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Efficient Privacy-Preserving Data Sanitization over Cloud Using Optimal GSA algorithm

COMPUTER JOURNAL 2018年第10期61卷 1577-1588页

作者： Renuga, S. Jagatheeshwari, S. S. K. Ponjesly Coll Engn CSE Dept Nagercoil 629003 Tamil Nadu India Ponjesly Coll Engn EEE Dept Nagercoil 629003 Tamil Nadu India

Data sharing is one of the important tasks in cloud computing application. When large volume of data is stored in the cloud, there is a chance for malicious attacks on the sensitive information stored in the cloud due to traditional poor privacy-preserving methods in the cloud. In this paper, an efficient data transform in the cloud with privacy-preserving method using sanitization algorithm is proposed. The optimal key is chosen here to process the sanitization algorithm effectively and gravitational search algorithm (GSA) is used to select the optimal key. The optimal key value is difficult to predict while using GSA algorithm. So this work is more secure compared to the existing privacy-preserving approach. The performance of the proposed technique is compared with artificial bee colony algorithm (ABC), elliptic curve cryptography (ECC), and RSA (Rivest-Shamir-Adleman). From the experimental result, this proposed method serves more secure with minimum execution time and hiding failure, maximum dissimilarity value when compared to the existing method. Finally, the result obtained shows that the proposed system has improved the privacy performance in cloud computing. The implementation will be done in JAVA using cloud simulator.

关键词： sanitization gravitational search algorithm cloud computing privacy preserving data owner data user cloud service provider

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Network expansion planning with microgrid aggregators under uncertainty

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第9期12卷 2105-2114页

作者： Sardou, Iman Goroohi Azad-Farsani, Ehsan Univ Jiroft Dept Elect Engn Jiroft Iran Golpayegan Univ Technol Dept Elect Engn Golpayegan Iran

In this paper, a bi-objective robust model is proposed for network expansion planning (NEP) considering the integration of the microgrid aggregators. The objectives include minimization of both the expansion cost and transmission lines loading index. Forced outages of system components are taken into account as the system uncertainties. A hybrid method as the combination of gravitational search algorithm (GSA) and primal-dual interior point (PDIP) method is employed to solve the nonlinear programming (NLP) problem of the NEP. In the proposed hybrid method, the operation sub-problems are solved by the PDIP method under the worst-case single component contingencies, while the expansion plan is defined as scenario independent variables obtained by the GSA algorithm. To detect the worst-case single component contingencies with the severest effects on the system security, a subsidiary optimization problem is solved for each load level of the system. A realistic network of Qom as a part of Tehran Regional Electric Company, Iran, as well as IEEE 118-bus test system are analysed to evaluate the efficiency of the proposed method. A key conclusion is that the stochastic model may not provide sufficient security level, and a robust model is required to ensure the system security against the severe contingencies.

关键词： distributed power generation power distribution planning substations search problems nonlinear programming power system security network expansion planning microgrid aggregators NEP problem power system planner substations effective biobjective robust model expansion cost transmission lines loading index system components system uncertainties hybrid method gravitational search algorithm GSA primal-dual interior point method nonlinear programming problem NLP operation subproblems PDIP method worst-case single component contingencies scenario independent variables first stage decision variables subsidiary optimisation problem Qom Tehran Regional Electric Company IEEE 118-bus test system Iran stochastic model security level economical solution

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Tuned-TSC based SVC for reactive power compensation and harmonic reduction in unbalanced distribution system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第3期12卷 571-585页

作者： Das, Sankar Chatterjee, Debashis Goswami, Swapan K. Govt Coll Engn & Text Technol Elect Engn Dept Berhampur Murshidabad India Jadavpur Univ Elect Engn Dept Kolkata India

