Event-Triggered H

This paper deals with H∞ controller design problem for event-triggered networked control systems (NCSs), where the next task release time and finishing time are predicted based on the sampled states. A new model of NCSs that involves the network conditions, state, and event-triggered communication strategy is proposed. Based on this model, some novel criteria for the asymptotic stability analysis and H∞ state feedback controller design of the event-triggered NCSs with timevarying delay are established to guarantee a prescribed H∞ disturbance rejection attenuation level. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method

Microstructure evolution and elemental diffusion behavior near the interface of Cr2AlC and single crystal superalloy DD5 at elevated temperatures

As one of the promising MAX phase materials for high-temperature applications, Cr2AlC is considered as a potential substitution bond coat material in thermal barrier coating systems. In this paper, the microstructure evolution and elemental diffusion behavior near the interface of the diffusion couple composed of Cr2AlC and single crystal superalloy DD5 were investigated at 1100 °C, 1150 °C, and 1200 °C. Elemental interdiffusion between Cr2AlC and DD5 occurs significantly, resulting in the formation of a thick layer of Kirkendall holes after 20 h heat treatment at 1100 °C and higher temperatures. The outward diffusion of Ni into Cr2AlC and the inward diffusion of Al into DD5 alloy causes the formation of β-NiAl matrix embedded with dispersed Cr7C3 phase. Simultaneously, the precipitation of σ-TCP phase and degradation of the γ/γ′ matrix occurs in the alloy. Additionally, TaC, M2C (where M = Ta, W, Cr), and M23C6 (M = Cr, Re, W) compounds are formed near the interface along with the dissolution of σ-TCP phases. It is further found that Al in Cr2AlC exhibits the highest average effective diffusion coefficient among the four dominant diffusing elements. It also displays the lowest diffusion activation energy which is due to its relatively weak Cr-Al and Al-Al bonds

3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses

3D-aware face generators are typically trained on 2D real-life face image datasets that primarily consist of near-frontal face data, and as such, they are unable to construct one-quarter headshot 3D portraits with complete head, neck, and shoulder geometry. Two reasons account for this issue: First, existing facial recognition methods struggle with extracting facial data captured from large camera angles or back views. Second, it is challenging to learn a distribution of 3D portraits covering the one-quarter headshot region from single-view data due to significant geometric deformation caused by diverse body poses. To this end, we first create the dataset 360{\deg}-Portrait-HQ (360{\deg}PHQ for short) which consists of high-quality single-view real portraits annotated with a variety of camera parameters (the yaw angles span the entire 360{\deg} range) and body poses. We then propose 3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator that learns a canonical 3D avatar distribution from the 360{\deg}PHQ dataset with body pose self-learning. Our model can generate view-consistent portrait images from all camera angles with a canonical one-quarter headshot 3D representation. Our experiments show that the proposed framework can accurately predict portrait body poses and generate view-consistent, realistic portrait images with complete geometry from all camera angles

An Early Evaluation of GPT-4V(ision)

In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such as depth, thermal, video, and audio. To estimate GPT-4V's performance, we manually construct 656 test instances and carefully evaluate the results of GPT-4V. The highlights of our findings are as follows: (1) GPT-4V exhibits impressive performance on English visual-centric benchmarks but fails to recognize simple Chinese texts in the images; (2) GPT-4V shows inconsistent refusal behavior when answering questions related to sensitive traits such as gender, race, and age; (3) GPT-4V obtains worse results than GPT-4 (API) on language understanding tasks including general language understanding benchmarks and visual commonsense knowledge evaluation benchmarks; (4) Few-shot prompting can improve GPT-4V's performance on both visual understanding and language understanding; (5) GPT-4V struggles to find the nuances between two similar images and solve the easy math picture puzzles; (6) GPT-4V shows non-trivial performance on the tasks of similar modalities to image, such as video and thermal. Our experimental results reveal the ability and limitations of GPT-4V and we hope our paper can provide some insights into the application and research of GPT-4V.Comment: Technical Report. Data are available at https://github.com/albertwy/GPT-4V-Evaluatio

SDM-NET: Deep Generative Network for Structured Deformable Mesh

We introduce SDM-NET, a deep generative neural network which produces structured deformable meshes. Specifically, the network is trained to generate a spatial arrangement of closed, deformable mesh parts, which respect the global part structure of a shape collection, e.g., chairs, airplanes, etc. Our key observation is that while the overall structure of a 3D shape can be complex, the shape can usually be decomposed into a set of parts, each homeomorphic to a box, and the finer-scale geometry of the part can be recovered by deforming the box. The architecture of SDM-NET is that of a two-level variational autoencoder (VAE). At the part level, a PartVAE learns a deformable model of part geometries. At the structural level, we train a Structured Parts VAE (SP-VAE), which jointly learns the part structure of a shape collection and the part geometries, ensuring a coherence between global shape structure and surface details. Through extensive experiments and comparisons with the state-of-the-art deep generative models of shapes, we demonstrate the superiority of SDM-NET in generating meshes with visual quality, flexible topology, and meaningful structures, which benefit shape interpolation and other subsequently modeling tasks.Comment: Conditionally Accepted to Siggraph Asia 201