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Novel polynomial Abet data augmentation algorithm with GRU paradigm for nuclear power prediction

Annals of Nuclear Energy 2024年 201卷

作者： Khan, Saifullah Awan, Saeed Ehsan Muhammad, Yasir Jadoon, Ihtesham Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 0.3 Yunlin Douliou64002 Taiwan

In current study innovative approach is presented to improve the accuracy and efficiency of machine learning models on the dataset for each of the 31 US states, solely comprising a single power output column in Terra watt-hours (TWh) spanning from January 2013 to November 2020 by exploiting the knacks of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), hybrid GRU-LSTM, Polynomial Feature Transform (PFT) and Polynomial Abet (PA) data augmentation with GRU for efficacious prediction. Achieving sufficient accuracy, a small univariate dataset of 95 rows was augmented through the PFT method, resulting in a significant reduction in prediction errors. Encouraged by this improvement, a novel algorithm PA data augmentation with GRU is introduced for further enhancing the accuracy, correlation, and computational efficiency of the prediction model. This research not only demonstrates the superior performance of the proposed PA data augmentation method but also highlights the benefit of using 31 individual univariate datasets for each state, allowing comprehensive testing and validation of the novel algorithm, providing compelling evidence of its efficacy in nuclear power prediction. The results from the proposed algorithms are evaluated using Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RRMSE). A quantitative overview of PA and PFT data augmentation techniques’ comparison show the result in terms of accuracy (PA's 0.464% RRMSE versus PFT's 1.088 % RRMSE), correlation (PA's −0.5877 versus PFT's 0.4892), and computational efficiency (PA's 14.02 % less computational time then PFT) for the nuclear power prediction model. The research framework introduced in this study, with its focus on utilizing state-specific data for nuclear energy prediction, holds significant implications for governmental decision-making policies. It provides a comprehensive overview of the trends in power generation and can be utilized for both renewable and thermal sources ac

关键词： Long short-term memory

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Category re-balancing based domain adaptive semantic segmentation 11

Category re-balancing based domain adaptive semantic segment...

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11th International Conference on Information technology and Quantitative Management, ITQM 2024

作者： Zhu, Siyu Zhou, Fenfen Li, Kai Liu, Minghao Wen, Haonan Computer School Beijing Information Science and Technology University 102206 No.55 Taihang RoadChangping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China College of Robotics Beijing Union University Beijing100027 China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China Yuquan Road Shijingshan District Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing100190 China

The semantic segmentation task faces the bottleneck of high manual annotation costs. Domain adaptive learning provides an effective solution through inter domain knowledge transfer. However, existing domain adaptive semantic segmentation tasks face the situation of category asymmetry between scenes when dealing with complex scenes. Existing methods lack handling of class imbalance in the prediction process. In domain adaptive semantic segmentation tasks, class imbalance limits the performance of inter domain transfer. Our work focuses on the impact of category distribution on domain adaptive semantic segmentation tasks in image pairs. Inspired by long tail learning, we divided categories into head, middle, and tail categories, and designed effective strategies to enhance the contribution of tail categories in domain adaptation tasks. We introduce the concept of category re-balancing into domain adaptive image matching, and our method is lightweight and effective, proving its effectiveness on general datasets. © 2024 The Authors.

关键词： Semantic Segmentation

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Radar Probing Optimization for Joint Beamforming and UAV Trajectory Design in UAV-Enabled Integrated Sensing and Communication

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IEEE Transactions on Communications 2024年

作者： Liu, Yaxi Mao, Wencan He, Boxin Huangfu, Wei Huang, Tianyao Zhang, Haijun Long, Keping University Of Science And Technology Beijing Beijing Advanced Innovation Center For Materials Genome Engineering Beijing Engineering And Technology Research Center For Convergence Networks And Ubiquitous Services School Of Computer And Communication Engineering Beijing100083 China University Of Science And Technology Shunde Innovation School Beijing China National Institute Of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task;ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Composability Verification of Complex Systems Using Colored Petri Nets

Composability Verification of Complex Systems Using Colored ...

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2021 Winter Simulation Conference, WSC 2021

作者： Mahmood, Imran Askari, Syed Hassan Sarjoughian, Hessam S. Center for Research in Modeling Simulation School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan

ISBN: (纸本)9781665433112

The discipline of component-based modeling and simulation offers promising gains including reductions in development cost, time, and system complexity. This paradigm promotes the use and reuse of modular components for adequate development of complex simulations. Achieving effective and meaningful model reuse through the composition of components still remains a daunting challenge. 'Composability', an integral part of this challenge, is the capability to select and assemble model components in various combinations to satisfy specific user requirements. In this paper, we propose the use of Colored Petri Nets for component-oriented model development, model composition, and the verification of composed models using state-space analysis technique. We present a case study of an elevator model as a proof of concept. Our case study explains the proposed process of developing and composing CPN-based model components and verifying the composed model using state-space analysis. © 2021 IEEE.

