Seismic performance analysis of steel beam to CFST column connection with ductility and energy dissipation components

Concrete-filled steel tubular (CFST) column to steel beam joint with the ductility and energy dissipation components is a type of connection which is used in prefabricated structures, to improve the capacity of connections and construction efficiency. In this paper, two different type of steel beam to CFST column connections with the penetrated high-strength bolts and end-plate are investigated, i.e., steel beam to CFST column connection with end plate (CJ-1), and T-stub bolted (CJ-2) connections. The finite element model (FEM) of steel beam to CFST column connection with the penetrated high-strength bolts under cyclic loading are conducted based on the whole process of the nonlinear explicit analysis method using ABAQUS. The feasibility of FEM is verified by a set of experimental results performed by our research group, as well as available test results from other researchers. The failure modes, bearing capacity, energy dissipation capacity and ductility and rigidity degeneration were studied. As a result, the load-displacement hysteretic loop curve of CJ-2 connection is plump. However, the hysteresis curve of CJ-1 shows pinching phenomenon. The value of buckling load and ultimate load of CJ-2 increased by 28 % and 30 % respectively, compared with CJ-1. With respect of stress analysis, the plastic strain accumulation position distribution is relatively uniform duo to the T-stub connection, avoiding the penetrated high-strength bolt early yield or fracture. The results show that the steel beam to CFST column connection with penetrated bolts and T-stub connection has good seismic capacity

High-Resolution ADCs Design in Image Sensors

This paper presents design considerations for high-resolution and high-linearity ADCs for biomedical imaging ap-plications. The work discusses how to improve dynamic spec-ifications such as Spurious Free Dynamic Range (SFDR) and Signal-to-Noise-and-Distortion Ratio (SNDR) in ultra-low power and high-resolution analog-to-digital converters (ADCs) including successive approximation register (SAR) for biomedical imaging application. The results show that with broad range of mismatch error, the SFDR is enhanced by about 10 dB with the proposed performance enhancement technique, which makes it suitable for high resolution image sensors sensing systems

Language Semantic Graph Guided Data-Efficient Learning

Developing generalizable models that can effectively learn from limited data and with minimal reliance on human supervision is a significant objective within the machine learning community, particularly in the era of deep neural networks. Therefore, to achieve data-efficient learning, researchers typically explore approaches that can leverage more related or unlabeled data without necessitating additional manual labeling efforts, such as Semi-Supervised Learning (SSL), Transfer Learning (TL), and Data Augmentation (DA). SSL leverages unlabeled data in the training process, while TL enables the transfer of expertise from related data distributions. DA broadens the dataset by synthesizing new data from existing examples. However, the significance of additional knowledge contained within labels has been largely overlooked in research. In this paper, we propose a novel perspective on data efficiency that involves exploiting the semantic information contained in the labels of the available data. Specifically, we introduce a Language Semantic Graph (LSG) which is constructed from labels manifest as natural language descriptions. Upon this graph, an auxiliary graph neural network is trained to extract high-level semantic relations and then used to guide the training of the primary model, enabling more adequate utilization of label knowledge. Across image, video, and audio modalities, we utilize the LSG method in both TL and SSL scenarios and illustrate its versatility in significantly enhancing performance compared to other data-efficient learning approaches. Additionally, our in-depth analysis shows that the LSG method also expedites the training process.Comment: Accepted by NeurIPS 202

High Linearity SAR ADC for Smart Sensor Applications

This paper presents capacitive array optimization technique to improve the Spurious Free Dynamic Range (SFDR) and Signal-to-Noise-and-Distortion Ratio (SNDR) of Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) for smart sensor application. Monte Carlo simulation results show that capacitive array optimization technique proposed can make the SFDR, SNDR and (Signal-to-Noise Ratio) SNR more concentrated, which means the differences between maximum value and minimum value of SFDR, SNDR and SNR are much smaller than the conventional calibration techniques, more stable performance enhancement can be achieved, and the averaged SFDR is improved from 72.9 dB to 91.1 dB by using the capacitive array optimization method, 18.2 dB improvement of SFDR is obtained with only little expense of digital logic circuits, which makes it good choice for high resolution and high linearity smart sensing systems

Diffraction limit of light in curved space

Overcoming diffraction limit is crucial for obtaining high-resolution image and observing fine microstructure. With this conventional difficulty still puzzling us and the prosperous development of wave dynamics of light interacting with gravitational fields in recent years, how spatial curvature affect the diffraction limit is an attractive and important question. Here we investigate the issue of diffraction limit and optical resolution on two-dimensional curved spaces - surfaces of revolution (SORs) with constant or variable spatial curvature. We show that the diffraction limit decreases and resolution is improved on SORs with positive Gaussian curvature, opening a new avenue to super-resolution. The diffraction limit is also influenced by propagation direction, as well as the propagation distance in curved space with variable spatial curvature. These results provide a possible method to control optical resolution in curved space or equivalent waveguides with varying refractive index distribution and may allow one to detect the presence of non-uniform strong gravitational effect by probing locally the optical resolution