Xiang Zhang

Bio: Xiang Zhang is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 154, co-authored 1733 publications receiving 117576 citations. Previous affiliations of Xiang Zhang include University of California, Berkeley & University of Texas MD Anderson Cancer Center.

Impact damage in composite aircraft structures-experimental testing and numerical simulation

Abstract: This paper describes a strategy for predicting internal damage in a laminated composite structure, when subjected to low-velocity impact. The aim was to obtain a better understanding of and cure for the notorious reduction in strength of aircraft compression panels when they suffered barely visible impact damage (BVID). A finite element model is presented which incorporates the non-linear behaviour due to gross deformation, interlaminar delamination and in-plane fibre and matrix failure. The strategy is validated by impact tests for a wide range of carbon/epoxy composite structures ranging from small stiff plates to realistic aircraft compression panels. It is demonstrated that the finite element model is capable of predicting impact damage in laminated composite structures and thus could be used as a design tool.

Atomic-scale ion transistor with ultrahigh diffusivity

Abstract: Biological ion channels rapidly and selectively gate ion transport through atomic-scale filters to maintain vital life functions. We report an atomic-scale ion transistor exhibiting ultrafast and highly selective ion transport controlled by electrical gating in graphene channels around 3 angstroms in height, made from a single flake of reduced graphene oxide. The ion diffusion coefficient reaches two orders of magnitude higher than the coefficient in bulk water. Atomic-scale ion transport shows a threshold behavior due to the critical energy barrier for hydrated ion insertion. Our in situ optical measurements suggest that ultrafast ion transport likely originates from highly dense packing of ions and their concerted movement inside the graphene channels.

Epitaxial Single-Layer MoS2 on GaN with Enhanced Valley Helicity.

Abstract: This work was supported by the National Key R&D Program of China (Grant No. 2017YFA0206301), the National Basic Research Program of China (Grant No. 2013CB921901), the National Natural Science Foundation of China (Grant Nos. 61521004, 11474007, and 11674005), the King Abdulah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2016-CRG5-2996, the National Science Foundation (NSF) under grant 1753380, and the “Youth 1000 Talent Plan” Fund. Y.Y. thanks Ting Cao from University of California, Berkeley, for help discussions.

Functional Molecularly Imprinted Polymer Microstructures Fabricated Using Microstereolithography

Abstract: Synthesis: Fibrous V 3 O 7 ´H 2 O template crystals were prepared hydrother-mally according to Yamamoto and co-workers [30]: an aqueous solution of VOSO 4 (0.15 M) was sealed in a poly(tetrafluoroethylene)-lined autoclave (Parr bomb 4749, 23 mL capacity) and heated at 180±220 C for 1±2 days. The resulting suspension was filtered, washed several times with water and dried overnight under vacuum (~ 10 ±3 mbar). Coating of the as-prepared green, paper like template as well as the subsequent core removal were performed in one pot. The fibrous solid (35 mg) was dispersed in a 250 mL glass flask containing an isopropanol/ammonia/water solution (respective volumes [mL]: 200:8.3:7.5) by means of an ultrasonic bath set at 40 C (Bandelin Sonorex DK 255 P apparatus, 35 kHz, 320 W). After addition of 0.1 mL TEOS, the ultra-sound intensity was maintained at ~ 200 W during the whole coating reaction (75 min). Then, 1 mL H 2 O 2 was added directly into the dispersion, which was further stirred for about 45 min. The solid was collected by filtration, washed extensively with isopropanol, and afterwards with water. To achieve complete core dissolution as well as elemental purity, the product was redispersed in a diluted H 2 O 2 aqueous solution (0.3 M; 30 mL), stirred for 48 h, washed several times with water, and dried under vacuum. Characterization: Samples were investigated in glass capillaries with a STOE STADI P X-ray powder diffractometer equipped with a curved Ge monochro-mator, a linear position sensitive detector, and using Cu Ka radiation. Scanning electron microscopy (SEM) was performed on a LEO 1530 Gemini apparatus, which was operated at low acceleration voltage (V acc = 1 kV) to minimize charging of the as-synthesized samples. For transmission electron microscopy (TEM), the samples were deposited on a holey carbon foil supported on a copper grid. TEM images were recorded on a CM30 microscope (Philips, Eindhoven, V acc = 300 kV, LaB 6 cathode). Elemental maps of vanadium were obtained at the L ionization edge applying the three-window method [33] on a Tecnai 30F apparatus (Philips, Eindhoven, V acc = 300 kV, field emission gun) equipped with a GIF (Gatan imaging filter). Laser elemental analysis was carried out on a pressed sample pellet using a Perkin Elmer/Sciex Elan 6100 DRC LA-ICP-MS machine. A defining trend in sensing and diagnostics is miniaturiza-tion, the reduction in the size of devices and components …

Deep Residual Learning for Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Very Deep Convolutional Networks for Large-Scale Image Recognition

Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Deep Residual Learning for Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Going deeper with convolutions

Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.