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Generation of controllable spectrum in multiple positions from speckle patterns

OPTICS AND LASER TECHNOLOGY 2022年 149卷 107820-107820页

作者： Li, Haoran Wu, Xiaoyan Liu, Guodong Vinu, R. V. Wang, Xiaoyan Chen, Ziyang Pu, Jixiong Huaqiao Univ Coll Informat Sci & Engn Fujian Key Lab Light Propagat & Transformat Xiamen 361021 Fujian Peoples R China China Acad Engn Phys Inst Fluid Phys Mianyang 621900 Sichuan Peoples R China China Acad Engn Phys Key Lab Sci & Technol High Energy Laser Mianyang 621900 Sichuan Peoples R China

Feedback-based wavefront shaping has been proposed to modulate the speckle field generated by coherent light transmitting through scattering media. Different from a monochromatic light, a colorful speckle pattern is generated when polychromatic light transmits through scattering media. Although single-position spectrum modulation has been realized, multiple-position spectrum modulation is a much more complicated problem. Based on non-dominated sorting genetic algorithm ii (NSGA2), we design a step-by-step strategy to solve this problem. The results show that modulated spectra in two spatial positions with controllable spectral shape, range and magnitude can be achieved. This research is expected to be applied in the field of adaptive spectral control ranging from advanced spectral filtering to optical fiber dispersion and multi-spectral imaging.

关键词： Wavefront shaping Spectrum modulation non-dominated sorting genetic algorithm ii

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Multi-objective optimization-based medical image watermarking scheme for securing patient records

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期118卷

作者： Singh, Roop Pal, Raju Mittal, Himanshu Joshi, Deepak Indian Inst Technol Delhi Ctr Biomed Engn New Delhi 110016 India Gautam Buddha Univ Sch Informat & Commun Technol Dept Comp Sci & Engn Greater Noida 201312 Uttar Pradesh India Indira Gandhi Delhi Tech Univ Women Dept Artificial Intelligence & Data Sci New Delhi 110006 India Noida Inst Engn & Technol Greater Noida 201306 Uttar Pradesh India All India Inst Med Sci AIIMS Dept Biomed Engn New Delhi 110029 India

The integrity and authenticity of electronic patient records (EPRs) are essential concerns in the healthcare industry. To address these concerns, this paper introduces a novel, robust watermarking scheme using integer wavelet transform -singular value decomposition (IWT-SVD). The proposed scheme is divided into logo embedding and EPR text embedding, aiming to mitigate false -positive issues and maintain EPR integrity and authenticity. In the first part, medical image and watermark logo are encrypted using DMA (Diffused Mandelbrot set -Arnold map) to enhance security. Further, the encrypted medical image is segmented into four subbands (CA, CH, CV, CD) by one -level IWT transform. Then SVD is applied on sub -band CD to extract the principal component (PC), where the encrypted logo is embedded due to its higher magnitudes, thereby mitigating false -positive errors. Embedding the encrypted logo into the hybrid IWT-SVD domain enhances security and robustness. Moreover, for embedding, multiple optimal embedding factors (MOEFs) are determined through non -dominated sorting genetic algorithm ii (NSGA-ii) to balance imperceptibility and robustness. In the second part, EPR text is embedded into watermarked logo using SVD to verify authenticity. The performance of the proposed scheme is validated on standard medical image datasets against various attacks in terms of SSIM, PSNR, BER, and NC. The proposed scheme improves PSNR values by 48.44% and 19.66% for grayscale and color images, respectively. Similarly, NC values are also improved by 10%-12% for grayscale and color images. Therefore, the proposed scheme enhances security, imperceptibility, and robustness while reducing complexity compared to the state-of-the-art schemes. Moreover, it is also false -positive -free and maintain integrity simultaneously.

