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Evolutionary algorithms For k-Anonymity In Social Networks Based On Clustering Approach

COMPUTER JOURNAL 2020年第7期63卷 1039-1062页

作者： Yazdanjue, Navid Fathian, Mohammad Amiri, Babak Iran Univ Sci & Technol Sch Ind Engn Tehran *** Iran

The usage of social networks shows a growing trend in recent years. Due to a large number of online social networking users, there is a lot of data within these networks. Recently, advances in technology have made it possible to extract useful information about individuals and the interactions among them. In parallel, several methods and techniques were proposed to preserve the users' privacy through the anonymization of social network graphs. In this regard, the utilization of the k-anonymity method, where k is the required threshold of structural anonymity, is among the most useful techniques. In this technique, the nodes are clustered together to form the super-nodes of size at least k. Our main idea in this paper is, initially, to optimize the clustering process in the k-anonymity method by means of the particle swarm optimization (PSO) algorithm in order to minimize the normalized structural information loss (NSIL), which is equal to maximizing 1-NSIL. Although the proposed PSO-based method shows a higher convergence rate than the previously introduced genetic algorithm (GA) method, it did not provide a lower NSIL value. Therefore, in order to achieve the NSIL value provided by GA optimization while preserving the high convergence rate obtained from the PSO algorithm, we present hybrid solutions based on the GA and PSO algorithms. Eventually, in order to achieve indistinguishable nodes, the edge generalization process is employed based on their relationships. The simulation results demonstrate the efficiency of the proposed model to balance the maximized 1-NSIL and the algorithm's convergence rate.

关键词： social network k-anonymity particle swarm optimization algorithm genetic algorithm structural information loss

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An Optimized Mass-spring Model with Shape Restoration Ability Based on Volume Conservation

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2020年第4期14卷 1738-1756页

作者： Zhang, Xiaorui Wu, Hailun Sun, Wei Yuan, Chengsheng Nanjing Univ Informat Sci & Technol Sch Comp & Software Engn Res Ctr Digital Forens Jiangsu Engn Ctr Network MonitoringMinist Educ Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China

To improve the accuracy and realism of the virtual surgical simulation system, this paper proposes an optimized mass-spring model with shape restoration ability based on volume conservation to simulate soft tissue deformation. The proposed method constructs a soft tissue surface model that adopts a new flexion spring for resisting bending and incorporates it into the mass-spring model (MSM) to restore the original shape. Then, we employ the particle swarm optimization algorithm to achieve the optimal solution of the model parameters. Besides, the volume conservation constraint is applied to the position-based dynamics (PBD) approach to maintain the volume of the deformable object for constructing the soft tissue volumetric model base on tetrahedrons. Finally, we built a simulation system on the PHANTOM OMNI force tactile interaction device to realize the deformation simulation of the virtual liver. Experimental results show that the proposed model has a good shape restoration ability and incompressibility, which can enhance the deformation accuracy and interactive realism.

关键词： Virtual surgery soft tissue deformation mass-spring model position-based dynamics particle swarm optimization algorithm

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A generalized pseudo-rigid-body PPRR model for both straight and circular beams in compliant mechanisms

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MECHANISM AND MACHINE THEORY 2020年 154卷 104054-104054页

作者： Xu, Hao Gan, Jinqiang Zhang, Xianmin China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China South China Univ Technol Guangdong Prov Key Lab Precis Equipment & Mfg Tec Guangzhou 510640 Peoples R China

The pseudo-rigid-body (PRB) models have been widely applied for modeling of straight beams in compliant mechanisms. In this paper, a generalized PRB PPRR model with two mutually perpendicular linear springs is established for both straight and circular beams in compliant mechanisms. In the developed model, the static equations of the deflected beams are derived from Bernoulli's equation. The characteristic parameters of the developed model are identified by the reasonably organized particle swarm optimization (PSO) algorithm. A simulation experiment of tip locus of a circular beam is conducted to evaluate the developed model. The simulation results show the developed model has higher accuracy than the PRB 2R, 3R, and RRHR model. In addition, two numerical examples are further implemented to demonstrate the effectiveness of the developed model. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Compliant mechanism Pseudo-rigid-body model Circular beam particle swarm optimization algorithm

