Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

Liu, Yang; Dang, Lanxue; Li, Shenshen; Cai, Kun; Zuo, Xianyu

doi:10.1109/jstars.2021.3085893

Cited by 6 publications

(3 citation statements)

References 96 publications

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“…First, companies can change their conservative financing strategies into moderate financing strategies and use short-term financing to raise funds. Secondly, the enterprise itself can issue additional stocks to raise funds, increase equity financing, reduce debt financing, or can use the form of a "debt-to-equity swap" for related optimization, at the same time, it can also delay the enterprise's expenditure on interest, form "natural financing" and increase the utilization of funds [19]. Finally, enterprises can further optimize the industrial structure through fixed increase fundraising, actively respond to the impact of the power system reform on the traditional power business, and improve the layout of the distribution network energysaving service business.…”

Section: Rational Formulation Of Corporate Financingmentioning

confidence: 99%

Application of Big Data and Internet of Things Technology in the Management of Financial Operating Income of Electric Power Enterprises

2022

International Transactions on Electrical Energy Systems

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Aim. In order to effectively integrate and rationally allocate the complex information flow of people, finance, and materials in the daily operation of the enterprise and improve the daily operation efficiency and management effect of the enterprise, the author proposes a method for the application of big data and Internet of Things technology in the management of financial operating income of electric power enterprises. The method specifically includes an overview of the relevant theories of financial management based on ERP and an analysis of the needs of a power company’s financial management system based on ERP so as to construct an evaluation index system for the financial management system. The experimental results show that the return on assets of the experimental enterprises has increased from 27.48% in 2015 to 55.18% in 2019, an increase of 27.7%, and the profitability has been continuously enhanced. In 2019, the company achieved a net profit of 392 million yuan, an increase of about 4 times compared with the previous 77 million yuan in 2015. Conclusion. The application can further optimize the allocation of various resources of the enterprise and ensure the effective implementation of the long-term strategy and management goals of the enterprise on the basis of realizing the management needs of the enterprise.

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Section: Rational Formulation Of Corporate Financingmentioning

confidence: 99%

Application of Big Data and Internet of Things Technology in the Management of Financial Operating Income of Electric Power Enterprises

2022

International Transactions on Electrical Energy Systems

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“…In addition, Liu et al [37], and Wang et al [38] demonstrate an application of federated learning in anomaly detection in industrial environments using hybrid models. Furthermore, [26], [38] deploy a decentralized solution where edge devices collaborate to train anomaly detection models. Besides, [19] presents two anomaly detection approaches running on the device.…”

Section: B Hybrid Modelsmentioning

confidence: 99%

“…Related to the literature, the most representative anomaly detection techniques are as follows: noise reduction ( [3], [21]), outlier detection ( [22]- [24]), and tamper/threat detection ( [17], [25]). Based on [18], [26], we conducted an extensive literature review focused on determining the underlying algorithms and how these could be ported to emerging microcontrollers with better computational capacity. Therefore, we explore a range of possible algorithms to determine how well they work on small IoT devices.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Anomaly Detection Model on Trusted IoT Devices

Rosero-Montalvo

István

Tözün

et al. 2023

IEEE Internet Things J.

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Most machine learning proposals in the Internet of Things (IoT) are designed and evaluated on pre-processed datasets, where data acquisition and cleaning steps are often considered a black box. In addition, IoT environments have numerous challenges related to acquiring data from sensors, where sensitive data can be threatened by malicious users who seek to interfere with the communication channel or storage. Additionally, sensor data can also be affected by noise. Therefore, differentiating the type of threat/anomaly requires additional energy and computational resources. We propose to carry out data cleaning/anomaly techniques on the IoT device itself, not in the cloud servers but closer to the data source. Therefore, the IoT device sends trusted data to the Cloud. Among the benefits of this is the considerable reduction in the cost of implementation due to less movement of data between IoT devices and the Cloud. Consequently, we define three anomaly detection steps using smoothing filters, unsupervised learning, and deep learning techniques (i.e., hybrid model) to detect different variations of anomalies and threats while focusing on a small computational/memory footprint. The deployment of the hybrid model on AVR, Tensilica, and ARM microcontrollers showed that the last ones are an adequate target to implement the model because they best satisfy the necessary hardware requirements. The proposed model consumes 50KB of Flash and 12KB of RAM and processes data locally, achieving a bandwidth reduction of 60%. Finally, the hybrid model was tested in external datasets.

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