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Joint Damage Identification in Frame Structures by Integrating a New Damage Index with Equilibrium Optimizer algorithm

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2022年第5期22卷

作者： Aval, Seyed Bahram Beheshti Mohebian, Pooya KN Toosi Univ Technol Fac Civil Engn Tehran Iran

Beam-column joints are responsible for maintaining the integrity and stability of frame structures, and any damage to these critical components can endanger the overall safety and reliability of the structure. Hence, early detection of structural joint damage is of paramount importance. However, most of the available structural damage identification methods focus on identifying damage in structural members, and relatively fewer methods have been developed so far for assessing damage in structural joints. In view of this, the present study proposes a new two-stage method for joint damage identification of frame structures. In the first stage, an efficient damage indicator, called residual moment-based joint damage index (RMBJDI), is developed and applied to detect the location of potentially damaged joints. This damage indicator can help to reduce the number of involved damage variables by excluding healthy joints from the problem. In the second stage, the reduced dimension damage identification problem is formulated as an optimization problem and is further tackled by employing a robust meta-heuristic algorithm, namely equilibrium optimizer (EO), to determine the damage severity of suspected damaged joints. In order to assess the capability and effectiveness of the presented joint damage identification method, two numerical examples of frame structures are conducted under both noise-free and noisy conditions. The results demonstrate that the proposed two-stage method, which integrates RMBJDI with EO, is a highly accurate and powerful tool for localizing and quantifying the joint damage in frame structures.

关键词： Joint damage identification vibration method damage indicator modal residual moment optimization algorithm frame structures

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A Full D-Band Low Noise Amplifier in 130 nm SiGe BiCMOS using Zero-Ohm Transmission Lines 15

A Full D-Band Low Noise Amplifier in 130 nm SiGe BiCMOS usin...

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15th European Microwave Integrated Circuits Conference (EuMIC) / 50th European Microwave Conference (EuMC)

作者： Maiwald, Tim Potschka, Julian Kolb, Katharina Dietz, Marco Aufinger, Klaus Visweswaran, Akshay Weigel, Robert Friedrich Alexander Univ Erlangen Nuremberg Inst Elect Engn Cauerstr 9 D-91058 Erlangen Germany Infineon Technol D-85579 Neubiberg Germany IMEC Kapeldreef 75 B-3001 Leuven Belgium

ISBN: (纸本)9782874870606

This paper presents the design, analysis and optimization of a three-stage cascode full D-Band Low Noise Amplifier (LNA) in an industrial established 130nm SiGe BiCMOS technology with a f(t)/f(max) of 250/370 GHz from Infineon Technologies. Transmission lines with a low characteristic impedance, also known as Zero-Ohm transmission lines, are used to control the impedance at the base of the common base stage of the cascode. The proposed amplifier features a 3dB bandwidth from 110-165 GHz with 23 dB peak gain at a measured noise figure between 6 and 7.9 dB over the entire D-Band. To achieve this result, a detailed analysis of the transistor characteristics, matching networks, parasitic extraction as well as self implemented optimization and synthesis procedure for the passive structures has been realized. Measured and simulated results are in good agreement.

关键词： low noise amplifier noise figure measurements D-Band passives SiGe optimization algorithm

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A novel RUL prediction method for Lithium-ion Batteries via hybrid modeling and BAS based particle filter 40

A novel RUL prediction method for Lithium-ion Batteries via ...

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40th Chinese Control Conference (CCC)

作者： He, Ning Qian, Cheng Li, Ruoxia Xian Univ Architecture & Technol Sch Mech & Elect Engn Xian 710055 Peoples R China Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China

ISBN: (纸本)9789881563804

As an important energy source, lithium-ion batteries have vast applications in daily production and life. Therefore, the life prediction of such batteries is of great significance, which can help us maintain the efficacy and reliability of the system powered by lithium-ion batteries. In this paper, a hybrid battery model and an improved particle filter (PF) are developed to better estimate the remaining useful life (RUL) of lithium-ion battery. Firstly, a hybrid model is constructed as the degradation model of the battery. The developed hybrid model is a combination of the empirical battery model and neural network model such that it could characterize both the internal mechanism of the battery and the change of external environment in a more accurate way. Then, the beetle antennae search (BAS) based PF (BAS-PF) is utilized to compute the battery parameters online based on the proposed hybrid model. Compared with the existing PF based method, BAS-PF can well handle the problem of diversity loss and improve the estimation accuracy. Finally, the battery data set published by NASA is used as the experiment object. Experiments show that the proposed hybrid model can well represent the internal and external changes of the battery, and the BAS-PF based method has a great improvement in the estimation accuracy. Those results illustrate that the developed technique could achieve an improved life prediction for lithium-ion batteries.

