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International Conference on Neural Networks, Information and Communication Engineering

作者： Gu, Shenkai Cheng, Li Wang, Jing Li, Xianglong Nanjing Tech Univ Coll Comp Sci Nanjing 211816 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China Jingling Inst Technol Nanjing 211816 Peoples R China

ISBN: (数字)9781510647381

ISBN: (纸本)9781510647381;9781510647374

Recently, many artificial bee colony-based localization algorithms have been used to obtain more accurate location information. However, the situation of co-linear anchor nodes and measurement errors lead to the problem of flip ambiguities. In the current researches on the localization algorithm of artificial bee colony, the lack of detection and correction mechanism for flipped nodes causes large errors. In this paper, we propose an improved artificial bee colony localization algorithm. We firstly qualify the initial search area based on the measured distance. Then, we introduce a robustness criterion to evaluate the localization nodes, which will avoid using the possible flipped nodes as assistant anchor nodes and thus improve the localization accuracy. Simulation results show that our algorithm can effectively avoid the flip ambiguities and achieve higher accuracy than existing ABC-based and other metaheuristic-based localization algorithms.

关键词： wireless sensor network localization range-based optimization algorithm flip ambiguities detection

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An Integer Linear Programming Approach for Phasor Measurement Unit Placement

An Integer Linear Programming Approach for Phasor Measuremen...

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IEEE URUCON Conference (IEEE URUCON)

作者： Ferraz, Renato S. F. Ferraz, Rafael S. F. Rueda-Medina, Augusto C. Paiva, Marcia H. M. Univ Fed Espirito Santo UFES PPGEE Postgrad Program Elect Engn Vitoria ES Brazil

ISBN: (纸本)9781665424431

Phasor Measurement Units (PMUs) are essential devices in the monitoring and protection of modern power systems. However, its prohibitive cost may limit the wide installation of this device in the networks. For this reason, in this paper, the problem of optimal PMU placement was solved using Integer Linear Programming and some metaheuristic techniques, such as, the Genetic algorithms, Particle Swarm optimization and Cuckoo Search. In addition, the test feeders were modeled as graphs, in order to introduce the domination concepts of graph theory in this problem formulation and, using the upper and lower bounds of the domination number, evaluate the optimization algorithms results. The proposed methodology was performed using the IEEE 13, 34, 37 and 123 node test feeders, and it can be generalized for feeders with higher dimensions. Based on the results, it is possible to conclude that the Integer Linear Programming is the most suitable for this application, presenting the lowest number of PMUs and the fastest convergence.

关键词： Graph theory optimization algorithm Phasor Measurement Unit Power system observability

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Improved political optimizer for complex landscapes and engineering optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 182卷 115178-115178页

作者： Askari, Qamar Younas, Irfan GIFT Univ Dept Comp Sci Gujranwala Pakistan Natl Univ Comp & Emerging Sci FAST Sch Comp Lahore Pakistan

Political Optimizer (PO) is a recently proposed meta-heuristic with excellent convergence speed and exploitation capability. However, it is found that PO prematurely converges for complex problems because of not giving enough time to the exploration. In this paper, the exploration capability and balance of PO are improved by making multiple modifications to propose an Improved Political Optimizer (IPO). To improve the exploration capability, the condition of an equal number of parties and constituencies is relaxed, and switching with a random member of a random party is incorporated in the party-switching phase. Moreover, the balance between exploration and exploitation is enhanced by modifying the position-updating strategy (RPPUS) in the election campaign phase and replacing the tunable party-switching rate with a self-adaptive parameter. The exploitation is further improved by utilizing the best solution of the population in the parliamentary affairs phase. In addition to improvement in PO, this paper also highlights a correction in the party-switching phase of the original PO. The performance of IPO is evaluated using 30 CEC-2014 benchmarks, 29 CEC-BC-2017 benchmarks, and 6 mechanical engineering problems. It is shown through non-parametric statistical Wilcoxon's rank-sum test that IPO significantly outperforms PO. Moreover, IPO is also compared with 10 of the well-cited and 14 latest optimization algorithms published in 2020. It is shown by using the Friedman mean-rank test that IPO secures the first rank for both types of benchmark functions. Moreover, the comparison of IPO with PO and a few well-known algorithms for 6 of the engineering problems shows that IPO performs better or equivalently to the compared optimization algorithms.

