44 research outputs found
Small-signal modelling of AC/MTDC hybrid power systems using Multi-Layer Component Connection Method
When Do Program-of-Thoughts Work for Reasoning?
The reasoning capabilities of Large Language Models (LLMs) play a pivotal
role in the realm of embodied artificial intelligence. Although there are
effective methods like program-of-thought prompting for LLMs which uses
programming language to tackle complex reasoning tasks, the specific impact of
code data on the improvement of reasoning capabilities remains under-explored.
To address this gap, we propose complexity-impacted reasoning score (CIRS),
which combines structural and logical attributes, to measure the correlation
between code and reasoning abilities. Specifically, we use the abstract syntax
tree to encode the structural information and calculate logical complexity by
considering the difficulty and the cyclomatic complexity. Through an empirical
analysis, we find not all code data of complexity can be learned or understood
by LLMs. Optimal level of complexity is critical to the improvement of
reasoning abilities by program-aided prompting. Then we design an
auto-synthesizing and stratifying algorithm, and apply it to instruction
generation for mathematical reasoning and code data filtering for code
generation tasks. Extensive results demonstrates the effectiveness of our
proposed approach. Code will be integrated into the EasyInstruct framework at
https://github.com/zjunlp/EasyInstruct.Comment: Work in progres
Novel adaptive stability enhancement strategy for power systems based on deep reinforcement learning
As the access rate of wind energy in a power system has significantly increased, stabilizing the power system has become challenging. Among these challenges, low-frequency oscillation is one of the most harmful problems, effectively resolved by adding a damping controller according to the relevant properties of the low-frequency oscillation. However, the controller often fails to adapt to the constantly changing wind energy system owing to the lack of a targeted dynamic change strategy. Thus, to address this issue, an adaptive stabilization strategy that uses a static var compensator with an additional damping controller structure is proposed. Specifically, the entire power system is equivalently represented as a generalized regression neural network, with a deep reinforcement learning algorithm called soft actor-critic introduced to train the agent based on the generalized regression neural network model. After the training process, the agent can provide additional efficient static var compensator damping controller parameters under different operating conditions, vastly improving the system stability. Simulation results verify the improved performance using the proposed strategy compared to other optimization methods, regardless of whether the low-frequency oscillations were suppressed in the time or frequency domains
OceanGPT: A Large Language Model for Ocean Science Tasks
Ocean science, which delves into the oceans that are reservoirs of life and
biodiversity, is of great significance given that oceans cover over 70% of our
planet's surface. Recently, advances in Large Language Models (LLMs) have
transformed the paradigm in science. Despite the success in other domains,
current LLMs often fall short in catering to the needs of domain experts like
oceanographers, and the potential of LLMs for ocean science is under-explored.
The intrinsic reason may be the immense and intricate nature of ocean data as
well as the necessity for higher granularity and richness in knowledge. To
alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean
domain, which is expert in various ocean science tasks. We propose DoInstruct,
a novel framework to automatically obtain a large volume of ocean domain
instruction data, which generates instructions based on multi-agent
collaboration. Additionally, we construct the first oceanography benchmark,
OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though
comprehensive experiments, OceanGPT not only shows a higher level of knowledge
expertise for oceans science tasks but also gains preliminary embodied
intelligence capabilities in ocean technology. Codes, data and checkpoints will
soon be available at https://github.com/zjunlp/KnowLM.Comment: Work in progress. Project Website:
https://zjunlp.github.io/project/OceanGPT