A reactive power compensation scheme along with harmonic reduction technique for an unbalanced four-wire system has been addressed in this study. The proposed compensation scheme can mitigate a wider range of thyristor controlled reactor (TCR) injected harmonics and load harmonics up to specified range. The scheme is realised by using a combination of thyristor controlled delta ()-connected and star (Y)-connected static VAr compensator (SVC). Each SVC consists of a TCR and a tuned-thyristor switched capacitor (t-TSC). The proposed SVC scheme can eliminate negative sequence current with source power factor improvement through -SVC and zero sequence current caused by unbalanced load is removed by Y-SVC. For harmonic compensation, the tuned-TSC is used to filter harmonics generated by the non-linear load or due to TCR switching in SVC. An optimised switching function is also adopted in the proposed scheme to minimise TCR harmonic injection in the SVC which reduces the required filter size. The optimised switching angles are computed off-line using gravitational search algorithm and stored in microcontroller memory for on-line applications. The proposed scheme has been validated through proper simulations using MATLAB software backed by suitable experimental results on a practical system.

关键词： static VAr compensators distribution networks reactive power harmonics suppression thyristor applications thyristors power factor search problems tuned-TSC based SVC unbalanced distribution system reactive power compensation scheme harmonic reduction technique unbalanced four-wire system thyristor controlled reactor TCR injected harmonics load harmonics thyristor controlled delta-connected SVC thyristor controlled star-connected SVC negative sequence current source power factor improvement -SVC zero sequence current Y-SVC harmonic compensation TCR switching optimised switching function TCR harmonic injection optimised switching angles gravitational search algorithm microcontroller memory MATLAB software

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Development of hybrid extreme learning machine based chemo-metrics for precise quantitative analysis of LIBS spectra using internal reference pre-processing method

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ANALYTICA CHIMICA ACTA 2018年 1030卷 33-41页

作者： Owolabi, Taoreed O. Gondal, Mohammed A. King Fahd Univ Petr & Minerals Ctr Excellence Nanotechnol Phys Dept Laser Res Grp Box 5047 Dhahran 31261 Saudi Arabia

Laser induced breakdown spectroscopy (LIBS) is a versatile spectroscopic technique that requires little or no sample preparation and capable of simultaneous elemental sample analysis. Quantitative analysis of its spectra has been a major challenge due to self-absorption of the emitted radiation during plasma cooling and inadequate description of non-linear complex interactions taking place in the laser induced plasma. This work presents a novel chemo-metric tool, extreme learning machine (ELM) and its hybrid HHELM (homogenously hybridized ELM), for the first time in modeling the complex interactions of laser induced plasma and quantification of LIBS spectra. Internal reference preprocessing (IRP) method is also proposed as a novel method of enhancing the performance of ELM based chemo-metrics. Since the proposed chemo-metrics (ELM and HHELM) determine their input weights as well as their hidden biases in a random manner, ELM and HHELM are respectively hybridized with gravitational search algorithm (GSA) for optimization of the number of hidden neurons. Effect of IRP, obtained by normalizing the emission spectra intensities with the emission intensity that has highest upper level excitation energy and lowest transition probability, on the performance of the proposed GSA-ELM and GSA-HHELM chemo-metrics is investigated. The proposed models are implemented using spectra of seven bronze standard samples. Chemo-metrics with IRP (GSA-ELM-IRP and GSA-HHELM-IRP) show better generalization performance than those without IRP (GSA-ELM-WIRP and GSA-HHELM-WIRP) while GSA-HHELM based chemo-metrics perform better than their counterparts. The outstanding performance demonstrated by the proposed chemo-metrics and their self-absorption correction ability would definitely widen the applicability of LIBS and improve its precision for the quantitative analysis. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Laser induced breakdown spectroscopy Chemo-metric Extreme learning machine gravitational search algorithm Internal reference preprocessing Laser induced plasma

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A developed system based on nature-inspired algorithms for DNA motif finding process

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NEURAL COMPUTING & APPLICATIONS 2018年第7期30卷 2059-2069页

作者： Mabrouk, Mai S. Abdelhalim, Mohamed B. Elewa, Ebtehal S. Misr Univ Sci & Technol Dept Biomed Engn 6 October Giza Egypt Arab Acad Sci Technol & MaritimeTransport Coll Comp & Informat Technol Cairo Egypt

