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Application of a new machine learning model to improve earthquake ground motion predictions

NATURAL HAZARDS 2024年第1期120卷 729-753页

作者： Joshi, Anushka Raman, Balasubramanian Mohan, C. Krishna Cenkeramaddi, Linga Reddy Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 Uttarakhand India Indian Inst Technol Hyderabad Dept Comp Sci & Engn Sangareddy 502285 Telangana India Univ Agder Dept Informat & Commun Technol N-4879 Grimstad Norway

A cross-region prediction model named SeisEML (an acronym for Seismological Ensemble Machine Learning) has been developed in this paper to predict the peak ground acceleration (PGA) at a given site during an earthquake. The SeisEML model consists of hybridized models, kernel-based algorithms, tree regression algorithms, and regression algorithms. The model ablation study is conducted to examine the performance and the selection of meta-machine learning models in the SeisEML. The training and testing data-set consists of 20852 and 6256 accelerograms recorded by the Kyoshin Network, Japan. The mean absolute error (MAE) and root mean square error (RMSE) have been utilized to compare the predicted peak ground acceleration (PGA) for the test data. The SeisEML model yields approximately half the MAE and RMSE values obtained with conventional attenuation relations. The SeisEML model has been used to compute Japan's iso acceleration contour map of three earthquakes (M-JMA 7.4, 6.6, and 6.1). The qualitative comparison of iso acceleration contours obtained from actual and predicted PGA using SeisEML clearly shows that the model can reliably predict the PGA distribution during an earthquake compared to the regional ground motion prediction equation (GMPE). The cross-region prediction was performed on the datasets of the Iranian earthquakes using SeisEML. The comparison of predicted and observed peak ground acceleration in terms of MAE and RMSE shows that the machine learning model's performance is superior to the regional attenuation relation. The predictions of PGA from the developed ML model indicate that this trained model has the potential for predicting regional and global scenarios with similar tectonic setups. The ML model developed in this paper can considerably enhance the reliability of PGA prediction for seismic hazard mapping of any region and can serve as a reliable substitute for GMPEs.

关键词： Machine learning Ensemble model PGA Earthquake meta heuristic algorithms

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Hybrid MPPT algorithms for Photovoltaic Systems 4

Hybrid MPPT Algorithms for Photovoltaic Systems

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IEEE 4th Southern Power Electronics Conference (SPEC)

作者： Omine, Leandro T. de Brito, Moacyr A. G. Pinto, Joao O. P. Garcia, Raymundo C. Fed Univ Mato Grosso do Sul UFMS Dept Elect Engn Campo Grande MS Brazil

ISBN: (纸本)9781538682579

Photovoltaic generation systems under partial shading conditions are difficult to optimize using conventional maximum power point tracking (MPPT) algorithms. Most of the techniques developed for these conditions fail to track dynamically the MPP and leads to energy losses during normal operation. This paper presents hybrid MPPT algorithms combining global and local MPPTs to extract the most available energy from the system under any condition. The methods are compared through simulations using Matlab/Simulink (R), where the tracking factor (TF) and power characteristics during time are evaluated.

关键词： Global optimization Maximum power point tracking (MPPT) meta heuristic algorithms Partial shading Photovoltaic

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Population Initialization of Seagull Optimization Algorithm with Pseudo Random Numbers for Continous Optimization 18

Population Initialization of Seagull Optimization Algorithm ...

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18th International Conference on Frontiers of Information Technology (FIT)

作者： Pervaiz, Sobia Bangyal, Waqas Haider Nisar, Kashif Rehman, Najeeb Ur Abasyn Univ Dept Comp Sci Islamabad Campus Islamabad Pakistan Univ Gujrat Dept Comp Sci Gujrat Pakistan Univ MalaysiaGujrat Fac Comp Kota Kinabalu Sabah Malaysia Univ MalaysiaGujrat Univ Informat Kota Kinabalu Sabah Malaysia UMS Kota Kinabalu Sabah Malaysia

ISBN: (纸本)9781665408301

This study is presenting a new nature-inspired algorithm termed as Seagull Optimization (SOA) to decipher the complex computational problems. The major concern of this approach is the attacking and migration attribute of seagull. To focus on the exploitation (local search) and exploration (global search) in the defined region, this study proposed two novel initialization approaches of SOA called SOA with SIMD-oriented Fast Mersenne Twister Generator (SOA-SOFMTG) and SOA with Combined Multiple Recursive Generator (SOA-CMRG). The convergence rate of the presented variants has been examined with simulation results and convergence graph of eight benchmark test functions. The effectiveness of proposed variants is investigated with Kruskal-Wallis statistical tests. The computational results conclude that the proposed variants of SOA is capable to resolve the complex optimization problems as compared to the standard SOA.

关键词： Pseudo Random Numbers meta heuristic algorithms Premature convergence Exploration Exploitation

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Decrypting the Transposition Cipher Using a New Move Operator on Particle Swarm Optimization 3

Decrypting the Transposition Cipher Using a New Move Operato...

