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Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders

ENERGIES 2018年第1期11卷 138页

作者： Oprea, Simona-Vasilica Bara, Adela Reveiu, Adriana Bucharest Univ Econ Studies Dept Econ Informat & Cybernet Romana Sq 6 Bucharest 010374 Romania

Although in 2012 the European Union (EU) has promoted energy efficiency in order to ensure a gradual 20% reduction of energy consumption by 2020, its targets related to energy efficiency have increased and extended to new time horizons. Therefore, in 2016, a new proposal for 2030 of energy efficiency target of 30% has been agreed. However, during the last years, even if the electricity consumption by households decreased in the EU-28, the largest expansion was recorded in Romania. Taking into account that the projected consumption peak is increasing and energy consumption management for residential activities is an important measure for energy efficiency improvement since its ratio from total consumption can be around 25-30%, in this paper, we propose an informatics solution that assists both electricity suppliers/grid operators and consumers. It includes three models for electricity consumption optimization, profiles, clustering and forecast. By this solution, the daily operation of appliances can be optimized and scheduled to minimize the consumption peak and reduce the stress on the grid. For optimization purpose, we propose three algorithms for shifting the operation of the programmable appliances from peak to off-peak hours. This approach enables the supplier to apply attractive time-of-use tariffs due to the fact that by flattening the consumption peak, it becomes more predictable, and thus improves the strategies on the electricity markets. According to the results of the optimization process, we compare the proposed algorithms emphasizing the benefits. For building consumption profiles, we develop a clustering algorithm based on self-organizing maps. By running the algorithm for three scenarios, well-delimited profiles are obtained. As for the consumption forecast, highly accurate feedforward artificial neural networks algorithm with backpropagation is implemented. Finally, we test these algorithms using several datasets showing their performance and integra

关键词： consumption management programmable appliances informatics solution optimization algorithms load profile forecast web-services prototype

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Neural network combined with nature-inspired algorithms to estimate overall heat transfer coefficient of a ribbed triple-tube heat exchanger operating with a hybrid nanofluid

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MEASUREMENT 2021年 174卷 108967-108967页

作者： Bahiraei, Mehdi Foong, Loke Kok Hosseini, Siavash Mazaheri, Nima Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Islamic Azad Univ Kermanshah Branch Young Researchers & Elite Club Kermanshah Iran

Four nature-inspired optimizers are combined with a multilayer perceptron neural network for reaching an optimal structure aimed at predicting the overall heat transfer coefficient of a ribbed triple-tube heat exchanger in terms of the rib pitch, rib height, and nanoparticle concentration. The heat exchanger works with a hybrid nanofluid having graphene nanoplatelet/Pt nanocomposite powder. The applied algorithms incorporate Artificial Bee Colony (ABC), Ant Colony optimization (ACO), Ant Lion Optimizer (ALO), and Biogeography-Based optimization (BBO). The required data are provided via computational solutions. The BBO algorithm is determined as the best method to predict the output because of its higher accuracy. The highest performance of the BBO is obtained from population of 450. The overall heat transfer coefficient is estimated with root mean square errors of 0.030 and 0.025 for the training data and testing data, respectively. Furthermore, the ACO algorithm shows the lowest computational time among all the methods.

关键词： Artificial neural network optimization algorithms Ribbed triple-tube heat exchanger Hybrid nanofluid Graphene nanoplatelets Overall heat transfer coefficient

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Mravenčí kolonie

Mravenčí kolonie

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作者： Hart, Pavel Brno University of Technology

Práce se zabývá rešerší optimalizačních algoritmů a dále pak implementací a porovnáním tří z nich. Jedná se o algoritmus mravenčí kolonie, zakázané... 详细信息

Práce se zabývá rešerší optimalizačních algoritmů a dále pak implementací a porovnáním tří z nich. Jedná se o algoritmus mravenčí kolonie, zakázané prohledávání a simulované žíhání. Implementace algoritmů byla uzpůsobena k řešení problému obchodního cestujícího. U všech zmíněných algoritmů byla zkoumána a zhodnocena jejich časová náročnost a kvalita nalezeného řešení. U algoritmů mravenčí kolonie bylo navíc provedeno zhodnocení vlivu řídicích parametrů na kvalitu nalezeného řešení.