关键词： Political optimizer Global optimization optimization algorithm Improved algorithm Meta-heuristic Engineering optimization

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Novel surface optimization for trajectory reconstruction in industrial robot tasks

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2021年第6期18卷

作者： Angel Funes-Lora, Miguel Vega-Alvarado, Eduardo Rivera-Blas, Raul Barbara Calva-Yanez, Maria Sepulveda-Cervantes, Gabriel Univ Michigan Ann Arbor MI 48109 USA Inst Politecn Nacl Ciudad De Mexico Mexico

This study presents a novel algorithm implementation that optimizes manually recorded toolpaths with the use of a 3D-workpiece model to reduce manual error induced. The novel algorithm has three steps: workpiece declaration, manual toolpath declaration, and toolpath optimization using steepest descent algorithm. Steepest descent finds the surface route wherein the manually recorded toolpaths traverse over a 3D-workpiece surface. The optimized toolpaths were simulated and tested with an industrial robot showing minimal error compared to the desired optimized toolpaths. The results obtained from the presented implementation on three different trajectories demonstrate that the proposed methodology can reduce the manual error induced using as a reference the CAD-workpiece surface.

关键词： Free-form surface industrial robot optimization algorithm point cloud data

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Statistical Learning for Solid Propellant Performance

Statistical Learning for Solid Propellant Performance

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AIAA Propulsion and Energy Forum / IEEE Electric Aircraft Technologies Symposium (EATS)

作者： Hartfield, Roy Carpenter, Mark Cervantes, Noel Auburn Univ Aerosp Engn 211 Davis Hall Auburn AL 36830 USA Auburn Univ Math & Stat Auburn AL 36830 USA

ISBN: (数字)9781624106118

ISBN: (纸本)9781624106118

This paper describes a methodology to rapidly model the performance of solid rocket motors and classify motors according to their geometries using statistical learning approaches. Star grain configurations are considered in this analysis. A variety of statistical learning approaches including neural networks have been applied to the problem of mapping pressure time curves for solid rocket motors and, using convolutional neural networks, backwardly map pressure time curves to the motor geometries which produced the pressure time profiles. This classification application is useful in reverse engineering applications and in identifying a range of possible options for achieving a given design goal. This paper contains a description of the physical model, the statistical learning methods required to robustly replicate the physics model, and the convolutional neural network approach used for the classification, along with results for both functions.

关键词： Solid Propellants Convolutional Neural Network Solid Rocket Motor Numerical Analysis Scatter Plot Computational Fluid Dynamics Monte Carlo Simulation Aerospace Engineering Open Source Software optimization algorithm

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GRAPH SIGNAL DENOISING USING NESTED-STRUCTURED DEEP algorithm UNROLLING

GRAPH SIGNAL DENOISING USING NESTED-STRUCTURED DEEP ALGORITH...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Nagahama, Masatoshi Yamada, Koki Tanaka, Yuichi Chan, Stanley H. Eldar, Yonina C. Tokyo Univ Agr & Technol Tokyo Japan Purdue Univ W Lafayette IN 47907 USA Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781728176055

In this paper, we propose a deep algorithm unrolling (DAU) based on a variant of the alternating direction method of multiplier (ADMM) called Plug-and-Play ADMM(PnP-ADMM) for denoising of signals on graphs. DAU is a trainable deep architecture realized by unrolling iterations of an existing optimization algorithm which contains trainable parameters at each layer. We also propose a nested-structured DAU: Its submodules in the unrolled iterations are also designed by DAU. Several experiments for graph signal denoising are performed on synthetic signals on a community graph and U.S. temperature data to validate the proposed approach. Our proposed method outperforms alternative optimization- and deep learning-based approaches.

关键词： Graph signal processing optimization algorithm deep learning signal denoising deep algorithm unrolling

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Short-term multi-objective optimal scheduling of the integrated power grid-abandoned coal mine energy system

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RESULTS IN ENGINEERING 2024年 22卷

作者： Liu, Jing Li, Rui Wu, Tao China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China

In order to meet the urgent needs of upgrading the coal industry, energy exploitation of abandoned coal mines which may be rich in water resources and renewable resources brooks no delay. At present, the framework design and simple energy flow analysis for the reuse of abandoned coal mine resources have been basically formed. However, profound studies on the complex interaction behavior and the coordinated optimal scheduling of the integrated power grid-abandoned coal mine energy system (IPGACMES) with distributed renewable energy and underground pumped hydro energy storage (UPHES) are still lacking. In this paper, a novel multi-objective optimal coordinated scheduling model of IPGACMES is proposed aiming to achieve the lowest operation cost and the minimal carbon emissions. Especially, load balance constraint as well as operation constraints of thermal units, wind/PV units, UPHES, power-to-gas (P2G), power to hydrogen (P2H) and hydrogen fuel cells (HFC) are all considered. Different case studies have been carried out to illustrate the feasibility and effectiveness of the presented optimal scheduling model. Better economic and environmental benefits are achieved in terms of a 3.52 % decline in operation cost and a 4.58 % reduction in carbon emissions. In addition, the renewable energy curtailment is decreased 83.7% whereby the use of P2G and P2H units.