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Iterative convergence control method for planar underactuated manipulator based on support vector regression model

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NONLINEAR DYNAMICS 2020年第4期102卷 2711-2724页

作者： Wang, Ya-Wu Yang, Hui-Qing Zhang, Pan China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China

An iterative convergence control method (ICCM) based on the support vector regression (SVR) is proposed to realize the position-posture control of the planar four-link underactuated manipulator with a passive second link. Firstly, the particle swarm optimization (PSO) algorithm is used to obtain the target angles of all links according to the position-posture control objective. Then, two prediction models for the coupling relationship between the first link and the passive link, and the third link and the passive link are established based on the SVR, whose optimal parameters are selected by the chaos particle swarm optimization (CPSO) algorithm. By repeatedly controlling the first link or the third link to rotate an angle which is calculated by the trained SVR model, the passive link gradually converges to its target angle after several iterations. Next, the active links are controlled to rotate to their target angles with low speeds, and the passive link does not rotate due to friction. Finally, the experimental results verify the effectiveness and feasibility of the proposed method.

关键词： Underactuated manipulator Position– posture control Support vector regression particle swarm optimization algorithm Coupling relationship

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Environment-dependent edge reconstruction of transition metal dichalcogenides: a global search

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MATERIALS TODAY ADVANCES 2020年 8卷

作者： Li, D. Ding, F. Jilin Univ Coll Phys State Key Lab Superhard Mat Changchun 130012 Peoples R China Inst Basic Sci Ctr Multidimens Carbon Mat Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol UNIST Dept Mat Sci & Engn Ulsan 44919 South Korea

The properties of an edge of a transition metal dichalcogenide (TMDC) sensitively depend on its atomic structure;therefore, knowing the exact structure is a precondition for predicting the applications of TMDC, such as in catalysis. Although some edge reconstructions of TMDCs have been reported in previous studies, a global search of TMDC edge structures and calculations of their stabilities are still absent. Here, we propose an approach to explore all possible edge reconstructions of a monolayer TMDC by employing the particle swarm optimization algorithm and first-principles calculations. Taking the most studied TMDC material, MoS2, as a representative, we have built a database of the edge structures of TMDCs. Including the five experimentally observed edges, the thirty-four most stable edges with various sulfur concentrations are predicted for the first time. The most stable edge structures of 1H-MoS2 in different environments along both the armchair and zigzag directions have been predicted and agree well with experimental observations. A specific edge of 1H-MoS2 in the database can be stabilized by modulating the chemical composition of the atoms, which offers an efficient way to tune the properties of TMDC nanostructures, such as TMDC quantum dots and/or nanoribbons. (C) 2020 The Author(s). Published by Elsevier Ltd.

关键词： Transition metal dichalcogenide Molybdenum disulfide Edge reconstruction particle swarm optimization algorithm First-principles calculation

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Time series prediction for output of multi-region solar power plants

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APPLIED ENERGY 2020年 257卷 114001-000页

作者： Zheng, Jianqin Zhang, Haoran Dai, Yuanhao Wang, Bohong Zheng, Taicheng Liao, Qi Liang, Yongtu Zhang, Fengwei Song, Xuan China Univ Petr Natl Engn Lab Pipeline Safety MOE Key Lab Petr Engn Beijing Key Lab Urban Oil & Gas Distribut Technol Fuxue Rd 18 Beijing 102249 Peoples R China Univ Tokyo Ctr Spatial Informat Sci 5-1-5 Kashiwanoha Kashiwa Chiba 2778568 Japan Wayne State Univ Dept Comp Sci Comp & Syst Secur Lab Detroit MI 48202 USA