关键词： Petri nets

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Enhancement of Neural Networks Model’s Predictions of Currencies Exchange Rates by Phase Space Reconstruction and Harris Hawks’ Optimization

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Computational Economics 2023年第2期63卷 835-860页

作者： Khan, Haider A. Ghorbani, Shahryar Shabani, Elham Band, Shahab S. Josef Korbel School of International Studies University of Denver Denver United States Department of Production Management Faculty of Management Sakarya University Serdivan Turkey Department of Agriculture Economics Faculty of Agriculture University of Tabriz Tabriz Iran Department of Computer Science and Information Engineering Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan

Predictions of variations in exchange rates of other currencies to a vehicle currency such as the Dollar (USD) are vital in order to reduce the risks for international transactions. In this study, we use a heuristic algorithm of Harris Hawks’ optimization (HHO) along with phase space reconstructions (PSRs) coupled to the ANN (PSR-ANNHHO) to predict the daily data of GBP/USD and CAD/USD exchange rates. In this new hybrid model, unlike the previous ones, the input of the model is based on the two parameters of time delay and the embedding dimension. The HHO algorithm increases the performance of ANN, which has can model non-linear systems in a natural manner. The performance of the PSR-ANNHHO model can be compared with the ANN and the ANN hybridized with metaheuristic Algorithm of Innovative Gunner (AIG) model (ANN-AIG). The Modified Diebold–Mariano test indicates the statistical difference between the accuracy of the models. Based on the statistical measures and graphs, the PSR-ANNHHO model predicts exchange rates considerably better than stand-alone ANN and ANN-AIG model in each case. Hence, implementing PSR along with using the heuristic algorithms could increase the accuracy of the model. This model’s precise performance supports the case for it to be employed to predict future exchange rate variations, in order to decrease transactions risks in the global markets. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Artificial neural network Exchange rate Harris hawks’ optimization Phase space reconstruction

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SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction 21

SumPA: Efficient Pattern-Centric Graph Mining with Pattern A...

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Proceedings of the 30th International Conference on parallel Architectures and Compilation Techniques

作者： Chuangyi Gui Xiaofei Liao Long Zheng Pengcheng Yao Qinggang Wang Hai Jin National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665442787

Graph mining aims to explore interesting structural information of a graph. Pattern-centric systems typically transform a generic-purpose graph mining problem into a series of subgraph matching problems for high performance. Existing pattern-centric mining systems reduce the substantial search space towards a single pattern by exploring a highly-optimized matching order, but inherent computational redundancies of such a matching order itself still suffer severely, leading to significant performance degradation. The key innovation of this work lies in a general redundancy criterion that characterizes computational redundancies arising in not only handing a single pattern but also matching multiple patterns simultaneously. In this paper, we present SumPA, a high-performance pattern-centric graph mining system that can sufficiently remove redundant computations for any complex graph mining problems. SumPA features three key designs: (1) a pattern abstraction technique that can simplify numerous complex patterns into a few simple abstract patterns based on pattern similarity, (2) abstraction-guided pattern matching that completely eliminates (totally and partially) redundant computations during subgraph enumeration, and (3) a suite of system optimizations to maximize storage and computation efficiency. Our evaluation on a wide variety of real-world graphs shows that SumPA outperforms the two state-of-the-art systems Peregrine and GraphPi by up to 61.89× and 8.94×, respectively. For many mining problems on large graphs, Peregrine takes hours or even days while SumPA finishes in only a few minutes.

关键词： data reuse

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A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization 1st

A Comparative Study of the Coulomb’s and Franklin’s Laws I...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Ghasemi, Mojtaba Zare, Mohsen Zahedi, Amir Hemmati, Rasul Abualigah, Laith Forestiero, Agostino Department of Electronics and Electrical Engineering Shiraz University of Technology Shiraz Iran Department of Electrical Engineering Faculty of Engineering Jahrom University Fras Jahrom Iran Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Computer Engineering Marquette University MilwaukeeWI United States Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town11800 Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Institute for High Performance Computing and Networking National Research Council of Italy Rende Italy

ISBN: (纸本)9783031314681

Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available at https://***/projects/cfa, https://***/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Charged particles