关键词： Abandoned coal mines Integrated energy system optimization algorithm P2G/P2H Renewable energy

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An enhanced mechanism for detection of spam emails by deep learning technique with bio-inspired algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 8卷

作者： Nicholas, Neomi Nelin Nirmalrani, V. Faculty of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

This study aims to create a highly effective system for classifying email spam, with the key objective of improving performance and accuracy in classification. Rigorous pre-processing techniques, including lemmatization and tokenization, are applied to refine the dataset. The impact of optimal feature selection on deep learning algorithm stability with varying training datasets is investigated and to improve classifier performance, pre-stages such as feature extraction and selection using bag of words are utilized. The hybrid model is further enhanced by combining feature extraction and classification algorithms with optimization. The inclusion of multiple features, such as n-gram features, addresses the 'Curse of Dimensionality,' and optimal feature selection is employed to improve the spam detection process. The proposed system undergoes three main modifications: multiple feature extraction, feature selection, and an enhanced hybrid model, all aimed at improving spam detection accuracy. The e-mail classification phase involves mapping between training and testing sets, with deep learning algorithms utilized for feature extraction and classification. The recently introduced Sand Cat Swarm optimization algorithm is employed to refine weights during each epoch, enhancing accuracy and minimizing loss. The proposed system extends its capabilities to identify phishing attacks within the network, offering a comprehensive approach to email security. © 2024

关键词： Bag of words Curse of dimensionality Email spam classification optimization algorithm Sand Cat Swarm Tokenization

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algorithmic Analysis and Statistical Inference of Sparse Models in High Dimension

Algorithmic Analysis and Statistical Inference of Sparse Mod...

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作者： Bu, Zhiqi University of Pennsylvania

学位级别：Ph.D., Doctor of Philosophy

The era of machine learning features large datasets that have high dimension of features. This leads to the emergence of various algorithms to learn efficiently from such high-dimensional datasets, as well as the need to analyze these algorithms from both the prediction and the statistical inference viewpoint. To be more specific, an ideal model is expected to predict accurately on the unseen new data, and to provide valid inference so as to harness the uncertainty in the model. Unfortunately, the high dimension of features poses a great challenge on the analysis of many prevalent models, rendering them either inapplicable or difficult to study. This thesis leverages the approximate message passing (AMP) algorithm, the optimization theory, and the Sorted L-One Penalized Estimation (SLOPE) to study several important problems of the sparse models. The first chapter introduces various l penalties including but not limited to the SLOPE, a relatively new convex optimization procedure via the sorted l1 penalty, in the general machine learning models. We then focus on the linear models and demonstrate some basic properties of SLOPE, especially its advantages over the Lasso. Next, we cover the AMP algorithm in terms of convergence behavior and asymptotic statistical characterization. The second chapter extends the AMP algorithms from Lasso to SLOPE and provides an asymptotically tight characterization of the SLOPE solution. Note that SLOPE is a relatively new convex optimization procedure for high-dimensional linear regression via the sorted l1 penalty: the larger the rank of the fitted coefficient, the larger the penalty. This non-separable penalty renders many existing techniques invalid or inconclusive in analyzing the SLOPE solution. We develop an asymptotically exact characterization of the SLOPE solution under Gaussian random designs through solving the SLOPE problem using approximate message passing (AMP). This algorithmic approach allows us to approximate the SLOPE

关键词： High-dimensional statistics Linear models Machine learning optimization algorithm Sparse models Statistical inference

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Combustion system optimization for the integration of e-fuels (Oxymethylene Ether) in compression ignition engines

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FUEL 2021年 305卷 121580-121580页

作者： Novella, Ricardo Bracho, Gabriela Gomez-Soriano, Josep Fernandes, Cassio S. Lucchini, Tommaso Univ Politecn Valencia CMT Motores Term Valencia Spain Politecn Milan Milan Italy

In this study, a numerical methodology for the optimization of the combustion chamber in compression ignited engines using OME as fuel is presented. The objective is to obtain a dedicated combustion system for an engine that is fueled with this alternative fuel improving the efficiency and reducing the emissions of NOx. This article proposes the integration between the optimization algorithm and CFD codes to evaluate the behavior of an engine fuelled with the low sooting fuel OME. Based on a diesel model validated against experimental data, a further model for OME fuel was implemented for evaluating the performance of the engine. The particle swarm algorithm (PSO) was modified based on the Novelty Search concepts and used as optimization algorithm. Several tools are coupled in order to create each CFD case where all the tools and optimization algorithm are coupled in a routine that automates the entire process. The result is an optimized combustion system that provides an increase of the efficiency (about 2.2%) and a NOx reduction (35.7%) in comparison with the baseline engine with conventional fuel. In addition, a neuronal network was trained with all the results of all simulations performed during the optimization process, studying the influence of each parameter on the emissions and efficiency. From this analysis it was concluded that the EGR rate and injection pressure affects the NOx emissions with a range of variability of 63% and 38% respectively.

关键词： Internal combustion engines CFD codes optimization algorithm Alternative fuel OME

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