metal dichalcogenides (TMDs) exhibit high mobility, strong spin-orbit coupling, and large effective masses, which simultaneously leads to a rich wealth of Landau quantizations and inherently strong electronic interactions. However, in spite of their extensively explored Landau levels (LL) structure, probing electron correlations in the fractionally filled LL regime has not been possible due to the difficulty of reaching the quantum limit. Here, we report evidence for fractional quantum Hall (FQH) states at filling fractions 4/5 and 2/5 in the lowest LL of bilayer MoS$_{2}$, manifested in fractionally quantized transverse conductance plateaus accompanied by longitudinal resistance minima. We further show that the observed FQH states sensitively depend on the dielectric and gate screening of the Coulomb interactions. Our findings establish a new FQH experimental platform which are a scarce resource: an intrinsic semiconducting high mobility electron gas, whose electronic interactions in the FQH regime are in principle tunable by Coulomb-screening engineering, and as such, could be the missing link between atomically thin graphene and semiconducting quantum wells.Comment: 10 pages, 4 figure

Analytical modeling of material constitutive behaviors and process mechanics in precision machining and additive manufacturing

Manufacturing processes, including precision machining and metal additive manufacturing, transform the raw materials into finished products with desired geometry and functionality. Physics-based analytical modeling methodology allows process planning and optimization because of the significant computational advantage without resorting to the finite element method or any iteration-based simulations. However, the analytical models are not readily available due to the complexity of those processes. In the study of precision machining, an inverse analysis methodology was employed with the mechanics model and gradient search method for the identification of material constitutive model parameters, namely the Johnson-Cook model constants. Analytical temperature models were developed based on the calculation of materials flow stress at the chip formation zone using constitutive model and mechanics model. The machining forces and machining temperatures were calculated in the machining ultra-fine-grained pure titanium, which was prepared by a severe plastic deformation method, namely equal channel angular extrusion. Ultra-fine-grained titanium was studied because of its increasing usefulness in biomedical and engineering applications. In the study of metal additive manufacturing, the temperature, thermal stress, thermal-stress induced distortion, residual stress, part distortion, and part porosity were calculated through analytical modeling based on thermal analysis and process mechanics. The exiting temperature models were significantly improved with considerations of scan strategy, boundary heat transfer, and powder material properties, which improved the predictive accuracy without significantly compromising the computational efficiency. The thermal stress and residual stress were calculated from thermal elasticity and elastoplastic relaxation procedure respectively. The thermal stress was employed in the calculation of in-situ thermal distortion. The residual stress was employed in the calculation of post-process part distortion, Furthermore, the part porosity due to lack-of-fusion was calculated from the areal thermal analysis, and powder bed porosity that calculated with statistical powder size distribution. Experimental validations are included with various materials for the validation of the presented models.Ph.D

Stock Market Simulation

After researching basic knowledge of stock market and stock trading methods, the two-member group conducted two five-week stock trading simulations individually via Investopedia Stock Simulator with two different trading strategies, which were Day trading Strategy and Position Trading Strategy. The stock trading results were compared to determine the most profitable trading skills. The trading experiences learned from this Interactive Qualifying Project (IQP) will benefit team members with successful stock investment in the future

Predictive Modeling of Machining Temperatures with Force–Temperature Correlation Using Cutting Mechanics and Constitutive Relation

Elevated temperature in the machining process is detrimental to cutting tools—a result of the effect of thermal softening and material diffusion. Material diffusion also deteriorates the quality of the machined part. Measuring or predicting machining temperatures is important for the optimization of the machining process, but experimental temperature measurement is difficult and inconvenient because of the complex contact phenomena between tools and workpieces, and because of restricted accessibility during the machining process. This paper presents an original analytical model for fast prediction of machining temperatures at two deformation zones in orthogonal cutting, namely the primary shear zone and the tool–chip interface. Temperatures were predicted based on a correlation between force and temperature using the mechanics of the cutting process and material constitutive relation. Minimization of the differences between calculated material flow stresses using a mechanics model and a constitutive model yielded an estimate of machining temperatures. Experimental forces, cutting condition parameters, and constitutive model constants were inputs, while machining forces were easily measurable by a piezoelectric dynamometer. Machining temperatures of AISI 1045 steel were predicted under various cutting conditions to demonstrate the predictive capability of each presented model. Close agreements were observed by verifying them against documented values in the literature. The influence of model inputs and computational efficiency were further investigated. The presented model has high computational efficiency that allows real-time prediction and low experimental complexity, considering the easily measurable input variables

Prediction of Temperature Distribution in Orthogonal Machining Based on the Mechanics of the Cutting Process Using a Constitutive Model

This paper presents an original method of predicting temperature distribution in orthogonal machining based on a constitutive model of various materials and the mechanics of their cutting process. Currently, temperature distribution is commonly investigated using arduous experiments, computationally inefficient numerical analyses, and complex analytical models. In the method proposed herein, the average temperatures at the primary shear zone (PSZ) and the secondary shear zone (SSZ) were determined for various materials, based on a constitutive model and a chip-formation model using measurements of cutting force and chip thickness. The temperatures were determined when differences between predicted shear stresses using the Johnson–Cook constitutive model (J–C model) and those using a chip-formation model were minimal. J–C model constants from split Hopkinson pressure bar (SHPB) tests were adopted from the literature. Cutting conditions, experimental cutting force, and chip thickness were used to predict the shear stresses. The temperature predictions were compared to documented results in the literature for AISI 1045 steel and Al 6082-T6 aluminum in multiple tests in an effort to validate this methodology. Good agreement was observed for the tests with each material. Thanks to the reliable and easily measurable cutting forces and chip thicknesses, and the simple forms of the employed models, the presented methodology has less experimental complexity, less mathematical complexity, and high computational efficiency