Solar energy, as a renewable and clean energy source, has developed rapidly and has attracted considerable attention. The integration of solar energy into a power grid requires precise prediction of the power output of solar plants. Accurate solar power output prediction can promote power dispatch, maintaining the normal operation of power systems. However, research on multi-region solar power is still rare. In this study, long shortterm memory and a particle swarm optimization algorithm contribute to solar power prediction considering time series. In order to improve the prediction accuracy, particle swarm optimization is used to optimize the parameters of the long short-term memory model. In addition, different long short-term memory structures are illustrated to determine the final prediction model with sensitivity analysis. Experiments are carried out to verify the effectiveness of the proposed method. The mean absolute error and root mean square error of the proposed method is the smallest among the prediction methods in four cases containing different seasons. In terms of prediction accuracy, results indicate that the proposed prediction model outperforms basic long short-term memory, artificial neural network, and extreme gradient boosting.

关键词： Solar power output prediction Time series Multi-region Long short-term memory particle swarm optimization algorithm Sensitivity analysis

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Modeling and simulation to optimize direct power control of DFIG in variable-speed pumped-storage power plant using teaching-learning-based optimization technique

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SOFT COMPUTING 2020年第22期24卷 16895-16915页

作者： Hosseini, Seyed Mohammad Hassan Rezvani, Alireza Islamic Azad Univ South Tehran Branch Dept Elect Engn Tehran Iran Islamic Azad Univ Saveh Branch Dept Elect Engn Tehran Iran

The issues related to the optimal control of large-scale storage systems in electric power systems such as pumped storage (PS) plant have turned into vital challenges in the way of integrating renewable energy sources into power systems to provide reliable and economical electric energy. In this regard, this paper uses the direct power control strategy to model and simulate a variable-speed PS plant, which includes a doubly fed induction generator (DFIG). The active and the reactive power of the stator would be able to be controlled, separately. This approach has a better dynamic performance compared to other methods, while it would be quite simple to implement. But there are some shortfalls with this method, such as high ripple relating to the active power as well as reactive power together with the current harmonics. In this respect, the space vector modulation (SVM) is applied to eliminate these shortfalls. In the proposed control technique, including SVM, the dynamic performance of the studied DFIG unit is controlled using the proportional-integral (PI) controller. It should be noted that the teaching-learning-based optimization (TLBO) method is employed to tune the PI controller for controlling the DFIG system in the PS plant. Finally, in order to validate the performance of the suggested framework, a comparison is made between the results obtained by the TLBO and the ones reported by other optimization methods. The obtained results using the TLBO algorithm indicate better performance of the PI controller to reduce the ripples of the active and reactive power of the stator as well as the harmonic power.

关键词： Pumped-storage power plant Doubly fed induction generator Teaching-learning-based optimization Imperialist competitive algorithm particle swarm optimization algorithm

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V-REP-based navigation of automated wheeled robot between obstacles using PSO-tuned feedforward neural network

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2020年第4期7卷 427-434页

作者： Pandey, Anish Panwar, Vikas Singh Hasan, Md Ehtesham Parhi, Dayal R. KIIT Deemed Univ Bhubaneswar India NIT Rourkela Rourkela Odisha India

This paper describes the navigation of an automated Pioneer P3-DX wheeled robot between obstacles using particle swarm optimization (PSO) algorithm tuned feedforward neural network (FNN). This PSO algorithm minimizes the mean square error between the actual and predicted values of the FNN. In this work, 2 separate DC motors and 16 ultrasonic sensors have been used for making differential drive steering angle and for collecting the distance from obstacles, respectively. The proposed without tuned FNN and PSO-tuned FNN receives obstacle's distance as inputs form ultrasonic sensors and control the steering angle of a differential drive of automated Pioneer P3-DX wheeled robot as output. We have compared the results between without tuned FNN and PSO-tuned FNN, and it has been found that PSO-tuned FNN gives a better trajectory and takes less distance to reach the target. Virtual Robot Experimentation Platform software has been used to design the real-time simulation results. A comparative study between without tuned FNN and PSO-tuned FNN verifies the effectiveness of PSO-tuned FNN for automated Pioneer P3-DX wheeled robot navigation. Also, we have compared this winner PSO-tuned FNN to the previously developed PSO-optimized Fuzzy Logic Controller navigational technique to show the authenticity and real-time implementation of PSO-tuned FNN.

