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Optimize the Efficiency of Lossy Matching Network: A Top-Down Splitting algorithm Based on Generalized Quality-Based Equation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第8期68卷 2750-2754页

作者： He, Fei Xie, Qian Wang, Zheng Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China

For the lossy matching network, the efficiency of transferring the power is one of the most important factors. In this brief, a novel generalized quality-based equation has been derived which can accurately calculate the efficiency. This generalized equation covers all the situations that the source and load impedance are both complex and consider both the loss of inductor and capacitor. Meanwhile, the SPICE simulations have been carried out to validate the accuracy of the proposed equation. Based on this generalized equation, a top-down splitting algorithm is proposed to further optimize the efficiency of multi-stage matching network. The proposed method shows better improvement of efficiency than prior works.

关键词： Mathematical model Impedance Inductors Q-factor Capacitors Simulation Shunts (electrical) Lossy matching network efficiency optimization algorithm generalized quality-based equation multi-stage network

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A review on intelligence algorithms for THD minimization in multi-level inverter

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2024年

作者： Sharma, P. V. V. Raghava Neelshetty, K. Visvesvaraya Technol Univ Guru Nanak Dev Engn Coll Belagavi & Res Ctr Elect & Elect Engn Dept Bidar Karnataka India Maturi Venkata Subba Rao MVSR Engn Coll EEE Dept Hyderabad Telangana India Guru Nanak Dev Engn Coll EEE Dept IEEE Bidar Karnataka India

The multi-level inverter (MLI) is advantageous for DC to AC voltage conversion in power distribution networks. The low-power applications require square and quasi-square waveforms, whereas the sinusoidal waveform is required in high-power applications. The pure sinusoidal waveform of an inverter is obtained by improving the number of levels in the inverter. Hence, more switches and DC sources are used in the inverters to produce multi-levels at the output. The large number of switches and DC sources increases harmonics in the system. The modulation techniques are used with the MLI topologies to alleviate these issues. Recently, modulation techniques based on optimization algorithms have been used to minimize Total Harmonic Distortion (THD). This paper reviews optimization algorithm-based modulation techniques in the recent literature. This review aims to provide an effective solution for enhancing THD in MLI. Different MLI types are initially explained, and then the recent algorithms are explained for THD minimization.

关键词： Lower order harmonics Modulation index multi-level inverter optimization algorithm Switching angles Total harmonic distortion

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Bearings-Only Guidance in Cis-Lunar Rendezvous

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第10期44卷 1862-1874页

作者： D'Onofrio, Fabio Bucchioni, Giordana Innocenti, Mario Univ Pisa Dept Informat Engn Via Caruso 16 I-56122 Pisa Italy

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Research on detection mission scheduling strategy for the LEO constellation to multiple targets

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JOURNAL OF DEFENSE MODELING AND SIMULATION-APPLICATIONS METHODOLOGY TECHNOLOGY-JDMS 2021年第2期18卷 87-104页

作者： Sun, Fuyu Wang, Hua Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China

The space-based infrared LEO constellation has the ability of surveillance and tracking of ballistic missiles during entire phases, which has played an important role in the ballistic missile defense system (BMDS). This paper focuses on the scheduling strategy for the LEO constellation to multiple targets. The concept of average contribution of the satellite is firstly introduced, transforming the dynamic scheduling process to continuous intervals. The switching frequency and relaxation degree are also considered as decision variables. Then the multilayer coding genetic algorithm is improved in order to solve the scheduling problem. Finally, a scheduling demonstration validates the correctness and effectiveness of the presented method. The theory analysis and simulation results of this paper can provide powerful support for future design of the LEO constellation and research on the BMDS.

关键词： LEO constellation multi-target tracking scheduling strategy decision variable optimization algorithm

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A collaborative numerical simulation-soft computing approach for earth dams first impoundment modeling

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COMPUTERS AND GEOTECHNICS 2023年 164卷

作者： Shakouri, Behzad Mohammadi, Mirali Safari, Mir Jafar Sadegh Hariri-Ardebili, Mohammad Amin Urmia Univ Fac Engn Dept Civil Engn POB 165 Orumiyeh *** Iran Yasar Univ Dept Civil Engn Izmir Turkiye Univ Maryland Coll Comp Math & Nat Sci College Pk MD 20742 USA Univ Colorado Dept Civil Engn Boulder CO 80309 USA

