860 research outputs found
Adhesion Strength Measurement of Multilayer Structures with Vertical Crack by Four Point Bending Test
Current microelectronic packages consist of multilayer systems. Adhesion strength is one of the most important factors to the reliability of these systems. Previous studies have used four point bending tests as a method for characterizing the energy release rate to obtain the adhesion strength of bilayer systems. An extension of this work is proposed in this study, where a four point bending test of multilayer structures with a vertical crack is used to measure the adhesion strength, assisted by the presence of a predefined area. The predefined area allows for a weak adhesion horizontal accurate pre-crack which permits crack propagation under loading as well as reducing scatter within the values of critical loads. A numerical analysis is conducted to compute the energy release rate from the critical loads using the concept of the J-integral. Two sets of multilayer specimens were fabricated and tested in the study: one for investigating crack front behavior relative to the compliance change in the load-displacement profile by using transparent substrates, and the other using the previous set as a guideline for testing metal substrates under certain environmental conditions. Experimental results along with visual evidence support the consistent behavior between crack front behavior and compliance change. This correlation can be used as a baseline for testing other electronic packages for interfacial failure
MARA-Net: Single Image Deraining Network with Multi-level connections and Adaptive Regional Attentions
Removing rain streaks from single images is an important problem in various
computer vision tasks because rain streaks can degrade outdoor images and
reduce their visibility. While recent convolutional neural network-based
deraining models have succeeded in capturing rain streaks effectively,
difficulties in recovering the details in rain-free images still remain. In
this paper, we present a multi-level connection and adaptive regional attention
network (MARA-Net) to properly restore the original background textures in
rainy images. The first main idea is a multi-level connection design that
repeatedly connects multi-level features of the encoder network to the decoder
network. Multi-level connections encourage the decoding process to use the
feature information of all levels. Channel attention is considered in
multi-level connections to learn which level of features is important in the
decoding process of the current level. The second main idea is a wide regional
non-local block (WRNL). As rain streaks primarily exhibit a vertical
distribution, we divide the grid of the image into horizontally-wide patches
and apply a non-local operation to each region to explore the rich rain-free
background information. Experimental results on both synthetic and real-world
rainy datasets demonstrate that the proposed model significantly outperforms
existing state-of-the-art models. Furthermore, the results of the joint
deraining and segmentation experiment prove that our model contributes
effectively to other vision tasks
Structural System Reliability: Overview of Theories and Applications to Optimization
This paper provides an overview of theories and applications of structural system reliability (SSR). The paper defines SSR problems and discusses the growing needs for SSR analysis and technical challenges. Detailed literature reviews are provided for three subtopics: SSR methods for Boolean system events, SSR methods for sequential failures, and SSR-based design/topology optimization. Discussions of each subtopic define the target problem using mathematical formulations and categorize existing SSR methods in terms of the characteristics of the problems and approaches. The paper summarizes SSR methods that are considered critical in the history and have introduced notable technological developments in recent years. In each subtopic or category, the reviewed methods are compared with each other in terms of accuracy, computational efficiency, and implementation issues to allow identifying apposite methods for SSR applications. The paper concludes with remarks on future research needs and opportunities
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs
The discovery of the disentanglement properties of the latent space in GANs
motivated a lot of research to find the semantically meaningful directions on
it. In this paper, we suggest that the disentanglement property is closely
related to the geometry of the latent space. In this regard, we propose an
unsupervised method for finding the semantic-factorizing directions on the
intermediate latent space of GANs based on the local geometry. Intuitively, our
proposed method, called Local Basis, finds the principal variation of the
latent space in the neighborhood of the base latent variable. Experimental
results show that the local principal variation corresponds to the semantic
factorization and traversing along it provides strong robustness to image
traversal. Moreover, we suggest an explanation for the limited success in
finding the global traversal directions in the latent space, especially W-space
of StyleGAN2. We show that W-space is warped globally by comparing the local
geometry, discovered from Local Basis, through the metric on Grassmannian
Manifold. The global warpage implies that the latent space is not well-aligned
globally and therefore the global traversal directions are bound to show
limited success on it.Comment: 23 pages, 19 figure
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