An approach to XBRL interoperability based on Ant Colony Optimization algorithm

Yaghoobirafi, Kamaleddin; Nazemi, Eslam

doi:10.1016/j.knosys.2018.08.038

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…With the gradual construction of the power grid, the total length of long-distance, large-capacity AC and DC transmission lines of various voltage levels are gradually increasing. Most of the overhead transmission lines are erected in the wild, passing through hills, mountains, rivers, and other terrains, and are extremely susceptible to various extreme weather and harsh environments and cause failures [3,4]. e precise location of the fault point of the transmission line can reduce the manpower and material resources consumed by the line inspection work after the fault and improve the reliability of the power supply [5].…”

Section: Introductionmentioning

confidence: 99%

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Liu

2021

Security and Communication Networks

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After a transmission line fails, quickly and accurately find the fault point and deal with it, which is of great significance to maintaining the normal operation of the power system. Aiming at the problems of low accuracy of traditional traveling wave fault location methods and many affected factors, this paper relies on distributed traveling wave monitoring points arranged on transmission lines to study methods to improve the accuracy of traveling wave fault location on transmission lines. First, when a line fails, a traveling wave signal that moves to both ends will be generated and transmitted along the transmission line. We use the Radon transform algorithm to process the traveling wave signal. Then, this paper uses ant colony algorithm to analyze and verify the location and extent of transmission line faults and then optimizes high-precision collection and processing. Finally, the simulation distance measurement is carried out on double-terminal transmission lines and multiterminal transmission lines (T-shaped lines) with branches. The results show that, for double-ended transmission lines, the algorithm increases the speed of matrix calculations and at the same time makes the fault location error of the transmission grid still maintain the improved effect.

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Section: Introductionmentioning

confidence: 99%

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Liu

2021

Security and Communication Networks

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“…Connected to comparability aspects, several sources have been reviewed that are related to the semantic issue of industrial classification. Being a driver of electronic data interchange, XBRL data sets are constructed from multiple identifying tags and numerical data that can be processed by computer software [3]. While the technical background on data-centric analysis is available [4] 2013 [5] 2014, [6], it is uncommon in the industry-specific research literature that XBRL databases are used as the primary source of data.…”

Section: Xbrl Utilization In Industry-specific Data Analysismentioning

confidence: 99%

XBRL utilization as an automated industry analysis

Suta¹,

Tóth²

2020

Hung. J. Ind. Chem.

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In the last two decades, electronic financial reporting went through a significant evolution, where to date, eXtensible Business Reporting Language (XBRL) has become the leading platform that is already obligatory for listed entities in the United States and was also legislated in the European Union from January 1, 2020. The primary objective of this research was to review the US-listed companies' 2018 quarterly reports. The study generated an automated industry analysis for the automotive industry from the aspect of four main financial item categories as an alternative to statistics-based, manually prepared industry analyses. Statistical tests were carried out between two industrial classification methodologies, the securities' industry identification marks and the reported Standard Industrial Classification (SIC) codes. The results showed a significant difference between the industry classification methodologies. Automated reporting was more precise with regard to the identification of the listed and reporting entities, however, the data fields of SIC codes within the XBRL data set provided an inaccurate classification, which is a potential area of improvement along with additional recommendations outlined in the Conclusion.

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“…Adapun cara kerja XBRL dengan melakukan penandaan tiap elemen data untuk memfasilitasi inter-operabilitas dari dokumen bisnis [2][3][4]. XBRL memungkinkan pelaporan dan pemantauan secara real-time karena kemudahan penanganan data.…”

Section: Pendahuluanunclassified

“…XBRL merupakan XML (Extensible Markup Language) yang dikembangkan sebagai bahasa pemrograman dalam pelaporan bisnis dengan cara memberi identifikasi atau penandaan masing-masing data, yang bertujuan untuk meningkatkan inter-operabilitas [4]. Interoperabilitas mengacu kepada penggunaan antar sistem yang berbeda dari berbagai entitas/pihak secara terpadu sehingga memfasilitasi kerja sama, kolaborasi, dan keselarasan operasi, termasuk inter-operabilitas data [29].…”

Section: Kajian Pustaka 21 Extensible Business Reporting Language (Xbrl)unclassified

The Impact of XBRL Adoption on Audit Delay in the Financial and Non-Financial Industries in Indonesia

Saputro¹,

Achjari²

2020

JATI

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Penerapan teknologi informasi di lingkungan akuntansi diantaranya adalah eXtensible Business Reporting Language (XBRL). Penelitian ini bertujuan untuk memperoleh bukti empiris dampak penggunaan XBRL dan jenis industri terhadap audit delay di Indonesia. Perbedaan audit delay sebelum dan sesudah penggunaan XBRL berdasarkan jenis industri keuangan dan non-keuangan juga diteliti. Sampel penelitiannya adalah 88 perusahaan publik yang diperoleh dari Bursa Efek Indonesia. Data yang terkumpul dianalisa dengan menggunakan regresi Generalized Least Square dan uji beda Wilcoxon Signed Rank. Hasil penelitian menunjukkan tidak ada pengaruh negatif penggunaan XBRL terhadap audit delay. Penelitian ini juga menemukan bahwa jenis industri berhubungan dengan perbedaan audit delay. Hasil penelitian ini menunjukkan tidak ada perbedaan audit delay sebelum dan sesudah penggunaan XBRL di industri keuangan. Sebaliknya, industri non-keuangan menunjukkan adanya perbedaan audit delay sebelum dan sesudah penggunaan XBRL. Penelitian ini berkontribusi dalam menunjukkan pengaruh penerapan XBRL terhadap audit delay di Indonesia ternyata sensitif terhadap sektor industri. Industri non-keuangan menunjukkan perbedaan audit delay, sementara industri keuangan tidak menunjukkan perbedaan.

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An approach to XBRL interoperability based on Ant Colony Optimization algorithm

Cited by 12 publications

References 25 publications

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

XBRL utilization as an automated industry analysis

The Impact of XBRL Adoption on Audit Delay in the Financial and Non-Financial Industries in Indonesia

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