This paper employs deep learning technique to perform the research of text mining for power grid networks, focusing on fundamental elements such as loss and activation functions. Through some analysis and formulas, we explain how these functions contribute to deep learning. We also introduce major deep learning training models, including CNN and RNN, and provide visual aids to aid understanding. To demonstrate the impact of various factors on deep learning training, we employ control variable experiments to analyze the influence of factors such as learning rate, batch size, and data noise on model training trends. While the influence of hyperparameters and data noise are covered in this paper, other factors such as CPU and memory frequency, as well as GPU performance, also play a crucial role in deep learning training. Therefore, continuous adjustments to various factors are necessary to achieve optimal training results for deep learning models in power grid networks.

https://publications.eai.eu/index.php/sis/article/download/3094/2378

Research on the Performance of Text Mining and Processing in Power Grid Networks

Motivated by the progress in artificial intelligence such as deep learning and IoT networks, this paper presents an intelligent flink framework for real-time voltage computing systems in autonomous and controllable environments. The proposed framework employs machine learning algorithms to predict voltage values and adjust them in real-time to ensure the optimal performance of the power grid. The system is designed to be autonomous and controllable, enabling it to adapt to changing conditions and optimize its operation without human intervention. The paper also presents experimental results that demonstrate the effectiveness of the proposed framework in improving the accuracy and efficiency of voltage computing systems. Simulation results are provided to verify that the proposed intelligent flink framework can work well for real-time voltage computing systems in autonomous and controllable environments, compared with the conventional DRL and cross-entropy methods, in terms of convergence rate and estimation result. Overall, the intelligent flink framework presented in this paper has the potential to significantly improve the performance and reliability of power grids, leading to more efficient and sustainable energy systems.

/pdf/intelligent-flink-framework-aided-real-time-voltage-tqeqe5qg.pdf

Intelligent Flink Framework Aided Real-Time Voltage Computing Systems in Autonomous and Controllable Environments

Motivated by the progress in artificial intelligence and edge computing, this paper proposes a real-time distributed computing model for low-voltage flow data in digital power grids under autonomous and controllable environments. The model utilizes edge computing through wireless offloading to efficiently process and analyze data generated by low-voltage devices in the power grid. Firstly, we evaluate the performance of the system under consideration by measuring its outage probability, utilizing both the received signal-to-noise ratio (SNR) and communication and computing latency. Subsequently, we analyze the system’s outage probability by deriving an analytical expression. To this end, we utilize the Gauss-Chebyshev approximation to provide an approximate closed-form expression. The results of our experimental evaluation demonstrate the effectiveness of the proposed model in achieving real-time processing of low-voltage flow data in digital power grids. Our model provides an efficient and practical solution for the processing of low-voltage flow data, making it a valuable contribution to the field of digital power grids.

/pdf/real-time-distributed-computing-model-of-low-voltage-flow-3qwdrlty.pdf

Real-time Distributed Computing Model of Low-Voltage Flow Data in Digital Power Grid under Autonomous and Controllable Environments

Hydrogel polymer networks are non-covalently crosslinked soft materials that exhibit unique mechanical and excellent bio-characteristics such as adapting to environmental forces and strengthening to the conditions they experience. Quantifying these characteristics are important for their usage in multitude of applications especially where new formulations or composites of hydrogels being prepared such as for 3D printing. Often such characteristics are quantified using destruc-tive methods and/or using complex mathematical models, which can destroy samples, are limiting and time-consuming. Herein, we describe non-destructive hyperspectral imaging (HSI) based machine vision approach that enable faster characterisation of hydrogel's mechanical properties. Several measurements of hydrogel samples were collected using HSI to develop a database used to train a Gaussian Process Regression (GPR) based learning model. From the confusion matrix results, we observed the >90% accuracy, which we think can provide relatively good performance compared to other traditional methods. We conclude that HSI with machine vision could simplify the measurement procedure, allowing for a more straightforward characterisation of hydrogel samples, is fast and reproducible to provide serve as both an in-process and a quality control method. 

Exploring non-destructive mechanical characterisation of hydrogels using hyperspectral imaging and machine vision

This paper aims to investigate an intrusion detection network for digital power grid networks, which consists of an edge server and two computational nodes that work collaboratively to detect any potential intrusion in the network. The primary objective of this study is to enhance the effectiveness of intrusion detection in the network. To achieve this objective, we first define the outage probability of the intrusion detection system under consideration. This is done to provide a measure of the probability that the system fails to detect an intrusion when it occurs. We then derive a closed-form expression for the outage probability to enable further analysis on the system behavior. Since the system resources, such as transmit power, are limited, we further design a transmit power allocation strategy to improve the system performance. This strategy seeks to optimize the allocation of transmit power across the different nodes of the intrusion detection network to maximize the likelihood of detecting intrusions while minimizing the resource usage. Finally, to evaluate the performance of the proposed system, we conduct simulations and provide results that demonstrate the accuracy of the closed-form expression and the effectiveness of the transmit power allocation strategy. These simulation results serve as evidence of the efficacy of the proposed approach in detecting intrusions in a resource-constrained network, especially for the digital power grid networks.

/pdf/research-on-intrusion-detection-technology-of-computing-83aafe7b.pdf

Research on Intrusion Detection Technology of Computing Nodes in Digital Power Grid based on Artificial Intelligence

This work addresses testing the independence of two continuous and finite-dimensional random variables from the design of a data-driven partition. The empirical log-likelihood statistic is adopted to approximate the sufficient statistics of an oracle test against independence (that knows the two hypotheses). It is shown that approximating the sufficient statistics of the oracle test offers a learning criterion for designing a data-driven partition that connects with the problem of mutual information estimation. Applying these ideas in the context of a data-dependent tree-structured partition (TSP), we derive conditions on the TSP's parameters to achieve a strongly consistent distribution-free test of independence over the family of probabilities equipped with a density. Complementing this result, we present finite-length results that show our TSP scheme's capacity to detect the scenario of independence structurally with the data-driven partition as well as new sampling complexity bounds for this detection. Finally, some experimental analyses provide evidence regarding our scheme's advantage for testing independence compared with some strategies that do not use data-driven representations.

Data-Driven Representations for Testing Independence: Modeling, Analysis and Connection with Mutual Information Estimation

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Data-Driven Representations for Testing Independence: Modeling, Analysis and Connection with Mutual Information Estimation