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3rd International Conference on Computer Science and Engineering (UBMK)

作者： Demirci, Huseyin Yurtay, Nilufer Yildiz, Tuba Karagul Sakarya Univ Fac Comp & Informat Sci Sakarya Turkey Duzce Univ Fac Technol Duzce Turkey

ISBN: (纸本)9781538678930

Transposition cipher encryption is a historical encryption method which is based on swapping letter of columns on base text according to a secret key. In this paper, we present a new swap based move operator for the particle swarm optimization algorithm. We have tested the operator on transposition cipher encryption with a text size of 125, 250, 500 and 750 letter and 5, 10, 15 key lengths. The algorithm we developed has recovered the exact original key in some tests. In most cases, particularly, the algorithm have recovered 70% of the keys and in the worst case the algorithm have recovered only sequences of the original key which can make the encrypted text barely readable for the human eye and most of the words are decrypted.

关键词： Coptanalysis Transposition Cipher Particle Swarm Optimization meta heuristic algorithms

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Short Term Hybrid PV/Wind Power Forecasting for Smart Grid Application using Feedforward Neural Network (FNN) Trained by a Novel Atomic Orbital Search (AOS) Optimization Algorithm 18

Short Term Hybrid PV/Wind Power Forecasting for Smart Grid A...

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18th International Conference on Frontiers of Information Technology (FIT)

作者： Zafar, Muhammad Hamza Khan, Noman Mujeeb Khan, Umer Amir Capital Univ Sci & Technol Islamabad Pakistan

ISBN: (纸本)9781665408301

The un-predictable behavior of renewable energy sources due to their intermittent nature renders them very difficult to forecast the generated power. The photovoltaic and wind systems area a significant part of current working power systems. In this paper, a feed forward neural network (FNN) trained by atomic orbital search (AOS) optimization algorithms based technique is presented for the short term power forecasting of hybrid PV/Wind energy systems. The proposed technique is then compared with a feed forward neural network trained with grey wolf optimizer (GWO-NN), Barnacle mating optimizer (BMO-FNN) and whale optimization algorithm (WOA-FNN). The proposed technique effectively trains the feed-forward neural network and achieves less testing error, training error and relative error and also takes less time as compared to in-comparison techniques. AOS-FNN have capability to effectively, predict the power of hybrid PV/Wind Energy System under varying environmental Conditions.

关键词： meta heuristic algorithms Intelligent Control System Atomic Orbital Search Algorithm Hybrid PV/Wind Power Regression

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Comparison of HSRNAFold and RNAFold algorithms for RNA Secondary Structure Prediction

Comparison of HSRNAFold and RNAFold Algorithms for RNA Secon...

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IEEE Region 10 Conference 2009

作者： Mohsen, Abdulqader M. Khader, Ahamad Tajudin Ramachandram, Dhanesh Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

ISBN: (纸本)9781424445462

Ribonucleic Acid (RNA) has important structural and functional roles in the cell and plays roles in many stages of protein synthesis. The structure of RNA largely determines its function. Current physical methods for structure determination are time-consuming and expensive, thus the methods for the computational prediction of structure are necessary. Various algorithms that have been used for RNA structure prediction based in minimum free energy include dynamic programming (DP) and meta heuristic algorithms. One of the most recent meta heuristic algorithms is Musician's behavior-inspired harmony search (HS) algorithm that has been successful in numerous complex optimization problems. This paper builds on the previous work of the harmony search algorithm (HSRNAFold) which was used to find the RNA secondary structure with minimum free energy. In this paper, the accuracy of prediction is compared to the dynamic programming technique RNAFold. The results show that HSRNAFold is able to predict more accurate structures than RNAFold for all test sequences.

关键词： meta heuristic algorithms Dynamic programming algorithms RNA folding Minimum Free Energy

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An adaptive artificial electric field algorithm for continuous optimization problems

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EXPERT SYSTEMS 2023年第9期40卷 e13380-e13380页

作者： Chauhan, Dikshit Yadav, Anupam Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math Jalandhar Punjab India Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math Jalandhar 144011 Punjab India

The comprehensive learning strategy is a meticulous method for enhancing the optimization ability of population-based optimization algorithms. This article proposes an adaptive artificial electric field algorithm (iAEFA), which is developed by embedding a comprehensive learning strategy into AEFA. The proposed algorithm utilizes a novel adaptive approach for developing a better learning strategy in which an agent's velocity is updated using the comprehensive influence of the entire population. The developed scheme has shown a stronger potential to discover better candidate solutions in each iteration. The objective of the proposed method is to develop an efficient optimizer for continuous optimization problems. The performance of the proposed iAEFA is evaluated using a set of 13 classical benchmark test problems and the CEC 2019 (100-digit challenge) benchmark functions. The experimental results are compared to seven state-of-the-art optimization algorithms. Using the Wilcoxon signed-rank test, the statistical significance of the results is confirmed. This article also discusses the theoretical convergence of the proposed algorithm, along with other significant findings about the proposed scheme. The experimental results and the theoretical analysis shows that the proposed scheme can be an excellent choice for the function optimization task compared to other existing algorithms.

