Engineering topics

Zhang, Yuhan

Publications and source records attributed to Zhang, Yuhan.

6 records

DOE OSTI2023

Selective Formation of Acetic Acid and Methanol by Direct Methane Oxidation Using Rhodium Single-Atom Catalysts

Atomically dispersed catalysts such as single-atom catalysts have been shown to be effective in selectively oxidizing methane, promising a direct synthetic route to value-added oxygenates such as acetic acid or methanol. However, an important challenge of this approach has been that the loading of active sites by single-atom catalysts is low, leading to a low overall yield of the products. We report an approach that can address this issue. It utilizes a metal–organic framework built with porphyrin as the linker, which provides high concentrations of binding sites to support atomically dispersed rhodium. It is shown that up to 5 wt% rhodium loading can be achieved with excellent dispersity. When used for acetic acid synthesis by methane oxidation, a new benchmark performance of 23.62 mmol·gcat –1 ·h –1 was measured. Furthermore, the catalyst exhibits a unique sensitivity to light, producing acetic acid (under illumination, up to 66.4% selectivity) or methanol (in the dark, up to 65.0% selectivity) under otherwise identical reaction conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Selective Methane Oxidation by Heterogenized Iridium Catalysts

Oxidative methane (CH 4 ) carbonylation promises a direct route to the synthesis of value-added oxygenates such as acetic acid (CH 3 COOH). Here, in this paper, we report a strategy to realize oxidative CH 4 carbonylation through immobilized Ir complexes on an oxide support. Our immobilization approach not only enables direct CH 4 activation but also allows for easy separation and reutilization of the catalyst. Furthermore, we show that a key step, methyl migration, that forms a C–C bond, is sensitive to the electrophilicity of carbonyl, which can be tuned by a gentle reduction to the Ir centers. While the as-prepared catalyst that mainly featured Ir(IV) preferred CH 3 COOH production, a reduced catalyst featuring predominantly Ir(III) led to a significant increase of CH 3 OH production at the expense of the reduced yield of CH 3 COOH.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2012

Composite Wavelet Filters for Enhanced Automated Target Recognition

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

automated target recognition↗

NASA NTRS2010

Feature Extraction and Selection Strategies for Automated Target Recognition

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

computer vision↗

NASA NTRS2010

Optimization of a Multi-Stage ATR System for Small Target Identification

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

false alarm rate↗

NASA NTRS2010

Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

neural network↗