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artificial neural network analysis of the day of the week anomaly in cryptocurrencies

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Financial Innovation 2023年第1期9卷 2558-2581页

作者： Nuray Tosunoğlu Hilal Abacı Gizem Ateş Neslihan SaygılıAkkaya Faculty of Economics and Administrative Sciences Ankara HacıBayram Veli UniversityAnkaraTurkey Faculty of Economics and Administrative Sciences ÇankırıKaratekin UniversityÇankırıTurkey Faculty of Economics and Administrative Sciences İnönüUniversityMalatyaTurkey Institute of Graduate Studies Ankara HacıBayram Veli UniversityAnkaraTurkey

Anomalies,which are incompatible with the efficient market hypothesis and mean a deviation from normality,have attracted the attention of both financial investors and researchers.A salient research topic is the existence of anomalies in cryptocurrencies,which have a different financial structure from that of traditional financial markets.This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market,which is hard to predict.It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods.An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies.On October 6,2021,Bitcoin(BTC),Ethereum(ETH),and Cardano(ADA),which are the top three cryptocurrencies in terms of market value,were selected for this study.The data for the analysis,consisting of the daily closing prices for BTC,ETH,and ADA,were obtained from the *** website from January 1,2018 to May 31,2022.The effectiveness of the established models was tested with mean squared error,root mean squared error,mean absolute error,and Theil’s U1,and R2 OOS was used for out-of-sample.The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models.When the models created with feedforward artificial neural networks are examined,the existence of the day-of-the-week anomaly is established for BTC,but no day-of-the-week anomaly for ETH and ADA was found.

关键词： Cryptocurrency Bitcoin Ethereum Cardano Day-of-the-week anomaly artificial neural network

artificial neural network-based one-equation model for simulation of laminar-turbulent transitional flow

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Theoretical & Applied Mechanics Letters 2023年第1期13卷 50-57页

作者： Lei Wu Bing Cui Zuoli Xiao State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing 100871China Inspur Electronic Information Industry Co. LtdBeijing 100085China HEDPS and Center for Applied Physics and Technology College of EngineeringPeking UniversityBeijing 100871China Nanchang Innovation Institute Peking UniversityNanchang 330008China

A mapping function between the Reynolds-averaged Navier-Stokes mean flow variables and transition intermittency factor is constructed by fully connected artificial neural network(ANN),which replaces the governing equation of the intermittency factor in transition-predictive Spalart-Allmaras(SA)-γmodel.By taking SA-γmodel as the benchmark,the present ANN model is trained at two airfoils with various angles of attack,Mach numbers and Reynolds numbers,and tested with unseen airfoils in different flow states.The a posteriori tests manifest that the mean pressure coefficient,skin friction coefficient,size of laminar separation bubble,mean streamwise velocity,Reynolds shear stress and lift/drag/moment coefficient from the present two-way coupling ANN model almost coincide with those from the benchmark SA-γmodel.Furthermore,the ANN model proves to exhibit a higher calculation efficiency and better convergence quality than traditional SA-γmodel.

关键词： Transition Turbulence Eddy-viscosity model artificial neural network Intermittency factor

artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

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International Journal of Agricultural and Biological Engineering 2023年第2期16卷 38-47页

作者： Apsornrat Numsong Jetsada Posom Somchai Chuan-Udom Department of Agricultural Engineering Faculty of EngineeringKhon Kaen UniversityThailand Applied Engineering for Important Crops of the North East Research Group Khon Kaen UniversityThailand

This research proposes an artificial neural network(ANN)-based repair and maintenance(R&M)cost estimation model for agricultural machinery.The proposed ANN model can achieve high estimation accuracy with small data requirement.In the study,the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters.The model inputs are geographical regions,harvest area,and curve fitting coefficients related to historical cost data;and the ANN output is the estimated R&M cost.Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm.The R&M costs are estimated using the ANN-based model,and results are compared with those of conventional mathematical estimation model.The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%,indicating the proposed ANN model’s high predictive accuracy.The proposed ANN-based model is useful for setting the service rates of agricultural machinery,given the significance of R&M cost in profitability.The novelty of this research lies in the use of curve-fitting coefficients in the ANN-based estimation model to improve estimation accuracy.Besides,the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility.Moreover,with minor modifications,the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin.

关键词： repair and maintenance cost estimation model artificial neural network curve fitting coefficients combine harvesters

artificial neural network potential for gold clusters

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Chinese Physics B 2020年第11期29卷 86-91页

作者：曹凌志王鹏举赛琳伟付洁段香梅 School of Physical Science and Technology Ningbo UniversityNingbo 315211China Laboratory of Clean Energy Storage and Conversion Ningbo UniversityNingbo 315211China Key Laboratory of Materials Modification by Laser Ion and Electron BeamsMinistry of EducationDalian University of TechnologyDalian 116024China College of Science Hohai UniversityChangzhou 213022Chin

In cluster science, it is challenging to identify the ground state structures(GSS) of gold(Au) clusters. Among different search approaches, first-principles method based on density functional theory(DFT) is the most reliable one with high precision. However, as the cluster size increases, it requires more expensive computational cost and becomes impracticable.In this paper, we have developed an artificial neural network(ANN) potential for Au clusters, which is trained to the DFT binding energies and forces of 9000 Au N clusters(11 ≤ N ≤ 100). The root mean square errors of energy and force are 13.4 meV/atom and 0.4 eV/A, respectively. We demonstrate that the ANN potential has the capacity to differentiate the energy level of Au clusters and their isomers and highlight the need to further improve the accuracy. Given its excellent transferability, we emphasis that ANN potential is a promising tool to breakthrough computational bottleneck of DFT method and effectively accelerate the pre-screening of Au clusters’ GSS.