关键词： Mravenčí kolonie ACO Optimalizační algoritmy Zakázané prohledávání Simulované žíhání Problém obchodního cestujícího TSP implementace srovnání algoritmů Ant colony algorithm ACO optimization algorithms Tabu search Simulated annealing Travelin

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Measure and optimize sample confidence of acoustic signal for fault identification in ships

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JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2019年第3期146卷 EL198-EL204页

作者： Zhang, Linke Wei, Na Du, Xuhao Wuhan Univ Technol Minist Educ Key Lab High Performance Ship Technol Wuhan 430063 Hubei Peoples R China Nanjing Univ Key Lab Modern Acoust Nanjing 210000 Jiangsu Peoples R China Nanjing Univ Inst Acoust Nanjing 210000 Jiangsu Peoples R China Univ Western Australia Dept Mech Engn Perth WA 6009 Australia Wuhan Univ Technol Key Lab Marine Power Engn & Technol Wuhan 430063 Hubei Peoples R China

The shortage of sufficient "real" acoustic data under fault conditions on ships has been a challenge for accurate structure diagnosis. The common solution is generating semi-artificial data while the data quality is unknown. Thus, a kernel-based confidence measure (KBCM) is proposed for evaluating the expanded acoustic data. The deviation between the expanded and real data has been deduced to establish a KBCM model for measuring confidence. Furthermore, an optimization algorithm termed as the maximum class separability is formulated for the kernel optimization. Successful applications on experimental acoustics datasets under various fault conditions have demonstrated the proposed method's effectiveness.

关键词： Functions and functionals Acoustic signal processing Acoustic modeling simulation and analysis optimization problems Water transportation Gaussian processes optimization algorithms Vibration testing Mathematical optimization Nonparametric statistics

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A data-driven approach to model calibration for nonlinear dynamical systems

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JOURNAL OF APPLIED PHYSICS 2019年第24期125卷 244901-244901页

作者： Greve, C. M. Hara, K. Martin, R. S. Eckhardt, D. Q. Koo, J. W. Texas A&M Univ Dept Aerosp Engn College Stn TX 77843 USA Air Force Res Lab Edwards Afb CA 93524 USA

A data-driven approach to model calibration is developed to accurately obtain the input parameters for nonlinear dynamical systems. The paper focuses on the convergence properties of the proposed method, which play a significant role in understanding the validity and usefulness of any data-driven model. The input parameters of nonlinear dynamical systems are optimized to a reference solution, which can be experimental data or results from a high-fidelity computer simulation, using the Wasserstein metric and a phase-space representation of a set of time-dependent signals. Test cases shown in this paper include the Lorenz system and the discharge plasma of a Hall effect thruster to characterize the numerical uncertainties of the proposed data-driven approach, given a constructed reference solution. Distinct wells in the cost function, the Wasserstein metric, are obtained relative to the reference solution, illustrating the applicability of the proposed method to dynamical problems. The numerical uncertainties associated with the phase-space portrait and sampling time are discussed.

关键词： Dynamical systems Hall effect thruster Lorenz system Phase space distribution optimization algorithms Plasma dynamics Non linear dynamics Numerical methods

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A committee machine-based estimation of shear velocity log by combining intelligent systems and rock-physics model using metaheuristic algorithms

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期126卷

作者： Nasrnia, Behzad Falahat, Reza Kadkhodaie, Ali Vijouyeh, Ali Gholami Sahand Univ Technol Sahand Iran Tabriz Univ Tabriz Iran