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Vehicle theft detection by generative adversarial networks on driving behavior

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engineering Applications of Artificial Intelligence 2023年第PartB期117卷

作者： Tseng, Pei-Yu Lin, Po-Ching Kristianto, Edy Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Computer Science and Information Engineering National Chung Cheng University Minshiung Chiayi Taiwan

Human driving behavior can be a unique fingerprint to identify individual drivers and can be used for vehicle theft detection. Prior research often uses supervised learning to classify drivers’ behaviors. However, that method is not suitable for vehicle theft detection because it is not possible to derive a training dataset which comprehends all possible thieves. A few studies have instead leveraged unsupervised learning, such as k-means, but such approaches cannot achieve acceptable accuracy or provide a method of tolerating the false outcomes from classifying individual data points. Moreover, the influence of environment and road conditions on the driver classification was not included further limiting its usability. In this work, we propose a redesigned Generative Adversarial Network (GAN) model, Convolutional Long short-term GAN (CLGAN), which comprises long short-term memory (LSTM) and a convolutional neural network (CNN). This model ensures the ability of feature extraction from the convolutional layer and feature preservation from the LSTM layer, while simultaneously minimizing overfitting. It is instructive to analyze and benchmark CLGAN with the various GAN models, such as DCGAN, RNN-GAN, and AAE. Applying a public dataset which was generated by an electronic control unit (ECU) and collected through in-vehicle Controller Area Network (CAN) bus, our model achieves high accuracy of 98.5%, and is more robust against various driving conditions on multiple types of roads. In addition, we leverage threshold random walk to ensure the reliability of detection as well as adopting Principal Component Analysis (PCA) on feature pre-processing to improve the accuracy by approximately 20%. This work sheds light on a feasible and practical way of implementing vehicle theft detection. © 2022 Elsevier Ltd

关键词： Principal component analysis

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Bacterial population dynamics and biosafety assessment in a closed Bioregenerative Life Support System during the “Lunar Palace 365” experiment

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Science China Life Sciences 2025年

作者： Yuming Fu Yueying Lu Jianlou Yang Hong Liu Key Laboratory of Biomechanics and Mechanobiology (Beihang University) Ministry of EducationKey Laboratory of Innovation and Transformation of Advanced Medical DevicesMinistry of Industry and Information TechnologyNational Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering)School of Biological Science and Medical EngineeringBeihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang University International Joint Research Center of Aerospace Biotechnology&Medical Engineering Beihang University School of Sport Management Shandong Sport University Innovation Center for Medical Engineering&Engineering Medicine Hangzhou International Innovation InstituteBeihang University

Our study investigates the dynamic variation of bacterial populations within the Bioregenerative Life Support System(BLSS), a critical component for sustainable habitats in extraterrestrial environments supporting human survival. We utilized the ground-based BLSS test platform, referred to as “Lunar Palace 1”(LP1), to examine bacterial dynamics in an ecosystem integrating plant production, human cultivation, and equipment maintenance. Our findings reveal distinct bacterial compositions among crew members and functional areas within the LP1 system while preserving temporal stability across the 370-day experiment. Importantly, we observed a low abundance of potential pathogens, minimal antibiotic resistance, and negligible impact of the isolated bacterial strains on equipment materials, suggesting favorable biosafety attributed to plant integration. Our work enhances the understanding of microbial communities in BLSS and underscores the importance of incorporating green plants to bolster environmental biosafety and sustainability in long-term space exploration missions.

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Potential of Vision Transformer in Leukemia Detection

Potential of Vision Transformer in Leukemia Detection

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Rayan Ahmed Laraib Nadeem Maryam Shafique Zameera Saleem Ayesha Iqbal Hafsa Iqbal School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Shaukat Khanum Memorial Cancer Hospital & Research Center Lahore Pakistan

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Leukemia, a bone marrow-related disease, arises from the abnormal proliferation of cells. It is the predominant form of childhood cancer, representing about one-third of cases annually. This study focuses on incorporating AI models to enhance the early detection of Leukemia. A public dataset is employed for testing purposes which comprises of 15,114 images in total. Advancing the field, this research employs vision transformer techniques, previously unexplored in leukemia detection, showing promising efficiency for such applications. Emphasizing the critical role of precision in medical imaging, particularly in the context of Leukemia detection, this study contributes to the ongoing efforts to improve the diagnostic accuracy and patient outcomes.

关键词： computer vision Machine learning algorithms Image analysis Accuracy Leukemia Transformers Medical diagnosis Intelligent systems Medical diagnostic imaging Testing

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