In this paper, recent algorithms are suggested to repair the issue of motif finding. The proposed algorithms are cuckoo search, modified cuckoo search and finally a hybrid of gravitational search and particle swarm optimization algorithm. Motif finding is the technique of handling expressive motifs successfully in huge DNA sequences. DNA motif finding is important because it acts as a significant function in understanding the approach of gene regulation. Recent results of existing motifs finding programs display low accuracy and can not be used to find motifs in different types of datasets. Practical tests are implemented first on synthetic datasets and then on benchmark real datasets that are based on nature-inspired algorithms. The results revealed that the hybridization of gravitational search algorithm and particle swarm algorithms provides higher precision and recall values and provides average enhancement of F-score up to 0.24, compared to other existing algorithms and tools, and also that cuckoo search and modified cuckoo search have been able to successfully locate motifs in DNA sequences.

关键词： Cuckoo search gravitational search algorithm Particle swarm optimization DNA motif finding Nature-inspired algorithms

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A surrogate model for thermal characteristics of stratospheric airship

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ADVANCES IN SPACE REsearch 2018年第12期61卷 2989-3001页

作者： Zhao, Da Liu, Dongxu Zhu, Ming Beihang Univ Sch Aeronaut Sci & Engn Beijing 100191 Peoples R China Beihang Univ Minist Ind & Informat Technol Key Lab Aircraft Adv Design Technol Beijing 100191 Peoples R China

A simple and accurate surrogate model is extremely needed to reduce the analysis complexity of thermal characteristics for a stratospheric airship. In this paper, a surrogate model based on the Least Squares Support Vector Regression (LSSVR) is proposed. The gravitational search algorithm (GSA) is used to optimize hyper parameters. A novel framework consisting of a preprocessing classifier and two regression models is designed to train the surrogate model. Various temperature datasets of the airship envelope and the internal gas are obtained by a three-dimensional transient model for thermal characteristics. Using these thermal datasets, two-factor and multi-factor surrogate models are trained and several comparison simulations are conducted. Results illustrate that the surrogate models based on LSSVR-GSA have good fitting and generalization abilities. The pre-treated classification strategy proposed in this paper plays a significant role in improving the accuracy of the surrogate model. (C) 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Stratospheric airship Surrogate model Thermal characteristics Least squares support vector regression gravitational search algorithm Learning algorithm

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RF MEMS for Reconfigurable Antenna Using gravitational search Optimization and Artificial Neural Network

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2018年第8期27卷

作者： Balarajuswamy, T. A. Nakkeeran, R. Pondicherry Univ Elect & Commun Engn Pondicherry Tamil Nadu India Pondicherry Univ Sch Engn & Technol Dept Elect Engn Pondicherry Tamil Nadu India

The projected method explains about the problems occurred in the combination of the MEMS switches and the complete scheme plan is resolved through choosing the finest devise limits for the plan. The devise limits, namely, length of beam, width of beam, torsion arm length, switch thickness, holes and gap were measured. At this point, the finest value of the devise limit is forecast by the aid of artificial neural network (ANN). Furthermore, the method contains the optimization method of gravitational search algorithm (GSA) to optimize the input signal and so dropping the Mean Square Error (MSE). The complete scheme is executed in the operational platform of MATLAB and the outcomes were examined.

关键词： gravitational search algorithm mean square error reconfigurable antenna

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Two-step fault diagnosis framework for rolling element bearings with imbalanced data based on GSA-WELM and GSA-ELM

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2018年第16期232卷 2937-2947页

作者： Lan, Yuan Han, Xiaohong Zong, Weiwei Ding, Xiaojian Xiong, Xiaoyan Huang, Jiahai Ma, Bing Taiyuan Univ Technol Key Lab Minist Educ Adv Transducers & Intelligent Control Taiyuan Shanxi Peoples R China Taiyuan Univ Technol Inst Mechatron Engn Sch Mech Engn Taiyuan Shanxi Peoples R China Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Huawei Technol Co Ltd Nanjing Jiangsu Peoples R China China Elect Technol Grp Corp Res Inst 2 Taiyuan Shanxi Peoples R China