关键词： AEFA scheme artificial intelligence benchmark functions meta heuristic algorithms optimization

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Clustering at the Edge: Load balancing and energy efficiency for the IoT

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AD HOC NETWORKS 2024年 156卷

作者： Sreenivasamurthy, Shesha Obraczka, Katia Univ Calif Santa Cruz Comp Sci Santa Cruz CA 95064 USA

This paper explores clustering as a technique to improve energy efficiency for a variety of current and emerging IoT application scenarios. We introduce a novel load balancing clustering algorithm based on Simulated Annealing whose main goal is to increase network lifetime while maintaining adequate sensing coverage in scenarios where sensor nodes produce non -uniform data traffic. Through simulations on the Cooja/Contiki simulation-emulation platform, we compare the proposed algorithm to leading state-of-the-art clustering approaches and show that our algorithm is able to improve both time until first clusterhead failure as well as network coverage by keeping more sensor nodes alive for a longer periods of time. We also explore different clustering criteria applied to our simulated annealing clustering framework and investigated different network lifetime metrics. We show that accounting for both workload and communication cost improves network lifetime by prolonging clusterhead lifetime and that clustering solely on the basis of physical distance improves network coverage.

关键词： Clustering IoT Simulated annealing Load balancing meta heuristic algorithms Unequal load

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Multi-Objective Optimization of Microgrids Based on Recent metaheuristic Methods

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第6期28卷 1839-1848页

作者： Tanyildizi Agir, Tuba Aydogmus, Zafer Alatas, Bilal Batman Univ Dept Elect & Elect Engn Batman Turkey Firat Univ Dept Elect & Elect Engn Elazig Turkey Firat Univ Dept Software Engn Elazig Turkey

As the technology develops in the modern world, the need for electrical energy has increased. Renewable energy sources have emerged as an alternative energy source to fossil energy sources. Micro grids are the hybrid energy sources for both renewable and non-renewable energy sources. The choice of the microgrid depends on meeting the supply and low cost requirements while avoiding environmental pollution. Therefore, emission, reliability and sizing of a micro grid have been investigated in the present study. In addition, Swallow Swarm Optimization (SSO) and Hybrid Particle Swallow Swarm Optimization (HPSSO) algorithms were not found in micro grid related optimization studies. Performance of SSO and HPSSO algorithms was also evaluated. Particle Swarm Optimization (PSO), SSO, and HPSSO were adjusted in this study as multi-objective optimization method for increasing the reliability, decreasing emission and sizing energy resources of a microgrid feeding a 10 MW residence. A microgrid consisting of 8 MW solar panel, 4,5 MW wind turbine, 15 MW diesel generator, and 4 MW battery has been taken into consideration. The efficiencies of these algorithms were compared for different iterations and populations. In this study, the best results were obtained with the SSO algorithm. Loss of power supply probability (LPSP) = 0, Renewable factor (RF) = 1, with this algorithm our micro-grid has achieved a safe energy and minimum emission to feed the residence. In addition, a system that connects and disconnects the energy resources in varying load conditions was actualized with the SSO algorithm. With this algorithm LPSP = 0, RF = 1, P-size = 0,001. Maximum reliability, zero emission and minimum sizing of the energy sources in our microgrid were achieved with loads of up to 50%. Moreover, LPSP = 0.39, RF = 0.086, P-size = 0,21 values were obtained for loads 50% and above and good results were obtained for reliability, emission and sizing of energy sources.

关键词： hybrid particle swallow swarm optimization meta heuristic algorithms microgrid particle swarm optimization swallow swarm optimization

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A Novel Dynamic Optimization Technique for Finding Optimal Trust Weights in Cloud

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2022年第6期16卷 2060-2073页

作者： Prasad, Aluri V. H. Sai Rajkumar, Ganapavarapu V. S. Neil Gogte Inst Technol Dept Comp Sci & Engn Hyderabad 500088 Telangana India GITAM Univ GITAM Inst Technol Dept Comp Sci & Engn Visakhapatnam 530045 Andhra Pradesh India

Cloud Computing permits users to access vast amounts of services of computing power in a virtualized environment. Providing secure services is essential. There are several problems to real-world optimization that are dynamic which means they tend to change over time. For these types of issues, the goal is not always to identify one optimum but to keep continuously adapting to the solution according to the change in the environment. The problem of scheduling in Cloud where new tasks keep coming over time is unique in terms of dynamic optimization problems. Until now, there has been a large majority of research made on the application of various Evolutionary algorithms (EAs) to address the issues of dynamic optimization, with the focus on the maintenance of population diversity to ensure the flexibility for adapting to the changes in the environment. Generally, trust refers to the confidence or assurance in a set of entities that assure the security of data. In this work, a dynamic optimization technique is proposed to find an optimal trust weights in cloud during scheduling.

关键词： Cloud Computing Cluster based dynamic optimization techniques Dynamic programming Evolutionary algorithms Task scheduling meta heuristic algorithms Trust

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