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2015 International Conference on Cognitive Computing and Information Processing (CCIP)

作者： Sachnev, Vasily

ISBN: (纸本)9781479971718

In this paper, we propose an efficient steganalysis method by using Cartesian calibrated JPEG Rich Models (ccJRM) features set. Proposed steganalysis scheme contains two steps: 1) search a subset of features (among set of 22510 features) with the most promising performances, and 2) build an cognitive ensemble classifier for efficient steganalysis. In the first step we used binary coded genetic algorithm (BCGA) coupled with Extreme Learning machine to collect few subset of features with promising performances and corresponding ELM models. In the second step we used another BCGA for searching the best combination of few ELM models computed in the first step. Chosen combination of ELM models is used to build a cognitive ensemble classifier. Proposed steganalysis scheme shows an improvement compared to existing JPEG steganalysis schemes.

关键词： binary coded genetic algorithm Ensemble model Extreme learning machine JPEG steganalysis

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Frequency stabilisation of a hybrid two-area power system by a novel quasi-oppositional harmony search algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第15期9卷 2167-2179页

作者： Mahto, Tarkeshwar Mukherjee, Vivekananda Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India

Modelling, simulation and performance analysis of a two-area thermal-hybrid distributed generation (HDG) power system having different sources of power generation has been carried out in this study. The thermal power plant is consisting of re-heat type thermal system, whereas the HDG system includes the combination of wind turbine generator and diesel generator. In the studied model, superconducting magnetic energy storage (SMES) device is considered in both the areas. Additionally, a flexible ac transmission system (FACTS) device such as static synchronous series compensator (SSSC) is also considered in the tie-line. The different tunable parameters of the proportional-integral-derivative (PID) controllers, SMES and SSSC are optimised using a novel quasi-oppositional harmony search (QOHS) algorithm. Optimisation performance of the novel QOHS algorithm is established while comparing its performance with binary coded genetic algorithm. From the simulation work it is observed that with the inclusion of SMES in both the areas, the system performances toward the achievement of minimal frequency and tie-line power oscillations are promising under different types of input loading perturbations. It is further revealed from the simulation results that the installation of an expensive FACTS device such as SSSC does not yield any significant improvement to the system performance.

关键词： frequency stability power system stability search problems genetic algorithms distributed power generation thermal power stations wind turbines diesel-electric generators flexible AC transmission systems superconducting magnet energy storage three-term control power generation control frequency stabilisation HDG power system two-area thermal-hybrid distributed generation power generation quasi-oppositional harmony search algorithm thermal power plant reheat type thermal system wind turbine generator diesel generator superconducting magnetic energy storage device SMES device flexible ac transmission system device FACTS device static synchronous series compensator SSSC QOHS algorithm optimisation performance binary coded genetic algorithm tie-line power oscillations input loading perturbations tunable parameters PID controllers

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Optimized multi area AGC simulation in restructured power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2010年第4期32卷 311-322页

作者： Bhatt, Praghnesh Roy, Ranjit Ghoshal, S. P. Charotar Inst Technol Dept Elect Engn Changa 388421 Gujarat India SV Natl Inst Technol Dept Elect Engn Surat 395007 Gujarat India Natl Inst Technol Dept Elect Engn Durgapur 713209 W Bengal India

In this paper, the traditional automatic generation control loop with modifications is incorporated for simulating automatic generation control (AGC) in restructured power system. Federal energy regulatory commission (FERC) encourages an open market system for price based operation. FERC has issued a notice for proposed rulemaking of various ancillary services. One of these ancillary services is load following with frequency control which comes broadly under Automatic Generation Control in deregulated regime. The concept of DISCO participation matrix is used to simulate the bilateral contracts in the three areas and four area diagrams. Hybrid particle swarm optimization is used to obtain optimal gain parameters for optimal transient performance. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Automatic generation control binary coded genetic algorithm CFA Deregulation Hybrid particle swarm optimization Real coded genetic algorithm

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Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer

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BMC BIOINFORMATICS 2015年第1期16卷 1-12页

作者： Sachnev, Vasily Saraswathi, Saras Niaz, Rashid Kloczkowski, Andrzej Suresh, Sundaram Catholic Univ Korea Dept Informat Commun & Elect Engn Bucheon South Korea Nationwide Childrens Hosp Res Inst Battelle Ctr Math Med Doha Qatar Ohio State Univ Nationwide Childrens Hosp Coll Med Battelle Ctr Math MedRes InstDept Pediat Columbus OH 43210 USA Nanyang Technol Univ Sch Comp Sci Nanyang Singapore

Background: Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific cancers. Identification of molecular markers that are related to different types of cancers might lead to discovery of drugs that are patient and disease specific. This study aims to use microarray gene expression cancer data to identify biomarkers that are indicative of different types of cancers. Our aim is to provide a multi-class cancer classifier that can simultaneously differentiate between cancers and identify type-specific biomarkers, through the application of the binary coded genetic algorithm (BCGA) and a neural network based Extreme Learning Machine (ELM) algorithm. Results: BCGA and ELM are combined and used to select a subset of genes that are present in the Global Cancer Mapping (GCM) data set. This set of candidate genes contains over 52 biomarkers that are related to multiple cancers, according to the literature. They include APOA1, VEGFC, YWHAZ, B2M, EIF2S1, CCR9 and many other genes that have been associated with the hallmarks of cancer. BCGA-ELM is tested on several cancer data sets and the results are compared to other classification methods. BCGA-ELM compares or exceeds other algorithms in terms of accuracy. We were also able to show that over 50% of genes selected by BCGA-ELM on GCM data are cancer related biomarkers. Conclusions: We were able to simultaneously differentiate between 14 different types of cancers, using only 92 genes, to achieve a multi-class classification accuracy of 95.4% which is between 21.6% and 38% higher than other results in the literature for multi-class cancer classification. Our findings suggest that computational algorithms such as BCGA-ELM can facilitate biomarker-driven integrated cancer research that can lead to a detailed understanding of the complexities of cancer.

