Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Microstructure and Mechanical Properties of Long Ti-6Al-4V Rods Additively Manufactured by Selective Electron Beam Melting Out of a Deep Powder Bed and the Effect of Subsequent Hot Isostatic Pressing

Abstract: An array of eight long Ti-6Al-4V rods (diameter: 12 mm; height: 300 mm) have been additively manufactured, vertically and perpendicular to the powder bed, by selective electron beam melting (SEBM). The purpose was to identify and understand the challenges of fabricating Ti-6Al-4V samples or parts from a deep powder bed (more than 200-mm deep) by SEBM and the necessity of applying post heat treatment. The resulting microstructure and mechanical properties of these Ti-6Al-4V rods were characterized along their building (i.e., axial) direction by dividing each rod into three segments (top, middle, and bottom), both before (i.e., as-built) and after hot isostatic pressing (HIP). The as-built microstructure of each rod was inhomogeneous; it was coarsest in the top segment, which showed a near equilibrium α-β lamellar structure, and finest in the bottom segment, which featured a non-equilibrium mixed structure. The tensile properties varied along the rod axis, especially the ductility, but all tensile properties met the requirements specified by ASTM F3001-14. HIP increased the relative density from 99.03 pct of the theoretical density (TD) to 99.90 pct TD and homogenized the microstructure thereby leading to highly consistent tensile properties along the rod axis. The temperature of the stainless steel substrate used in the powder bed was monitored. The as-built inhomogeneous microstructure is attributed to the temperature gradient in the deep powder bed. Post heat treatment is thus necessary for Ti-6Al-4V samples or parts manufactured from a deep powder bed by SEBM. This differs from the additive manufacturing of small samples or parts from a shallow powder bed (less than 100-mm deep) by SEBM.

An Improved Artificial Bee Colony Algorithm for Solving Hybrid Flexible Flowshop With Dynamic Operation Skipping

Abstract: In this paper, we propose an improved discrete artificial bee colony (DABC) algorithm to solve the hybrid flexible flowshop scheduling problem with dynamic operation skipping features in molten iron systems. First, each solution is represented by a two-vector-based solution representation, and a dynamic encoding mechanism is developed. Second, a flexible decoding strategy is designed. Next, a right-shift strategy considering the problem characteristics is developed, which can clearly improve the solution quality. In addition, several skipping and scheduling neighborhood structures are presented to balance the exploration and exploitation ability. Finally, an enhanced local search is embedded in the proposed algorithm to further improve the exploitation ability. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed DABC algorithm is favorably compared against several presented algorithms, both in solution quality and efficiency.

TransMed: Transformers Advance Multi-Modal Medical Image Classification.

Abstract: Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it cannot deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model on two datasets, parotid gland tumors classification and knee injury classification. Combining our contributions, we achieve an improvement of 10.1% and 1.9% in average accuracy, respectively, outperforming other state-of-the-art CNN-based models. The results of the proposed method are promising and have tremendous potential to be applied to a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to multi-modal medical image classification.

Command Filter Based Adaptive Fuzzy Finite-Time Control for a Class of Uncertain Nonlinear Systems With Hysteresis

Abstract: This article addresses an adaptive fuzzy finite-time control for a class of uncertain strict-feedback nonlinear systems with backlashlike hysteresis and stochastic disturbances At first, a novel criterion of semiglobally finite-time stability in probability (SGFSP) is established based on Lyapunov function method Under the proposed stability criterion, an adaptive fuzzy finite-time control scheme is designed In the design process of the controller, command filter technique is introduced to overcome the problems of “explosion of complexity” and “singularity” inhered in the traditional adaptive finite-time control based on the backstepping method Meanwhile, via constructing the corresponding error compensating systems, the effect of errors generated by the command filters is reduced, such that the original systems have more better tracking performance To cope with the influence of backlashlike hysteresis input, an auxiliary system is constructed, in which the output signal is applied to compensate the effect of the hysteresis It is shown that the tracking error can converge to a small neighborhood of original in finite time, and the closed-loop system is SGFSP under the constructed controller Finally, the effectiveness of the proposed control strategy is further verified by two simulation examples

Interacting model of new agegraphic dark energy: Cosmological evolution and statefinder diagnostic

Abstract: The statefinder diagnostic is a useful method for distinguishing different dark energy models. In this paper, we investigate the new agegraphic dark energy model with interaction between dark energy and matter component by using statefinder parameter pair {r, s} and study its cosmological evolution. We plot the trajectories of the new agegraphic dark energy model for different interaction cases in the statefinder plane. As a result, the influence of the interaction on the evolution of the universe is shown in the statefinder diagrams.