Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Sparse tensor discriminant analysis

Abstract: The classical linear discriminant analysis has undergone great development and has recently been extended to different cases. In this paper, a novel discriminant subspace learning method called sparse tensor discriminant analysis (STDA) is proposed, which further extends the recently presented multilinear discriminant analysis to a sparse case. Through introducing the L1 and L2 norms into the objective function of STDA, we can obtain multiple interrelated sparse discriminant subspaces for feature extraction. As there are no closed-form solutions, k-mode optimization technique and the L1 norm sparse regression are combined to iteratively learn the optimal sparse discriminant subspace along different modes of the tensors. Moreover, each non-zero element in each subspace is selected from the most important variables/factors, and thus STDA has the potential to perform better than other discriminant subspace methods. Extensive experiments on face databases (Yale, FERET, and CMU PIE face databases) and the Weizmann action database show that the proposed STDA algorithm demonstrates the most competitive performance against the compared tensor-based methods, particularly in small sample sizes.

Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement

Abstract: Fringe projection profilometry (FPP) has become a more prevalently adopted technique in intelligent manufacturing, defect detection, and some other important applications. In FPP, efficiently recovering the absolute phase has always been a great challenge. The stereo phase unwrapping (SPU) technologies based on geometric constraints can eliminate phase ambiguity without projecting any additional patterns, which maximizes the efficiency of the retrieval of the absolute phase. Inspired by recent successes of deep learning for phase analysis, we demonstrate that deep learning can be an effective tool that organically unifies phase retrieval, geometric constraints, and phase unwrapping into a comprehensive framework. Driven by extensive training datasets, the neural network can gradually “learn” to transfer one high-frequency fringe pattern into the “physically meaningful” and “most likely” absolute phase, instead of “step by step” as in conventional approaches. Based on the properly trained framework, high-quality phase retrieval and robust phase ambiguity removal can be achieved only on a single-frame projection. Experimental results demonstrate that compared with traditional SPU, our method can more efficiently and stably unwrap the phase of dense fringe images in a larger measurement volume with fewer camera views. Limitations about the proposed approach are also discussed. We believe that the proposed approach represents an important step forward in high-speed, high-accuracy, motion-artifacts-free absolute 3D shape measurement for complicated objects from a single fringe pattern.

Self-Standing Porous LiCoO2 Nanosheet Arrays as 3D Cathodes for Flexible Li-Ion Batteries

Abstract: Self-standing electrodes are the key to realize flexible Li-ion batteries. However, fabrication of self-standing cathodes is still a major challenge. In this work, porous LiCoO2 nanosheet arrays are grown on Au-coated stainless steel (Au/SS) substrates via a facile “hydrothermal lithiation” method using Co3O4 nanosheet arrays as the template followed by quick annealing in air. The binder-free and self-standing LiCoO2 nanosheet arrays represent the 3D cathode and exhibit superior rate capability and cycling stability. In specific, the LiCoO2 nanosheet array electrode can deliver a high reversible capacity of 104.6 mA h g−1 at 10 C rate and achieve a capacity retention of 81.8% at 0.1 C rate after 1000 cycles. By coupling with Li4Ti5O12 nanosheet arrays as anode, an all-nanosheet array based LiCoO2//Li4Ti5O12 flexible Li-ion battery is constructed. Benefiting from the 3D nanoarchitectures for both cathode and anode, the flexible LiCoO2// Li4Ti5O12 battery can deliver large specific reversible capacities of 130.7 mA h g−1 at 0.1 C rate and 85.3 mA h g−1 at 10 C rate (based on the weight of cathode material). The full cell device also exhibits good cycling stability with 80.5% capacity retention after 1000 cycles at 0.1 C rate, making it promising for the application in flexible Li-ion batteries.

A multi-objective optimization-based layer-by-layer blade-coating approach for organic solar cells: rational control of vertical stratification for high performance

Abstract: A major breakthrough in organic solar cells (OSCs) in the last thirty years was the development of the bulk heterojunction (BHJ) solution processing strategy, which effectively provided a nanoscale phase-separated morphology, aiding in the separation of Coulombically bound excitons and facilitating charge transport and extraction. Compared with the application of the layer-by-layer (LbL) approach proposed in the same period, the BHJ spin-coating technology shows overwhelming advantages for evaluating the performance of photovoltaic materials and achieving more-efficient photoelectric conversion. Thus, in this study, we have further compared the BHJ and LbL processing strategies via the doctor-blade coating technology because it is a roll-to-roll compatible high-throughput thin film fabrication route. We systematically evaluated multiple target parameters, including morphological characteristics, optical simulation, physical kinetics, device efficiency, and blend stability issues. It is worth emphasizing that our findings disprove the old stereotypes such as the BHJ processing method is superior to the LbL technology for the preparation of high-performance OSCs and the LbL approach requires an orthogonal solvent and donor/acceptor materials with special solubility. Our studies demonstrate that the LbL blade-coating approach is a promising strategy to effectively reduce the efficiency-stability gap of OSCs and even a superior alternative to the BHJ method in commercial applications.