Rolling element bearings constitute the key parts on rotating machinery, and their fault diagnosis is of great importance. In many real bearing fault diagnosis applications, the number of fault data is much less than the number of normal data, i.e. the data are imbalanced. Many traditional diagnosis methods will get low accuracy because they have a natural tendency to favor the majority class by assuming balanced class distribution or equal misclassification cost. To deal with imbalanced data, in this article, a novel two-step fault diagnosis framework is proposed to diagnose the status of rolling element bearings. Our proposed framework consists of two steps for fault diagnosis, where Step 1 makes use of weighted extreme learning machine in an effort to classify the normal or abnormal categories, and Step 2 further diagnoses the underlying anomaly in detail by using preliminary extreme learning machine. In addition, gravitational search algorithm is applied to further extract the significant features and determine the optimal parameters of the weighted extreme learning machine and extreme learning machine classifiers. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our Institute, and the empirical results show that our approach is really fast and can achieve the diagnosis accuracies more than 96%.

关键词： Two-step fault diagnosis framework fault diagnosis of rolling element bearings weighted extreme learning machine extreme learning machine gravitational search algorithm

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Proposed Energy Efficient algorithm for Clustering and Routing in WSN

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WIRELESS PERSONAL COMMUNICATIONS 2018年第3期103卷 2575-2598页

作者： Morsy, Nehad A. AbdelHay, Ehab H. Kishk, Sherif S. Mansoura Univ Fac Engn Dept Commun & Elect Mansoura Egypt

Clustering in WSN recently become big challenge and attracts many researches. Clustering is a way of grouping sensor nodes into clusters with CH responsible to receive from its members and send to base station BS, CH selection in efficient way prolongs network life time and stability region. So, improper CHs selection and distribution in sensing field will affect the performance of clustering. In WSN environment there is N sensor nodes and K CHs, there is N k different ways to create clusters, so it's difficult to identify optimal set of CHs without using search optimization algorithm. In this paper, the process of CH selection is formulated as single-objective optimization problem to find optimal set of CHs to form, one-hop clusters, in order to balance energy consumption, enhance stability and scalability using gravitational search algorithm (GSA). The problem has been solved using particle swarm optimization and GSA and compare the result against LEACH protocol. In this paper, several simulations have been done to demonstrate the efficiency of the proposed algorithm under different position for BS. Furthermore, new cost function has been proposed for Hierarchical Clustering. The objective of hierarchical clustering is to increase network lifetime and prolong network stability, several simulations have been done to compare the efficiency of multi-hop versus one-hop approach.

关键词： WSN clustering Energy efficient gravitational search algorithm Multi-hop network Life time Stability

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Optimal design of fractional order low pass Butterworth filter with accurate magnitude response

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DIGITAL SIGNAL PROCESSING 2018年 72卷 96-114页

作者： Mahata, Shibendu Saha, Suman Kumar Kar, Rajib Mandal, Durbadal NIT Durgapur Dept Elect & Commun Engn Durgapur India NIT Raipur Dept Elect & Telecommun Engn Raipur Madhya Pradesh India

The design of (1 + alpha) order, 0 < alpha < 1, low pass Butterworth filter approximated in terms of an integer order continuous-time transfer function using a nature-inspired optimization technique called gravitational search algorithm (GSA) is presented in this paper. While approximations of the non integer order Laplacian operator s(alpha) in terms of second order rational function using the continued fraction expansion method for the design of fractional order low pass Butterworth filters (FOLBFs) is recently reported in literature, however, such a design technique is non-optimal. In this work, the metaheuristic global search process of GSA efficiently explores and intensely exploits the nonlinear, non-uniform, multidimensional, and multimodal FOLBF design problem error landscape. At the end of the iterative search routine of GSA, the optimal values of the coefficients in terms of the third order rational approximations are achieved which accurately approximate the magnitude response of the ideal FOLBF. The proposed GSA based FOLBFs consistently achieve the best solution quality with the fastest convergence rate as compared with the designs based on Real coded Genetic algorithm (RGA) and Particle Swarm Optimization (PSO). Comparison with the recent literature also demonstrates the superiority of the proposed designs. SPICE simulations justify the design feasibility of the proposed FOLBF models. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Butterworth filter Fractional order gravitational search algorithm Metaheuristic optimization Signal processing Mann-Whitney U test

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