Rotation invariant multiview face detection (MVFD) aims to detect faces with arbitrary rotation-in-plane (RIP) and rotation-off-plane (ROP) angles in still images or video sequences. MVFD is crucial as the first step in automatic face processing for general applications since face images are seldom upright and frontal unless they are taken cooperatively. In this paper, we propose a series of innovative methods to construct a high-performance rotation invariant multiview face detector, including the Width-First-Search (WFS) tree detector structure, the Vector Boosting algorithm for learning vector-output strong classifiers, the domain-partition-based weak learning method, the sparse feature in granular space, and the heuristic search for sparse feature selection. As a result of that, our multiview face detector achieves low computational complexity, broad detection scope, and high detection accuracy on both standard testing sets and real-life images.

Recently, the fabrication methods of orthopedic implants and devices have been greatly developed. Additive manufacturing technology allows the production of complex structures with bio-mimicry features, and has the potential to overcome the limitations of conventional fabrication methods. This review explores open-cellular structural design for porous metal implant applications, in relation to the mechanical properties, biocompatibility, and biodegradability. Several types of additive manufacturing techniques including selective laser sintering, selective laser melting, and electron beam melting, are discussed for different applications. Additive manufacturing through powder bed fusion shows great potential for the fabrication of high-quality porous metal implants. However, the powder bed fusion technique still faces two major challenges: it is high cost and time-consuming. In addition, triply periodic minimal surface (TPMS) structures are also analyzed in this paper, targeting the design of metal implants with an enhanced biomorphic environment.

Due to the real working conditions and data acquisition equipment, the collected working data of bearings are actually limited. Meanwhile, as the rolling bearing works in the normal state at most times, it is easy to raise the imbalance problem of fault types which restricts the diagnosis accuracy and stability. To solve these problems, we present an imbalanced fault diagnosis method based on the generative adversarial network (GAN) and provide a comparative study in detail. The key idea is utilizing GAN, a kind of deep learning technique, to generate synthetic samples for minority fault class and then improve the generalization ability of the fault diagnosis model. First, this method applies fast Fourier transform to preprocess the original vibration signal and then obtains the frequency spectrum of fault samples. Second, it uses the spectrum data as the input of GAN to generate the synthetic minority samples following the data distribution of the real samples. Finally, it puts the synthetic samples into the training set and builds a stacked denoising auto encoder model for fault diagnosis. To testify the effectiveness of the proposed method, a series of comparative experiments is carried out on the CWRU bearing dataset. The results show that the proposed method can provide a better solution for imbalanced fault diagnosis on the basis of generating similar fault samples. As a comparative study, the proposed method is compared to several diagnostic methods with traditional time-frequency domain characteristics. Moreover, we also demonstrate that the proposed method outperforms three widely used sample synthesis techniques, such as random oversampling, synthetic minority oversampling technique, and the principal curve-based oversampling method in terms of diagnosis accuracy and numerical stability. INDEX TERMS Generative adversarial network, fault diagnosis, imbalanced fault, SDAE.

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Austenite reversion during tempering of a Fe-13.6Cr-0.44C (wt.%) martensite results in an ultrahigh strength ferritic stainless steel with excellent ductility. The austenite reversion mechanism is coupled to the kinetic freezing of carbon during low-temperature partitioning at the interfaces between martensite and retained austenite and to carbon segregation at martensite-martensite grainboundaries. An advantage of austenite reversion is its scalability, i.e., changing tempering time and temperature tailors the desired strength-ductility profiles (e.g. tempering at 400°C for 1 min. produces a 2 GPa ultimate tensile strength (UTS) and 14% elongation while 30 min. at 400°C results in a UTS of ~ 1.75 GPa with an elongation of 23%). The austenite reversion process, carbide precipitation, and carbon segregation have been characterized by XRD, EBSD, TEM, and atom probe tomography (APT) in order to develop the structure-property relationships that control the material's strength and ductility.