An integrated Process Planning and Robust Fixture Design System

Ph.DDOCTOR OF PHILOSOPH

A machining feature extraction approach for casting and forging parts

Master'sMASTER OF ENGINEERIN

Efficient analysis of radiated immunity of printed circuit boards using SPICE

This paper proposes an efficient method for simulating the radiated immunity of printed circuit board (PCB) with arbitrarily oriented traces. Existing methods usually deal with plane wave excited PCB with simple configuration, for which a SPICE model can be analytically derived. Nevertheless, it is difficult to analytically develop a SPICE model for a PCB whose traces are arbitrarily oriented. In this work, traces of a PCB are divided into small segments. SPICE model of the PCB is obtained by connecting equivalent circuits of all segments. Meanwhile, the incident field is converted to distributed current and voltage sources in the SPICE model. Radiated immunity of the PCB is then analyzed using SPICE. Since it is challenging to analyze all traces of the PCB simultaneously, uncoupled traces are individually simulated. Numerical examples are presented to illustrate the advantages of the proposed method. It is shown that the proposed method is about 17 times faster than a full wave analysis method

A High-Performance Convolutional Neural Network for Ground-Level Ozone Estimation in Eastern China

Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-h (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R2 of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants

A High-Performance Convolutional Neural Network for Ground-Level Ozone Estimation in Eastern China

Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-h (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R2 of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants

Strain-Tunable Visible-Light-Responsive Photocatalytic Properties of Two-Dimensional CdS/g-C₃N₄: A Hybrid Density Functional Study

By means of a hybrid density functional, we comprehensively investigate the energetic, electronic, optical properties, and band edge alignments of two-dimensional (2D) CdS/g-C 3 N 4 heterostructures by considering the effect of biaxial strain and pH value, so as to improve the photocatalytic activity. The results reveal that a CdS monolayer weakly contacts with g-C 3 N 4 , forming a type II van der Waals (vdW) heterostructure. The narrow bandgap makes CdS/g-C 3 N 4 suitable for absorbing visible light and the induced built-in electric field between the interface promotes the effective separation of photogenerated carriers. Through applying the biaxial strain, the interface adhesion energy, bandgap, and band edge positions, in contrast with water, redox levels of CdS/g-C 3 N 4 can be obviously adjusted. Especially, the pH of electrolyte also significantly influences the photocatalytic performance of CdS/g-C 3 N 4 . When pH is smaller than 6.5, the band edge alignments of CdS/g-C 3 N 4 are thermodynamically beneficial for oxygen and hydrogen generation. Our findings offer a theoretical basis to develop g-C 3 N 4 -based water-splitting photocatalysts

Hyperspectral Infrared Atmospheric Sounder (HIRAS) Atmospheric Sounding System

Accurate atmospheric temperature and moisture profiles are essential for weather forecasts and research. Satellite-based hyperspectral infrared observations are meaningful in detecting atmospheric profiles, especially over oceans where conventional observations can seldom be used. In this study, a HIRAS (Hyperspectral Infrared Atmospheric Sounder) Atmospheric Sounding System (HASS) was introduced, which retrieves atmospheric temperature and moisture profiles using a one-dimension variational scheme based on HIRAS observations. A total of 274 channels were optimally selected from the entire HIRAS spectrum through information entropy analyses, and a group of retrieval experiments were independently performed for different HIRAS fields of views (FOVs). Compared with the ECMWF reanalysis data version-5 (ERA5), the RMSEs of temperature (relative humidity) for low-, mid-, and high-troposphere layers were 1.5 K (22.3%), 1.0 K (33.2%), and 1.3 K (38.5%), respectively, which were similar in magnitude to those derived from other hyperspectral infrared sounders. Meanwhile, the retrieved temperature RMSEs with respect to the satellite radio occultation (RO) products increased to 1.7 K, 1.8 K, and 1.9 K for the low-, mid-, and high-troposphere layers, respectively, which could be attributed to the accurate RO temperature products in the upper atmospheres. It was also found that the RMSE varied with the FOVs and latitude, which may be caused by the current angle-dependent bias correction and unique background profiles