关键词： empirical potential artificial neural network gold cluster first-principles

artificial neural network based optimization of a six-step two-bed pressure swing adsorption system for hydrogen purification

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能源与人工智能(英文)

能源与人工智能(英文) 2021年第3期5卷 38-52页

作者： Liang Tong Pierre Bénard Yi Zong Richard Chahine Kun Liu Jinsheng Xiao Reliability Engineering Institute School of Energy and Power EngineeringWuhan University of TechnologyWuhanHubei 430063China Hubei Research Center for New Energy&Intelligent Connected Vehicle and Hubei Key Laboratory of Advanced Technology for Automotive Components School of Automotive EngineeringWuhan University of TechnologyHubei 430070China Hydrogen Research Institute Universitédu QuébecàTrois-RivièresQuebec G8Z 4M3Canada Center for Electric Power and Energy Department of Electrical EngineeringTechnical University of Denmark4000RoskildeDenmark Green Island Environment and Resources Institute School of Architectural and Civil EngineeringShenyang City UniversityLiaoning 110112China

The pressure swing adsorption(PSA)system is widely applied to separate and purify hydrogen from gaseous mixtures.The extended Langmuir equation fitted from the extended Langmuir-Freundlich isotherm has been used to predict the adsorption isothermal of hydrogen and methane on the zeolite 5A adsorbent bed.A six-step two-bed PSA model for hydrogen purification is developed and validated by comparing its simulation results with other works.The effects of the adsorption pressure,the P/F ratio,the adsorption step time and the pressure equalization time on the performance of the hydrogen purification system are studied.A four-step two-bed PSA model is taken into consideration,and the six-step PSA system shows higher about 13%hydrogen recovery than the four-step PSA system.The performance of the vacuum pressure swing adsorption(VPSA)system is compared with that of the PSA system,the VPSA system shows higher hydrogen purity than the PSA system.Based on the validated PSA model,a dataset has been produced to train the artificial neural network(ANN)model.The effects of the number of neurons in the hidden layer and the number of samples used for training ANN model on the predicted performance of ANN model are investigated.Then,the well-trained ANN model with 6 neurons in the hidden layer is applied to predict the performance of the PSA system for hydrogen purification.Multi-objective optimization of hydrogen purification system is performed based on the trained ANN model.The artificial neural network can be considered as a very effective method for predicting and optimizing the performance of the PSA system for hydrogen purification.

关键词： Hydrogen purification Pressure swing adsorption Isotherm artificial neural network Machine learning Optimization

artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling

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Underground Space 2021年第3期6卷 281-289页

作者： Gustavo Paneiro Manuel Rafael DECivil/CERENA Tecnico LisboaULisboaPortugal Orica Mining Services Portugal Portugal

Given their technical and economic advantages,the application of explosive substances to rock mass excavation is widely used.However,because of serious environmental restraints,there has been an increasing need to use complex tools to control environmental effects due to blast-induced ground vibrations.In the present study,an artificial neural network(ANN)with k-fold cross-validation was applied to a dataset containing 1114 observations that was obtained from published results;furthermore,quantitative and qualitative parameters were considered for ground vibration amplitude prediction.The best ANN model obtained has a maximum coefficient of determination of 0.840 and a mean absolute error of 5.59 and it comprises 17 input parameters,12 neurons in a one-layer hidden layer,and a sigmoid transfer function.Compared with the traditional models,the model obtained using the proposed methodology demonstrated better generalization ability.Furthermore,the proposed methodology offers an ANN model with higher prediction ability.

关键词： Rock blasting Excavation Ground vibrations artificial neural network K-fold cross-validation Modeling

artificial neural network with modified rider optimization for design and control of PV-integrated quasi Z-source cascaded multilevel inverter system

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第1期12卷 87-111页

作者： V.C.Harish Kumar S.Amala Shanthi Department of EEE NICHE ThuckalayKanyakumariTamil NaduIndia Department of ECE NICHEThuckalayKanyakumariTamil NaduIndia

