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Abstract The surge in demand for cost-effective, lightweight, and rapidly responsive sensors has propelled research in various fields, and traditional sensors face limitations in performing up to the mark due to their intrinsic properties and a lack of innovative fabrication techniques. Consequently, over the last decade, a notable shift has been toward harnessing naturally existing nanostructures to develop efficient and versatile sensing devices. One such nanostructure in morpho butterfly wings has attracted attention because of its vibrant uniqueness and diverse sensing properties. This review will explore recent interdisciplinary research endeavors on the nanostructure, including chemical, vapor, and acoustic detection. Furthermore, its potential as an infrared sensor, considerations related to heat transfer properties, and a brief overview of various replication techniques and challenges encountered in reproducing the intricate nanostructure are discussed.

Abstract This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep learning models in FWI behave when trained on openfwi, a collection of large-scale, multi-structural, synthetic datasets published recently. In particular, we train and evaluate the FWI models on a combination of 10 2D subsets in openfwi that contain 470 K pairs of seismic data and velocity maps in total. Our experiments demonstrate that training on the combined dataset yields an average improvement of 13.03% in MAE, 7.19% in MSE and 1.87% in SSIM compared to each split dataset, and an average improvement of 28.60%, 21.55% and 8.22% in the leave-one-out generalization test. We further demonstrate that model capacity needs to scale in accordance with data size for optimal improvement, where our largest model yields an average improvement of 20.06%, 13.39% and 0.72% compared to the smallest one.

The wake effect reduces the wind speed at downstream wind turbines (WTs), making it necessary for the central controller to collect wind power generation data from each WT. However, wind farms (WFs) face a more complex problem in maintaining the voltage stability at the WT terminal while following the transmission system operator (TSO) instructions due to the information collection as well as the possible data loss during transmission. Therefore, this study proposes a coordinated control method for WTs and multiple power sources based on model predictive control under wake disturbance conditions, aiming to reduce the average voltage deviation in WT terminals and go close to the rated voltage and ensure effective compliance with TSO commands in large-scale WFs. Accordingly, the Jensen wake model was utilized to accurately calculate the available active and reactive power limits for each WT. Energy storage systems and static Var generators were modeled to coordinate and maintain the voltage in all WT terminals within the feasible range, providing peak shaving and valley filling support to reduce wind energy waste and shortfall, thereby enhancing the economic and operational reliability of WF. Further, the effectiveness of the proposed method was validated in MATLAB/Simulink.

The integration of energy storage facilities into existing structures will result in increased costs. Therefore, it is of great significance to optimize the configuration of integrated power systems with multienergy flows to reduce the cost of the comprehensive utilization of energy. This study established a wind-solar-battery-fuel cell integrated power supply system to optimize the grid-connected regional power supply. First, the load is given with a known daily energy demand. The optimization goal is to minimize the average annual cost and the loss of power supply probability. The cost model includes the constraints of transportation costs and the benefits of selling hydrogen and oxygen. Then, an improved genetic algorithm is developed to optimize the structure of the wind-solar-battery-fuel cell integrated power supply system. The basic idea of the improved genetic algorithm is to change the coordination mode of the crossover operator and the mutation operator according to the size of the initial population fitness. Finally, according to the calculation results of the improved genetic algorithm, the optimal configuration of the capacity of devices in the system is obtained, verifying the effectiveness of the improved genetic algorithm. The cost calculation result of the genetic algorithm is 18.7% higher than that of the improved genetic algorithm, and it completely converges at approximately 70 steps. The cost calculation result of the particle swarm optimization is 17.1% higher than that of the improved genetic algorithm, and it completely converges at approximately 75 steps. The cost calculation result of the nondominated sorting genetic algorithm is 9.6% higher than that of the improved genetic algorithm, and it completely converges at approximately 58 steps. The system established in this research can fully meet the power demands for a given area and effectively reduce the local curtailment of wind energy and solar energy.

This paper aims to develop a generalized framework to systematically trade off computational complexity with output distortion in linear transforms such as the DCT, in an optimal manner. The problem is important in real-time systems where the computational resources available are time-dependent. Our approach is generic and applies to any linear transform and we use the DCT as a specific example. There are three key ideas: (a) a joint transform pruning and Haar basis projection-based approximation technique. The idea is to save computations by factoring the DCT transform into signal-independent and signal-dependent parts. The signal-dependent calculation is done in real-time and combined with the stored signal-independent part, saving calculations. (b) We propose the idea of the complexity-distortion framework and present an algorithm to efficiently estimate the complexity distortion function and search for optimal transform approximation using several approximation candidate sets. We also propose a measure to select the optimal approximation candidate set, and (c) an adaptive approximation framework in which the operating points on the C-D curve are embedded in the metadata. We also present a framework to perform adaptive approximation in real time for changing computational resources by using the embedded metadata. Our results validate our theoretical approach by showing that we can reduce transform computational complexity significantly while minimizing distortion.