关键词： Watermarking Medical images Electronic patient record Integer wavelet transform Singular value decomposition non-dominated sorting genetic algorithm ii

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A many-objective optimization charging scheme for wireless rechargeable sensor networks via mobile charging vehicles

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COMPUTER NETWORKS 2022年 215卷 1页

作者： Li, Jiahui Sun, Geng Wang, Aimin Lei, Ming Liang, Shuang Kang, Hui Liu, Yanheng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

In wireless rechargeable sensor networks (WRSNs), the energy can be transferred from the mobile charging vehicles (MCVs) to sensor nodes via the wireless medium, so that providing a new paradigm to prolong the network lifetime. However, the network lifetime of MCV-enabled WRSNs is affected by several factors such as the charging priority of sensor nodes. Moreover, the communication protocols can dynamically affect the energy consumptions of sensor nodes, which should be taken into account. In this paper, we consider a low energy adaptive clustering hierarchy (LEACH)-based WRSNs, and formulate an MCV deployment manyobjective optimization problem (MDMaOP) for replenishing energy to sensor nodes, in which the number of sensor nodes within the charging ranges of the MCVs, motion energy consumptions of the MCVs, remaining energy of the node with the least residual energy and distance between the MCVs and sensor nodes are simultaneously optimized, to prolong the network lifetime. Moreover, the formulated MDMaOP is analyzed and proven as NP-hard. Then, we propose a fast optimization approach and an accurate optimization approach to solve the formulated problem for satisfying the demands of calculation time and accuracy in different scenarios. Simulation results demonstrate that the proposed methods are effective for prolonging the network lifetime and outperform some other comparison algorithms.

关键词： Wireless rechargeable sensor networks Mobile charging vehicles Charging efficiency Many-objective optimization Firefly algorithm non-dominated sorting genetic algorithm ii

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Multi-objective optimization of high-speed on-off valve based on surrogate model for water hydraulic manipulators

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FUSION ENGINEERING AND DESIGN 2021年 173卷 112949-112949页

作者： Liu Qingtong Yin Fanglong Nie Songlin Hong Ruidong Ji Hui Beijing Univ Technol Beijing Key Lab Adv Mfg Technol Beijing 100124 Peoples R China

Water hydraulic manipulators are suitable for remote handling maintenance task in reactors. Water hydraulic servo valves are currently used in the manipulators, but water has the characteristics of low viscosity and poor lubricity, there are inevitable leakage and wear failure. High speed on-off valve (HSV) can be used to generate proportional output and could be used to replace servo valves. In this research, a water hydraulic HSV for water hydraulic manipulators is proposed. An optimization method supported surrogate model, non-dominated sorting genetic algorithm ii (NSGA-ii), and AMESim simulation is built. And the effect of the HSV structure parameters on dynamic characteristic is analyzed using the global sensitivity analysis. The Pareto frontier is investigated using decision-making tools with FUZZY, LINMAP, and TOPSIS methods, the best solutions are selected by using the deviation index. The HSV prototype is fabricated, and the static and dynamic characteristics experiments of the HSV are carried out. The simulation results showed that, after optimization, the opening response time is reduced by 17%, and the closing response time is reduced 9%. The experimental results show that the opening response time and closing response time reach 5.93 ms and 4.84 ms.

关键词： Decision-making methods Multi-objective optimization non-dominated sorting genetic algorithm ii Surrogate model Water hydraulic HSV

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Optimal allocation of phase shifting transformer with uncertain wind power based on dynamic programming

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FRONTIERS IN ENERGY RESEARCH 2023年 10卷

作者： Fu, Kang Du, Zhaobin Li, Feng Li, Zuohong Xia, Chengjun South China Univ Technol Guangzhou Peoples R China Guangdong Prov Key Lab Intelligent Operat & Contro Guangzhou Peoples R China Grid Planning & Res Ctr Guangdong Power Grid Corp Guangzhou Peoples R China

Phase Shifting Transformer (PST) can help improve the power flow distribution of the transmission section, which can increase the wind power consumption of the grid. In order to adapt the PST allocation to the grid evolution, this paper presents a dynamic programming method to allocate PST in each planning stage of the grid optimally. The optimal allocation model of PST under a single grid seeks to maximize the wind power consumption and the Total Transfer Capacity (TTC) between areas. A calculation method for TTC of grids containing PST and wind power is proposed. The non-dominated sorting genetic algorithm ii (NSGA2) is used to solve the Pareto sets under each planning stage of the grid. Then, the optimal planning path of PST is derived based on dynamic programming. The superiority of the proposed method is demonstrated by comparing the IEEE-118 system results of dynamic and static programming.