Uncertainty quantification plays a crucial role in the design, monitoring, and risk assessment of earth dams. To reduce the computational burden, we employ a combination of finite difference method and soft computing techniques to investigate material uncertainties in earth dams during the initial impoundment stage. The findings of sensitivity analysis with the Tornado diagram indicate that key material properties such as dry density, elasticity modulus, friction angle, and Poisson's ratio significantly influence the displacements and stress analysis. In our study, we explore four variants of extreme learning machines (ELMs): the standalone ELM, hybridized versions with the improved grey wolf optimizer algorithm, ant colony optimization for continuous domains, and artificial bee colony. These methods are assessed across various training sizes to predict multiple parameters, including horizontal and vertical displacements, stresses, and the factor of safety (FoS). The hybridized ELM with the improved grey wolf optimizer algorithm emerges as the superior choice for most of the response variables. A minimum of 200 numerical simulations is required to establish a stable and accurate meta-model with an average prediction error of less than 3% for responses and the FoS.

关键词： Soft Computing optimization algorithm Grey Wolf Optimizer Earth Dam FLAC2D

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Synchronization of Fractional-order Neural Networks via Intermittent Quantized Control: Optimal algorithm

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INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS 2024年第7期63卷 164-164页

作者： Jing, Taiyan He, Tongyang Henan Polytech Univ Sch Math & Informat Sci Jiaozuo 454000 Peoples R China

The biggest challenge of this article is how to maximize the rest time of intermittent controllers. This paper mainly uses intermittent quantized controller (IQC) to examine asymptotic synchronization between fractional-order neural networks (FONNs). Firstly, by utilizing the advantages of intermittent properties, a novel lemma with asymptotic stability inequalities is proposed. Secondly, combining intermittent properties with quantization technique, two different categories of aperiodically intermittent quantized controllers (AIQCs) are designed to ensure asymptotic convergence of FONNs. Due to the certain correlation between control interval, rest interval, and convergence rate parameters, thus, optimization algorithm becomes particularly important in maximizing rest time as much as possible. Thirdly, by constructing Lyapunov functions, several useful conditions are established for the asymptotic synchronization of FONNs. Finally, the rationality of the proposed theoretical analysis is confirmed by two numerical examples.

关键词： Fractional-order neural networks Asymptotic synchronization Intermittent quantized controller optimization algorithm

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Training artificial neural networks using self-organizing migrating algorithm for skin segmentation

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Diep, Quoc Bao Truong, Thanh-Cong Zelinka, Ivan Van Lang Univ Fac Mech Elect & Comp Engn Sch Technol Ho Chi Minh City Vietnam Univ Finance Mkt Fac Informat Technol Ho Chi Minh City Vietnam VSB TUO Fac Elect Engn & Comp Sci Dept Comp Sci 17 Listopadu 2172-15 Ostrava 70800 Czech Republic VSB Tech Univ Ostrava Quantum Comp Lab IT4Innovat 17 Listopadu 2172-15 Ostrava 70800 Czech Republic

This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network's accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks.

关键词： Skin segmentation Swarm intelligence optimization algorithm SOMA Artificial neural networks Computer vision

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Fast detection of volatile fatty acids in biogas slurry using NIR spectroscopy combined with feature wavelength selection

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SCIENCE OF THE TOTAL ENVIRONMENT 2023年第Part1期857卷 159282页

作者： Xu, Yonghua Liu, Jinming Sun, Yong Chen, Shaopeng Miao, Xinying Northeast Agr Univ Coll Elect & Informat Harbin 150030 Peoples R China Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Daqing 163319 Peoples R China Northeast Agr Univ Coll Engn Harbin 150030 Peoples R China Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn 5 Xinfeng Rd Hightech Zone Daqing Heilongjiang Peoples R China

To analyze the state of anaerobic digestion (AD), fast detection models of volatile fatty acids (VFAs) were constructed using near-infrared transmission spectroscopy combined with partial least squares regression to measure concentra-tions of the acetic acid (AA), propionic acid (PA) and total acid (TA) in biogas slurry. CARS-SA-BPSO algorithm was proposed based on competitive adaptive reweighted sampling (CARS) and simulated annealing binary particle swarm optimization algorithm (SA-BPSO) for selecting feature wavelengths of the AA, PA and TA. Regression models were established with the determination coefficient of prediction (Rp2) of 0.989, root mean squared error of prediction (RMSEP) of 0.111 and residual predictive deviation (RPD) of 9.706 for AA;Rp2 of 0.932, RMSEP of 0.116 and RPD of 3.799 for PA;Rp2 of 0.895, RMSEP of 0.689 and RPD of 3.676 for TA. It is sufficient to meet the fast detection needs of the AA and PA concentrations in biogas slurry, and basically meet the measuring demand of the TA concentration. CARS-SA-BPSO effectively improves the performance of the calibration model using sensitive wavelength selections, which provides theoretical support for establishing the spectral quantitative regression model to meet the require-ments of practical application.