The modeling of the controller for quasi Z-Source Cascaded Multilevel Inverter(qZSCMI)-dependent 3-phase grid-tie Photovoltaic(PV)power system is considered in this paper.The state-of-the-art controller requires precise conceptual models and sophisticated optimization principles based on the derived models.However,such processes are limited to known system models,which are uncertain in future systems.Here,the controller for 3-phase qZS-CMI is modeled based on two phases,and the source PV voltage and output grid current are controlled.In Phase I,optimized Proportional Integral(PI)controller is used for finding out the total PV voltage,and Phase II utilizes the optimized Proportional Resonant(PR)controller enabled with the artificial neural network(ANN)for controlling the grid current.For two phases,the modified optimization algorithm called Fitness Enabled-Rider Optimization Algorithm(FE-ROA)is used.Moreover,in Phase II,ANN is trained in an offline mode with the exact dataset arranged by the proposed FE-ROA,and it guarantees the control of grid current.The two phases plan to optimize the gain of both PI and PR controllers respectively using the same proposed algorithm.The main objective of phase I is to lessen the error among the reference PV voltage,and measured voltage,and phase II is to lessen the error among the reference and measured grid current.Hence,the grid-tie current injection is achieved by the developed module,and system-level control offers independent Maximum Power Point Tracking(MPPT).Lastly,the performance of the proposed controller for qZS-CMI is compared over the other controllers and substantiates the efficacy of the proposed one.

关键词： qZS-CMI PI controller PR controller artificial neural network fitness enabled-rider optimization algorithm

artificial neural network aided real-time simulation of electric traction system

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能源与人工智能(英文)

能源与人工智能(英文) 2020年第1期1卷 105-116页

作者： Hao Bai Chen Liu Elena Breaz Fei Gao FEMTO-ST Institute Universite Bourgogne Franche-ComteUTBMCNRSRue Ernest Thierry MiegF-90010 BelfortFrance FCLAB Universite Bourgogne Franche-ComteUTBMCNRSRue Ernest Thierry MiegF-90010 BelfortFrance School of Electrical Engineering Zhengzhou University450001 ZhengzhouChina Technical University of Cluj-Napoca 400604 Cluj-NapocaRomania

Real-time simulation-based validation plays an essential role in the early stage development of electric traction systems.The high-fidelity real-time simulation relies on the accurate modeling of the power electronics and motors and usually represents the nonlinear characteristics as many as possible while obeying the computing time constraints.In this paper,an artificial neural network(ANN)aided modeling approach is proposed for the field-programmable gate array(FPGA)-based real-time simulation to deal with the nonlinearities in permanent magnet synchronous motor(PMSM)and its drive.With the help of the ANN,the switches power losses,and the PMSM nonlinear flux linkage and electromagnetic torque can be modeled.An electro-thermal model of inverters and the finite element analysis(FEA)-based PMSM model are thus enabled in the FPGA-based real-time simulation,which can significantly improve the performances of the real-time simulation-based test bench.A two-level inverter fed PMSM drive system is modeled using the ANN-aided modeling approach and simulated in the National Instrument PXIe FlexRIO FPGA real-time system.The accuracy and effectiveness of the proposed approaches are tested and validated by comparing the results with the offline simulation tools.

关键词： Real-time system artificial neural network Permanent magnet synchronous motor (PMSM) Power losses Field-programmable gate array(FPGA)

artificial neural network model for predicting drill cuttings settling velocity

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Petroleum 2020年第4期6卷 340-352页

作者： Okorie E.Agwu Julius U.Akpabio Adewale Dosunmu Department of Chemical and Petroleum Engineering University of UyoAkwa Ibom StateNigeria Department of Petroleum and Gas Engineering University of PortHarcourtRivers StateNigeria

The traditional method of using the coefficient of drag–Reynolds number relationship to predict cuttings settling velocity involves an implicit procedure that requires repeated,time-consuming and tedious iterations using Newtonian or mostly non-Newtonian correlations.Usually,these correlations are limited to certain fluid flow regimes.Besides,most of the explicit and direct cuttings settling velocity models that exist are based on the assumption that the cuttings are spherical particles.However,in the field,the cuttings are a mixture of various shapes and are hardly spherical,hence these models when applied to field conditions come off with huge errors.The objective of this work was to use a nature-inspired algorithm(artificial neural network-ANN)to develop a model for estimating cuttings settling velocity that would be robust and useful in the field that would take into account the shape of the cuttings.The data used for this work was obtained from research experiments in the literature.The model was then evaluated using four performance metrics namely:mean squared error(MSE),root mean square error(RMSE),sum of squares error(SSE)and goodness of fit(R2).It was found that the model's predictions obtained in this work agreed with experimental evidence.Furthermore,the developed model possesses the capacity to generalize across new input datasets and can be applied to particles of any shape,hence,defining the novelty of this research and bridging the gap between theory and practice.When compared with state-of-the-art models,the developed models show a high degree of robustness,as the ANN model performed reasonably well with an MSE of 7.5×10−4,an R2 of 0.978,RMSE of 0.0274 and SSE of 0.25.To generalize the results across new input datasets,the developed model was cross-validated with new data that was not part of the training process.It was found that the ANN model had an MSE value 0.00807,RMSE of 0.0898,MAE of 0.065,SSE of 2.74 and MAPE of 0.675%.To ensure the replicability of the ANN model,the weights and biases for

关键词： Drill cuttings artificial neural network Mud Cuttings settling velocity