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Privacy-Preserving Constrained Quadratic optimization With Fisher Information

IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 545-549页

作者： Farokhi, Farhad CSIRO Data61 Docklands Vic 3008 Australia Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia

Noisy (stochastic) gradient descent is used to develop privacy-preserving algorithms for solving constrained quadratic optimization problems. The variance of the error of an adversary's estimate of the parameters of the quadratic cost function based on iterates of the algorithm is related to the Fisher information of the noise using the Cramer-Rao bound. This motivates using the Fisher information as a measure of privacy. Noting that the performance degradation in noisy gradient descent is proportional to the variance of the noise, a measure of utility is defined to be equal to the variance of the noise. Trade-off between privacy and utility is balanced by minimizing the Fisher information subject to a constraint on the variance of the noise. The optimal privacy-preserving noise is proved to be Gaussian, which implies that the developed privacy-preserving optimization algorithm also guarantees differential privacy.

关键词： Privacy Noise measurement Signal processing algorithms Differential privacy Cost function optimization algorithm constrained optimization quadratic cost noisy gradient descent Fisher information privacy-preserving optimization

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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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Rapid determination of rice protein content using near-infrared spectroscopy coupled with feature wavelength selection

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INFRARED PHYSICS & TECHNOLOGY 2023年 135卷

作者： Liu, Jinming Luo, Xin Zhang, Dongjie Wang, Chunqi Chen, Zhengguang Zhao, Xiaoyu Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Daqing 163319 Peoples R China Heilongjiang Bayi Agr Univ Postdoctoral Mobile Stn Agr Engn Daqing 163319 Peoples R China Natl Coarse Cereals Engn Technol Ctr Daqing 163319 Peoples R China Heilongjiang Bayi Agr Univ Coll Food Daqing 163319 Peoples R China

Protein content is a key indicator for rice nutritional value, and its accurate determination has become a necessary tool for rice quality evaluation and breeding. To solve the shortcomings of slow and costly testing by traditional methods, a rapid detection model of rice protein content was constructed based on near-infrared spectroscopy coupled with feature wavelength selection. The backward interval partial least squares (BiPLS) was combined with the genetic simulated annealing algorithm (GSA) and the simulated annealing binary particle swarm optimization algorithm (SABPSO), respectively, to construct BiPLS-GSA and BiPLS-SABPSO for selecting protein feature wavelengths, thereby establishing a corresponding partial least squares quantitative calibration model. Among them, the regression model established using feature wavelengths selected by BiPLS-SABPSO had the best performance. The determination coefficient of the model for the validation set and the independent test set were 0.949 and 0.956, the root mean square error were 0.174% and 0.214%, the relative root mean square error were 2.621% and 3.118%, and the residual predictive deviation being 4.235 and 4.500, respectively, which could meet the requirement for rapid and accurate determination of rice protein content. The combination of BiPLS-SABPSO and near-infrared spectroscopy has developed into a new method to achieve fast and reliable determination of rice protein content, providing an alternative strategy for rapid qualitative detection of related agricultural products.

关键词： Protein content Near -infrared spectroscopy Feature wavelengths Backward interval partial least squares Genetic simulated annealing algorithm Simulated annealing binary particle swarm optimization algorithm

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Breast cancer detection in thermograms using a hybrid of GA and GWO based deep feature selection method

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期219卷

作者： Pramanik, Rishav Pramanik, Payel Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn 188 Raja S C Mallick Rd Kolkata 700032 West Bengal India

Breast cancer is one of the most common reasons for the premature death of women worldwide. However, early detection and diagnosis of the same can save many lives. Hence, computer scientists across the world are also striving to develop reliable models to deal with this disease. One of the reliable methods of detecting breast cancer is through thermal images or thermograms. In medical image analysis, the current research trend is the use of deep learning (DL) models. However, many state-of-the-art DL models generate a large number of features and for processing those features we need a significant amount of memory and computation time. To address this issue, we have proposed a lightweight model to detect signs of abnormality in breast thermograms using a combination of transfer learning-based DL model and feature selection approaches. At first, we employed a deep learning model, called SqueezeNet 1.1 (pre-trained on the ImageNet dataset), fine-tuned on the breast cancer thermal images for the feature extraction purpose. Then a hybrid of Genetic algorithm (GA) and Grey Wolf Optimizer (GWO) is used to reduce the dimension of the obtained feature vector. Before that, a chaotic map is used to create the initial population of GA. The proposed model performs very well in detecting and differentiating malignant and healthy breasts. We have evaluated our model on a publicly available dataset, namely Database For Mastology Research (DMR-IR) and achieved 100% accuracy on the test set using only 3% features extracted by the SqueezeNet 1.1 model.

关键词： Breast cancer Thermal image Deep learning Feature selection optimization algorithm

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Multi-objective optimal design of a fuzzy adaptive robust fractional-order PID controller for a nonlinear unmanned flying system

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AEROSPACE SCIENCE AND TECHNOLOGY 2023年第1期141卷

作者： Ansarian, A. Mahmoodabadi, M. J. Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran Sirjan Univ Technol Dept Mech Engn Sirjan Iran

In this study, a fuzzy adaptive robust fractional-order proportional-integral-derivative (FARFOPID) controller is introduced for stabilization of a nonlinear unmanned flying system. At first, a proportional-integral-derivative scheme improved by the fractionalorder technique is employed to control the complicated dynamics of the considered system. An adaptive mechanism based on sliding surfaces is proposed to update the effective gains of the fractional-order PID controller. To improve the performance of the controller in the presence of disturbances and uncertainties, fuzzy systems are used to adjust the parameters of the sliding surfaces. The fuzzy systems, considered in this article, employ the singleton fuzzifier, product inference engine and center average defuzzifier as well as triangular-trapezoidal membership functions. Then, the multi-objective thermal exchange optimization (MOTEO), which has presented a high convergence speed and good performance for global search problems, is utilized to determine the controller parameters in such a way that control efforts and system errors would be minimized, simultaneously. Finally, simulation results clearly depict the effectiveness and efficiency of the proposed approach to handle the regarded unmanned flying system in comparison with other controllers.& COPY;2023 Elsevier Masson SAS. All rights reserved.