关键词： Volatile fatty acids Near-infrared spectroscopy Partial least squares Competitive adaptive reweighted sampling Simulated annealing binary particle swarm optimization algorithm

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Assessing the changeability of precipitation patterns using multiple remote sensing data and an efficient uncertainty method over different climate regions of Iran

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 221卷

作者： Fooladi, Mahmood Golmohammadi, Mohammad Hossein Rahimi, Iman Safavi, Hamid Reza Nikoo, Mohammad Reza Isfahan Univ Technol Dept Civil Engn Esfahan Iran Sultan Qaboos Univ Coll Engn Dept Civil & Architectural Engn Muscat Oman

While the rain gauge measurements are unevenly distributed in many world regions, it is necessary to use remote sensing-based precipitation data at high spatial and temporal resolutions. On the other hand, quantifying the uncertainty of precipitation is a vital issue for hydrometeorological applications globally. For this purpose, applying a new fusion framework using high-resolution remote sensing datasets can provide accurate precipi-tation evaluation against local observations with low uncertainty. This study examines three weighted fusion -based models containing the Ordered-Weighted-Averaged (OWA) family approach based on the ORLIKE method (OWA-ORLIKE) and ORNESS method (OWA-ORNESS) as well as the Entropy-weight (EW) method to combine different remote sensing precipitation products over different climate zones of Iran. In this case, mul-tiple monthly remotely sensed datasets, including ERA5, ERA5-Land, TerraClimate, GPM, PERSIANN-CDR, TRMM, and CHIRPS, are utilized to assess precipitation patterns versus local measurements, which were gath-ered by Google Earth Engine (GEE) platform. Furthermore, the K-means algorithm is employed to cluster ground -based precipitation stations based on the climate zones category across Iran. Additionally, the Genetic Optimi-zation algorithm (GOA) is applied to specify optimal values of weights in weighting-based models. The per-formance of single and combination datasets are evaluated using statistical error metrics, including Pearson correlation coefficient (PCC), root mean square error (RMSE), Kling Gupta efficiency (KGE), and bias. The Thiessen polygon method has been applied to calculate each cluster's mean precipitation to obtain the optimal weights of stations. Finally, as an efficient uncertainty approach, Cross Wavelet Transform (XWT) method has been used for uncertainty assessment of monthly and seasonal precipitation series. Results indicated that the OWA family as the best fusion model had almost the lowest uncertaint

关键词： Precipitation patterns Remotely sensed datasets K -means algorithm Weighted fusion -based models optimization algorithm Uncertainty assessment

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Optimized Computational Intelligence Model for Estimating the Flexural Behavior of Composite Shear Walls

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BUILDINGS 2023年第9期13卷 2358页

作者： Mirrashid, Masoomeh Naderpour, Hosein Kontoni, Denise-Penelope N. Jakubczyk-Galczynska, Anna Jankowski, Robert Nguyen, Tan N. Semnan Univ Fac Civil Engn Semnan *** Iran Univ Peloponnese Sch Engn Dept Civil Engn GR-26334 Patras Greece Hellenic Open Univ Sch Sci & Technol GR-26335 Patras Greece Gdansk Univ Technol Fac Civil & Environm Engn PL-80233 Gdansk Poland Sejong Univ Dept Architectural Engn 209 Neungdong Ro Seoul 05006 South Korea

This article presents a novel approach to estimate the flexural capacity of reinforced concrete-filled composite plate shear walls using an optimized computational intelligence model. The proposed model was developed and validated based on 47 laboratory data points and the Transit Search (TS) optimization algorithm. Using 80% of the experimental dataset, the optimized model was selected by determining the unknown coefficients of the network-based computational structure. The remaining 20% of the data was used to evaluate the accuracy of the model, and the best-performing structure was selected. Furthermore, the final neural network details were subjected to statistical analysis to extract a user-friendly formula, making it easier to apply in practice. The proposed ANN model and the proposed user-friendly formula were then compared with the AISC 341-16 and experimental results and demonstrated their efficacy in predicting the flexural behavior of composite shear walls. Overall, the proposed approach provides a more reliable and efficient framework for estimating the flexural behavior of composite shear walls.

关键词： composite shear wall reinforced concrete flexural behavior flexural capacity optimization algorithm soft computing computational intelligence

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