关键词： Remaining useful life optimization algorithm Lithium-ion battery Particle filter Hybrid model

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optimization of the eco-friendly solvent-free microwave extraction of Limnophila aromatica essential oil

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INDUSTRIAL CROPS AND PRODUCTS 2021年 165卷

作者： Yingngam, Bancha Brantner, Adelheid Treichler, Monika Brugger, Nadine Navabhatra, Abhiruj Nakonrat, Prasit Ubon Ratchathani Univ Fac Pharmaceut Sci Dept Pharmaceut Chem & Technol Ubon Ratchathani 34190 Thailand Karl Franzens Univ Graz Inst Pharmaceut Sci Dept Pharmacognosy Univ Pl 4-1 A-8010 Graz Austria Rangsit Univ Dept Pharmacol Coll Pharm Pathum Thani 12000 Thailand Ubon Ratchathani Univ Dept Elect & Elect Engn Fac Engn Ubon Ratchathani 34190 Thailand

Seasonal variation can alter the profile, quantity, and bioactivity of secondary metabolites in aromatic plants. This study aimed to establish solvent-free microwave extraction (SFME) conditions for the recovery of essential oil from Limnophila aromatica (Lam.) Merr. aerial parts and investigate the influence of seasonality on this oil. The effects of key SFME variables, including the microwave power and irradiation time, on the essential oil yield were evaluated, and these variables were optimized using response surface methodology. The chemical composition and antioxidant and antiproliferative capabilities of the essential oils obtained under the selected SFME conditions during different seasons of the year were then analyzed. The optimal SFME process conditions for achieving the maximal essential oil yield were 700 W for 25 min. The most important variable influencing oil recovery was the irradiation time (p < 0.001), followed by the microwave power (p < 0.01). The applied extraction method offered essential oil yields during the different seasons ranging from 0.20 % to 0.24 % (v/w). The obtained essential oils showed the presence of forty-seven constituents, mainly limonene, perillaldehyde, (E)-4-caranone, (Z)-4-caranone, and alpha-pinene. Monoterpene hydrocarbons were the dominant class of compounds in the oils. Seasonality affected both the antioxidant and antiproliferative properties to different degrees. These findings are not only useful for identifying eco-friendly essential oil extraction conditions but also serve as a useful guide for informing agronomic practices during the planting and cultivation of the studied medicinal plant.

关键词： Aromatic plants Essential oil Extraction conditions optimization algorithm Response surface methodology Seasonal variation

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Teaching-Learning-Based optimization of Neural Networks for Water Supply Pipe Condition Prediction

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WATER 2021年第24期13卷

作者： Elshaboury, Nehal Abdelkader, Eslam Mohammed Al-Sakkaf, Abobakr Alfalah, Ghasan Housing & Bldg Natl Res Ctr Construct & Project Management Res Inst Giza 12311 Egypt Cairo Univ Fac Engn Struct Engn Dept Giza 12613 Egypt Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ H3G 1M8 Canada Hadhramout Univ Coll Engn & Petr Dept Architecture & Environm Planning Mukalla 50512 Yemen King Saud Univ Coll Architecture & Planning Dept Architecture & Bldg Sci Riyadh 11421 Saudi Arabia

The bulk of water pipes experience major degradation and deterioration problems. This research aims at estimating the condition of water pipes in Shattora and Shaker Al-Bahery's water distribution networks, in Egypt. The developed models involve training the Elman neural network (ENN) and feed-forward neural network (FFNN) coupled with particle swarm optimization (PSO), genetic algorithms (GA), the sine cosine algorithm (SCA), and the teaching-learning-based optimization (TLBO) algorithm. For the Shattora network, the inputs to these models are pipe characteristics such as length, wall thickness, diameter, material, lining and coating, surface type, traffic distribution, cathodic protection, flow velocity, and c-factor. For the Shaker Al-Bahery network, the data gathered include length, material, age, diameter, depth, and wall thickness. Three assessment criteria are used to evaluate the suggested machine learning models, namely index of agreement (IOA), correlation coefficient (R), and root mean squared error (RMSE). The results reveal that coupling FFNN with the TLBO algorithm outperforms other prediction models. Therefore, the FFNN-TLBO model can be a valuable tool for simulating the water network pipe condition. This study could help the water municipality allocate the available budget effectively and plan the required maintenance and rehabilitation actions.