关键词： Global cancer map Cancer biomarkers binary coded genetic algorithm Extreme learning machine Hallmarks of cancer

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Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm

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Protection and Control of Modern Power Systems 2020年第1期5卷 153-165页

作者： Pabitra Kumar Guchhait Abhik Banerjee Department of Electrical Engineering National Institute of TechnologyYupiaArunachal PradeshIndia

Conventional proportional integral derivative(PID)controllers are being used in the industries for control *** is very simple in design and low in cost but it has less capability to minimize the low frequency noises of the ***,in this study,a low pass filter has been introduced with the derivative input of the PID controller to minimize the noises and to improve the transient stability of the *** paper focuses upon the stability improvement of a wind-diesel hybrid power system model(HPSM)using a static synchronous compensator(STATCOM)along with a secondary PID controller with derivative filter(PIDF).Under any load disturbances,the reactive power mismatch occurs in the HPSM that affects the system transient *** with PIDF controller is used to provide reactive power support and to improve stability of the *** controller parameters are also optimized by using soft computing technique for performance *** paper proposes the effectiveness of symbiosis organisms search algorithm for optimization *** coded genetic algorithm and gravitational search algorithm are used for the sake of comparison.

关键词： binary coded genetic algorithm Gravitational search algorithm Hybrid power system model PID controller with derivative filter Static synchronous compensator Symbiosis organisms search algorithm Transient stability

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Modelling of magnetostrictive vibration and acoustics in converter transformer

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IET ELECTRIC POWER APPLICATIONS 2021年第3期15卷 332-347页

作者： Yadav, Suman Mehta, Ram Krishna N Eastern Reg Inst Sci & Technol Dept Elect Engn Nirjuli Arunachal Prade India

The converter transformers are susceptible to more noise and vibration when compared to power transformers due to the presence of DC bias in the DC transmission line. DC bias occurs mostly due to inaccuracies in valve firing resulting in a small residual DC oscillating around zero. Measurement of magnetostriction becomes significant as it influences the vibration and noise from the core. Hence, a magnetostrictive model of a high-voltage DC converter transformer has been developed. This work analyses the vibration and noise acoustics under such an occurrence. First, the core of the transformer model is designed in the stepped configuration for 240 MVA;then, magnetostrictive vibration is analysed by using suitable modules of COMSOL Multiphysics at different magnitudes of DC bias. The physics of noise has been interfaced using the Acoustics Module, and the results are recorded. Finally, artificial neural network model is developed for the prediction of vibration and noise characteristics of the model. The fitting process of neural network was then remodelled using various optimisation techniques, namely teaching-learning-based optimisation, particle swarm optimisation, biogeography-based optimisation, simulated annealing and binary coded genetic algorithm, and their results were compared to obtain the best-suited method using % mean-squared-error evaluation.

关键词： Optimisation techniques mean-squared-error evaluation particle swarm optimisation artificial neural network model power engineering computing DC transmission line Transformers and reactors binary coded genetic algorithm apparent power 240.0 MVA DC transformers neural nets power transformers noise characteristics biogeography-based optimisation Power convertors and power supplies to apparatus COMSOL multiphysics DC bias power convertors genetic algorithms high-voltage DC converter transformer magnetostrictive vibration mean square error methods Interpolation and function approximation (numerical analysis) Power engineering computing noise acoustics simulated annealing residual DC acoustics module teaching-learning-based optimisation

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Optimization of Physical Parameters of an Underactuated Quadrupedal Robot 26

Optimization of Physical Parameters of an Underactuated Quad...

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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Karagoz, Osman Kaan Ankarali, Mustafa Mert Orta Dogu Tekn Univ Ankara Turkey

ISBN: (纸本)9781538615010

In this paper, we present the comparison of different optimization algorithms that are used to optimize the parameters of a simulated legged robotic platform. We compare the results obtained by applying different algorithms on the same model and show the relative advantages and disadvantages of these algorithms. The tested algorithms are Particle Swarm Optimization, binary coded genetic algorithm, Broyden-Fletcher-Goldfrab-Shannon algorithm and Method of Zoutendijk. We showed that the globally optimal parameter set reduces the total dissipated energy approximately 50% with respect to the reference paremeter set in the literature. The implemented optimization methods can also be applied to other legged platforms to obtain efficient systems without affecting the performance and the stability.

关键词： Particle swarm optimization binary coded genetic algorithm Broyden-Fletcher-Goldfrab-Shannon algorithm method of Zoutendijk efficiency legged robot

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