关键词： navigation automated Pioneer P3-DX wheeled robot feedforward neural network particle swarm optimization algorithm Virtual Robot Experimentation Platform

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Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions

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RENEWABLE ENERGY 2020年 145卷 2034-2045页

作者： Fan, Junliang Wu, Lifeng Ma, Xin Zhou, Hanmi Zhang, Fucang Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Henan Univ Sci & Technol Coll Agr Engn Luoyang 471003 Peoples R China

Increasing air pollutants significantly affect the proportion of diffuse (R-d) to global (R-s) solar radiation. This study proposed three new hybrid support vector machines (SVM) with particle swarm optimization algorithm (SVM-PSO), bat algorithm (SVM-BAT) and whale optimization algorithm (SVM-WOA) for predicting daily R-d in air-polluted regions. These models were further compared to standalone SVM, multivariate adaptive regression spline (MARS) and extreme gradient boosting (XGBoost) models. The results showed that models with suspended particulate matter with aerodynamic diameter smaller than 2.5 mu m and 10 mu m (PM2.5 and PM10) and ozone (O-3) produced more accurate daily R-d estimates than those without air pollution parameters, with average relative decreases in root mean square deviation (RMSD) of 11.1%, 10.0% and 10.4% for sunshine duration-based, R-s-based and combined models, respectively. SVM showed better accuracy than XGBoost and MARS. However, compared to SVM, SVM-BAT further enhanced the prediction accuracy and convergence rate in daily R-d modeling, followed by SVM-WOA and SVM-PSO, with relative decreases in RMSD of 2.9%-5.6%, 1.9%-4.9% and 1.1%-3.3%, respectively. The results highlighted the significance of incorporating air pollutants for more accurate estimation of daily R-d in air-polluted regions. Heuristic algorithms, especially BAT, are highly recommended for improving performance of standalone machine learning models. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： Air pollution Support vector machines Extreme gradient boosting particle swarm optimization algorithm Bat algorithm Whale optimization algorithm

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CirRNAPL: A web server for the identification of circRNA based on extreme learning machine

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2020年 18卷 834-842页

作者： Niu, Mengting Zhang, Jun Li, Yanjuan Wang, Cankun Liu, Zhaoqian Ding, Hui Zou, Quan Ma, Qin Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China Heilongjiang Prov Land Reclamat Headquarters Gen Rehabil Dept Harbin Peoples R China Northeast Forestry Univ Sch Informat & Comp Engn Harbin 150040 Peoples R China Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Univ Elect Sci & Technol China Ctr Informat Biol Chengdu 610054 Peoples R China

Circular RNA (circRNA) plays an important role in the development of diseases, and it provides a novel idea for drug development. Accurate identification of circRNAs is important for a deeper understanding of their functions. In this study, we developed a new classifier, CirRNAPL, which extracts the features of nucleic acid composition and structure of the circRNA sequence and optimizes the extreme learning machine based on the particle swarm optimization algorithm. We compared CirRNAPL with existing methods, including blast, on three datasets and found CirRNAPL significantly improved the identification accuracy for the three datasets, with accuracies of 0.815, 0.802, and 0.782, respectively. Additionally, we performed sequence alignment on 564 sequences of the independent detection set of the third data set and analyzed the expression level of circRNAs. Results showed the expression level of the sequence is positively correlated with the abundance. A user-friendly CirRNAPL web server is freely available at http://***./CirRNAPL/. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Circular RNA Extreme learning machine particle swarm optimization algorithm Identification Expression level

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