关键词： teaching-learning-based optimization optimized neural network machine learning optimization algorithm condition prediction

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Effects of dispatch strategies on optimum sizing of solar-diesel-battery energy storage-RO desalination hybrid scheme by efficient heuristic algorithm

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JOURNAL OF ENERGY STORAGE 2022年 54卷

作者： Liu, Hao Wu, Baojia Maleki, Akbar Yanbian Univ Coll Sci Dept Phys Jilin 133000 Peoples R China Shahrood Univ Technol Fac Mech Engn Shahrood Iran

Dispatch strategies on optimum sizing of hybrid renewable energy systems are crucial for attaining a costeffective and reliable power supply. In this paper, the effects of dispatch strategies on optimum sizing of photovoltaic/energy storage/diesel/ reverse osmosis desalination hybrid scheme for supplying the electrical loads and freshwater demand of a remote region are presented. The size of the components of the hybrid scheme are optimized to minimize the total net annual cost and minimizing the system's Levelized cost of energy and to minimize the cost of the desalinated water produced, and the reliability index. For this aim, two optimal dispatch strategies, load following, and cycle charging, are designed for integrated energy systems. So an efficient heuristic algorithm, tabu search, is used for optimum sizing of the hybrid energy scheme. The results are compared with the results obtained by well-known optimization method, harmony search. In each of the strategies, the effects of reliability index and economic factors, including diesel generator initial cost, fuel cost, the O&M cost of diesel generator, initial battery cost, photovoltaic initial cost, and the interest rate, on the optimization of the hybrid energy scheme have been investigated. It showed that the tabu search algorithm has potential benefit to reduce total cost by nearly 1.8% and average simulation time by nearly 1.65% as compared to the harmony search algorithm. Also, in a specific reliability index, the cycle charging strategy has potential benefit to reduce greenhouse gas emissions by nearly 11.5% per year and the cost criteria by nearly 7.5% as compared to the load following strategy.

关键词： Dispatch strategy Hybrid system Battery energy storage RO desalination optimization algorithm Reliability index Economic factors

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In-house digital image correlation technique for large structures

In-house digital image correlation technique for large struc...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Kumar, David Chiang, Chih-Hung Chaoyang Univ Technol Dept Aeronaut Engn Taichung 413310 Taiwan Chaoyang Univ Technol Ctr NDT Taichung 413310 Taiwan

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Monitoring and inspection of large structures such as wind turbines are challenging due to their size scale and the generic capabilities of available methods. The present study is based on performing 3D vibrations studies of large structures for health monitoring and designing purposes. An in-house digital image correlation (DIC) technique is developed to measure 3D displacements. The technique uses two externally-triggerable cameras to record the images simultaneously. The image-correlation method is incorporated with nonlinear optimization and distortion correction algorithms to ensure accuracy and acquire small in-plane and out-of-plane displacements. In a field experiment, the developed in-house DIC technique is used to perform static and dynamic testing of a light pole of 3m height. A large-sized checker-pattern-based calibration board is used for calibrating the designed stereovision system. The in-house DIC method can measure static and dynamic 3D displacements and accurately obtain the natural frequencies of the light pole. The fundamental natural frequency of the pole is 4.95 Hz, which is validated using a three-axis accelerometer.

关键词： Accelerometer Wind Turbine Non Destructive Testing optimization algorithm Flapping Wing Displacement Sensor Structural Dynamic Analysis Spline Interpolation Composite Materials Flexible Structures

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Heteroscedastic Gaussian Process Regression using Nearest Neighbor Point Estimates

Heteroscedastic Gaussian Process Regression using Nearest Ne...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Robani, Muhammad Daffa Palar, Pramudita Satria Zuhal, Lavi Rizki Inst Teknol Bandung Fac Mech & Aerosp Engn Bandung 40132 West Java Indonesia