Estimation of reservoir parameters such as porosity, lithology and determination of elastic moduli is based on well-logging data and seismic velocity information. However, the lack of shear velocity log in many wells, and the high costs of its acquisition have led to developing different approaches for shear velocity estimation. Currently, shear velocity estimation is accomplished by common industrial methods such as empirical equations, regression analysis, intelligent methods and rock-physics models (RPMs). The current study proposes that robust, accurate and cost-effective intelligent methods coupled with suitable RPM provide reasonable estimations of the shear velocity in an optimal processing time. Hybridizing intelligent models with RPMs in the framework of a committee machine is an optimal solution for shear velocity estimation of carbonate reservoirs. The intelligent methods used in this study include neural network (NN), fuzzy logic (FL) and an adaptive-neuro fuzzy inference system (ANFIS). In this research, for each individual intelligent model and a developed RPM, the optimal weights were allocated by Metaheuristic optimization algorithms including genetic algorithm (GA), ant colony optimization routing (ACOR) and simulated annealing (SA)). To obtain the best results with the lowest mean squared error (MSE), a total of 9408, 10801 and 12288 models were created and tested in MATLAB to optimally parametrize the GA, ACOR and SA, respectively. The results showed that the combination of the applied intelligent models with the developed RPMs leads to a higher correlation coefficient with reference to the measured Shear velocity in test dataset. The use of a combination of intelligent methods in the form of committee-machine instead of individual methods has led to a reduction in error and improved the results by up to 2% Considering the high sensitivity of shear wave velocity in geomechanical and reservoir characterization studies, even small improvements in it

关键词： Shear velocity Neural network Fuzzy logic Neuro-fuzzy optimization algorithms Rock-physics model Committee machine

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Time domain amplitude and frequency detection of gravitational waves from coalescing binaries

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Physical Review D 1997年第8期55卷 4537-4537页

作者： L. Milano F. Barone M. Milano Universitá di Napoli “Federico II ” Facoltá di Ingegneria Elettronica Via Claudio I-80125 Napoli Italy

We propose a multistep procedure for the on-line detection and analysis of the gravitational wave signals emitted during the coalescence of compact binaries. This procedure, based on a hierarchical strategy, consists of a rough on-line analysis of the gravitational wave signal using adaptive line enhancers filters and a fast off-line parameter estimate, using the controlled random search optimization algorithm. A more refined off-line analysis using the classic matched-filtering technique, with a greatly reduced computational burden, can follow to further improve the parameter estimate. The results of simulations for the rough analysis are quite promising both for the relatively small computational power needed and for the robustness of the algorithms used, so that it could be very helpful for gravitational wave detection with very large baseline interferometric detectors such as LIGO and VIRGO.

关键词： Adaptive line enhancer Black-hole binaries Compact binaries Neutron-star Circular orbit Radiation Noise Order Interferometer Parameters interferometers analysis of flowline on line detection BLACK HOLES (ASTRONOMY) Noise TIME-DOMAIN Gravitational wave Amplitude Radiation optimization algorithms Circular orbits

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Reduction of side lobes in the entire azimuth plane in a planar elliptical array antenna

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2022年第6期35卷 e5067-e5067页

作者： Misra, Bitan Mahanti, Gautam Kumar Panda, Ganapati Natl Inst Technol Dept Elect & Commun Engn Durgapur India CV Raman Global Univ Bhubaneswar India

A novel synthesis method to reduce the maximum side lobe in the entire azimuth plane and for achieving a desired value of directivity of a concentric elliptical array antenna is addressed in this paper. Three well-performing meta-heuristic optimization algorithms, teaching learning-based optimization (TLBO), symbiotic organism search (SOS), and quantum particle swarm optimization (QPSO), are employed to optimize the cost function and to obtain the desired radiation pattern. Different simulation-based experiments are performed to obtain the best possible set of angular positions and the distribution of isotropic antennas across the ellipses by rigorously maintaining non-overlapping constraints. The major benefit of the proposed design is the deprecation of interference in all phi planes. The statistical analysis of the simulation results demonstrate perceptible improvement compared with that of a uniformly spaced concentric elliptical array and hence show the efficiency of the proposed algorithms. In both simulation studies, the results obtained using TLBO, SOS, and QPSO are compared. Analysis of the results exhibits that for the stated objective, the TLBO- and SOS-based side lobe reduction approach performs superior to QPSO method. Proposed multi-objective research work could lead to reduce payload and can simultaneously achieve isoflux coverage in entire azimuth plane on the earth surface which can essentially be utilized in (Low Earth Orbit) LEO and (Geostationary Earth Orbit) GEO satellites applications.

