Chong Zhou's repositories
awesome-open-gpt
Collection of Open Source Projects Related to GPT/GPT相关开源项目合集🚀、精选🛠
Article-Summarizer
Uses frequency analysis to summarize text.
awesome-rl
Reinforcement learning resources curated
co-with-gnns-example
组合优化问题 用图神经网络
Computer-Network-A-Top-Down-Approach-Answer
计算机网络-自顶向下方法 习题/编程/实验答案
deap
Distributed Evolutionary Algorithms in Python
deep-symbolic-optimization
A deep learning framework for symbolic optimization.
dynamic_pointer_network
an implementation of Pointer Network using tensorflow
gluon-tutorials-zh
通过MXNet/Gluon来动手学习深度学习
Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models
In the case of static PV modeling (single, double, and three diode models), the load variation and switching operation of the inverter and DC/DC converter stages are not considered. Therefore, another type of PV model named integer order dynamic PV model (IOM) has been introduced, which is the most efficient and accurate model to handle the static models' aforementioned drawbacks. That is why the dynamic model is the preferable one for the design of the grid-connected PV systems. Recently, the theory of fractional calculus has been employed to reinforce the efficiency and flexibility of IOM. As a result, the fractional-order dynamic PV model (FOM) has been introduced as the latest trend in tackling the PV models' dynamic behavior. The accuracy of the dynamic PV models is mainly influenced by obtaining their parameters under different operating conditions. The manufacturers usually undefine the models’ parameters. Therefore, it is crucial to identify these parameters accurately with minimum execution time using the experimental load current–time (I-T) curve [1]-[2]-[3]. [1]AbdelAty AM, Radwan AG, Elwakil AS, Psychalinos C. Transient and steady-state response of a fractional-order dynamic PV model under different loads. J Circ Syst Comput 2018;27(02):1850023. https://doi.org/10.1142/s0218126618500238 [2] Yousri, D., Allam, D., Eteiba, M.B. and Suganthan, P.N., 2019. Static and dynamic photovoltaic models’ parameters identification using Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants. Energy conversion and management, 182, pp.546-563. [3] Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters March 2021 Energy Conversion and Management ( In proofing). Note: To implement the code for optimizing the fractional order model. The user should click on fomcon-1.21b right click and select add to the path ( then select folders and subfolders) to let all the inside files are readable. Then use main to implement the optimization process
learn-to-select-data
Code for Learning to select data for transfer learning with Bayesian Optimization
mealpy
A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)
MORL
Multi-Objective Reinforcement Learning
NLP-Project
including text classifier, language model, pre_trained model, multi_label classifier, text generator, dialogue. etc
NPHard
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
OpenCV-Python-Tutorial
OpenCV问答群1,QQ群号:187436093
qrcodejs
Cross-browser QRCode generator for javascript
RSBook
推荐系统 刘宏志 北大
weather-rescue
Data from various Weather Rescue initiatives