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

When noise corrupts a black-box function, the use of deterministic surrogate models is inadequate since it becomes difficult to distinguish the true function and the noise. Gaussian process is one type of surrogate model that is widely used in practice to handle noisy problems. In several stochastic simulators or physical experiments, the variance of the noise could vary in the input spaces (i.e., heteroscedasticity). This study focuses on the heteroscedastic Gaussian process (HGP) model that utilizes point estimates in learning the noise function without any additional replication. This paper proposes a modified HGP called Nearest Neighbor Point Estimates HGP (NNPEHGP) that uses two GP models similar to the typical HGP configuration. In our proposed model, k-nearest neighbor (kNN) regression is augmented to the procedure to learn the noise functions to overcome the overfitting problem that frequently appears when the number of training samples is small. We test the efficacy of the proposed method on four problems, consisting of two mathematical functions, one real-world dataset, and a heat transfer problem to demonstrate the superiority of the proposed NNPEHGP model in terms of accuracy and stability.

关键词： Probability Distribution Convective Heat Transfer Coefficient Numerical Stability Black Box Light Detection and Ranging Maximum Likelihood Estimation optimization algorithm Dirac Delta Function Thermal Conductivity Gamma Function

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Delamination detection in composite laminates using improved surrogate-assisted optimization

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COMPOSITE STRUCTURES 2021年 277卷

作者： Tong, Huawei Pan, Jingwen Singh, Hemant Kumar Luo, Weili Zhang, Zhifang Hui, David Guangzhou Univ Sch Civil Engn Guangzhou 510006 Peoples R China Guangzhou Univ Res Ctr Wind Engn & Engn Vibrat Guangzhou 510006 Peoples R China Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia Univ New Orleans Dept Mech Engn New Orleans LA 70148 USA

Vibration-based delamination detection can be posed as an optimization problem, wherein the discrepancy of the frequency shifts obtained from a numerical model with assumed delamination parameters is minimized from target values of the frequency shifts. Unlike cracks, delamination is characterized by a mix of continuous and discrete parameters. The continuous variables comprise the in-plane locations and sizes, whereas discrete parameters correspond to the interface where the damage occurs. While the recent studies have demonstrated the effectiveness of surrogate-assisted optimization in determining the locations and sizes of the delamination, the prediction of interface has received scarce attention. To improve on this aspect, in this paper, individual surrogate models are built corresponding to each of the interfaces. Furthermore, we also attempt to improve the underlying optimization method by using multiple populations instead of one in order to reduce the likelihood of being trapped in local minima. The performance of the proposed approach is numerically investigated by considering various factors, such as the different number of samples for training the surrogate model, the combinations of different modes of frequency shifts used as input for damage detection, and the addition of artificial noise to the numerical frequency shifts to simulate the unavoidable measurement errors. Experimental investigations are also conducted on six delaminated beam specimens and one intact specimen. The proposed approach showed significant improvements in identifying the damage parameters, and in particular improving the prediction accuracy of the damage interface.

关键词： Damage detection Vibration analysis Composite structure Surrogate model optimization algorithm

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Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target

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ENERGY 2021年 215卷

作者： Dong, Yunxuan Wang, Jing Xiao, Ling Fu, Tonglin Univ Macau Dept Elect & Comp Engn Macau Peoples R China Lanzhou Univ Technol Sch Law Lanzhou 730050 Gansu Peoples R China Chongqing Univ Posts & Telecommun Sch Econ & Management Chongqing 400065 Peoples R China LongDong Univ Sch Math & Stat Qingyang Gansu Peoples R China Chinese Acad Sci Shapotou Desert Res & Expt Stn Northwest Inst Ecoenvironm & Resources Lanzhou 730000 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Accurate and reliable wind speed forecasting (WSF) is crucial for wind power systems. As one of the effective forecast methods, machine learning (ML) methods are employed for wind speed time series forecasting because the excellent ability in fitting the relationship between data and cost function. However, the cost functions with non-convexity make the whole problem poor interpretability and poor robustness. In this paper, a novel hybrid supervised approach is proposed to solve the above problems. The proposed approach has adopted local convolutional neural networks (LCNNs) for convexity preserving of the cost function, in this way, a non-convex problem can be transformed as a convex problem so that heuristic optimization algorithms is adopted to find optimal parameters, and it helps to construct a more stable model. Highway Gate (HG) algorithm is adopted to decrease the computation complexity of the proposed model. The numerical simulation results indicate that the proposed method is not only effective for solving convergence problem cost by non-convexity, but also beneficial to improve accuracy and stability of the traditional ML for wind speed time series forecasting. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Wind speed forecasting Convolutional neural networks Hybrid forecast approach optimization algorithm

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