关键词： phase shifting transformer dynamic programming optimal allocation wind power uncertainty non-dominated sorting genetic algorithm ii

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Multi-Objective Structural Optimization of a Composite Wind Turbine Blade Considering Natural Frequencies of Vibration and Global Stability

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ENERGIES 2023年第8期16卷 3363页

作者： Couto, Lucas de Landa Moreira, Nicolas Estanislau Saito, Josue Yoshikazu de Oliveira Hallak, Patricia Habib Lemonge, Afonso Celso de Castro Univ Fed Juiz de Fora Fac Engn Dept Mech Engn BR-36036900 Juiz De Fora Brazil Univ Fed Juiz de Fora Fac Engn Dept Appl & Computat Mech BR-36036900 Juiz De Fora Brazil

Aspects concerning resonance and global stability of a wind turbine blade must be carefully considered in its optimal design. In this paper, a composite wind turbine blade with an external geometry based on the NREL 5 MW model was subjected to multi-objective structural optimization considering these aspects. Four multi-objective structural optimization problems are formulated considering the blade mass, the maximum blade tip displacement, the natural frequencies of vibration, and the critical load factor as objective functions. The design variables are the number of plies, material, and fiber orientation. The design constraints are the materials' margin of safety, the blade's allowable tip displacement, and the minimum load factor. The blade model is submitted to the loads determined by the actuator lines theory and discretized in a finite element parameterized model using the Femap software according to geometric design variables. Among many multi-objective evolutionary algorithms available in the literature concerning evolutionary computation, the NSGA-ii is the adopted evolutionary algorithm to solve the multi-objective optimization problems. Pareto fronts are obtained and performance indicators are used to evaluate the distribution of the non-dominated solutions. Multi-criteria decision-making is used to extract the solutions from the Pareto fronts according to the decision-maker's preferences. The values of the objective functions, design variables, and constraints are presented for each extracted solution. The proposed study is expected to contribute to the multi-objective optimization and the structural design of wind turbine blades.

关键词： wind turbine blade composite material multi-objective structural optimization non-dominated sorting genetic algorithm ii

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DETAILED DESIGN AND ANALYSIS FOR ADDITIVE MANUFACTURING OF TOPOLOGICALLY OPTIMIZED AND GENERATIVELY DESIGNED TI-6AL-4V HIP JOINT IMPLANT

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INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING 2024年第4期22卷 73-90页

作者： Kishor, Abhishek Burela, Ramesh Gupta Gupta, Ankit Shiv Nadar Inst Eminence DTU Dept Mech Engn Greater Noida Uttar Pradesh India

In this paper, a comprehensive investigation of the design and analysis of Ti-6Al-4V hip joint implants using generative design and topology optimization, along with laser powder bed fusion (LPBF), an additive manufacturing technique, has been presented. The study employed the NSGA-ii genetic algorithm for generative design, enabling the generation of diverse optimized designs and topology optimization with the solid isotropic material penalization approach, efficiently reducing implant mass of the design space by up to 75% while maintaining structural integrity. Finite element analysis revealed comparable von Mises stress and deformation levels between geometries obtained with generative design and topology optimization. However, the combined approach exhibited superior performance, namely, topology optimization followed by generative design, with a 40% reduction in deformation and a 15% reduction in von Mises stress compared to conventional models. LPBF simulations demonstrated the superiority of the optimized geometries, with a 30% reduction in thermal stress and a 66% reduction in deformation compared to conventional designs. It is observed that design input for generative design significantly impacts the output design. Also, geometry has a notable impact on the quality of the printed part.

