Zobrazeno 1 - 10
of 6 750
pro vyhledávání: '"Yanzhao An"'
Publikováno v:
Case Studies in Thermal Engineering, Vol 61, Iss , Pp 105085- (2024)
The air inlet holes on the swirl combustor are crucial for high-performance micro gas turbine (MGT), affecting internal airflow, fuel-air mixing, temperature distribution, and cooling. However, there are seldom studies that pay attention to the influ
Externí odkaz:
https://doaj.org/article/606ab5c9be564c5895882d99a23ed3ab
Publikováno v:
Green Energy and Resources, Vol 1, Iss 1, Pp 100003- (2023)
Regional energy systems are designed to contribute to a green and “carbon neutral” economy of localities. In this system, the engine combustion is significant for power generation. Therefore, this study mainly investigated the effect of throttle
Externí odkaz:
https://doaj.org/article/61ac5d8cae7e4e159ffd181a32b98988
Autor:
Fang, Yanzhao
The goal of multi-object tracking (MOT) is to detect and track all objects in a scene across frames, while maintaining a unique identity for each object. Most existing methods rely on the spatial-temporal motion features and appearance embedding feat
Externí odkaz:
http://arxiv.org/abs/2411.10028
Autor:
Jin, Hongpeng, Wu, Yanzhao
Large Language Models (LLMs) have achieved remarkable success in serving end-users with human-like intelligence. However, LLMs demand high computational resources, making it challenging to deploy them to satisfy various performance objectives, such a
Externí odkaz:
http://arxiv.org/abs/2411.02829
Text embeddings are vital for tasks such as text retrieval and semantic textual similarity (STS). Recently, the advent of pretrained language models, along with unified benchmarks like the Massive Text Embedding Benchmark (MTEB), has facilitated the
Externí odkaz:
http://arxiv.org/abs/2410.15035
Autor:
Lin, Mingan, Yang, Fan, Shen, Yanjun, Sun, Haoze, Li, Tianpeng, Zhang, Tao, Zhu, Chenzheng, Zheng, Miao, Li, Xu, Zhou, Yijie, Chen, Mingyang, Qin, Yanzhao, Li, Youquan, Liang, Hao, Li, Fei, Li, Yadong, Wang, Mang, Dong, Guosheng, Fang, Kun, Xu, Jianhua, Cui, Bin, Zhang, Wentao, Zhou, Zenan, Chen, Weipeng
We introduce Nova, a suite of practical alignment techniques employed in a series of empirically validated high-performing models. This represents the first comprehensive account of alignment methodologies, offering valuable insights for advancing AI
Externí odkaz:
http://arxiv.org/abs/2410.14940
We propose a novel framework, Stable Diffusion-based Momentum Integrated Adversarial Examples (SD-MIAE), for generating adversarial examples that can effectively mislead neural network classifiers while maintaining visual imperceptibility and preserv
Externí odkaz:
http://arxiv.org/abs/2410.13122
Autor:
Jin, Hongpeng, Wu, Yanzhao
The Learning Rate (LR) has a high impact on deep learning training performance. A common practice is to train a Deep Neural Network (DNN) multiple times with different LR policies to find the optimal LR policy, which has been widely recognized as a d
Externí odkaz:
http://arxiv.org/abs/2410.07564
Autor:
Yin, Qingyu, He, Xuzheng, Deng, Luoao, Leong, Chak Tou, Wang, Fan, Yan, Yanzhao, Shen, Xiaoyu, Zhang, Qiang
Fine-tuning and in-context learning (ICL) are two prevalent methods in imbuing large language models with task-specific knowledge. It is commonly believed that fine-tuning can surpass ICL given sufficient training samples as it allows the model to ad
Externí odkaz:
http://arxiv.org/abs/2410.04691
Brain CT report generation is significant to aid physicians in diagnosing cranial diseases. Recent studies concentrate on handling the consistency between visual and textual pathological features to improve the coherence of report. However, there exi
Externí odkaz:
http://arxiv.org/abs/2409.19676