Probing the phonon surface interaction by wave packet simulation: effect of roughness and morphology

One way to reduce the lattice thermal conductivity of solids is to induce additional phonon surface scattering through nanostructures. However, how phonons interact with boundaries, especially at the atomic level, is not well understood. In this work, we performed two-dimensional atomistic wave packet simulations to investigate the phonon surface interaction. Emphasis has been given to the angular-resolved phonon reflection at smooth, periodically rough, and amorphous surfaces. We found that the acoustic phonon reflection at a smooth surface is not simply specular. Mode conversion can occur after reflection, and the detailed energy distribution after reflection will dependent on surface condition and polarization of incident phonon. At periodically rough surfaces, the reflected wave packet distribution does not follow the well-known Ziman's model, but shows a nonmonotonic dependence on the depth of surface roughness. When an amorphous layer is attached to the surface, the incident wave packet will be absorbed by the amorphous region, and results in quite diffusive reflection. Our results clearly show that the commonly used specular-diffusive model is not enough to describe the phonon reflection at a periodically rough surface, while an amorphous layer can induce strong diffusive reflection. This work provides a careful analysis of phonon reflection at a surface with different morphology, which is important to a better understanding of thermal transport in various nanostructures.Comment: 15pages, 9 figure

Coresets for Relational Data and The Applications

A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assumption may not hold in real-world scenarios. In this paper, we consider the problem of coresets construction over relational data. Namely, the data is decoupled into several relational tables, and it could be very expensive to directly materialize the data matrix by joining the tables. We propose a novel approach called ``aggregation tree with pseudo-cube'' that can build a coreset from bottom to up. Moreover, our approach can neatly circumvent several troublesome issues of relational learning problems [Khamis et al., PODS 2019]. Under some mild assumptions, we show that our coreset approach can be applied for the machine learning tasks, such as clustering, logistic regression and SVM

An Optimal NARX Neural Network Identification Model for a Magnetorheological Damper With Force-Distortion Behavior

This paper presents an optimal NARX neural network identification model for a magnetorheological (MR) damper with the force-distortion behavior. An intensive experimental study is conducted for designing the NARX network architecture to enhance modeling accuracy and availability, and the activation function selection, weights, and biases of the selected network are optimized by differential evolution algorithm. Different experimental training and validation samples are used for network training. The prediction capability of the optimal NARX model is verified by new measured test data. The test and comparative results show that the optimal NARX network model can satisfactorily emulate the dynamic behavior of MR damper and effectively capture its distortion behavior occurred with the increased current. The developed inverse NARX network model can effectively estimate the required current and track desired damping force. Moreover, the effects of different noise disturbance on the NARX network model performance are analyzed, and the model error varies slightly with a small noise disturbance. The accuracy of the results supports the use of this modeling technique for identifying irregular non-linear models of MR damper and similar devices

Using gyro stabilizer for active anti-rollover control of articulated wheeled loader vehicles

Articulated wheeled loader vehicles have frequent rollover accidents as they operate in the complex outdoor environments. This article proposes an active anti-rollover control method based on a set of single-frame control moment gyro stabilizer installed on the rear body of the vehicle. The rollover dynamic model is first established for articulated wheeled loader vehicle with gyro stabilizer. The proposed control strategy is then applied in simulation to verify the rollover control effect on the vehicle under steady-state circumferential conditions. Finally, a home-built articulated wheel loader vehicle with gyro stabilizer is used to further verify the proposed control strategy. The results show that the vehicle can quickly return to the stable driving state and effectively avoid the vehicle rollover when a suitable anti-roll control moment can be provided by the gyro stabilizer. As a result, the articulated wheeled loader vehicle is able to operate safely in a complex outdoor environment

Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles.

Articulated wheel loaders used in the construction industry are heavy vehicles and have poor stability and a high rate of accidents because of the unpredictable changes of their body posture, mass and centroid position in complex operation environments. This paper presents a novel distributed multi-sensor system for real-time attitude estimation and stability measurement of articulated wheel loaders to improve their safety and stability. Four attitude and heading reference systems (AHRS) are constructed using micro-electro-mechanical system (MEMS) sensors, and installed on the front body, rear body, rear axis and boom of an articulated wheel loader to detect its attitude. A complementary filtering algorithm is deployed for sensor data fusion in the system so that steady state margin angle (SSMA) can be measured in real time and used as the judge index of rollover stability. Experiments are conducted on a prototype wheel loader, and results show that the proposed multi-sensor system is able to detect potential unstable states of an articulated wheel loader in real-time and with high accuracy

CupCleaner: A Data Cleaning Approach for Comment Updating

Recently, deep learning-based techniques have shown promising performance on various tasks related to software engineering. For these learning-based approaches to perform well, obtaining high-quality data is one fundamental and crucial issue. The comment updating task is an emerging software engineering task aiming at automatically updating the corresponding comments based on changes in source code. However, datasets for the comment updating tasks are usually crawled from committed versions in open source software repositories such as GitHub, where there is lack of quality control of comments. In this paper, we focus on cleaning existing comment updating datasets with considering some properties of the comment updating process in software development. We propose a semantic and overlapping-aware approach named CupCleaner (Comment UPdating's CLEANER) to achieve this purpose. Specifically, we calculate a score based on semantics and overlapping information of the code and comments. Based on the distribution of the scores, we filter out the data with low scores in the tail of the distribution to get rid of possible unclean data. We first conducted a human evaluation on the noise data and high-quality data identified by CupCleaner. The results show that the human ratings of the noise data identified by CupCleaner are significantly lower. Then, we applied our data cleaning approach to the training and validation sets of three existing comment updating datasets while keeping the test set unchanged. Our experimental results show that even after filtering out over 30\% of the data using CupCleaner, there is still an improvement in all performance metrics. The experimental results on the cleaned test set also suggest that CupCleaner may provide help for constructing datasets for updating-related tasks

Isogeometric multi-patch topology optimization based on pix2pix

We present a novel approach that combines the power of pix2pix, an image-to-image translation framework, with the advanced capabilities of isogeometric multi-patch analysis for topology optimization. The proposed method adds the Nitsche’s methods into the advantages of Isogeometric analysis (IGA), thus gaining the ability to handle complex geometries by generating locally smooth and well-converged results. Additionally, the usage of generative adversarial network based pix2pix allows for a more efficient representation of the design space, reducing the computational cost of the optimization process. This approach has shown promising results in various numerical examples. This technique aims to improve the efficiency of conceptual design in complex engineering applications