关键词： computation concentric elliptical array antenna directivity optimization algorithms planar array side lobe level

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Nonmetric Correction of Lens Distortion Based on Entropy Measure

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SCIENTIFIC PROGRAMMING 2018年第Pt.1期2018卷 9-9页

作者： Chen, Tianfei Sun, Lijun Zhang, Qiuwen Wu, Xiang Wu, Defeng Henan Univ Technol Sch Elect Engn Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450002 Henan Peoples R China Jimei Univ Marine Engn Inst Xiamen 361021 Fujian Peoples R China

In the real vision system, lens always inevitably contains nonlinear distortion, which leads to geometric distortion of digital image, so it must be corrected. In this paper, a nonmetric correction algorithm for lens distortion based on entropy measure is proposed. The algorithm uses the imaging characteristics of the space line in the ideal perspective model, and the distortion entropy is defined to measure the degree of lens distortion. For distortion curves with different distribution, the calculation dimension of distortion entropy measure is uniform, which can reduce the influence of curve inhomogeneity. On this basis, the modified distortion entropy measure with normalized weight is put forward to enhance the capability of noise suppression, and the distortion correction performance of the traditional interior point optimization algorithm, basic artificial bee colony (ABC) algorithm, and Gbest-guided artificial bee colony (GABC) algorithm is compared and analyzed. The simulation experiments demonstrate that the correction performance of GABC to optimize the modified distortion entropy measure with normalized weight is best, and it has strong robustness to noise. Finally, the actual image distortion correction examples verify the effectiveness of the proposed algorithm.

关键词： Noise robustness Digital images optimization algorithms Nonlinear distortion algorithms entropy Noise suppression geometric distortion correction algorithm simulation test LENS DISTORTION

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Parameters' fine tuning of differential evolution algorithm

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COMPUTER SYSTEMS SCIENCE AND ENGINEERING 2015年第2期30卷 125-139页

作者： Al-Dabbagh, Rawaa Dawoud Mekhilef, Saad Baba, Mohd Sapiyan Univ Malaya Dept Artificial Intelligence Kuala Lumpur 50603 Selangor Malaysia Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab PEARL Kuala Lumpur 50603 Selangor Malaysia Gulf Univ Sci & Technol Kuwait Kuwait

Most heuristic search method's performances are dependent on parameter choices. These parameter settings govern how new candidate solutions are generated and then applied by the algorithm. They essentially play a key role in determining the quality of the solution obtained and the efficiency of the search. Their fine-tuning techniques are still an on-going research area. Differential Evolution (DE) algorithm is a very powerful optimization method and has become popular in many fields. Based on the prolonged research work on DE, it is now arguably one of the most outstanding stochastic optimization algorithms for real-parameter optimization. One reason for its popularity is its widely appreciated property of having only a small number of parameters to tune. This paper presents a detailed review of DE parameter tuning with a table compromised a recommended guidelines for these parameters, along with a full description of the basic DE algorithm and its corresponding operators, overlooked by previous studies. It is aimed at practitioners to help them achieve better results when adopting DE as an optimization method for their problems with less time and effort. Moreover, an experimental study has been conducted over fifteen test problems and the results obtained prove the reliability of the setting values.

关键词： optimization algorithms Natural Computing Evolutionary algorithms (EAs) Differential Evolution (DE) Parameter Setting Parameter Tuning Differential Evolution Mutation review

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