Analysis of Superconducting Magnetic Energy Storage Used in a Submarine HVAC Cable Based Offshore Wind System

Because of the booming development of offshore wind power around the world, a stable transmission system which is used for the connection between the offshore wind farms and the onshore grid is required. For the offshore wind farms not far from the coast, high voltage alternating current (HVAC) transmission system is the best choice. Aiming to study the transient problems caused by cable operation, a 60 km submarine cable is modeled in this paper using ATP-EMTP. The larger capacitance effect of HVAC submarine cables will cause more severe transient problems. Also, the variable wind power generated by offshore wind farm will bring undesired impact on the onshore power grid. This paper proposes a superconducting magnetic energy storage (SMES) system which can mitigate both the high frequency fluctuation of wind power and the transient over voltage of the HVAC cable system. In addition, SMES sizing study has been done to achieve the proposed functions

SiGe HBT X-Band LNAs for Ultra-Low-Noise Cryogenic Receivers

We report results on the cryogenic operation of two different monolithic X-band silicon-germanium (SiGe) heterojunction bipolar transistor low noise amplifiers (LNAs) implemented in a commercially-available 130 nm SiGe BiCMOS platform. These SiGe LNAs exhibit a dramatic reduction in noise temperature with cooling, yielding Teff of less than 21 K (0.3 dB noise figure) across X-band at a 15 K operating temperature. To the authors’ knowledge, these SiGe LNAs exhibit the lowest broadband noise of any Si-based LNA reported to date

IG Captioner: Information Gain Captioners are Strong Zero-shot Classifiers

Generative training has been demonstrated to be powerful for building visual-language models. However, on zero-shot discriminative benchmarks, there is still a performance gap between models trained with generative and discriminative objectives. In this paper, we aim to narrow this gap by improving the efficacy of generative training on classification tasks, without any finetuning processes or additional modules. Specifically, we focus on narrowing the gap between the generative captioner and the CLIP classifier. We begin by analysing the predictions made by the captioner and classifier and observe that the caption generation inherits the distribution bias from the language model trained with pure text modality, making it less grounded on the visual signal. To tackle this problem, we redesign the scoring objective for the captioner to alleviate the distributional bias and focus on measuring the gain of information brought by the visual inputs. We further design a generative training objective to match the evaluation objective. We name our model trained and evaluated from the novel procedures as Information Gain (IG) captioner. We pretrain the models on the public Laion-5B dataset and perform a series of discriminative evaluations. For the zero-shot classification on ImageNet, IG captioner achieves $> 18\%$ improvements over the standard captioner, achieving comparable performances with the CLIP classifier. IG captioner also demonstrated strong performance on zero-shot image-text retrieval tasks on MSCOCO and Flickr30K. We hope this paper inspires further research towards unifying generative and discriminative training procedures for visual-language models