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001-es BibID:BIBFORM092439
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Deep Learning-Based Approach for Detecting Trajectory Modifications of Cassini-Huygens Spacecraft / Ashraf Aldabbas, Zoltan Gal, Khawaja Moyeezullah Ghori, Muhammad Imran, Muhammad Shoaib
ISSN:2169-3536 2169-3536
Megjegyzések:There were necessary trajectory modifications of Cassini spacecraft during its last 14 years movement cycle of the interplanetary research project. In the scale 1.3 hour of signal propagation time and 1.4-billion-kilometer size of Earth-Cassini channel, complex event detection in the orbit modifications requires special investigation and analysis of the collected big data. The technologies for space exploration warrant a high standard of nuanced and detailed research. The Cassini mission has accumulated quite huge volumes of science records. This generated a curiosity derives mainly from a need to use machine learning to analyze deep space missions. For energy saving considerations, the communication between the Earth and Cassini was executed in non-periodic mode. This paper provides a sophisticated in-depth learning approach for detecting Cassini spacecraft trajectory modifications in post-processing mode. The proposed model utilizes the ability of Long Short Term Memory (LSTM) neural networks for drawing out useful data and learning the time series inner data pattern, along with the forcefulness of LSTM layers for distinguishing dependencies among the long-short term. Our research study exploited the statistical rates, Matthews correlation coefficient, and F1 score to evaluate our models. We carried out multiple tests and evaluated the provided approach against several advanced models. The preparatory analysis showed that exploiting the LSTM layer provides a notable boost in rising the detection process performance. The proposed model achieved a number of 232 trajectory modification detections with 99.98% accuracy among the last 13.35 years of the Cassini spacecraft life.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
Cassini-Huygens interplanetary project
complex event
sensory data
big data
neural network
pattern processing
knowledge representation
Megjelenés:IEEE Access. - 9 (2021), p. 39111-39125. -
További szerzők:Gál Zoltán (1966-) (informatikus) Ghori, Khawaja Moyeezullah (1982-) (informatikus) Imran, Muhammad (1981-) (informatikus) Shoaib Muhammad (1971-) (mérnök, informatikus)
Pályázati támogatás:FIKP-20428-3/2018/FEKUTSTRAT
Internet cím:Szerző által megadott URL
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001-es BibID:BIBFORM082861
Első szerző:Ghori, Khawaja Moyeezullah (informatikus)
Cím:Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection / Khawaja Moyeezullah Ghori, Rabeeh Ayaz Abbasi, Muhammad Awais, Muhammad Imran, Ata Ullah, Laszlo Szathmary
Megjegyzések:With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.
Tárgyszavak:Természettudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
Megjelenés:IEEE Access. - 8 (2020), p. 16033-16048. -
További szerzők:Abbasi, Rabeeh Ayaz Awais, Muhammad Imran, Muhammad (1981-) (informatikus) Ullah, Ata Szathmáry László (1977-) (programtervező-informatikus)
Pályázati támogatás:EFOP-3.6.3-VEKOP-16-2017-00002
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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