关键词： hip implant additive manufacturing generative design non-dominated sorting genetic algorithm ii laser powder bed fusion biomedical application design topology optimization finite element analysis solid isotropic material penalization

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Short-Term Electric Power Demand Forecasting Using NSGA ii-ANFIS Model

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ENERGIES 2019年第10期12卷 1891页

作者： Jadidi, Aydin Menezes, Raimundo de Souza, Nilmar de Castro Lima, Antonio Cezar Univ Fed Bahia Polytech Sch Dept Elect Engn BR-40210630 Salvador BA Brazil

Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm ii (NSGA ii) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA ii is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA ii is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to test the model and among the different tested models, NSGA ii-ANFIS-GA provides better accuracy. Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063, 65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively.

关键词： electric load forecasting non-dominated sorting genetic algorithm ii multi-layer perceptron adaptive neuro-fuzzy inference system meta-heuristic algorithms

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Optimal locations of piezoelectric patches for supersonic flutter control of honeycomb sandwich panels, using the NSGA-ii method

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SMART MATERIALS AND STRUCTURES 2016年第3期25卷

作者： Nezami, M. Gholami, B. Islamic Azad Univ Aerosp & Mech Engn Grp Firoozkooh Branch Firoozkooh Iran Amikabir Univ Technol Tehran Polytech Dept Biomed Engn Tehran Iran

The active flutter control of supersonic sandwich panels with regular honeycomb interlayers under impact load excitation is studied using piezoelectric patches. A non-dominated sorting-based multi-objective evolutionary algorithm, called non-dominated sorting genetic algorithm ii (NSGA-ii) is suggested to find the optimal locations for different numbers of piezoelectric actuator/sensor pairs. Quasi-steady first order supersonic piston theory is employed to define aerodynamic loading and the p-method is applied to find the flutter bounds. Hamilton's principle in conjunction with the generalized Fourier expansions and Galerkin method are used to develop the dynamical model of the structural systems in the state-space domain. The classical Runge-Kutta time integration algorithm is then used to calculate the open-loop aeroelastic response of the system. The maximum flutter velocity and minimum voltage applied to actuators are calculated according to the optimal locations of piezoelectric patches obtained using the NSGA-ii and then the proportional feedback is used to actively suppress the closed loop system response. Finally the control effects, using the two different controllers, are compared.

关键词： honeycomb sandwich panel active flutter control non-dominated sorting genetic algorithm ii piezoelectric patch direct proportional feedback control

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Fault Location in VSC-HVDC Systems Based on NSGA-ii and Discrete Wavelet Transform

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2022年第3期12卷 1347-1361页

作者： Rohani, Reza Koochaki, Amangaldi Siahbalaee, Jafar Islamic Azad Univ Dept Elect Engn Aliabad Katoul Branch Aliabad Katoul Iran

This paper introduces a novel fault location model based on Adaptive Neuro-Fuzzy Inference System (ANFIS). For the purpose of performance improvement, a meta-heuristic algorithm known as non-dominated sorting genetic algorithm type 2 (NSGA-ii), which has the ability of fast searching for the optimal point and escaping the local optimality trap, is used for ANFIS training. The fault current and voltage are usually the two parameters used as the inputs to the ANFIS, even though they cannot truly specify the fault characteristics on their own. Here, Discrete Wavelet transform (DWT) is used to extract the relevant features of the fault current. To demonstrate superiority of the proposed model, a comparative study is performed against the traditional Least-Squares and Back-Propagation (LS+BP) and Chaotic Dynamic Weight Particle Swarm Optimization (CDW-PSO) methods. The simulation results show that the proposed estimation model has a superior performance compared to the previous models in terms of both convergence speed and estimation error.

关键词： Fault location estimation VSC-HVDC system Adaptive neuro-fuzzy inference system non-dominated sorting genetic algorithm ii Discrete wavelet transform

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