关键词： PID controller Fractional-order calculus Adaptive robust control Fuzzy system Multi-objective thermal exchange optimization algorithm Unmanned flying system

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A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response

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RENEWABLE ENERGY 2023年第Part1期219卷

作者： Wei, Hui Wang, Wen-sheng Kao, Xiao-xuan China Univ Min & Technol Beijing Sch Management Beijing 100083 Peoples R China China Univ Min & Technol Beijing Res Inst Decis Making Sci & Big Data Beijing 100083 Peoples R China

Renewable energy generation significantly reduces harmful emissions and promotes sustainable development. Still, its volatility leads to extensive grid connections, affecting electricity system security. Diversifying forms of energy consumption further expands the peak valley difference in electricity demand. This study aims to investigate the hybrid dynamic environmental-economic dispatch problem of multi-energy complementary virtual power plant, considering the renewable energy generation uncertainty and demand response to promote renewable energy integration and mitigate the electricity system supply-demand mismatch. It proposes a novel approach based on the sine cosine and multi-objective particle swarm optimization algorithm to reduce the economic and environmental costs of the multi-energy complementary virtual power plant. First, a hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant is established, considering climbing power, equality, and inequality constraints. Second, targeting the multi objective, nonlinear, and high-dimension characteristics of the hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant, a sine cosine and multi-objective particle swarm optimization algorithm is proposed to optimize the particle position update method. Finally, simulation cases are constructed based on the development trend of the virtual power plant, setting various dispatching situations to validate the robustness of the proposed approach and determining a compromise solution by membership functions. The simulation results show that the lowest economic and environmental costs obtained by the sine cosine and multi-objective particle swarm optimization algorithm are at least 11.37 % and 2.79 % lower than those obtained by the NSGA-II algorithm and multi-objective particle swarm optimization algorithm when considering the renewable energy generation uncertainty and dem

关键词： Hybrid dynamic environmental-economic dispatch Multi-energy complementary virtual power plant Renewable energy generation uncertainty Demand response Sine cosine and multi-objective particle swarm optimization algorithm

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Parameters determination of the modified J-A model with an optimization algorithm

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2013年第3期41卷 259-266页

作者： Cong, Haoxi Zou, Liang Zhang, Li Li, Qingmin Shandong Univ Sch Elect Engn Jinan 250061 Shandong Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

J-A model is widely used in hysteresis modeling as well as performance simulation of the magnetic materials. To achieve preferable adequacy, a modified J-A model is given, however, accurate solution of the parameters is very important for the modified J-A model, especially for over-determined nonlinear equations. In response to solving the over-determined nonlinear equations, this paper first turned the problem of the over-determined nonlinear equations into solving the minimum value of a multivariate function by means of the least square method. While the multivariate function is of high nonlinearity (the function is not continuous and the matrix of the partial derivatives is singular), solution methods using derivative calculation were abandoned, and the direct seeking methods with no derivative calculations (simplex algorithm) were involved to solve the problem. In the end the solution was validated with the use of the genetic algorithm and the simulated annealing algorithm.

关键词： Modified J-A model overdetermined nonlinear equations least square method optimization algorithm simulation

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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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Spammer detection via ranking aggregation of group behavior

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期216卷

作者： Zhang, Zheng Zhou, Mingyang Wan, Jun Lu, Kezhong Chen, Guoliang Liao, Hao Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Nanyang Inst Technol Sch Comp & Software Nanyang 473004 Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Peoples R China

Online review networks have become a popular platform for disseminating information which attracts large numbers of spammers to influence the reputation of the online products by writing fake reviews. Thus, the spammer detection becomes crucial in controlling the fraud reviews which is valuable for practical applications. However, existing ranking-based methods usually focus on the rating value and textual features of reviews ignoring the features of group behavior which results in the sharp degradation of spammer detection's performance in case of large-scale spammer group attack. In this paper, we propose a novel ranking aggregation method based on features of collusive attack of spammer groups to optimize the spammer ranking algorithm by reassigning the spamicity score for each reviewer. Extensive experiments on various real-world datasets with spammer injection demonstrate that our proposed optimization method can significantly improve the accuracy and robustness of the spammer detection. The average improvements of performance for ranking before and after optimization achieve 10.73%, 42.86% and 4.32% respectively on the Recall, Precision and AUC metrics.

关键词： Spammer group Ranking aggregation Feature extraction Bipartite graph network optimization algorithm

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A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration

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ENERGIES 2023年第19期16卷 6959页

作者： Ahmadi, Bahman Shirazi, Elham Univ Twente Dept Elect Engn Math & Comp Sci NL-7522 NB Enschede Netherlands Univ Twente Dept Engn Technol NL-7522 NB Enschede Netherlands

The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid's voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid.

关键词： smart grid electric vehicle charging strategy optimization algorithm distribution network

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