惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Martin Fowler
Martin Fowler
Jina AI
Jina AI
J
Java Code Geeks
Microsoft Security Blog
Microsoft Security Blog
Recent Announcements
Recent Announcements
I
InfoQ
L
LangChain Blog
The Cloudflare Blog
IT之家
IT之家
博客园 - 叶小钗
Apple Machine Learning Research
Apple Machine Learning Research
B
Blog
A
About on SuperTechFans
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Last Week in AI
Last Week in AI
Blog — PlanetScale
Blog — PlanetScale
罗磊的独立博客
云风的 BLOG
云风的 BLOG
Microsoft Azure Blog
Microsoft Azure Blog
Engineering at Meta
Engineering at Meta
F
Fortinet All Blogs
博客园 - 聂微东
美团技术团队
博客园_首页

cs.NE updates on arXiv.org

MPCS: Neuroplastic Continual Learning via Multi-Component Plasticity and Topology-Aware EWC Combining Trained Models in Reinforcement Learning Training Non-Differentiable Networks via Optimal Transport ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance Benchmarking local Hebbian learning rules for memory storage and prototype extraction Robust volatility updates for Hierarchical Gaussian Filtering Spiking Sequence Machines and Transformers Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks Attractor FCM Physical Foundation Models: Fixed hardware implementations of large-scale neural networks When Does Structure Matter in Continual Learning? Dimensionality Controls When Modularity Shapes Representational Geometry Learning to Forget: Continual Learning with Adaptive Weight Decay Causal Learning with Neural Assemblies NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning Text-Utilization for Encoder-dominated Speech Recognition Models EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming Analysis and Explainability of LLMs Via Evolutionary Methods Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks Learn&Drop: Fast Learning of CNNs based on Layer Dropping Architecture-Induced Recoverability Bias in Differentiable Symbolic Regression Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction
Investigating Wave Energy Potential in Southern Coasts of...
Erfan Amini, Seyed Taghi Omid Naeeni, Pedram Ghaderi, Fereidoun · 2019-12-31 · via cs.NE updates on arXiv.org

There is a significantly accelerating trend in the application of the marine wave energy converters in recent years. As a result, it is imperative to adopt a suitable point for implementing these systems. Besides, the Caspian Sea, as one of the most important marine renewable energy sources in Asia, is capable of supplying the coastal areas with a large amount of energy. Therefore, areas around nine ports in the southern coasts of the Caspian Sea were selected to measure their wave energy potential. Initially, the amount of energy on these points was measured using the irregular energy theory. It was observed that the wave power was higher in the southwestern areas (within the Kiashahr coast and Anzali port) than the southeastern areas. A new approach was developed to compare these points and measure their fitnesses in supplying the maximum energy using the Grey Wolf optimizer (GWO) algorithm and time history analysis. In this method, the optimal parameters were first extracted from the algorithm for assessing the points within the southern areas of the Caspian Sea. These values were regarded as the assessment indices. Then, the fitness of each point was obtained using the correlation function and the norm vector to present the most optimal position with maximum wave energy exploitation potential. This new approach was validated with analytical data, and its accuracy in predicting and comparing the wave power on different points was approved. Finally, by a side-by-side comparison of the parameters affecting the wave energy, the optimum range of significant wave height and wave energy period was achieved.